Drawing structures is one of the foundational skills in chemistry education. Students assemble Lewis structures and illustrate small organic compounds to strengthen and demonstrate their understanding of fundamental principles as early as high school. While these activities are simple at first, these skills are essential for much of the work professional chemists do, such as writing academic publications and planning synthetic routes.

Of course, drawing chemicals isn’t limited to pencil and paper. Outside of the classroom, most scientists use computer software to present chemical structures. This is why chemical drawing software is increasingly becoming a component of chemistry education. One of the most popular applications for drawing chemical structures is ChemSketch. With over 2 million downloads, this application is used by scientists at all stages of their careers to communicate their chemistry.

“It’s very intuitive,” said David Dubins, an Associate Professor, Teaching Stream for the Leslie Dan Faculty of Pharmacy at the University of Toronto. He has been using ChemSketch for over 20 years, first for his graduate research and now for teaching.

How does ChemSketch support chemistry education? Here are five reasons this software is used by students and educators worldwide.

Teaching Chemistry Fundamentals with ChemSketch

ChemSketch isn’t simply a sketch pad with carbons and hydrogens added in. This software is chemically intelligent and understands how atoms and molecules work. Take the following image as an example—these three structures look different, but the software understands they are all methane.

This chemical intelligence helps to understand many fundamental concepts in chemistry, such as:

• Molecular weight: ChemSketch includes automatic molecular weight calculation, which is essential for calculating moles and yields
• Drawing organic structures: carbon-based chemicals automatically use hydrogens to fill vacant bonds and includes error messages when users draw impossible structures, such as pentavalent carbons
• Nomenclature: students can see how changing the size or composition of a chemical structure influences naming priority

Visualizing Stereochemistry with the 3D Viewer

One of the most essential concepts in organic chemistry is stereochemistry. The size and shape of a molecule can determine its chemical properties. However, it is challenging to visualize these molecules, especially when encountering them for the first time and drawing them out by hand. After all, an “H” on the page looks to be about the same size as a “C” or an “O,” so it is easy to become confused.

ChemSketch includes a 3D view function that allows users to generate models of their molecules quickly. Users can visualize their molecule, which can help them understand and predict stereochemical effects. “The first time I tried it, it really blew my mind. Where has this been all my life?” said Dubins.

These visualization aids are not just useful for undergraduate students–many applications are relevant to laboratory research. “My research area in graduate school was biophysics, so we were looking at structures and solvent-accessible surface areas,” explained Dubins. “ChemSketch was involved in helping me physically put together ball-and-stick models, roll solvent molecules around them, calculate things about them, and correlate that to some of the experiments I was doing in the lab.”

Learn more about Markush structures and stereochemical labels in ChemSketch.

Professionally Present Your Chemistry

Beyond exploring scientific concepts, preparing professional-grade chemical schemes is an essential skill that chemists should learn early on. Researchers must draw accurate chemical structures and reaction schemes for academic publications, patent applications, and presentations. Creating these high-quality structures requires practice, which can start as early as high school.

Of course, ChemSketch is also used by educators. Structures that are clearly drawn and labeled are often included in handouts, textbooks, slides, and exams. Dubins explained that his primary use for ChemSketch these days is preparing course materials and writing lab protocols.

ChemSketch includes many features that help teachers and students draw clear structures, including the “Clean Structure” function, prebuilt settings based on common academic journal formats, and report-building tools.

Learning to use Chemistry Software

ChemSketch can also help with basic computer skills. There has long been a stereotype that each generation would be more technologically literate than the last. In our increasingly digital world, kids would learn to use computers almost by osmosis. In reality, technological skills do not pass down so easily, and many students have not spent much time using the desktop and laptop computers necessary for their careers.

Dubins explains that there is a wide range of skills in his classrooms. “Some of them are on the forefront, and generating images with their computers and coding. Others will take their phone, take a picture of their textbook, and include this grainy picture in their report.”

Basic computer skills still need to be taught, such as adjusting settings, choosing a file format when saving, or copying and pasting information from one application to another. ChemSketch offers students a sandbox to explore and experiment with these options to develop the fundamental computer skills they will need later in their careers.

ChemSketch Convenience and Dependability in Chemistry Education

What many educators value the most about ChemSketch is its dependability and convenience. Many applications available for use or download over the internet are unreliable or may be incompatible with modern software. “If you are putting a curriculum together, it’s not really smart to introduce a tool that might not exist next year,” explains Dubins. “That happens a lot. You start to learn one piece of software, and then it’s gone.”

ChemSketch was first released in 1995 and has been available on-demand for download since 1998. Professors and teachers can have peace of mind knowing that ACD/Labs will continue to maintain the software and is committed to meeting the needs of chemists of all experience levels for years to come.

Learn more about ChemSketch

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Marine-derived peptides exhibit significant promise for nutraceutical and medicinal applications due to their diverse range of bioactivities, including antimicrobial, antiviral, antitumor, and antioxidative properties. Among these peptides, aspergillamides, which are unique modified tripeptides containing rare dehydrotryptamine moieties, are primarily sourced from marine-derived Aspergillus fungi. Despite displaying moderate cytotoxic effects, these aspergillamides demonstrate structural diversity resulting from geometric isomerization of double bonds and variations in amino acid composition. However, the number of reported natural aspergillamides remains limited to less than ten. To search novel and bioactive secondary metabolites from marine microorganisms, Xiao-Wei and coworkers [1] selected the Aspergillus terreus SCSIO 41008 for chemical investigations. As a result, they isolated and identified a new aspergillamide C (1).

Compound 1 was obtained as a colorless oil. It has a molecular formula of C₂₈H₃₄N₄O₄ as determined by the quasi-molecular ion peak at m/z 513.2482 [M + Na]⁺ (Calcd. 513.2478) observed from the HR-ESI-MS data. To elucidate the structure of this compound, 1D NMR, HSQC, HMBC, COSY and NOESY data were used (see Table 1).

Table 1. NMR spectroscopic data used for the structure elucidation of aspergillamide C

These data were entered into ACD/Structure Elucidator (ACD/SE) to challenge the software. A Molecular Connectivity Diagram (MCD) was created by the program and slightly edited manually (see Figure 1).

Checking the MCD for the presence of contradictions was completed with the program reporting proper data consistency. Therefore, strict structure generation was initiated, followed by ¹³C chemical shift prediction and structure filtering. Results: k = 2 162 → (Structure filtering) → 94 → (Duplicate removal) → 71, t_g = 11s.

The structures were ranked in increasing order of average deviations of calculated ¹³C chemical shifts from experimental ones. Interestingly the proposed structure 1 was not generated. The six top ranked structures are shown in Figure 2.

Figure 2 shows that the first ranked structure #1 (CASE solution) differs from compound 1. The difference can be seen clearer in Figure 3.

The authors of [1] admitted the presence of a HMBC nonstandard correlation between H-20 and CH₃-18, which allowed them to confirm their structural hypothesis. To allow the generation of structure 1, the procedure was repeated with the length of the mentioned correlation set to 2-4 chemical bonds. Results: k = 4276 → (Structure filtering) → 166 → (Duplicate removal) → 121, t_g = 19s.

Three top ranked structures are shown in Figure 4.

We see that structure 1 was placed in the second position by the ranking procedure and all average and maximum deviations indicate that the CASE solution is the most probable. The validity of this structure was also confirmed by the calculation of DP4 probabilities. According to the methodology suggested and described in works [2-6], the definite determination of the right structure could be done on the basis of conformational analysis and DFT based chemical shift prediction for both structures #1 and #2. Additionally, recording ¹⁵N correlation experiments (HMBC) could also reveal additional information confirming the correct structure.

