An artificial intelligence platform for automated PFAS subgroup classification: A discovery tool for PFAS screening

Since structural analyses and toxicity assessments have not been able to keep up with the discovery of unknown per- and polyfluoroalkyl substances (PFAS), there is an urgent need for effective categorization and grouping of PFAS. In this study, we presented PFAS-Atlas, an artificial intelligence-based platform containing a rule-based automatic classification system and a machine learning-based grouping model. Compared with previously developed classification software, the platform's classification system follows the latest Organization for Economic Co-operation and Development (OECD) definition of PFAS and reduces the number of uncategorized PFAS. In addition, the platform incorporates deep unsupervised learning models to visualize the chemical space of PFAS by clustering similar structures and linking related classes. Through real-world use cases, we demonstrate that PFAS-Atlas can rapidly screen for relationships between chemical structure and persistence, bioaccumulation, or toxicity data for PFAS. The platform can also guide the planning of the PFAS testing strategy by showing which PFAS classes urgently require further attention. Ultimately, the release of PFAS-Atlas will benefit both the PFAS research and regulation communities.

Potential safety concerns of volatile constituents released from coffee-ground-blended single-use biodegradable drinking straws: A chemical space perspective

Incorporating spent coffee grounds into single-use drinking straws for enhanced biodegradability also raises safety concerns due to increased chemical complexity. Here, volatile organic compounds (VOCs) present in coffee ground straws (CGS), polylactic acid straws (PLAS), and polypropylene straws (PPS) were characterized using headspace - solid-phase microextraction and migration assays, by which 430 and 153 VOCs of 10 chemical categories were identified by gas chromatography - mass spectrometry, respectively. Further, the VOCs were assessed for potential genetic toxicity by quantitative structure-activity relationship profiling and estimated daily intake (EDI) calculation, revealing that the VOCs identified in the CGS generally triggered the most structural alerts of genetic toxicity, and the EDIs of 37.9% of which exceeded the threshold of 0.15 μg person-1 d-1, also outnumbering that of the PLAS and PPS. Finally, 14 VOCs were prioritized due to their definite hazards, and generally higher EDIs or detection frequencies in the CGS. Meanwhile, the probability of producing safer CGS was also illustrated. Moreover, it was uncovered by chemical space that the VOCs with higher risk potentials tended to gather in the region defined by the molecular descriptor related to electronegativity or octanol/water partition coefficient. Our results provided valuable references to improve the chemical safety of the CGS, to promote consumer health, and to advance the sustainable development of food contact materials.

Chemoinformatic approaches for navigating large chemical spaces

INTRODUCTION

Large chemical spaces (CSs) include traditional large compound collections, combinatorial libraries covering billions to trillions of molecules, DNA-encoded chemical libraries comprising complete combinatorial CSs in a single mixture, and virtual CSs explored by generative models. The diverse nature of these types of CSs require different chemoinformatic approaches for navigation.

AREAS COVERED

An overview of different types of large CSs is provided. Molecular representations and similarity metrics suitable for large CS exploration are discussed. A summary of navigation of CSs in generative models is provided. Methods for characterizing and comparing CSs are discussed.

EXPERT OPINION

The size of large CSs might restrict navigation to specialized algorithms and limit it to considering neighborhoods of structurally similar molecules. Efficient navigation of large CSs not only requires methods that scale with size but also requires smart approaches that focus on better but not necessarily larger molecule selections. Deep generative models aim to provide such approaches by implicitly learning features relevant for targeted biological properties. It is unclear whether these models can fulfill this ideal as validation is difficult as long as the covered CSs remain mainly virtual without experimental verification.

SpaceGrow: efficient shape-based virtual screening of billion-sized combinatorial fragment spaces

The growing size of make-on-demand chemical libraries is posing new challenges to cheminformatics. These ultra-large chemical libraries became too large for exhaustive enumeration. Using a combinatorial approach instead, the resource requirement scales approximately with the number of synthons instead of the number of molecules. This gives access to billions or trillions of compounds as so-called chemical spaces with moderate hardware and in a reasonable time frame. While extremely performant ligand-based 2D methods exist in this context, 3D methods still largely rely on exhaustive enumeration and therefore fail to apply. Here, we present SpaceGrow: a novel shape-based 3D approach for ligand-based virtual screening of billions of compounds within hours on a single CPU. Compared to a conventional superposition tool, SpaceGrow shows comparable pose reproduction capacity based on RMSD and superior ranking performance while being orders of magnitude faster. Result assessment of two differently sized subsets of the eXplore space reveals a higher probability of finding superior results in larger spaces highlighting the potential of searching in ultra-large spaces. Furthermore, the application of SpaceGrow in a drug discovery workflow was investigated in four examples involving G protein-coupled receptors (GPCRs) with the aim to identify compounds with similar binding capabilities and molecular novelty.

"DompeKeys": a set of novel substructure-based descriptors for efficient chemical space mapping, development and structural interpretation of machine learning models, and indexing of large databases

The conversion of chemical structures into computer-readable descriptors, able to capture key structural aspects, is of pivotal importance in the field of cheminformatics and computer-aided drug design. Molecular fingerprints represent a widely employed class of descriptors; however, their generation process is time-consuming for large databases and is susceptible to bias. Therefore, descriptors able to accurately detect predefined structural fragments and devoid of lengthy generation procedures would be highly desirable. To meet additional needs, such descriptors should also be interpretable by medicinal chemists, and suitable for indexing databases with trillions of compounds. To this end, we developed-as integral part of EXSCALATE, Dompé's end-to-end drug discovery platform-the DompeKeys (DK), a new substructure-based descriptor set, which encodes the chemical features that characterize compounds of pharmaceutical interest. DK represent an exhaustive collection of curated SMARTS strings, defining chemical features at different levels of complexity, from specific functional groups and structural patterns to simpler pharmacophoric points, corresponding to a network of hierarchically interconnected substructures. Because of their extended and hierarchical structure, DK can be used, with good performance, in different kinds of applications. In particular, we demonstrate how they are very well suited for effective mapping of chemical space, as well as substructure search and virtual screening. Notably, the incorporation of DK yields highly performing machine learning models for the prediction of both compounds' activity and metabolic reaction occurrence. The protocol to generate the DK is freely available at https://dompekeys.exscalate.eu and is fully integrated with the Molecular Anatomy protocol for the generation and analysis of hierarchically interconnected molecular scaffolds and frameworks, thus providing a comprehensive and flexible tool for drug design applications.

Octanol/water partition coefficients estimated using retention times in reverse-phase liquid chromatography and calculated in silico as one of the determinant factors for pharmacokinetic parameter estimations of general chemical substances

The octanol/water partition coefficient P (logP) is a hydrophobicity index and is one of the determining factors for the pharmacokinetics of orally administered substances because it influences membrane permeability. To illustrate the wide-ranging variety of compounds in the chemical space, a two-dimensional data plot consisting of 25 blocks was previously proposed based on a substance's in silico chemical descriptors. The logP values of approximately 200 diverse chemicals (test plus reference compounds covering all 25 blocks of the chemical space) were estimated experimentally using retention times in reverse-phase liquid chromatography; these values were compared with those of authentic reference compounds with established logP values (available for 17 of 60 reference substances in the Organization for Economic Co-operation and Development Test Guideline 117). The logP values of 140 of 165 chemicals successfully estimated using four different mobile phase conditions (pH 2, 4, 7, and 10 for molecular forms) correlated significantly with those calculated using the in silico packages ChemDraw and ACD/Percepta (r > 0.72). Although substances that neighbored authentic compounds in the chemical space had precisely correlated logP values estimated experimentally and in silico, some compounds that were more distant from authentic substances showed lower logP values than those estimated in silico. These results indicate that additional authentic reference materials with wider ranging chemical diversity and their logP values from reverse-phase liquid chromatography should be included in the international test guidance to promote simple and reliable estimation of octanol/water partition coefficients, which are important determinant factors for the pharmacokinetics of general chemicals.

