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In order to better foramize it, the notorious inverse-QSAR problem (finding structures of given QSAR-predicted properties) is considered in this paper as a two-step process including (i) finding "seed" descriptor vectors corresponding to user-constrained QSAR model output values and (ii) identifying the chemical structures best matching the "seed" vectors. The main development effort here was focused on the latter stage, proposing a new attention-based conditional variational autoencoder neural-network architecture based on recent developments in attention-based methods. The obtained results show that this workflow was capable of generating compounds predicted to display desired activity while being completely novel compared to the training database (ChEMBL). Moreover, the generated compounds show acceptable druglikeness and synthetic accessibility. Both pharmacophore and docking studies were carried out as "orthogonal"

Nowadays, drug discovery is inevitably intertwined with the usage of large compound collections. Understanding of their chemotype composition and physicochemical property profiles is of the highest importance for successful hit identification. Efficient polyfunctional tools allowing multifaceted analysis of constantly growing chemical libraries must be Big Data-compatible. Here, we present the freely accessible ChemSpace Atlas (https://chematlas.chimie.unistra.fr), which includes almost 40K hierarchically organized Generative Topographic Maps (GTM) accommodating up to 500 M compounds covering fragment-like, lead-like, drug-like, PPI-like, and NP-like chemical subspaces. They allow users to navigate and analyze ZINC, ChEMBL, and COCONUT from multiple perspectives on different scales: from a bird's eye view of the entire library to structural pattern detection in small clusters. Around 20 physicochemical properties and almost 750 biological activities can be visualized (associated with map zones), supporting activity profiling and analogue search. Moreover, ChemScape Atlas will be extended toward new chemical subspaces (e.g., DNA-encoded libraries and synthons) and functionalities (ADMETox profiling and property-guided de novo compound generation).

In chemical library analysis, it may be useful to describe libraries as individual items, rather than as collections of compounds. This is particularly true for ultra-large non-cherry pickable compound mixtures, such as DNA-Encoded Libraries (DELs). In this sense, the Chemical Library Space (CLS) is useful for the management of a portfolio of libraries, just like Chemical Space (CS) helps managing a portfolio of molecules. Several possible CLSs were previously defined using vectorial library representations obtained from Generative Topographic Mapping (GTM). Given the steadily growing number of DEL designs, the CLS becomes “crowded”, and requires analysis tools beyond pairwise library comparison. Therefore, herein we investigate the cartography of CLS on meta-(µ)GTMs – “meta” to remind that these are maps of the CLS, itself based on responsibility vectors issued by regular CS GTMs. 2,5K DELs and ChEMBL (reference) were projected on the µGTM, producing landscapes of library-specific properties. These describe both inter-library similarity and intrinsic library characteristics in the same view, herewith facilitating the selection of the best project-specific libraries.

Autoencoders represent a promising technique for the inverse quantitative structure-activity relationship (QSAR) task. However, undesirable bias, such as atom ordering, affects the neighbourhood behaviour of autoencoders’ latent space and, consequently, usage of the latent vectors as variables in machine-learning models. Here, we report a graph-based autoencoder which implements vector quantisation operation (VQGAE). The latter allows to learn vectorial representation of molecular fragments in an unsupervised manner. The latent vectors or fragment count vectors of VQGAE are permutation invariant and perform well in similarity ranking. In QSAR benchmarks, the VQGAE’s latent vectors outperform those derived by some earlier developed SMILES-based and graph-based autoencoders. Finally, VQGAE autoencoder was used in the inverse QSAR task in order to design new A2A adenosine receptor inhibitors.

The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of a community effort, the “Billion molecules against Covid-19 challenge”, to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 potentially active molecules, which were subsequently ranked to find ‘consensus compounds’. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (Nsp12 domain), and (alpha) spike protein S. Overall, 27 potential inhibitors were experimentally confirmed by binding-, cleavage-, and/or viral suppression assays and are presented here. All results are freely available and can be taken further downstream without IP restrictions. Overall, we show the effectiveness of computational techniques, community efforts, and communication across research fields (i.e., protein expression and crystallography, in silico modeling, synthesis and biological assays) to accelerate the early phases of drug discovery.

The computational design of chiral organic catalysts for asymmetric synthesis is a promising technol-ogy, which may significantly reduce the material and human resources required for the preparation of enantiopure compounds. Herein, for the modelling of catalysts enantioselectivity, we propose to use the Multi-Instance Learning (MIL) approach accounting for multiple catalyst conformers and requir-ing neither conformers selection nor their spatial alignment. A catalyst was represented by an ensem-ble of conformers, each encoded by 3D pmapper descriptors. A catalyzed chemical transformation was converted into a single molecular graph - ondensed Graph of Reaction (CGR) - encoded by 2D fragment descriptors. A whole chemical reaction was finally encoded by concatenated 3D catalyst and 2D transformation descriptors. The performance of the proposed method was demonstrated in the modelling of enantioselectivity of homogeneous and phase-transfer reactions and compared with some state-of-the-art approaches