Nevertheless, it is very probable that structure 1 can be revised to the structure selected by CASE:

References

1. Xiao-Wei, L.Yun, L. Yong-Jun, Z. Xue-Feng, L. Yong-Hong. (2019). Peptides and polyketides isolated from the marine sponge-derived fungus Aspergillus terreus SCSIO 41008. Chinese Journal of Natural Medicines, 17(2), 149-154.
2. V. Buevich, M. E. Elyashberg. (2016). Synergistic combination of CASE algorithms and DFT chemical shift predictions: a powerful approach for structure elucidation, verification and revision. J. Nat. Prod., 79(12), 3105–3116.
3. V. Buevich, M. E. Elyashberg. (2018). Towards unbiased and more versatile NMR-based structure elucidation: A powerful combination of CASE algorithms and DFT calculations. Magn. Reson. Chem., 56, 493–504., DOI: 10.1002/mrc.4645
4. V. Buevich, M.E. Elyashberg. (2020). Enhancing Computer Assisted Structure Elucidation with DFT analysis of J-couplings. Magn. Reson. Chem., 58(6), 594-606, DOI: 10.1002/mrc.4996
5. Elyashberg, I.M. Novitskiy, R. Bates, A.G. Kutateladze, C.M. Williams. (2022). Reassignment of Improbable Natural Products Identified through Chemical Principle Screening. Eur. J. Org. Chem., e202200572. https://doi.org/10.1002/ejoc.202200572
6. Elyashberg, S. Tyagarajan, M. Mandal, A.V. Buevich. (2023). Enhancing Efficiency of Natural Product Structure Revision: Leveraging CASE and DFT over Total Synthesis. Molecules, 28(9), 3796.

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The steps involved in MS include ionization, ion separation by a mass analyzer, detection, deflection, and data processing. Understanding the different ionization methods in mass spectrometry is crucial, as the ionization process is the heart of MS.

What is Ionization in Mass Spectrometry?

Ionization is the process of converting neutral molecules into charged ions for analysis. The original sample can be solid, liquid, or gas. In cases where samples are solid or liquid, they are transformed into a gaseous state and then subjected to ionization through an ion source. This process involves the loss of an electron, leading to the formation of a positively charged cation.

The ionization chamber is maintained under vacuum conditions to prevent/minimize interaction with airborne molecules. A positively charged metal plate facilitates the movement of the samples to the next part of the apparatus.

Spectrometers work in either positive or negative ion mode, and it is crucial to select the most appropriate setting for accurate data analysis. The method of ionization plays a critical role in interpreting mass spectra.

Understanding Mass Spectrometry Ionization Methods

Ionization methods can be categorized into two groups: hard ionization and soft ionization.

There are a variety of ionization techniques available, and the technique used depends on the experimental goals and sample characteristics. To learn more about how the ionization source impacts the interpretation of mass spectra, read Common Adduct and Fragment Ions in Mass Spectrometry.

Hard Ionization Techniques

Hard ionization techniques involve applying excessive energy to the sample during ionization, leading to sample fragmentation. During this process molecules lose an electron (become ionized) and become highly excited. As they relax, extensive fragmentation occurs resulting in numerous positive ions with  various masses less than the mass of the molecular ion.

Electron Ionization (EI)

EI was one of the very first mass spectrometry techniques developed. It is a popular hard ionization method in which high-energy electrons produce ions after interacting with atoms or molecules in a solid or gas phase. Since it uses high energy to generate ions, it creates a lot of ion fragmentation which can be helpful for determining the structure of unknown compounds. When EI is combined with other separation techniques, it can be used to detect other thermally stable and volatile compounds in solid, liquid, and gas states. EI has high ionization efficiency and sensitivity, and it can provide a lot of structural information.

• Analysis: Useful for organic compounds with molecular weights below 600 Da
• Detection: EI can only detect positive ions
• Applications: EI has many different applications including, but not limited to, environmental analysis, archaeological analysis, forensic analysis, and pharmaceutical analysis

Inductively Coupled Plasma (ICP) ionization

ICP is a hard ionization technique used to measure elements at trace levels in biological fluids at atmospheric pressure. ICP ionizes the sample via an inductively coupled plasma (usually argon plasma) coupled to energy via an induction coil. The sample must be in either gas or vapor form, which then decomposes into its elements and these elements are transformed into ions. These ions can be detected and stored based on their mass.

• Analysis: Useful for detecting trace levels of metals and non-metals in liquid samples
• Detection: Metals and non-metals in liquid samples at low concentrations and different isotopes of the same element
• Applications: Trace element analysis in the clinical laboratory, as well as environmental, pharmaceutical, and geochemical analysis

Soft Ionization Techniques

Soft ionization techniques, apply less energy to ionize the sample, resulting in minimal fragmentation. Here, the spectra contain mainly the most abundant molecular ion peak.

Atmospheric Pressure Chemical Ionization (APCI)

APCI is a soft ionization technique that uses gas-phase ion-molecule reactions at atmospheric pressure to produce primary ions on a solvent spray. It is commonly coupled with high-performance liquid chromatography (HPLC). APCI produces a singly charged product and although this avoids signal overlap, it gives limited structural information due to the production of few fragment ions.

• Analysis: Molecular weight less than 1500 Da
• Detection: Polar and thermally stable non-polar compounds
• Applications: Analysis of drugs, pesticides, various organic compounds, and nonpolar lipids

Atmospheric Pressure Photo Ionization (APPI)

APPI is a soft ionization technique often coupled to liquid chromatography (LC). It uses photochemical action to ionize samples in the gas phase. The solvent and the sample from liquid chromatography form a gaseous analyte which is then ionized to interact with photons emitted by the light source at atmospheric pressure. The APPI light source can be an argon lamp or more frequently a xenon lamp.  The ions are then introduced into the mass spectrometer for analysis. It can simultaneously ionize polar and non-polar small molecules, so more compounds can be analyzed in a single pass.

• Analysis: Samples in gas phase
• Detection: Weakly polar and non-polar compounds
• Applications: APPI is used for analyzing pesticides, steroids, and drug metabolites lacking polar functional groups. It is being extensively deployed in security applications for explosives detection.

Electrospray Ionization (ESI)

ESI is a soft ionization method using electrospray to apply a high voltage to a liquid to produce an aerosol, producing multiply charged ions. Using ESI, you can choose from positive ion mode or negative ion mode. In ESI, higher molecular weight molecules tend to carry multiple charges, the distribution of charge states accurately quantifies molecular weight, resulting in accurate molecular mass and structural information. The experimental parameters and choice of solvent used must be carefully selected.

The process of electrospray ionization consists of three stages:

1. Droplet formation is where the sample solution is ejected from the capillary to form a charged droplet in the presence of a high-voltage electric field.
2. Desolvation is when the droplets enter the spray chamber and are evaporated by the countercurrent of the heated dry gas, the diameter of the droplets gets smaller, and the surface charge density increases. When the repulsive forces between the charges are enough to counteract the surface tension of the droplet, the droplets will split into smaller charged droplets.
3. Then there is gas phase ion formation. Here the size of the charged droplets decreases, and the ions turn into gas phase ions.

Coupling ESI with tandem mass spectrometry (ESI-MS/MS) helps to overcome the obstacle of obtaining very little structural information from the simple ESI mass spectrum. ESI can also be coupled with high-performance liquid chromatography (HPLC) for the analysis of both small and large molecules. ESI has improved sensitivity and accuracy over other MS techniques, allowing for a broadened application in the protein field.

• Analysis: Both small and large molecules of various polarities in more complex biological samples
• Detection: Analysis of large non-volatile molecules, inorganic substances, organometallic ion complexes, and biomacromolecules
• Applications: Analysis of peptides, proteins, and nucleotides.

Chemical Ionization (CI)

CI is a soft ionization technique that uses a reagent gas to ionize sample molecules through ion-molecule reactions in the gas phase. Samples to be analyzed must be in vapor form or vaporized before being introduced to the ion source. The first step is for the reagent gas to undergo electron ionization, generating a molecular ion. This molecular ion will then fragment as it reacts with other reagent gas molecules and ions, creating analyte ions.  It is important to select the most appropriate reagent ion as it increases the selectivity and response time of CI.