Graph Attention Network based mapping of knowledge relations between chemical spaces of Nuclear factor kappa B and Centella asiatica

The confounding nature of the innate immunity target Nuclear Factor kappa B (NF-κB) and its interaction with Centella asiatica (CA) molecules necessitate the intervention of advanced technologies, such as deep learning methods. The integration of chemical space concepts with deep learning technologies is a new way of knowledge mapping used to explore drug-target interactions, especially in molecular libraries derived from traditional medicine based molecular sources. The current constraint of virtual screening for mechanistic target hunting is the use of a binary classification model that includes active and inactive molecules from in vitro experiments to explore drug-target interaction. This study aims to explore the regulatory nature of the molecules from the inhibition and activation of the NF-κB bioassay data set and map this information for a knowledge-based analysis against the molecules of CA, a low-growing tropical plant. This finding has led to a new direction in the field, transitioning from the conventional active-inactive framework to a more comprehensive active-inactive-regulatory model. This approach can be thoroughly explored by leveraging a graph-based deep learning system. The study presents an innovative approach using a Graph Attention Network (GAT) to rank CA molecules in chemical space based on their similarity with NF-κB bioassay molecules, enabling the efficient analysis of complex relationships between molecules and their regulatory function. Graph Attention Network (GAT) overcomes the limitations of traditional deep learning models such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in handling non-Euclidean graph data and allows for a more precise understanding of similarity ranking by utilizing molecular graphs and attention behavior. By measuring similarity and arranging a matrix of similarity ranking based on GAT, deep neural ranking-based algorithms confirmed the regulatory behaviour of an innate immunity target NF-κB with the support of underlying inverse mapping in the surjective chemical spaces of NF-κB bioassays and CA molecular spaces. Overall, the study introduces new techniques for exploring the regulatory behaviour of complex targets like NF-κB. We then used t-SNE for clustering in chemical space and scaffold hunting for scaffold property analysis and identified nine CA molecules that exhibit regulatory behavior of NF-κB target and are recommended for further investigation.

Molecular Property Diagnostic Suite Compound Library (MPDS-CL): a structure-based classification of the chemical space

Molecular Property Diagnostic Suite Compound Library (MPDS-CL) is an open-source Galaxy-based cheminformatics web portal which presents a structure-based classification of the molecules. A structure-based classification of nearly 150 million unique compounds, obtained from 42 publicly available databases and curated for redundancy removal through 97 hierarchically well-defined atom composition-based portions, has been done. These are further subjected to 56-bit fingerprint-based classification algorithm which led to the formation of 56 structurally well-defined classes. The classes thus obtained were further divided into clusters based on their molecular weight. Thus, the entire set of molecules was put into 56 different classes and 625 clusters. This led to the assignment of a unique ID, named as MPDS-AadharID, for each of these 149,169,443 molecules. MPDS-AadharID is akin to the unique number given to citizens in India (similar to SSN in the US and NINO in the UK). The unique features of MPDS-CL are (a) several search options, such as exact structure search, substructure search, property-based search, fingerprint-based search, using SMILES, InChIKey and key-in; (b) automatic generation of information for the processing for MPDS and other galaxy tools; (c) providing the class and cluster of a molecule which makes it easier and fast to search for similar molecules and (d) information related to the presence of the molecules in multiple databases. The MPDS-CL can be accessed at https://mpds.neist.res.in:8086/ .

Art driven by visual representations of chemical space

Science and art have been connected for centuries. With the development of new computational methods, new scientific disciplines have emerged, such as computational chemistry, and related fields, such as cheminformatics. Chemoinformatics is grounded on the chemical space concept: a multi-descriptor space in which chemical structures are described. In several practical applications, visual representations of the chemical space of compound datasets are low-dimensional plots helpful in identifying patterns. However, the authors propose that the plots can also be used as artistic expressions. This manuscript introduces an approach to merging art with chemoinformatics through visual and artistic representations of chemical space. As case studies, we portray the chemical space of food chemicals and other compounds to generate visually appealing graphs with twofold benefits: sharing chemical knowledge and developing pieces of art driven by chemoinformatics. The art driven by chemical space visualization will help increase the application of chemistry and art and contribute to general education and dissemination of chemoinformatics and chemistry through artistic expressions. All the code and data sets to reproduce the visual representation of the chemical space presented in the manuscript are freely available at https://github.com/DIFACQUIM/Art-Driven-by-Visual-Representations-of-Chemical-Space- . Scientific contribution: Chemical space as a concept to create digital art and as a tool to train and introduce students to cheminformatics.

A molecule perturbation software library and its application to study the effects of molecular design constraints

Computational molecular design can yield chemically unreasonable compounds when performed carelessly. A popular strategy to mitigate this risk is mimicking reference chemistry. This is commonly achieved by restricting the way in which molecules are constructed or modified. While it is well established that such an approach helps in designing chemically appealing molecules, concerns about these restrictions impacting chemical space exploration negatively linger. In this work we present a software library for constrained graph-based molecule manipulation and showcase its functionality by developing a molecule generator. Said generator designs molecules mimicking reference chemical features of differing granularity. We find that restricting molecular construction lightly, beyond the usual positive effects on drug-likeness and synthesizability of designed molecules, provides guidance to optimization algorithms navigating chemical space. Nonetheless, restricting molecular construction excessively can indeed hinder effective chemical space exploration.

Which boronic acids are used most frequently for synthesis of bioactive molecules?

Boronic acids are essential building blocks used for the synthesis of bioactive molecules, the generation of chemical libraries and the exploration of structure-activity relationships. As a result, more than ten thousand boronic acids are commercially available. Medicinal chemists are therefore facing a challenge; which of them should they select to maximize information obtained by the synthesis of new target molecules. The present article aims to help them to make the right choices. The boronic acids used frequently in the synthesis of bioactive molecules were identified by mining several large molecular and reaction databases and their properties were analyzed. Based on the results a diverse set of boronic acids covering well the bioactive chemical space was selected and is suggested as a basis for library design for the efficient exploration of structure-activity relationships. A Boronic Acid Navigator web tool which helps chemists to make their own selection is also made available at https://bit.ly/boronics.

Non-targeted analysis (NTA) and suspect screening analysis (SSA): a review of examining the chemical exposome

Non-targeted analysis (NTA) and suspect screening analysis (SSA) are powerful techniques that rely on high-resolution mass spectrometry (HRMS) and computational tools to detect and identify unknown or suspected chemicals in the exposome. Fully understanding the chemical exposome requires characterization of both environmental media and human specimens. As such, we conducted a review to examine the use of different NTA and SSA methods in various exposure media and human samples, including the results and chemicals detected. The literature review was conducted by searching literature databases, such as PubMed and Web of Science, for keywords, such as "non-targeted analysis", "suspect screening analysis" and the exposure media. Sources of human exposure to environmental chemicals discussed in this review include water, air, soil/sediment, dust, and food and consumer products. The use of NTA for exposure discovery in human biospecimen is also reviewed. The chemical space that has been captured using NTA varies by media analyzed and analytical platform. In each media the chemicals that were frequently detected using NTA were: per- and polyfluoroalkyl substances (PFAS) and pharmaceuticals in water, pesticides and polyaromatic hydrocarbons (PAHs) in soil and sediment, volatile and semi-volatile organic compounds in air, flame retardants in dust, plasticizers in consumer products, and plasticizers, pesticides, and halogenated compounds in human samples. Some studies reviewed herein used both liquid chromatography (LC) and gas chromatography (GC) HRMS to increase the detected chemical space (16%); however, the majority (51%) only used LC-HRMS and fewer used GC-HRMS (32%). Finally, we identify knowledge and technology gaps that must be overcome to fully assess potential chemical exposures using NTA. Understanding the chemical space is essential to identifying and prioritizing gaps in our understanding of exposure sources and prior exposures. IMPACT STATEMENT: This review examines the results and chemicals detected by analyzing exposure media and human samples using high-resolution mass spectrometry based non-targeted analysis (NTA) and suspect screening analysis (SSA).

Harnessing polyhydroxylated pyrrolidines as a stabilizer of acid alpha-glucosidase (GAA) to enhance the efficacy of enzyme replacement therapy in Pompe disease

To discover small molecules as acid alpha-glucosidase (GAA) stabilizers for potential benefits of the exogenous enzyme treatment toward Pompe disease cells, we started from the initial screening of the unique chemical space, consisting of sixteen stereoisomers of 2-aminomethyl polyhydroxylated pyrrolidines (ADMDPs) to find out two primary stabilizers 17 and 18. Further external or internal structural modifications of 17 and 18 were performed to increase structural diversity, followed by the protein thermal shift study to evaluate the GAA stabilizing ability. Fortunately, pyrrolidine 21, possessing an l-arabino-typed configuration pattern, was identified as a specific potent rh-GAA stabilizer, enabling the suppression of rh-GAA protein denaturation. In a cell-based Pompe model, co-administration of 21 with rh-GAA protein significantly improved enzymatic activity (up to 5-fold) compared to administration of enzyme alone. Potentially, pyrrolidine 21 enables the direct increase of ERT (enzyme replacement therapy) efficacy in cellulo and in vivo.

Exploring chemical space in non-targeted analysis: a proposed ChemSpace tool

Non-targeted analysis (NTA) using high-resolution mass spectrometry allows scientists to detect and identify a broad range of compounds in diverse matrices for monitoring exposure and toxicological evaluation without a priori chemical knowledge. NTA methods present an opportunity to describe the constituents of a sample across a multidimensional swath of chemical properties, referred to as "chemical space." Understanding and communicating which region of chemical space is extractable and detectable by an NTA workflow, however, remains challenging and non-standardized. For example, many sample processing and data analysis steps influence the types of chemicals that can be detected and identified. Accordingly, it is challenging to assess whether analyte non-detection in an NTA study indicates true absence in a sample (above a detection limit) or is a false negative driven by workflow limitations. Here, we describe the need for accessible approaches that enable chemical space mapping in NTA studies, propose a tool to address this need, and highlight the different ways in which it could be implemented in NTA workflows. We identify a suite of existing predictive and analytical tools that can be used in combination to generate scores that describe the likelihood a compound will be detected and identified by a given NTA workflow based on the predicted chemical space of that workflow. Higher scores correspond to a higher likelihood of compound detection and identification in a given workflow (based on sample extraction, data acquisition, and data analysis parameters). Lower scores indicate a lower probability of detection, even if the compound is truly present in the samples of interest. Understanding the constraints of NTA workflows can be useful for stakeholders when results from NTA studies are used in real-world applications and for NTA researchers working to improve their workflow performance. The hypothetical ChemSpaceTool suggested herein could be used in both a prospective and retrospective sense. Prospectively, the tool can be used to further curate screening libraries and set identification thresholds. Retrospectively, false detections can be filtered by the plausibility of the compound identification by the selected NTA method, increasing the confidence of unknown identifications. Lastly, this work highlights the chemometric needs to make such a tool robust and usable across a wide range of NTA disciplines and invites others who are working on various models to participate in the development of the ChemSpaceTool. Ultimately, the development of a chemical space mapping tool strives to enable further standardization of NTA by improving method transparency and communication around false detection rates, thus allowing for more direct method comparisons between studies and improved reproducibility. This, in turn, is expected to promote further widespread applications of NTA beyond research-oriented settings.

Molecular Similarity for Drug Discovery, Target Prediction and Chemical Space Visualization

Similar drug molecules often have similar properties and activities. Therefore, quantifying molecular similarity is central to drug discovery and optimization. Here I review computational methods using molecular similarity measures developed in my group within the interdisciplinary network NCCR TransCure investigating the physiology, structural biology and pharmacology of ion channels and membrane transporters. We designed a 3D molecular shape and pharmacophore comparison algorithm to optimize weak and unselective inhibitors by scaffold hopping and discovered potent and selective inhibitors of the ion channels TRPV6 and TRPM4, of endocannabinoid membrane transport, and of the divalent metal transporters DMT1 and ZIP8. We predicted off-target effects by combining molecular similarity searches from different molecular fingerprints against target annotated compounds from the ChEMBL database. Finally, we created interactive chemical space maps reflecting molecular similarities to facilitate the selection of screening compounds and the analysis of screening results. These different tools are available online at https://gdb.unibe.ch/tools/.

Scavengome of an antioxidant

The term "scavengome" refers to the chemical space of all the metabolites that may be formed from an antioxidant upon scavenging reactive oxygen or nitrogen species (ROS/RNS). This chemical space covers a wide variety of free radical metabolites with drug discovery potential. It is very rich in structures representing an increased chemical complexity as compared to the parent antioxidant: a wide range of unusual heterocyclic structures, new CC bonds, etc. may be formed. Further, in a biological environment, this increased chemical complexity is directly translated from the localized conditions of oxidative stress that determines the amounts and types of ROS/RNS present. Biomimetic oxidative chemistry provides an excellent tool to model chemical reactions between antioxidants and ROS/RNS. In this chapter, we provide an overview on the known metabolites obtained by biomimetic oxidation of a few selected natural antioxidants, i.e., a stilbene (resveratrol), a pair of hydroxycinnamates (caffeic acid and methyl caffeate), and a flavonol (quercetin), and discuss the drug discovery perspectives of the related chemical space.

Scaffold Generator: a Java library implementing molecular scaffold functionalities in the Chemistry Development Kit (CDK)

The concept of molecular scaffolds as defining core structures of organic molecules is utilised in many areas of chemistry and cheminformatics, e.g. drug design, chemical classification, or the analysis of high-throughput screening data. Here, we present Scaffold Generator, a comprehensive open library for the generation, handling, and display of molecular scaffolds, scaffold trees and networks. The new library is based on the Chemistry Development Kit (CDK) and highly customisable through multiple settings, e.g. five different structural framework definitions are available. For display of scaffold hierarchies, the open GraphStream Java library is utilised. Performance snapshots with natural products (NP) from the COCONUT (COlleCtion of Open Natural prodUcTs) database and drug molecules from DrugBank are reported. The generation of a scaffold network from more than 450,000 NP can be achieved within a single day.