Due to having little excess energy from the ions, there is little fragmentation that occurs, and resultantly, little structural information is obtained. Because there are few fragments formed, the products are usually ions of the analyte, which makes it possible to get the exact molecular weight of the analyte.

• Analysis: Both small and large molecules of various polarities in more complex biological samples
• Detection: Molecules that typically fragment a lot under EI conditions
• Applications: Identification, structure elucidation, and quantification of organic compounds, and some use in biochemical analysis

Matrix-Assisted Laser Desorption/Ionization (MALDI)

MALDI is a soft ionization technique that creates ions from large molecules with minimal fragmentation using a laser energy-absorbing matrix. The sample is prepared and ionized before analysis with the mass spectrometer.

The MALDI method consists of 3 steps:

1. A large quantity of suitable matrix material is mixed in with the sample and applied to a metal plate. The matrix must coexist with the sample and ensure minimal interaction between the molecules of the sample.
   Choosing the matrix is the most crucial step of the MALDI analysis. The ideal matrix should have strong electron absorption at the laser wavelength being used, lower vapor pressure, better stability in a vacuum, and be miscible with the solid analyte. Matrices are commonly solid and organic, but they can produce background peaks that can interfere with the characterization of the sample compounds. Compounds that have been shown to reduce background interference include inorganic materials, porous silicon, and surfactant-inhibiting substrates.
2. A pulsed laser (usually a nitrogen laser) exerts high-intensity energy on the sample/matrix material mix so that it can desorb from the metal plate with minimal fragmentation. The matrix will absorb the UV light and convert it into heat energy. A small part of the analyte/matrix will rapidly heat and vaporize. The matrix absorbs the UV light to protect the analyte from being damaged and transmits that energy to the sample to vaporize and ionize the sample.
3. The analyte molecules either protonate or deprotonate, creating ions that can then be analyzed by the mass spectrometer. This transferring of protons is a gentler and less destructive technique.

It produces a single mass spectrum which is helpful for analyzing multi-component samples. It can be coupled with Fourier transform ion cyclotron resonance mass spectrometry and resolution is further improved (FTICR-MS).

Most commonly, MALDI is coupled with a Time-of-Flight mass spectrometer (TOF-MS) in which the ions become separated based on their individual masses and charges as they travel the length of the machine. Once they reach the end of the spectrometer they can be detected, and data collected.

• Analysis: Polar, non-volatile, and thermally unstable samples
• Detection: Analyze biomolecules (DNA, proteins, peptides, carbohydrates) and organic molecules as these tend to be more fragile and more likely to fragment when ionized by other ionization methods
• Applications: an analytical tool in pathology to identify and quantify proteins, peptides, drugs, and metabolites

In mass spectrometry, ionization plays a pivotal role in turning molecules into charged ions, setting the stage for their analysis and characterization. Understanding ionization methods enables selection of the most appropriate technique to accurately analyze your data.

To refresh your knowledge about the basic principles of MS, read our previous blog post: A Beginners Guide to Mass Spectrometry.

You can learn more about ACD/Labs mass spectrometry software tools here.

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Many of the small molecules under investigation in pharmaceutical and biopharmaceutical labs are susceptible to ionization. While the composition of positively charged molecules (cations), negatively charged molecules (anions), and molecules with groups that are both positively and negatively charged (zwitterions) differs by therapeutic area and drug target class (the protein family being targeted); the vast majority are ionizable. A study by process development scientists at Amgen¹ finds this to be true for 70% of the oral molecules approved by the FDA between 1900 and 2020. Furthermore, they report that this trend has not changed in the last 40–50 years.

Ion Class Distribution of FDA-Approved Oral Molecules Over 120 Years

Ionization Considerations in Drug Discovery and Development

An understanding of ionization is critical. From screening assays in early discovery to identifying a suitable lead series, to the final formulation of drug product to ensure delivery to patients in a stable form, ionization influences important decisions.

What pK_a Reveals in Screening & Lead Optimization

In vitro biological assays are used to screen molecular entities for activity against a chosen target. These assays require strict pH and osmolarity control to ensure that their constituents function as they would in vivo. Predominantly solution-based, assays are buffered to achieve a functional pH.

By predicting pK_a for each test material, ionizable functional group(s) are revealed and scientists can review the overall charge state (apparent pK_a) and microspecies present in the assay to focus on ‘lead-like’ or ‘drug-like’ compounds. This data early in the process can significantly impact decisions to shorten lead to candidate timelines and produce novel analogs that address ADMET (absorption, distribution, metabolism, excretion, toxicity) deficiencies:

• When ionization is seen to influence/modulate the bioactivity of an entity or entities, it helps focus lead optimization efforts on that lead series/molecular entities
• If charge state differences do not influence bioassay results within a lead series, medicinal chemists may be able to modify the structure to enhance off-target properties such as ADME/Tox without impacting potency (inhibition, agonism, degradation, etc.)
• If ionizable groups are not seen to contribute to potency, it gives project teams the freedom to make changes to that part of the molecule to make the molecule ‘more druggable’ (e.g., improve solubility, modulate dissolution rates, etc.)
• When localized charge is a potency-meditating factor, medicinal chemists know to protect that part of the molecule from modification which can reduce design-make-test-analyze (DMTA) cycles

pK_a in Drug Formulation

Ionization and related molecular characteristics also impact formulation. Since pH adjustment is a powerful way to enhance solubility and dissolution, salt formation is an effective strategy for clinical development. Knowledge of pK_a helps scientists identify suitable formulations for delivery and select stable physical forms (salt formation through pH modification).

Other Applications of Ionization Data in Pharmaceutical R&D

pK_a determines ionization behavior in many environments, not only in vivo but also in daily work carried out in R&D laboratories.

Ionization Descriptors and Structure Activity Relationships (SAR)

Ionization has a direct impact on SAR. It relates to aqueous solubility and the distribution coefficient, logD (a measurement of the lipophilicity of ionizable compounds) and ADME/Tox properties—absorption, bioavailability, volume of distribution, permeability, hERG toxicity, and drug clearance. Computational chemists and modelers rely on accurate ionization calculators to model these complex in vivo behaviors. Learn more about how ionization influences ADME/Tox parameters.

Ionization in Chromatography

Chromatographers and separation scientists use pK_a and related logD values to optimize HPLC and UHPLC separations. An understanding of the ionic form(s) of the analyte(s), and the pH of the mobile phase helps them choose the most appropriate pH (buffer solution) and stationary phase (column) for separations. Using physicochemical properties to design robust chromatographic methods is part of a quality by design (QbD) approach to method development and optimization.

Ionization in Synthetic Chemistry

Acid/base strength or the order of acidity/basicity is used by synthetic chemists (medicinal chemists and process chemists) to predict/understand how reactions occur, and to select appropriate reagents for syntheses. Knowing the pK_a of a carboxylic acid, for example, allows them to select the correct base for that synthetic step. When there is more than one labile (acidic) proton, an understanding of pK_a values allows the scientist to select reactivity around a molecule.

Find tables of pK_a values in various media here.

Software for Ionization Prediction

In silico calculation of the acid dissociation constant (pK_a) has long been an alternative to physical experiments. A reliable pK_a calculator can negate the need for hundreds or thousands of measurements, saving time, materials, and human effort. This not only helps make projects more efficient and productive, it also supports green chemistry initiatives and sustainable practices. Scientists can use predicted pK_a values to make data-driven decisions throughout pharmaceutical discovery and development. Calculated ionization data can be provided to researchers ‘on-demand’ or integrated into other applications. Learn about ACD/Labs’ acid dissociation calculator.