Generation of a poly-functionalized indolizine scaffold and its anticancer activity in pancreatic cancer cells

A highly efficient domino [4 + 2] annulation process was employed to construct a novel indolizine chemical scaffold. Biological investigation led us to identify 6w as a potent anticancer agent. 6w significantly inhibited cell viability in BxPC3 pancreatic cancer, MCF7 breast cancer, and PC3 prostate cancer cell lines with IC50 values of 0.47 ± 0.04, 1.82 ± 0.08 and 2.68 ± 0.08 µM, respectively. Remarkably, 6w showed a weak effect on cell viability of nontumorigenic human keratinocyte cell line HaCaT compared to the above three types of cancer cells. 6w most potently inhibited cell viability of BxPC3 cells, and 6w also potently reduced cell migration and induced apoptosis in BxPC3 cells through activation of caspase-3 and cleavage of PARP in a dose-dependent manner. These results suggest that 6w can be used for the development of potential anticancer drugs for the treatment of pancreatic cancer.

On modeling and utilizing chemical compound information with deep learning technologies: A task-oriented approach

A large number of chemical compounds are available in databases such as PubChem and ZINC. However, currently known compounds, though large, represent only a fraction of possible compounds, which is known as chemical space. Many of these compounds in the databases are annotated with properties and assay data that can be used for drug discovery efforts. For this goal, a number of machine learning algorithms have been developed and recent deep learning technologies can be effectively used to navigate chemical space, especially for unknown chemical compounds, in terms of drug-related tasks. In this article, we survey how deep learning technologies can model and utilize chemical compound information in a task-oriented way by exploiting annotated properties and assay data in the chemical compounds databases. We first compile what kind of tasks are trying to be accomplished by machine learning methods. Then, we survey deep learning technologies to show their modeling power and current applications for accomplishing drug related tasks. Next, we survey deep learning techniques to address the insufficiency issue of annotated data for more effective navigation of chemical space. Chemical compound information alone may not be powerful enough for drug related tasks, thus we survey what kind of information, such as assay and gene expression data, can be used to improve the prediction power of deep learning models. Finally, we conclude this survey with four important newly developed technologies that are yet to be fully incorporated into computational analysis of chemical information.

Synthesis of Nitrone-derived Pyrrolidine Scaffolds and Their Combinatorial Libraries to Develop Selective α-l-Rhamnosidase Inhibitors

A general and flexible approach toward the development of α-l-rhamnosidase (α-l-Rha-ase) inhibitors is described. Five enantiopure poly-substituted pyrrolidine-based scaffolds bearing the C1-aminomethyl moiety were designed and synthesized from five-membered cyclic nitrones. Each structurally diversified amide library of these scaffolds was rapidly generated via combinatorial parallel synthesis and applied for in-situ inhibition study against α-l-Rha-ase, allowing us to efficiently identify new inhibition hits. Surprisingly, all promising inhibitors are derived from the same scaffold 3. Among them, the most potent and selective inhibitor is pyrrolidine 19 with Ki =0.24 μM, approximately 24-fold more potent than the reference compound DAA (Ki =5.7 μM). It is the first study to comprehensively prepare pyrrolidine-based scaffolds and libraries for inhibition study against α-l-Rha-ase.

Differential Organic Contaminant Ionization Source Detection and Identification in Environmental Waters by Nontargeted Analysis

The development of nontargeted analysis (NTA) methods to assess environmental contaminants of emerging concern, which are not commonly monitored, is paramount, especially when no previous knowledge on the identity of the pollution source is available. We compared complementary ionization techniques, namely electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI), in the detection and identification of organic contaminants in tap and surface waters from South Florida. Furthermore, the performance of a simple rationalized NTA method was assessed by analyzing 10 complex mixtures as part of the US Environmental Protection Agency's Non-targeted Analysis Collaborative Trial interlaboratory study, where limitations of the NTA approach have been identified (e.g., number of employed databases, false positives). Different water bodies displayed unique chemical features that can be used as chemical fingerprints for source tracking and discrimination. The APCI technique detected at least threefold as many chemical features as ESI in environmental water samples, corroborating the fact that APCI is more energetic and can ionize certain classes of compounds that are traditionally difficult to ionize by liquid chromatography-mass spectrometry. Kendrick mass defect plots and Van Krevelen diagrams were applied to elucidate unique patterns and theoretical chemical space regions of anthropogenic organic compounds belonging to homologous series or similar classes covered by ESI and APCI. Overall, APCI and ESI were established as complementary, expanding the detected NTA chemical space which would otherwise be underestimated by a single ionization source operated in a single polarity setting. Environ Toxicol Chem 2022;41:1154-1164. © 2021 SETAC.

Are we ready to design oral PROTACs®?

PROTACs® are expected to strongly impact the future of drug discovery. Therefore, in this work we firstly performed a statistical study to highlight the distribution of E3 ligases and POIs collected in PROTAC-DB, the main online database focused on degraders. Moreover, since the emerging technology of protein degradation deals with large and complex chemical structures, the second part of the paper focuses on how to set up a property-based design strategy to obtain oral degraders. For this purpose, we calculated a pool of seven previously ad hoc selected 2D descriptors for the 2258 publicly available degraders in PROTAC-DB (average values: MW= 972.9 Da, nC= 49.5, NAR= 4.5, PHI= 17.3, nHDon= 4.5, nHAcc= 17.7 and TPSA= 240 Ų) and compared them to a dataset of 50 bRo5 orally approved drugs. Then, a chemical space based on nC, PHI and TPSA was built and subregions with optimal permeability and bioavailability were identified. Bioavailable degraders (ARV-110 and ARV-471) tend to be closer to the Ro5 region, using mainly semi-rigid linkers. Permeable degraders, on the other hand, are placed in an average central region of the chemical space but chameleonicity could allow them to be located closer to the two Arvinas compounds.

Has Artificial Intelligence Impacted Drug Discovery?

Artificial intelligence (AI) tools find increasing application in drug discovery supporting every stage of the Design-Make-Test-Analyse (DMTA) cycle. The main focus of this chapter is the application in molecular generation with the aid of deep neural networks (DNN). We present a historical overview of the main advances in the field. We analyze the concepts of distribution and goal-directed learning and then highlight some of the recent applications of generative models in drug design with a focus into research work from the biopharmaceutical industry. We present in some more detail REINVENT which is an open-source software developed within our group in AstraZeneca and the main platform for AI molecular design support for a number of medicinal chemistry projects in the company and we also demonstrate some of our work in library design. Finally, we present some of the main challenges in the application of AI in Drug Discovery and different approaches to respond to these challenges which define areas for current and future work.

Substituents of life: The most common substituent patterns present in natural products

Comparison of substituents present in natural products with the substituents found in average synthetic molecules reveals considerable differences between these two groups. The natural products substituents contain mostly oxygen heteroatoms, are structurally more complex, often containing double bonds and are rich in stereocenters. Substituents found in synthetic molecules contain nitrogen and sulfur heteroatoms, halogenes and more aromatic and particularly heteroaromatic rings. The characteristics of substituents typical for natural products identified here can be useful in the medicinal chemistry context, for example to guide the synthesis of natural product-like libraries and natural product-inspired fragment collections. The results may be used also to support compound derivatization strategies and the design of pseudo-natural natural products.

Synthesis of sp3-rich chiral bicyclo[3.3.1]nonanes for chemical space expansion and study of biological activities

Chiral sp³-rich bicyclo[3.3.1]nonane scaffolds 10-12 were synthesized as single diastereomers from aldehyde 9, which was prepared from 4,4-dimethoxycyclohexa-2,5-dienone through a copper-catalyzed enantioselective reduction. Three different types of intramolecular addition reactions were studied: SmI₂-mediated reductive cyclization, base-promoted aldol reaction, and one-pot Mannich reaction. We succeeded in introducing three side-chains to scaffold 11 and construct an sp³-rich compound library in both enantiomeric variants by simply changing the chirality of the ligands. The biological evaluation revealed that all synthesized compounds exhibited a concentration-dependent inhibition of hypoxia-inducible factor-1 (HIF-1) transcriptional activity, with IC₅₀ values in the range of 17.2-31.7 µM, whereas their effects on cell viability were varied (IC₅₀ = 3.5 to > 100 µM). The most active compound 16f inhibits the accumulation of HIF-1α protein and mRNA in hypoxia, indicating that it has a mechanism of action distinctly different from other known compounds bearing the common bicyclo[3.3.1]nonane skeleton.

Analysis of the uncharted, druglike property space by self-organizing maps

Physicochemical properties are fundamental to predict the pharmacokinetic and pharmacodynamic behavior of drug candidates. Easily calculated descriptors such as molecular weight and logP have been found to correlate with the success rate of clinical trials. These properties have been previously shown to highlight a sweet-spot in the chemical space associated with favorable pharmacokinetics, which is superior against other regions during hit identification and optimization. In this study, we applied self-organizing maps (SOMs) trained on sixteen calculated properties of a subset of known drugs for the analysis of commercially available compound databases, as well as public biological and chemical databases frequently used for drug discovery. Interestingly, several regions of the property space have been identified that are highly overrepresented by commercially available chemical libraries, while we found almost completely unoccupied regions of the maps (commercially neglected chemical space resembling the properties of known drugs). Moreover, these underrepresented portions of the chemical space are compatible with most rigorous property filters applied by the pharma industry in medicinal chemistry optimization programs. Our results suggest that SOMs may be directly utilized in the strategy of library design for drug discovery to sample previously unexplored parts of the chemical space to aim at yet-undruggable targets.

Classifying natural products from plants, fungi or bacteria using the COCONUT database and machine learning

Natural products (NPs) represent one of the most important resources for discovering new drugs. Here we asked whether NP origin can be assigned from their molecular structure in a subset of 60,171 NPs in the recently reported Collection of Open Natural Products (COCONUT) database assigned to plants, fungi, or bacteria. Visualizing this subset in an interactive tree-map (TMAP) calculated using MAP4 (MinHashed atom pair fingerprint) clustered NPs according to their assigned origin ( https://tm.gdb.tools/map4/coconut_tmap/ ), and a support vector machine (SVM) trained with MAP4 correctly assigned the origin for 94% of plant, 89% of fungal, and 89% of bacterial NPs in this subset. An online tool based on an SVM trained with the entire subset correctly assigned the origin of further NPs with similar performance ( https://np-svm-map4.gdb.tools/ ). Origin information might be useful when searching for biosynthetic genes of NPs isolated from plants but produced by endophytic microorganisms.

Twitter integration of chemistry software tools

Social media activity on a research article is considered to be an altmetric, a new measure to estimate research impact. Demonstrating software on Twitter is a powerful way to attract attention from a larger audience. Twitter integration of software can also lower the barriers to trying the tools and make it easier to save and share the output. We present three case studies of Twitter bots for cheminformatics: retrosynthetic analysis, 3D molecule viewer, and 2D chemical structure editor. These bots make software research more accessible to a broader range of people and facilitate the sharing of chemical knowledge, concepts, and ideas.

Identification of novel Atg3-Atg8 inhibitors using virtual screening for autophagy modulation

A collection of 9050 natural products, their derivatives, and mimetics, was virtually screened against the human Atg3-Atg8 (Atg - autophagy) binding scaffold. By blocking this interaction, the lipidation of Atg8 does not occur and the formation of autophagosomes is inhibited. Forty-three (43) potential ligands were tested using enhanced Green Fluorescent Protein (eGFP) tagged LC3, the human ortholog of Atg8, in MCF7 breast cancer cells. Three hits showed single digit µM IC₅₀ values with AT110, an isoflavone derivative, being the best at 1.2 ± 0.6 µM. Molecular modelling against Atg8 in conjunction with structural activity relationship (SAR) strongly supports the binding to this target. Testing in a panel of cancer cell lines showed little cytotoxic effect as compared to chloroquine. However, same concentration of AT110 was shown to be toxic to young zebrafish embryos. This can be explained in terms of the autophagy process being very active in the zebrafish embryos rendering them susceptible to AT110 whereas in the cancer cells tested the autophagy is not usually active. Nevertheless, AT110 blocks autophagy flux in the zebrafish confirming that the ligand is modulating autophagy. A small molecule non-cytotoxic autophagy inhibitor would open the door for adjunct therapies to bolster many established anticancer drugs, reducing their efficacious concentration thus limiting undesirable site effects. In addition, since many cancer types rely on the autophagy mechanism to survive a therapeutic regime, recurrence can potentially be reduced. The discovery of AT110 is an important step in establishing such an adjunct therapy.

Investigation of the Click-Chemical Space for Drug Design Using ZINClick

This chapter provides a brief overview of the applications of ZINClick virtual library. In the last years, we have investigated the click-chemical space covered by molecules containing the triazole ring and generated a database of 1,2,3-triazoles called ZINClick, starting from literature reported alkynes and azides synthesizable in no more than three synthetic steps from commercially available products. This combinatorial database contains millions of 1,4-disubstituted 1,2,3-triazoles that are easily synthesizable. The library is regularly updated and can be freely downloaded from http://www.ZINClick.org . This virtual library is a good starting point to explore a new portion of chemical space.

Recent efforts for drug identification from phytochemicals against SARS-CoV-2: Exploration of the chemical space to identify druggable leads

Nature, which remains a central drug discovery pool, is always looked upon to find a putative druggable lead. The natural products and phytochemical derived from plants are essential during a global health crisis. This class represents one of the most practical and promising approaches to decrease pandemic's intensity owing to their therapeutic potential. The present manuscript is therefore kept forth to give the researchers updated information on undergoing research in allied areas of natural product-based drug discovery, particularly for Covid-19 disease. The study briefly shreds evidence from in vitro and in silico researches done so far to find a lead molecule against Covid-19. Following this, we exhaustively explored the concept of chemical space and molecular similarity parameters for the drug discovery about the lead(s) generated from in silico-based studies. The comparison was drawn using FDA-approved anti-infective agents during 2015–2020 using key descriptors to evaluate druglike properties. The outcomes of results were further corroborated using Molecular Dynamics studies which suggested the outcomes in alignment with chemical space ranking. In a nutshell, current research work aims to provide a holistic strategic approach to drug design, keeping in view the identified phytochemicals against Covid-19.

One-pot parallel synthesis of 1,3,5-trisubstituted 1,2,4-triazoles

An implementation of the three-component one-pot approach to unsymmetrical 1,3,5-trisubstituted-1,2,4-triazoles into combinatorial chemistry is described. The procedure is based on the coupling of amidines with carboxylic acids and subsequent cyclization with hydrazines. After the preliminary assessment of the reagent scope, the method had 81% success rate in parallel synthesis. It was shown that over a billion-sized chemical space of readily accessible ("REAL") compounds may be generated based on the proposed methodology. Analysis of physicochemical parameters shows that the library contains significant fractions of both drug-like and "beyond-rule-of-five" members. More than 10 million of accessible compounds meet the strictest lead-likeness criteria. Additionally, 195 Mln of sp³-enriched compounds can be produced. This makes the proposed approach a valuable tool in medicinal chemistry.