1. Agarwal, J, Huckle, J. Newman, D. L. Reid. (2022). Trends in small molecule drug properties: A developability molecules assessment perspective. Drug Discovery Today, 27(12), 103366. https://www.sciencedirect.com/science/article/abs/pii/S1359644622003592

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Aspergillus, a widely researched fungal genus in endophytes, stands out as a dominant presence. The Aspergillus genus, as reported by The World Data Centre for Microorganisms (WDCM), encompasses approximately 380 species. Within the timeframe of 2015–2019, over 350 new fungal metabolites were identified, further establishing these filamentous fungi as an abundant and diverse source of bioactive secondary metabolites with significant chemical and biological variations. In the study described by Quader et al [1], a new natural product, aspochalasin H1 (1), together with nine known compounds, were isolated from a Hawaiian plant-associated endophytic fungus Aspergillus sp. FT1307.

1

The structure of aspochalasin H1 was elucidated using 1D and 2D NMR (¹H-¹H COSY, HSQC, HMBC, ROESY and 1D NOE), high-resolution electrospray ionization mass spectroscopy (HRESIMS), and comparisons with the reported literature.

Compound 1 was obtained as an oil. The molecular formula C₂₄H₃₅NO₅ with eight degrees of unsaturation was established by the positive mode quasi-molecular ion peaks at m/z 418.2592 for [M + H] ⁺ (calcd. 418.2593 for C₂₄H₃₆NO₅) and m/z 440.2398 for [M + Na] ⁺ (calcd. 440.2413 for C₂₄H₃₅NO₅Na) in combination with its NMR data.

The molecular formula and NMR spectroscopic data of aspochalasin H1 (Table 1) were entered into ACD/Structure Elucidator.

Table 1. NMR spectroscopic data of aspochalasin H1

The Molecular Connectivity Diagram (MCD) created by the program is presented in Figure 1.

Figure 1. Molecular connectivity diagram (MCD) of aspochalasin H1. The hybridizations of carbon atoms are marked by the corresponding colors: sp² – violet, sp3 – blue, not sp (sp² or sp³) – light blue. The labels “ob” and “fb” are set by the program to carbon atoms for which neighboring with heteroatom is either obligatory (ob) or forbidden (fb). HMBC connectivities are marked by green arrows, while COSY connectivities by blue arrows. Ambiguous connectivities are marked by dotted lines. The ambiguous connectivities are accounted for by the presence of two identical ¹H chemical shifts 1.76 ppm (marked by red font in Table 1) assigned to protons which are attached to carbons CH₃ (20.1) and CH₂ (31.0)

No edits were done for the data presented in the MCD, and structure generation accompanied by ¹³C chemical shift prediction and structural filtering was initiated. Results: k = 10,006 → (Filter) → 787 → (Duplicate removal) → 787, tg = 38 s. The structures of the output file were ranked in increasing order of average deviations of calculated ¹³C chemical shifts from the experimental ones. The six top ranked structures are presented in Figure 2.

Figure 2. The ranked output file. ¹³C chemical shift prediction was carried out using the HOSE code-based method, the neural networks, and the incremental approach. Average deviations of ¹³C chemical shifts determined by these methods are denoted as d_A, d_N and d_I correspondingly. Each atom is colored to mark a difference between its experimental and calculated ¹³C chemical shifts. The green color represents a difference between 0 to 3 ppm, yellow was >3 to <15 ppm and red > 15 ppm.

Figure 2 shows that the best structure of the ranked file (#1) is identical to the structure of aspochalasin H1 deduced by the authors [1]. Although all other structures are similar to structure 1, they are characterized with larger averaged and maximum deviations, which confirms the reliability of the found solution to the problem.

Thus, the CASE-based structure elucidation was carried out automatically without any user intervention in half a minute. The structure of aspochalasin H1 with ¹³C chemical shift assignments is shown below:

References

1. M. Qader, K. A. U. Zaman, Z. Hu , C. Wang, Xi. Wu, S. Cao. (2021). Aspochalasin H1: A New Cyclic Aspochalasin from Hawaiian Plant-Associated Endophytic Fungus Aspergillus sp. FT1307. Molecules, 26, 4239. https://doi.org/10.3390/molecules26144239

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Software and data are rapidly changing the field of chemistry. Between advanced analytical data processing tools, machine learning algorithms, and automation, research done at state-of-the-art facilities has changed substantially in the last 30 years. Pharmaceutical companies and chemical manufacturers are looking for ways to improve the efficiency of their research using technology.

As industry stays at the cutting edge of technology, has academia kept up? In most cases, the answer is “no”. Chemistry education today often looks the same as it did 10, 20, or even 30 years ago. While the fundamentals of science are as important as ever, students also need to develop a broad technological skillset to help them meet the needs of industry. This includes experience with:

• Managing chemical data
• Working with machine learning and automation
• Practicing green chemistry

Managing Chemical Data—Moving Beyond Microsoft

All labs must efficiently collect, manage, access, and store data. 92% of respondents to our 2022 Analytical Data Management Report said they need analytical data on a daily basis, but 68% said that data was difficult to access and share. Where is this data being stored? 80% of respondents said they use Microsoft applications. These applications weren’t designed for chemistry data, making it time-consuming for scientists to collect, interpret, and share that data.

Many research organizations are improving efficiency by adopting analytical data management systems or cloud-based solutions. When data is easily accessible across an organization, chemists can review each other’s experiment data to uncover insights.

To prepare for industry-level pharmaceutical and chemical research laboratories, students must learn to manage analytical data outside of Excel spreadsheets and Word documents.  Universities can offer students this experience by deploying chemistry software, cloud-based applications, and analytical data management systems that are user-friendly and support better productivity.

Introducing Students to Data Science, Machine Learning, and Automation

Machine learning and data science are already impacting lab workflows, and this will only grow in the years ahead. Data science is one of the most in-demand skills for students graduating from universities, and many companies are already using artificial intelligence to support drug discovery.

However, there is no ChatGPT for chemistry. The machine learning and data science models used in the pharmaceutical and chemical industries are not plug-and-play tools. They need a steady diet of high-quality information to be valuable. Students should learn about the importance of digitalizing and harmonizing analytical data to supply machine learning and data science applications with the reliable data they need to deliver actionable insights.

Automation is another growing trend in the pharmaceutical and chemical industries. Researchers can use automation to eliminate or simplify the most tedious and time-consuming lab workflows. While students need to be familiar with each step in their laboratory workflows, they should also learn about the hardware and software involved in automation. For example, high throughput experimentation (HTE) is a type of chemistry where many small-scale reactions run in parallel. This includes adding reagents to the wells of a plate, performing a chemical conversion, and analyzing the results. HTE is done with the help of robots and software such as Katalyst D2D. Experience with these tools will help enable the automated labs of the future.

In addition to preparing students for the workforce, this also allows the students to spend less time measuring out reagents or cleaning glassware. That time can be spent completing more experiments, interpreting their results, or engaging with the scientific literature.

Green Chemistry Technology and Chemistry Education

Fighting climate change and environmental degradation are some of the most pressing concerns for the 21^st century. Between petrochemicals, plastics, and pesticides, chemists have a massive environmental impact. Chemical synthesis often has an enormous carbon footprint, as exemplified by the pharmaceutical industry, which is more emissions-intensive than the automotive industry.

If we want the world to be habitable in the future, today’s students need to learn and adopt the principles of green chemistry and understand how they can be implemented. Use of predictive software can help reduce the number of experiments that need to be run, saving considerable resources and reducing waste.

Method Selection Suite is an excellent example of this. Chromatographic methods often require many experiments, leading to massive volumes of waste solvents. Method Selection Suite helps chemists efficiently explore a wide range of solvents to help uncover the methods without mindless trial-and-error. Not only that, it is also helpful for finding methods that avoid using environmentally hazardous solvents.

Students can also practice using chemical property predictors. Tools such as Tox Suite allow chemists to use chemical structures to predict aquatic toxicity, mutagenicity, hERG inhibition, and more. Familiarity with this type of software will help chemists think deeply about environmental impact and will help them develop safer products in the future.

Chemistry Education and the Future

Chemists will probably always need to learn fundamental laboratory skills. Hands-on experience measuring reagents, running reactions, and performing extractions is foundational for understanding chemistry.