MolFinder: an evolutionary algorithm for the global optimization of molecular properties and the extensive exploration of chemical space using SMILES

Here, we introduce a new molecule optimization method, MolFinder, based on an efficient global optimization algorithm, the conformational space annealing algorithm, and the SMILES representation. MolFinder finds diverse molecules with desired properties efficiently without any training and a large molecular database. Compared with recently proposed reinforcement-learning-based molecule optimization algorithms, MolFinder consistently outperforms in terms of both the optimization of a given target property and the generation of a set of diverse and novel molecules. The efficiency of MolFinder demonstrates that combinatorial optimization using the SMILES representation is a promising approach for molecule optimization, which has not been well investigated despite its simplicity. We believe that our results shed light on new possibilities for advances in molecule optimization methods.

Exploiting cheminformatic and machine learning to navigate the available chemical space of potential small molecule inhibitors of SARS-CoV-2

The current life-threatening and tenacious pandemic eruption of coronavirus disease in 2019 (COVID-19) has posed a significant global hazard concerning high mortality rate, economic meltdown, and everyday life distress. The rapid spread of COVID-19 demands countermeasures to combat this deadly virus. Currently, there are no drugs approved by the FDA to treat COVID-19. Therefore, discovering small molecule therapeutics for treating COVID-19 infection is essential. So far, only a few small molecule inhibitors are reported for coronaviruses. There is a need to expand the small chemical space of coronaviruses inhibitors by adding potent and selective scaffolds with anti-COVID activity. In this context, the huge antiviral chemical space already available can be analysed using cheminformatic and machine learning to unearth new scaffolds. We created three specific datasets called "antiviral dataset" (N = 38,428) "drug-like antiviral dataset" (N = 20,963) and "anticorona dataset" (N = 433) for this purpose. We analyzed the 433 molecules of "anticorona dataset" for their scaffold diversity, physicochemical distributions, principal component analysis, activity cliffs, R-group decomposition, and scaffold mapping. The scaffold diversity of the "anticorona dataset" in terms of Murcko scaffold analysis demonstrates a thorough representation of diverse chemical scaffolds. However, physicochemical descriptor analysis and principal component analysis demonstrated negligible drug-like features for the "anticorona dataset" molecules. The "antiviral dataset" and "drug-like antiviral dataset" showed low scaffold diversity as measured by the Gini coefficient. The hierarchical clustering of the "antiviral dataset" against the "anticorona dataset" demonstrated little molecular similarity. We generated a library of frequent fragments and polypharmacological ligands targeting various essential viral proteins such as main protease, helicase, papain-like protease, and replicase polyprotein 1ab. Further structural and chemical features of the "anticorona dataset" were compared with SARS-CoV-2 repurposed drugs, FDA-approved drugs, natural products, and drugs currently in clinical trials. Using machine learning tool DCA (DMax Chemistry Assistant), we converted the "anticorona dataset" into an elegant hypothesis with significant functional biological relevance. Machine learning analysis uncovered that FDA approved drugs, Tizanidine HCl, Cefazolin, Raltegravir, Azilsartan, Acalabrutinib, Luliconazole, Sitagliptin, Meloxicam (Mobic), Succinyl sulfathiazole, Fluconazole, and Pranlukast could be repurposed as effective drugs for COVID-19. Fragment-based scaffold analysis and R-group decomposition uncovered pyrrolidine and the indole molecular scaffolds as the potent fragments for designing and synthesizing the novel drug-like molecules for targeting SARS-CoV-2. This comprehensive and systematic assessment of small-molecule viral therapeutics' entire chemical space realised critical insights to potentially privileged scaffolds that could aid in enrichment and rapid discovery of efficacious antiviral drugs for COVID-19.

Chemical space and diversity of seaweed metabolite database (SWMD): A cheminformatics study

Seaweeds have attracted attention in the past decade as a biological source of highly diverse secondary metabolites with great potential in the industrial and pharmaceutical sciences. Herein, we represent a comprehensive cheminformatics study to compare the chemical diversity of seaweed metabolites based on their taxonomic source. Seaweed Metabolite Database (SWMD) was utilized in this study. The compounds were manually categorized into three datasets, namely red algae (Rhodophyta, n = 645), brown algae (Phaeophyta, n = 220), and green algae (Chlorophyta, n = 32). The compounds in each dataset were curated to generate six chemical descriptors of pharmaceutical interest for each molecule, which were later used to visualize the chemical space of these metabolites by principal component analysis. Scaffolds were generated by removing side chains and keeping the core part of each molecule. Scaffold diversity among the tested datasets was quantified using Cyclic System Retrieval Curves. Green algae metabolites in SWMD possessed the highest scaffold diversity followed by brown and red algae metabolites, respectively. Three structural binary fingerprints, including ECFP_4, MACCS keys, and PubChem were computed indicating that the red algae metabolites had the highest fingerprint diversity followed by the green and brown algae metabolites respectively. Finally, Consensus Diversity Plots were generated to assess the global diversity considering both scaffold and fingerprint diversity. It was concluded that green algae metabolites in the SWMD are the most diverse regarding chemical descriptors of pharmaceutical relevance and scaffolds. While red algae possess the highest fingerprint diversity.

A cheminformatic study on chemical space characterization and diversity analysis of 5-LOX inhibitors

The process of blocking 5-lipoxygenase (5-LOX) catalyzed leukotriene biosynthesis has been recognized for the past few decades as a promising therapeutic strategy for acute inflammatory, allergic, and respiratory diseases. Due to the toxicity effect of FDA approved 5-LOX inhibitor zileuton, novel 5-LOX inhibitors have been sought by the scientific community. As a result, a significant and relevant amount of information on the structure-activity of 5-LOX inhibitors has been released and stored in public databases. In this study, we aimed at the comprehensive cheminformatic characterization of the diversity and complexity of the chemical space of 5-LOX inhibitors and its activating protein FLAP inhibitors by comparing it with the Approved drug space and virtual LOX library. The visual representation of the property space indicates some compounds in the 5-LOX inhibitors space broaden the traditional medicinal space. The structural diversity of the databases is computed using complementary approaches, including Physicochemical Property (PCP) descriptors, molecular fingerprints, and molecular scaffold. With the apparent exception of approved drugs, the 5-LOX dataset shows more diversity compared to FLAP and LOX virtual library set. This study was able to identify the underlying patterns in the chemical and pharmacological properties space that were decisive for the drug discovery and development of 5-LOX inhibitors.

One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome

Background

Molecular fingerprints are essential cheminformatics tools for virtual screening and mapping chemical space. Among the different types of fingerprints, substructure fingerprints perform best for small molecules such as drugs, while atom-pair fingerprints are preferable for large molecules such as peptides. However, no available fingerprint achieves good performance on both classes of molecules.

Results

Here we set out to design a new fingerprint suitable for both small and large molecules by combining substructure and atom-pair concepts. Our quest resulted in a new fingerprint called MinHashed atom-pair fingerprint up to a diameter of four bonds (MAP4). In this fingerprint the circular substructures with radii of r = 1 and r = 2 bonds around each atom in an atom-pair are written as two pairs of SMILES, each pair being combined with the topological distance separating the two central atoms. These so-called atom-pair molecular shingles are hashed, and the resulting set of hashes is MinHashed to form the MAP4 fingerprint. MAP4 significantly outperforms all other fingerprints on an extended benchmark that combines the Riniker and Landrum small molecule benchmark with a peptide benchmark recovering BLAST analogs from either scrambled or point mutation analogs. MAP4 furthermore produces well-organized chemical space tree-maps (TMAPs) for databases as diverse as DrugBank, ChEMBL, SwissProt and the Human Metabolome Database (HMBD), and differentiates between all metabolites in HMBD, over 70% of which are indistinguishable from their nearest neighbor using substructure fingerprints.