But while these legacy techniques are still essential, more is needed. Today’s chemistry education must be preparing students for the coming wave of digital innovation and be equipped to meet the needs of society. Providing access to chemistry software is one of the best ways to meet these objectives.

The post Building the Future of Chemistry Education: How Universities Support Better Science appeared first on ACD/Labs.

Liang, Wu, and coworkers [1] disclosed a phytochemical study of Salvia plebeia R. Br. in 2015. In it, a novel, highly oxidized abietane, plebeianiol A, was isolated and was assigned with structure 1.

1

Recently Vanderwal and coworkers [2] revised this structure and confirmed the revision by total synthesis. The need for structure revision was explained by the following arguments: The authors of [1] showed that plebeianiol A has radical-scavenging properties and inhibits the production of both reactive oxygen species and nitric oxide. Based on 2D NMR experiments, the authors proposed a structure with the canonical octahydrophenanthrene abietane substructure having hydroxyl groups in carbons with chemical shifts 81.8, 66.8, 65.3, and 143 ppm, which are often found to be oxidized in abietane compounds. In an unusual twist the authors proposed the presence of an isopropyl group on the B ring at the carbon at 26.4 ppm and a phenolic hydroxyl group in the location typical of the abietane isopropyl group at the carbon at 141.6 ppm. The authors noted that this proposal was unusual given the typical structures of abietane natural products (Figure 1) but nonetheless proposed a highly unusual biosynthesis of this compound.

Figure 1. Typical structures of abietane natural products

On these grounds and after careful re-examination of the NMR spectral data reported for plebeianiol A, Vanderwal and coworkers [2] were led to conclude that the structure had likely been misassigned and might, in fact, be 2, with the typical features of an abietane diterpenoid.

2

Liang and coworkers had previously isolated a related abietane (3) from Ajuga forrestii, to which 2 is likely a biosynthetic precursor.

3

The authors of [2] assumed that a similar approach could be followed to access the proposed revised structure of plebeianiol A (2) and in doing so correct its structure. As a result, structure 2 was synthesized, and its NMR spectra shown to be identical with those presented in article [1].

The comparison of structures 1 and 2 shows that the authors [1] incorrectly interpreted at least two peaks in the HSQC spectrum: the quaternary carbon at 126.3 ppm and the methylene group at 31.7 ppm were attributed as CH’s. Obviously, the correct structure could not be deduced from these NMR data.

As shown by [3-6], ACD/SE is a powerful tool for the revision of incorrectly determined structures. Many corrections of erroneous structures published in the literature have been made with the help of this expert system. NMR spectra taken from the corresponding original articles are introduced into the program, which usually allows to find the right solution to the problem. The cause for the publication of an incorrect structure is most often the erroneous interpretation of HMBC data.

We were interested to know what the result of using ACD/SE for the revision of the plebeianiol A structure would be if the NMR data presented in the work of Liang and collaborators [1] were used for this.

Structure 1 was introduced into the Proposed Structure (PS) window of ACD/SE and verified by ¹³C chemical shift prediction using three empirical methods provided for in ACD/SE. The results are shown in Figure 2.

Figure 2.The structure of plebeianiol A (1) proposed by Liang et al [1 for which ¹³C chemical shift prediction was carried out using the HOSE code-based method, the neural networks, and the incremental approach. The average deviations of ¹³C chemical shifts determined by these methods are denoted as d_A, d_N and d_I correspondingly. Each atom is colored to mark a difference between its experimental and calculated ¹³C chemical shifts. The green color represents a difference between 0 to 3 ppm, yellow was >3 to 15 ppm, red >15 ppm.

We see that the average and maximum deviations calculated for structure 1 are very large, which is evidence of structure error. Note that this conclusion was made without any chemical considerations. The next step should be an attempt to find the most probable structure that could be contingent from the NMR data presented in [1]. These data included a table of ¹³C and ¹H chemical shifts associated with carbon atoms for which numbers of attached hydrogens were determined. Only some key correlations from the HMBC spectrum were drawn on the picture of the supposed structure 1. This spectroscopic information is summarized in Table 1.

Table 1. NMR spectroscopic information for plebeianiol A [1]. The incorrect interpretations of HSQC and HMBC spectra were marked by red. The five OH groups which were identified by the authors are not shown on the table.

Though the low resolution of the HSQC spectrum is shown in SI to [1] does not allow analyzing the spectrum in detail, it is clear that there is no signal of the cross peak 126.3/6.53 ppm. Nevertheless, to verify the consequences from the authors “axioms”, the data presented in Table 1 and the molecular formula C₂₀H₃₀O₅ were entered into ACD/SE, and a Molecular Connectivity Diagram (MCD) was created (Figure 3).

Figure 3. Molecular connectivity diagram (MCD) of plebeianiol A. Hybridizations of carbon atoms are marked by the corresponding colors: sp2 – violet, sp3 – blue. Labels “ob” and “fb” are set by the program to carbon atoms for which neighboring with heteroatom is either obligatory (ob) or forbidden (fb). HMBC connectivities are marked by green arrows.

It is expected that number of generated structures will be large because the MCD contains a quaternary atom at 143.0 ppm having no HMBC correlations. Structure generation accompanied with ¹³C chemical shift prediction and structural filtering was initiated, which was completed with the following results: k = 90,470 → (spectral and structural filtering) → 5,897 → (filtering by Bredt’s rule) → 4,497 → (duplicate removal) → 2,291, t_g =3 m 50 s.

The six top structures of the output file ranked in increasing order of ¹³C chemical shift deviations are shown in Figure 4.

Figure 4. The six top ranked structures of the output file.

The figure shows that the best structures have average deviations two times smaller than structure 1 had (Figure 2), but they still exceed the deviation values which are typical for correct structures. In addition, the possibility of dimethyl and isopropyl groups neighboring as substituents at cyclohexane ring in a natural product is not evident: for instance, only one structure possessing this property was found among 226,000 items included into NMR DB:

We can conclude that no acceptable structure can be logically deduced from the NMR data published in [1]. To find the correct solution, it is necessary to revise the NMR data. However, it was impossible in our case because neither authors of [1] nor of [2] presented full NMR data.

Taking this into account, we made the corrections to HSQC and HMBC data (see Table 1) according to 2 and repeated the structure generation. The structures obtained did not make chemical sense and also had large deviations. From this we can conclude that in reality not all HMBC correlations noted by the authors as key ones have a standard length (2-3 chemical bonds). In this regard, an attempt was made to perform fuzzy structure generation, allowing for the presence of one nonstandard HMBC correlation, the length of which is unknown. As one might expect, the generation was very long. It was stopped after 2 hours, when a message was received from the program that structure 2 was generated (this structure was in the PS window during the generation). For 2 hours, the program produced ~3,900,000 structures, of which 18,427 were included in the output file. The first 6 structures of the ranked output file are shown in Figure 5.

Figure 5. The first 6 structures of the ranked output file produced using fuzzy structure generation.

The figure shows that the best structure, #1, is identical to the revised structure 2 that was confirmed by chemical synthesis. To compare the chemical shift assignments, both structures, 2 and #1, revised by using the two methods are shown in Figure 6.

Figure 6. Comparison of chemical shift assignments for structure 2 and the best hit suggested by ACD/SE.

We see that both structures have similar deviations and differ only in the permutation of two chemical shifts -126.3 and 131.9 ppm.

From this example, it can be concluded that the use of ACD/SE made it possible to confidently establish the error of structure 1 and to show that it is impossible to derive any acceptable structure from the published NMR spectra. After correction of the HSQC fuzzy structure generation helped to overcome the uncertainty of HMBC data and identify the correct structure, although the generation process was stopped before it was completed. Obviously, based on relevant and complete NMR data (only the HMBC correlations which the authors [1] declared to be key ones were available), the correct structure would be found by the program in a routine mode and at a short time.

References

1. Zhang, B.-B.; He, B.-Q.; Sun, J.-B.; Zeng, B.; Shi, X.-J.; Zhou, Y.; Niu, Y.; Nie, S.-Q.; Feng, F.; Liang, Y.; Wu, F.-H. (2015). Diterpenoids from Saliva plebeia R. Br. and Their Antioxidant and Anti-Inflammatory Activities. Molecules, 20(8), 14879-14888.
2. L. K. Johnson, S.W. Niman, D. Vrubliauskas, C.D. Vanderwal. (2021). Stereocontrolled Synthesis and Structural .Revision of Plebeianiol A. Org. Lett., 23, 9569−9573.
3. M. Elyashberg, A. Williams, K. Blinov. (2010). Structural revisions of natural products by Computer Assisted Structure Elucidation (CASE) Systems. Nat. Prod. Rep., 27(9), 1296-1328.
4. M. Elyashberg, K. Blinov, S. Molodtsov, A.J. Williams. (2013). Structure Revision of Asperjinone Using Computer-Assisted Structure Elucidation Methods., J. Nat. Prod., 76, 113−116.
5. M. Elyashberg, I. M. Novitskiy, R. Bates, A. G. Kutateladze, C. M. Williams. (2022). Reassignment of Improbable Natural Products Identified through Chemical Principle Screening. Eur. J. Org. Chem., e202200572. https://doi.org/10.1002/ejoc.202200572
6. Elyashberg, M.; Tyagarajan, S.; Mandal, M.; Buevich, A. V. (2023). Enhancing Efficiency of Natural Product Structure Revision: Leveraging CASE and DFT over Total Synthesis. Molecules, 28(9), 3796.

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Actinomycetes are a very well-known group of aerobic and anaerobic Gram-positive mycelial bacteria. They are known to produce a variety of bioactive secondary metabolites. In fact, more than 70% of the currently used antibiotics were originally isolated from Streptomyces, the largest genus of actinomycetes. Rare actinomycetes have also been regarded as potential sources for the discovery of bioactive compounds, including antibiotics. In the past five years, 31% of new bioactive compounds were isolated from rare actinomycete strains, although the Streptomyces genus continues to dominate this field, contributing 65% of the reported bioactive compounds.

The genus Saccharomonospora, a rare actinomycete, was first described in 1971. Analysis of the chemical components from the culture broth of the marine bacterium Saccharomonospora sp. CNQ-490 carried out by Fenical and coworkers [1] has yielded three novel compounds, including saccharobisindole (1). Its chemical structure was elucidated by the interpretation of 1D, 2D NMR (HSQC, HMBC and COSY) , and high-resolution mass spectrometry (HR-MS) data.

1

Saccharobisindole (1) was obtained as a pale, yellow oil. The molecular formula of this compound (C₂₆H₂₈N₂O₃) was deduced from high-resolution fast atom bombardment mass spectrometry (HR-FAB-MS) which showed an ion at m/z 417.2181 [M+H]⁺ (calcd for C26H₂₉N₂O₃, 417.2178) indicating 14 degrees of unsaturation. The IR spectrum of this compound indicated the presence of a hydroxy group at 3431 cm^-1, a carbonyl group at 1676 cm^-1, and a double bond at 1638 cm^-1.

1D, HSQC and HMBC data tabulated in article [1] along with the molecular formula were entered into ACD/Structure Elucidator (Table 1).

Table 1. NMR spectroscopic data of saccharobisindole (1).

The Molecular Connectivity Diagram (MCD) created by the program is shown in Figure 1.

Figure 1. Molecular connectivity diagram. Carbon atoms hybridizations are marked by the corresponding colors: sp² – violet, sp³ – blue, not sp (sp³ or sp²) – light blue. Labels “fb” are set by the program to carbon atoms for which neighboring with heteroatom is forbidden. The HMBC connectivities are marked by green arrows.

The MCD contains 8 light blue carbon atoms characterized by ambiguous hybridizations. No carbons received the “ob” label, which indicates those atoms which should be connected to a heteroatom. At the same time, as seen in Table 1, reliably determined multiplicities are found for all signals in the proton spectrum. Therefore, the fields “Number of Hydrogens on Neighbor Atoms” were filled in by corresponding figures in the dialog window “Edit Properties of Atom…”. No manual MCD edits were made.

Checking the MCD for consistency showed that there were no contradictions in the 2D NMR data, therefore strict structure generation accompanied with ¹³C chemical shift prediction and structure filtering was initiated. Results: k = 20 → (structure filtering) → 3, t_g = 17 s.

The resulting structural file ranked in increasing order of the average deviations d_A(¹³C ) calculated ¹³C chemical shifts from the experimental ones is presented in Figure 2.

Figure 2. The ranked output file. ¹³C chemical shift prediction was carried out using the HOSE code-based method, the neural networks, and the incremental approach. Average deviations of ¹³C chemical shifts determined by these methods are denoted as d_A, d_N and d_I correspondingly. Each atom is colored to mark the difference between its experimental and calculated ¹³C chemical shifts. The green color represents a difference between 0 to 3 ppm, yellow was >3 to 15 ppm, red > 15 ppm,

We see that the first ranked structure is identical to that suggested by authors[1] and its DP4 probabilities calculated for all three methods of chemical shift prediction are equal to 100%.

It was interesting to see which solution would be obtained if the multiplicities in ¹H spectrum were ignored. A new MCD was created, and structure generation was repeated. Results: k = 176 → (structure filtering) → 26 → (duplicate removal) → 7 → (check by Bredt’s rule) → 6, t_g = 55 s.

The new ranked output file is presented in Figure 3.

Figure 3. The ranked output file obtained by ignoring multiplicities in ¹H NMR spectrum.

Figure 3 shows that the number of generated structures is double while the generation time increased three times. All additional structures contain hydrogen atoms whose multiplicities contradict those indicated in Table 1. The effect would be more noticeable in cases when the expected generation time is longer.

Thus, using ACD/Structure Elucidator allowed us to determine the structure of saccharobisindole in a fully automatic way quickly and reliably. The resulting structure together with the assigned ¹³C chemical shifts is shown below:

References

1. Kim, S.; Le, T. C.; Han, S.-A.; Hillman, P. F.; Hong, A.; Hwang, S.; Du, Y. E.; Kim, H.; Oh, D.-C.; Cha, S.-S.; Lee, J.; Nam, S.-J.; Fenical, W. Saccharobisindole, Neoasterric Methyl Ester, and 7-Chloro-4(1H)-quinolone: Three New Compounds Isolated from the Marine Bacterium Saccharomonospora sp. Marine Drugs 2022, 20, (1), 3. https://doi.org/10.3390/md20010035

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How did you become involved in analytical data management?

Nichola: I represent the very early research functions at AstraZeneca—medicinal chemistry groups who are generating thousands of samples to test and select candidates. We wanted a centralized, cloud-based solution that would make analytical data accessible to all functions within the organization and that is how I got involved in the Global Analytical Database project.

Mark: I started down this digital journey as a lab manager, managing a team and dealing with chromatography and mass spectrometry, with purely selfish goals: to make my own job easier and improve my team’s efficiency. Once I made a start, I discovered there was so much more we could be doing with data.

Graham: I’ve been involved with numerous analytical data management projects for our customers. We see many commonalities in the challenges they face and their approaches to address them. The experience of the ACD/Labs team means we can provide an informed perspective to help organizational leaders attain their data management goals.

Analytical Data—An Enterprise Asset

Nichola: There needs to be a real transition to move away from thinking of data as a single use piece of information that’s consumed and then no longer usable. The data in itself can be an incredibly valuable resource. Now with the potential for AI and machine learning to derive further insight from that data, it’s really important to think with that mind set. To think of a data management strategy that helps you to realize the benefits.