Conclusion

MAP4 is a new molecular fingerprint suitable for drugs, biomolecules, and the metabolome and can be adopted as a universal fingerprint to describe and search chemical space. The source code is available at https://github.com/reymond-group/map4 and interactive MAP4 similarity search tools and TMAPs for various databases are accessible at http://map-search.gdb.tools/ and http://tm.gdb.tools/map4/.

Diversity-oriented generation and biological evaluation of new chemical scaffolds bearing a 2,2-dimethyl-2H-chromene unit: Discovery of novel potent ANO1 inhibitors

Chemical territory bearing a 2,2-dimethyl-2H-chromene motif was expanded by utilizing an o-hydroxy aldehyde group of 5-hydroxy-2,2-dimethyl-2H-chromene-6-carbaldehyde as a synthetic handle to install distinctive morphology and functionality of each scaffold. Cell based assays and in silico docking analysis led us to discover that these new compounds exhibit inhibitory effect on anoctamin1 (ANO1). ANO1 is amplified and highly expressed in various carcinomas including prostate cancer, esophageal cancer, breast cancer, and pancreatic cancer. Biological assays revealed that (E)-1-(7,7-dimethyl-7H-furo[2,3-f]chromen-2-yl)-3-(1H-pyrrol-2-yl)prop-2-en-1-one (3n, Ani-FCC) is a novel, potent and selective ANO1 inhibitor with an IC₅₀ value of 1.23 μM. 3n showed 144 times stronger activity on ANO1 inhibition than ANO2 inhibition and did not alter the chloride channel activity of CFTR and the intracellular calcium signaling. Notably, 3n strongly decreased cell viability of PC-3 and FaDu cells expressing high levels of ANO1 with a decrease in ANO1 protein levels. In addition, 3n significantly enhanced apoptosis via activation of caspase 3 and cleavage of PARP in PC-3 and FaDu cells. This study shows that a novel ANO1 inhibitor, 3n, can be a potential candidate for the treatment of cancers overexpressing ANO1, such as prostate cancer and esophageal cancer.

Chemical space exploration based on recurrent neural networks: applications in discovering kinase inhibitors

With the rise of artificial intelligence (AI) in drug discovery, de novo molecular generation provides new ways to explore chemical space. However, because de novo molecular generation methods rely on abundant known molecules, generated molecules may have a problem of novelty. Novelty is important in highly competitive areas of medicinal chemistry, such as the discovery of kinase inhibitors. In this study, de novo molecular generation based on recurrent neural networks was applied to discover a new chemical space of kinase inhibitors. During the application, the practicality was evaluated, and new inspiration was found. With the successful discovery of one potent Pim1 inhibitor and two lead compounds that inhibit CDK4, AI-based molecular generation shows potentials in drug discovery and development.

Enabling design of screening libraries for antibiotic discovery by modeling ChEMBL data

It is critical to identify novel antibiotics. Yet, the scientific community has struggled in this pursuit because we do not understand which molecules will penetrate the bacterial outer envelope. In this work, we have identified a large dataset of compounds known to reach their targets in bacterial cells (penetrators) and compared them with molecules that do not (non-penetrators). Our dataset, extracted from the ChEMBL database, is a useful tool to guide the selection of molecules for antibiotic screening. Simple random forest classification models are able to correctly identify penetrators from non-penetrators. The model demonstrated ~87% accuracy, with high precision (~88%) and recall (~97%) in identifying penetrators of Gram-positive bacteria. A paucity of data for non-penetrators was a major hurdle to model-building; we observed a ~86% negative predictive value, but only a ~57% specificity. Accumulation of data on non-penetrators is therefore necessary. Data for Gram-negative bacteria was also sparse, but a larger fraction of these data represented non-penetrators. Correspondingly, the resultant models performed well in predicting those molecules that would fail to enter Gram-negative cells, but were relatively weaker in correctly predicting penetrators. A comparison of physicochemical properties of penetrators and non-penetrators suggests only marginal differences exist. Therefore, it may be difficult to identify overarching rules for generation of screening libraries for antibiotic discovery, based purely on physicochemical properties alone. Instead, models such as ours should be of use. Our models are highly preliminary and based on phenotypic data, but a similar large dataset directly addressing accumulation of chemical matter in bacterial cells is currently unavailable. Hence, our models represent the cutting edge in design of screening libraries for antibiotic discovery until appropriate data can be compiled.

Craig plot 2.0: an interactive navigation in the substituent bioisosteric space

Bioisosteric replacement is a standard technique that is used in medicinal chemistry to design analogs of bioactive molecules with similar biological activity and with additional improved characteristics. Successful application of this technique relies on a good knowledge of physicochemical properties of common organic substituents and an efficient way to navigate their space. In this study the properties of the most common substituents present in bioactive molecules are analysed and a freely-available web tool https://bit.ly/craigplot that allows visualization, analysis and selection of bioisosteric substituents is presented.

Expansion of chemical space based on a pyrrolo[1,2-a]pyrazine core: Synthesis and its anticancer activity in prostate cancer and breast cancer cells

In connection with our continued research to generate new aza-fused heteroaromatic chemical scaffolds, we developed a highly atom-economical three-component route to novel 3,4-dihydropyrrolo[1,2-a]pyrazine ring skeleton multi-functionalized on the pyrazine unit. This [4+1+1] annulation approach led us to gain access to a new N-fused bicyclic chemical space having two distinctive functional groups (heteroaryl and aroyl) in a trans manner. Investigation of anticancer activity of the synthesized compounds and their derivatives revealed that (3R*,4S*)-3-(4-bromophenyl)-4-(4-fluorobenzoyl)-2-(2-oxo-2-phenylethyl)-3,4-dihydropyrrolo[1,2-a]pyrazin-2-ium bromide (3h) has potent anticancer activity. 3h significantly inhibited cell viability in prostate cancer cells (PC-3) and breast cancer cells (MCF-7) with IC₅₀ value of 1.18 ± 0.05 μM and 1.95 ± 0.04 μM, respectively. In addition, 3h strongly reduced cell migration in a dose dependent manner, and induced apoptosis via caspase-3 activation and cleavage of PARP in PC-3 and MCF-7 cells. Our results in this study imply that 3h can be a potential anticancer agent against prostate cancer and breast cancer.

The Groebke-Blackburn-Bienaymé Reaction

Imidazo[1,2-a]pyridine is a well-known scaffold in many marketed drugs, such as Zolpidem, Minodronic acid, Miroprofen and DS-1 and it also serves as a broadly applied pharmacophore in drug discovery. The scaffold revoked a wave of interest when Groebke, Blackburn and Bienaymé reported independently a new three component reaction resulting in compounds with the imidazo[1,2-a]-heterocycles as a core structure. During the course of two decades the Groebke Blackburn Bienaymé (GBB-3CR) reaction has emerged as a very important multicomponent reaction (MCR), resulting in over a hundred patents and a great number of publications in various fields of interest. Now two compounds derived from GBB-3CR chemistry received FDA approval. To celebrate the first 20 years of GBB-chemistry, we present an overview of the chemistry of the GBB-3CR, including an analysis of each of the three starting material classes, solvents and catalysts. Additionally, a list of patents and their applications and a more in-depth summary of the biological targets that were addressed, including structural biology analysis, is given.