Graham: Years ago, data use was by humans, but there is increasing demand for data to be used by machines and the interaction of humans and machines. The paradigm shift in the last decade has been that more and more organizations are viewing data as an asset that has value. Not just in solving immediate scientific problems, but that the data can lead to financial benefit for the organization.

In today’s R&D organizations, people are trying to interact with each other around data. That change is happening and it depends on a data lifecycle that involves taking original data and converting it into information and then being able to use that in ways that generate value. There is increasing awareness that there needs to be investment in data management technologies and I think that this goes hand in hand with the recognition that data has value. As tough as the pandemic was, I think it served to heighten awareness that data and data management are critically important to the success and health of organizations.

The Data Access Challenge

Graham: Data accessibility issues exist with current technologies in a variety of ways but consistently across R&D, data is siloed in one way or another. It’s either frozen into a location, a document format, or some image format. I’m sure there is some data that is still not digitalized. It’s in notebooks which may not be electronic, or instrument records which are not fully digitalized.

“Finding data within a lab or a site is easier than in the organization as a whole. When you can’t find data, it doesn’t exist.” – Mark Kwasnik

Nichola: I can find my own data readily, but if I want to be able to search pools of data to look for trends, or gain insights, or even access data from other company functions or geographies, that’s a real challenge. We use a lot of external contract research organizations and accessing the data they generate is even more difficult.

Mark: I agree, finding data within a lab or a site is easier than in the organization as a whole. It’s easier for scientists to re-prep and re-run a sample than to search through forty computers and find existing data. Solvay is a global organization with sites all around the world. Even if the data at your particular site is structured and easy to use, that doesn’t mean you could reach across the continental divide and understand someone else’s data or data structure, even if they’re running the same methods and analyzing the same samples. And when you can’t find data, it doesn’t exist.

Why Analytical Data Heterogeneity Is Here to Stay

Graham: One of the findings of a recent analytical data management survey we carried out was that data heterogeneity is a key challenge. Scientists are using more than one analytical technique, using perhaps two or more instruments, followed by two more software applications for data analysis, and then trying to bring that all together. 90% of the people that we surveyed were dealing with diverse types of data.

The Legacy Instrument Reality

Mark: At our global R&I centers, within Solvay, not only do we have a whole plethora of techniques to be able to serve our customers, but even within the techniques you will see a handful of different instruments. If you pick a single technique, like gas chromatography, in a newly built lab, you’ll find the same vendors and software—it’s nice and homogenized. But not every lab is new.

Existing labs have legacy equipment from different vendors, or even from the same vendor but different software platforms. Not only does this make it more difficult to run the lab itself, because the team must learn how to maintain, run, and operate all these different GCs and software platforms (from a scientist’s perspective, it’s challenging); putting on my data hat, when you want to come ingest and process the data, vendors tend to do things slightly differently.

To me as a scientist a chromatogram is a chromatogram, but to me as a data guy a chromatogram from seven different vendors is seven different “techniques”. Having said that, it is difficult to justify a large capital investment in hardware or software without new capabilities, just for the sake of standardization.

“To me as a scientist a chromatogram is a chromatogram, but to me as a data guy a chromatogram from seven different vendors is seven different ‘techniques’.” – Mark Kwasnik

Nichola: I’ve seen it from both sides—as a scientist and a manager. There are lots of reasons for the choices that you make in the lab environment. Sometimes it’s about familiarity, sometimes it’s to do with the support platforms you have in place. There’s a big overhead to keeping large analytical facilities running.

Scientific Freedom to Choose the Best Tool for the Job

Nichola: We consistently need to use at least two different types of data to characterize and confirm our structures and purities, to meet testing requirements. I agree that there is some merit in trying to consolidate to fewer vendor platforms, but as we’re looking to drug more and more challenging targets with increasing molecular complexity in Pharma, we want to be able to choose the best instrument for addressing our analytical needs. Sometimes you need a little heterogeneity.

“We want to be able to choose the best instrument for addressing our analytical needs.” – Nichola Davies

Mark: Agreed, as a purely data guy harmonization of instruments and data is great, but as a scientist I don’t want IT dictating what equipment and techniques I have access to. I want the freedom and flexibility to pick the best tool on the market.

Mergers and Acquisitions

Graham: With mergers and acquisitions in the pharma industry, we’ve seen that when two large companies merge, who individually have done great work on their tech standardization, considerable additional effort is needed in managing instrument data.

Nicola: Yes, absolutely. We see that in mergers of large organizations, each company tends to have their preferred solution. That’s quite a big overhead for IT governance.

The Path to Effective Analytical Data Management

What data should be managed? All data, curated data, or just interpreted results?

Nichola: My preference would be all data. We don’t know what the future will hold yet in terms of how we’re going to be able to use data. If we’re not capturing it now, organizing it, tagging it appropriately with metadata, then we’re preventing that future use.

Mark: I agree, all data if it’s tagged properly. From an IT perspective all data is quite an overhead—data storage and architecture are expensive; but so is generating precious samples, using the hazardous materials and the time to prep them, using an expensive high resolution mass spectrometer or NMR to generate the data and the analysts time to process it. Those also have a fixed cost associated with them.

Graham: Curated data is absolutely essential to proper data science and reference data also needs to be properly curated to be reliable in an organization. Tagging data to be able to understand it and access curated packets is a must.

Data Needs Context to Be Reusable

Mark: Even if I have beautifully digitized data, and access to the processed and raw data files, without context it means nothing to anyone else in the organization. You need instrument metadata associated with it, and the analytical testing data. A chromatogram is great but if I don’t know for GC, for example, if a 5 or 60 meter column was used, or at what temperature, it’s essentially useless. It’s just taking up digital hard drive space.

“Just because data is digital, doesn’t mean you can use it again…without context it means nothing to anyone else in the organization.” – Mark Kwasnik

Graham: That must be part of the strategy—to determine what context is necessary. It’s great to have a vocabulary around metadata, for example, but you need to bring the right stakeholders together to ensure that the vocabulary captures the important information about sample preparation, methods, and equipment.

Mark: Context requirements may be very different, depending on where and how the data will be used. People generating data want very different metadata than internal or external customers submitting samples. The big dream for everyone is AI and machine learning and those have very different data needs to make sense and use of gigantic datasets. Different users need slightly different things tied to the data to be able to leverage it. Just because data is digital, doesn’t mean you can use it again. You have to lay the foundations before you start building your house.

Bring Together the Right Stakeholders

Graham: At the outset of projects with customers we go through “define and design.” We sit down with a variety of stakeholders and example data to talk through what they want from it. We find those are very useful for organizations, strategically.

Nichola: It’s critical to involve the data scientists right at the beginning of developing your analytical data management strategy because if you aren’t capturing their needs at that point, it’s really difficult to build that in later.

Get Commitment

Nichola: I probably underestimated the amount of time I needed to dedicate to this project. There are many other pulls on my time. If you’re embarking on a big project like this, making sure you’ve got that buy-in from management to dedicate time towards supporting this type of activity is essential. Commitment across the board is essential. You have to have top-down support from management, a strategy, and the funding to implement it, but you need buy-in from middle management and those in the labs doing the legwork, because they already have a lot on their plate, and you are now adding more.

Mark: Absolutely, you can’t expect someone already working at 100% to put in extra time because you want data tagged. Everyone needs to know the benefit to them and to the organization.

Automate, Automate, Automate

Mark: If I’m going to ask scientists to fill in 5-10 metadata fields to make the data they’re capturing better usable in the future, I will try to include automation along the process to save them time elsewhere. Perhaps by-passing manual data entry or eliminating manual report creation.

Think More Broadly About Harmonization

Mark: It’s important to remember that even if two labs are doing the same work, they might not really be the same. Labs in two different countries might have different languages, or different date formats, or commas versus periods. Harmonizing on these little things makes a big difference. It may not be your job as the overarching person deploying the strategy, but you need to get the scientists in the different labs together to figure out what works best for them.

Closing Thoughts

Nichola: Within the drug discovery and development process, the initial synthesis of drug product happens several years before it transitions into the development environment. In that transition from discovery to development, I’ve traditionally seen a lot of re-work. Development receives the compound and they regenerate all the analytical data again—they redevelop methods, reassign NMR spectra— which is terrible duplication of effort. Finding mechanisms to easily share our learnings later in the R&D process helps to streamline and facilitate our end goal—to deliver safe and effective therapies to patients, faster.

Mark: Effective analytical data management makes labs more efficient. In a global organization you have multiple sites working on the same thing. Being able to exchange information means you don’t have to redevelop methods or rerun samples. Labs can generate data faster, the plants can produce faster, research and innovation can do their job faster. Even if an experiment was a failure and didn’t meet a range of specifications for this application today, it might be exactly what you need 6–9 months later for a different request. Not having to start from scratch and being able to use that as a starting point accelerates the innovation pipeline.

Watch the webinar recording “How to Overcome the Challenges of Analytical Data Management” for more details, including a short discussion from Nichola about AstraZeneca’s Global Analytical Database, or read the article published by The Analytical Scientist “Demystifying Analytical Data Management”.

The post Insights & Best Practices in Analytical Data Management from Industry Experts appeared first on ACD/Labs.

This is where green chemistry comes in—an approach that aims to design chemical processes and products that are environmentally sustainable and safe for human health. However, applying the principles of green chemistry can feel intimidating, especially to scientists who are not used to applying this concept.

Luckily there are a wide variety of software tools that can support scientists who want to improve the sustainability of their research. But before exploring the applications of software to sustainable chemistry, it is worth first discussing the fundamentals of green chemistry.

What are the Principles of Green Chemistry?

Green chemistry is the practice of reducing the use or generation of hazardous substances in chemical processes. The goals of green chemists include:

• Decreasing the amount and harmfulness of chemical waste
• Creating more environmentally friendly products
• Avoiding the release of greenhouse gases

Green chemistry and environmental chemistry are sometimes confused but are quite distinct. Environmental chemists analyze compounds found in the environment and explain how chemicals affect natural processes or ecosystems. Green chemists are more focused on synthetic chemistry than fieldwork or analytical chemistry.

One popular framework in this field is the 12 principles of green chemistry. Paul Anastas and John Warner originally proposed them in the book “Green Chemistry, Theory and Practice” published in 1998. Since then, these concepts have become rules of thumb for scientists who want to reduce the environmental impact of their chemistry. Institutions like the American Chemical Society and the European Commission have also advocated for these 12 principles.

Here, we will explore six of these principles in more detail and discuss how scientific software supports sustainability.

Prevention—Eliminating Waste with Scientific Software

What kind of chemical waste is easiest to treat? The one you didn’t create in the first place!

The first principle of green chemistry is “prevention”. Chemists should design synthetic pathways to reduce the amount of waste they generate. This also applies to research scientists who can reduce the environmental impact of their work by avoiding unnecessary experiments.

How software can help: researchers can prevent waste by using software predictions instead of running real-life experiments. Property prediction software like PhysChem Suite that can predict the solubility, pK_a, and logP of chemicals without experimental measurement can help avoid waste.

Reduce Use of Hazardous Chemicals of Your Syntheses

Chemistry often requires the use of hazardous chemicals. Strong acids and bases can cause burns, heavy metal catalysts are toxic, and many solvents are flammable. In fact, many of the chemicals we use in organic synthesis are chosen because they are highly reactive, such as hydride donors and Grignard reagents.

While these chemicals are necessary, chemists should try to find alternatives when possible. This may mean substituting reagents or developing alternative synthetic routes. Within pharmaceutical R&D, development teams are responsible for improving synthetic methods, which includes increasing yield and efficiency. Considering chemical hazards can help reduce the environmental impact of manufacturing processes.

How software can help: There aren’t enough hours in the day to test every possible method to make a molecule. High throughput experimentation (HTE) supported by Katalyst D2D allows research teams to investigate a broader range of synthetic routes, quickly. This can lead to more sustainable synthetic methods that avoid using environmentally harmful reagents.

Designing Safer Chemicals

Is your chemical product hazardous? Hopefully, the answer is “no,” but developing safe compounds while meeting functional needs can be challenging. Many chemicals react with unintended biological targets, which can cause environmental harm.

This is particularly important for chemicals that will be exposed to the environment, such as agrochemicals, paints, or coatings. Surfactants used in dish or laundry detergents are a fascinating example: the chemicals need to retain their function while in use, but they also need to degrade after they go down the drain.

See how Unilever is applying computational chemistry to create more environmentally friendly products.

How software can help: synthesizing and testing novel chemicals is a tremendous amount of work, especially if these molecules fail safety testing once they are made. Using predictive toxicity software such as Tox Suite allows you to use chemical structures to predict aquatic toxicity, mutagenicity, hERG inhibition, and more; supporting scientists that want to develop safer products.

Solvents—Reducing Volume and Risk with Scientific Software

Solvents are a means to an end in chemical synthesis. Some solvents are better than others for specific reactions or are more expensive, but the first goal is a that the reaction works.

This is not true when it comes to green chemistry. By weight, solvents are the main ingredient in almost all organic chemistry reactions, which is why “safer solvents and auxiliaries” is one of the 12 principles of green chemistry. Researchers have found that some solvents, such as short-chain alcohols, are relatively innocuous, while halogenated solvents are hazardous to the environment. Scientists should switch to less harmful solvents and reduce the total volume used (when possible).

Liquid chromatography can use a massive amount of organic solvent. Developing a separation method that avoids hazardous solvents and decreases the solvent needed will reduce your environmental impact.

How software can help: Method Selection Suite combines predicted and experimental information to identify chromatographic conditions optimized for your needs. This decreases the environmental impact of your final method and reduces the number of experiments needed to optimize it.

Innovating with Renewable Feedstocks

Petrochemicals are the foundational building blocks used to create many products we take for granted. Fuels, plastics, and even medicine have relied on non-renewable feedstocks for many years. Now that oil reserves are becoming depleted, we must transition to renewable starting materials.

Unfortunately, switching to renewables can be challenging. An example of this is using recycled plastics. Everyone loves recycling, in theory, but incorporating recycled material into new products requires extra steps to ensure the final product meets safety and performance standards.

Scientists must use their knowledge and ingenuity to develop better ways to incorporate more recycled material into their products.

How software can help: NMR software from ACD/Labs can be used to analyze recycled materials so they can be used in new plastic products. This is demonstrated by Steve Clemens, a scientist at NOVA Chemicals who uses macros in the software to ensure recycled polyethylene meets quality needs.

Reduce Derivatives

Synthesizing complex molecules takes many steps. Some reactions can’t be avoided, but some can. Each step requires additional solvent, energy, and reagents, so shorter synthetic pathways are often better than longer ones.

For example, avoiding specific protection and deprotection reactions is sometimes possible. Protecting groups are substructures that can be added to reactive sections of a molecule to prevent unwanted side products. Complex synthetic pathways will often use multiple protecting groups throughout a synthesis. Is it possible to avoid or consolidate some of these protection/deprotection steps without compromising yield or purity?

These question are challenging. In the case of pharmaceutical research, the development team, who include process chemists and engineers must find the synthetic method that will be used in manufacturing. Avoiding these unnecessary protection/deprotection steps both improves efficiency and reduces environmental impact.

How software can help: Luminata supports development teams in developing better synthetic pathways by allowing scientists to review experimental results across multiple routes. Researchers can use this information to find methods that maximize performance while avoiding unnecessary derivatives.

Applying Sustainable Science Software

Green chemistry is a rich field with many unanswered questions. Chemists should take advantage of all the tools possible to find more environmentally friendly methods of producing chemicals. While this work is challenging, it supports a growing body of knowledge that will lead to a more sustainable world.

Learn more about how software can support sustainable chemistry.

The post Enabling Green Chemistry—6 Ways Scientific Software Support Sustainability appeared first on ACD/Labs.