Cheminformatics Explorations of Natural Products

The chemistry of natural products is fascinating and has continuously attracted the attention of the scientific community for many reasons including, but not limited to, biosynthesis pathways, chemical diversity, the source of bioactive compounds and their marked impact on drug discovery. There is a broad range of experimental and computational techniques (molecular modeling and cheminformatics) that have evolved over the years and have assisted the investigation of natural products. Herein, we discuss cheminformatics strategies to explore the chemistry and applications of natural products. Since the potential synergisms between cheminformatics and natural products are vast, we will focus on three major aspects: (1) exploration of the chemical space of natural products to identify bioactive compounds, with emphasis on drug discovery; (2) assessment of the toxicity profile of natural products; and (3) diversity analysis of natural product collections and the design of chemical collections inspired by natural sources.

Compositional space: A guide for environmental chemists on the identification of persistent and bioaccumulative organics using mass spectrometry

Since 2001, twenty-eight halogenated groups of persistent organic pollutants (POPs) have been banned or restricted by the Stockholm Convention. Identifying new POPs among the hundreds of thousands of anthropogenic chemicals is a major challenge that is increasingly being met by state-of-the-art mass spectrometry (MS). The first step to identification of a contaminant molecule (M) is the determination of the type and number of its constituent elements, viz. its elemental composition, from mass-to-charge (m/z) measurements and ratios of isotopic peaks (M + 1, M + 2 etc.). Not every combination of elements is possible. Boundaries exist in compositional space that divides feasible and improbable compositions as well as different chemical classes. This study explores the compositional space boundaries of persistent and bioaccumulative organics. A set of ~305,134 compounds (PubChem) was used to visualize the compositional space occupied by F, Cl, and Br compounds, as defined by m/z and isotope ratios. Persistent bioaccumulative organics, identified by in silico screening of 22,049 commercial chemicals, reside in more constrained regions characterized by a higher degree of halogenation. In contrast, boundaries surrounding non-halogenated chemicals could not be defined. Finally, a script tool (R code) was developed to select potential POPs from high resolution MS data. When applied to household dust (SRM 2585), this approach resulted in the discovery of previously unknown chlorofluoro flame retardants.

Discovery of novel indolylarylsulfones as potent HIV-1 NNRTIs via structure-guided scaffold morphing

For more in-depth exploration of the chemical space around the entrance channel of HIV-1 reverse transcriptase (RT), a series of novel indolylarylsulfones (IASs) bearing different chiral N-substituted pyrrolidine, azetidine or substituted sulfonamide groups at indole-2-carboxamide were designed and synthesized as potent HIV NNRTIs by structure-guided scaffold morphing approach. All the IASs exhibited moderate to excellent potency against wild-type HIV-1 with EC₅₀ values ranging from 0.0043 μM to 4.42 μM. Notably, compound 27 (EC₅₀ = 4.7 nM, SI = 5183) and 33 (EC₅₀ = 4.3 nM, SI = 7083) were identified as the most potent compounds, which were more active than nevirapine, lamivudine and efavirenz, and also reached the same order of etravirine. Furthermore, some compounds maintained excellent activity against various single HIV-1 mutants (L100I, K103 N, E138K, Y181C) as well as one double mutant (F227L/V106A) with EC₅₀ values in low-micromolar concentration ranges. Notably, 34 displayed outstanding potency against F227L/V106A (EC₅₀ = 0.094 μM), and also showed exceptional activity against E138K (EC₅₀ = 0.014 μM), L100I (EC₅₀ = 0.011 μM) and K103 N (EC₅₀ = 0.025 μM). Additionally, most compounds showed markedly reduced cytotoxicity (CC₅₀) compared to lead compounds, especially 36 (CC₅₀ > 234.91 μM, SI > 18727) and 37 (CC₅₀ > 252.49 μM, SI > 15152). Preliminary SARs and molecular modeling studies were also discussed in detail, which may provide valuable insights for further optimization.

Trivalent Phosphorus and Phosphines as Components of Biochemistry in Anoxic Environments

Phosphorus is an essential element for all life on Earth, yet trivalent phosphorus (e.g., in phosphines) appears to be almost completely absent from biology. Instead phosphorus is utilized by life almost exclusively as phosphate, apart from a small contingent of other pentavalent phosphorus compounds containing structurally similar chemical groups. In this work, we address four previously stated arguments as to why life does not explore trivalent phosphorus: (1) precedent (lack of confirmed instances of trivalent phosphorus in biochemicals suggests that life does not have the means to exploit this chemistry), (2) thermodynamic limitations (synthesizing trivalent phosphorus compounds is too energetically costly), (3) stability (phosphines are too reactive and readily oxidize in an oxygen (O₂)-rich atmosphere), and (4) toxicity (the trivalent phosphorus compounds are broadly toxic). We argue that the first two of these arguments are invalid, and the third and fourth arguments only apply to the O₂-rich environment of modern Earth. Specifically, both the reactivity and toxicity of phosphines are specific to aerobic life and strictly dependent on O₂-rich environment. We postulate that anaerobic life persisting in anoxic (O₂-free) environments may exploit trivalent phosphorus chemistry much more extensively. We review the production of trivalent phosphorus compounds by anaerobic organisms, including phosphine gas and an alkyl phosphine, phospholane. We suggest that the failure to find more such compounds in modern terrestrial life may be a result of the strong bias of the search for natural products toward aerobic organisms. We postulate that a more thorough identification of metabolites of the anaerobic biosphere could reveal many more trivalent phosphorus compounds. We conclude with a discussion of the implications of our work for the origin and early evolution of life, and suggest that trivalent phosphorus compounds could be valuable markers for both extraterrestrial life and the Shadow Biosphere on Earth.

Microfluidic triple-gradient generator for efficient screening of chemical space

Generation of a combinatorial gradient for multiple chemicals is essential for studies of biochemical stimuli, chemoattraction, protein crystallization and others. While currently available platforms require complex design/settings to obtain a double-gradient chemical matrix, we herein report for the first time a simple triple-gradient matrix (TGM) device for efficient screening of chemical space. The TGM device is composed of two glass slides and works following the concept of SlipChip. The device utilizes XYZ space to distribute three chemicals and establishes a chemical gradient matrix within 5 min. The established matrix contains 24 or 104 screening conditions depending on the device used, which covers a concentration range of [0.117-1, 0.117-1 and 0.686-1] and [0.0830-1, 0.0830-1, 0.686-1] respectively for the three chemicals. With the triple gradients built simultaneously, this TGM device provides order-of-magnitude improvement in screening efficiency over existing single- or double-gradient generators. As a proof of concept, we applied the device to screen the crystallization conditions for two model proteins of lysozyme and trypsin and confirmed the crystal structures using X-ray diffraction. Furthermore, we successfully obtained the crystallization condition of adhesin competence repressor, a protein that senses the alterations in intracellular zinc concentrations. We expect the TGM system to be widely used as an analytical platform for material synthesis and chemical screening beyond for protein crystallization.

Design, synthesis, and biological evaluation of novel pyrrolo[1,2-a]pyrazine derivatives

A pyrrolo[1,2-a]pyrazine-based chemical territory was expanded via construction of new chemical library with distinctive substitution patterns, which was made possible by regiodivergent electrophilic acylation followed by aldol condensation. Biological screening of the compounds in this class revealed that the viability of human lymphoma U937 cells was strongly inhibited by 6b with a methoxy group at the o-position of the aromatic ring, but not by compounds 6t-w bearing a halogen at the o-position. Furthermore, 6x having a 2,4-dimethoxyphenyl group inhibited the survival of U937 cells more potently than 6b. In contrast, 6y possessing a 2,5-dimethoxyphenyl moiety did not show effective inhibition, implying the importance of orientation of the substituent(s) around the benzene ring. The anticancer action of 6x with safe therapeutic window could be associated with the FTase-p38 signaling axis.