Increasing antimicrobial resistance (AMR) represents a global healthcare threat. Methods for rapid selection of optimal antibiotic treatment are urgently needed to decrease the spread of AMR and associated mortality. The use of machine learning (ML) techniques based on genomic data to predict resistance phenotypes serves as a solution for the acceleration of the clinical response prior to phenotypic testing. Nonetheless, many existing ML methods lack interpretability and do not implicitly incorporate visualization of the sequence space that can be useful for extracting insightful patterns from genomic data. Herein, we present a methodology for AMR prediction and visualization of sequence space based on the non-linear dimensionality reduction method □ generative topographic mapping (GTM). This approach applied to data on AMR of >5000 S. aureus isolates retrieved from the PATRIC database yielded GTM models with reasonable accuracy for all drugs (balanced accuracy values ≥0.75). The GTMs represent data in the form of illustrative 2D maps of the genomic space and allow for antibiotic-wise comparison of resistance phenotypes. In addition to that, the maps were found to be useful for the analysis of genetic determinants responsible for drug resistance based on the data from the PATRIC database. Overall, the GTM-based methodology is a useful tool for the illustrative exploration of the genomic sequence space and modelling AMR and can be used as a tool complementary to the existing ML methods for AMR prediction.Availabilityhttps://doi.org/10.5281/zenodo.7101559

The development of DNA-Encoded Library (DEL) technology introduced new challenges for the analysis of chemical libraries. Unlike classical HTS libraries, the DEL composition cannot be modified once synthesized and, therefore, it must be considered as a stand-alone chemoinformatic object represented both as a collection of independent molecules, and yet an individual entity. For the analysis of such collections, the concept of the Chemical Library Space (CLS) where entire libraries become objects is indispensable. In this article, we introduce, analyze and compare four vectorial library representations obtained using Generative Topographic Mapping (GTM), formally defining CLS. Such representations allow effective comparison of libraries, with the ability to tune and chemically interpret the similarity relationships. In particular, property-tuned CLS encodings enable to simultaneously compare libraries with respect to both property and chemotype distribution. We apply the various CLS encodings for the selection problem of DELs that optimally match a reference collection (here ChEMBL28), showing how the choice of the CLS descriptors may help to fine-tune the matching (overlap) criteria. The proposed CLS encodings are a new efficient way for polyvalent analysis of the space of thousands of chemical libraries.

This work introduces CGRdb2.0─an open-source database management system for molecules, reactions, and chemical data. CGRdb2.0 is a Python package connecting to a PostgreSQL database that enables native searches for molecules and reactions without complicated SQL syntax. The library provides out-of-the-box implementations for similarity and substructure searches for molecules, as well as similarity and substructure searches for reactions in two ways─based on reaction components and based on the Condensed Graph of Reaction approach, the latter significantly accelerating the performance. In benchmarking studies with the RDKit database cartridge, we demonstrate that CGRdb2.0 performs searches faster for smaller data sets, while allowing for interactive access to the retrieved data.

In order to analyze the Chimiothèque Nationale (CN) - The French National Compound Library - in the context of screening and biologically relevant compounds, the library was compared with ZINC in-stock collection and ChEMBL. This includes the study of chemical space coverage, physicochemical properties and Bemis-Murcko (BM) scaffold populations. More than 5K CN-unique scaffolds (in comparison with ZINC and ChEMBL collections) were identified. Generative Topographic Maps (GTMs) accommodating those libraries were generated and used to compare the compound populations. Hierarchical GTM («zooming») was applied to generate an ensemble of maps at various resolution levels, from global overview to precise mapping of individual structures. The respective maps were added to the ChemSpace Atlas website. The analysis of synthetic accessibility in the context of combinatorial chemistry showed that only 29,7% of CN compounds can be fully synthesized using commercially available building blocks.

We report a novel approach for grading chemical structure drawings for remote teaching, integrated into the Moodle platform. Typically, existing online platforms use a binary grading system, which often fails to give a nuanced evaluation of the answers given by the students. Therefore, such platforms are unevenly adapted to different disciplines. This is particularly true in the case of chemical structures, where most questions simply cannot be evaluated on a true/false basis. Specifically, a strict comparison of candidate and expected chemical structures is not sufficient when some tolerance is deemed acceptable. To overcome this limitation, we have developed a grading workflow based on the pairwise similarity score of two considered chemical structures. This workflow is implemented as a Moodle plugin, using the Chemdoodle engine for drawing structures and communicating with a REST server to compute the similarity score using molecular descriptors. The plugin (https://github.com/Laboratoire-de-Chemoinformatique/moodle-qtype_molsimilarity) is easily adaptable to any academic user; both embedding and similarity measures can be configured.

In order to better formalize the notorious Inverse-QSAR problem (finding structures of given QSAR-predicted properties) is considered in this paper as a two-step process1,2,3 including (i) finding “seed” descriptor vectors corresponding to user-constrained QSAR model output values and (ii) identifying the chemical structures best matching the “seed” vectors. The main development effort here was focused on the latter stage, proposing a new Attention-based Conditional Variational AutoEncoder (ACoVAE) neural-network architecture based on recent developments in attention-based methods. The obtained results show that this workflow was capable of generating compounds predicted to display desired activity, while being completely novel compared to the training database (ChEMBL). Moreover, the generated compounds show acceptable druglikeness and synthetic accessibility. Both pharmacophore and docking studies were carried out as “orthogonal” in silico validation methods, proving that some of de novo structures are, beyond being predicted active by 2D-QSAR models, clearly able to match binding 3D pharmacophores and bind the protein pocket

Graph-based architectures are becoming increasingly popular as a tool for structure generation. Here, we introduce novel open-source architecture HyFactor in which, similar to the InChI linear notation, the number of hydrogens attached to the heavy atoms was considered instead of the bond types. HyFactor was benchmarked on the ZINC 250K, MOSES, and ChEMBL data sets against conventional graph-based architecture ReFactor, representing our implementation of the reported DEFactor architecture in the literature. On average, HyFactor models contain some 20% less fitting parameters than those of ReFactor. The two architectures display similar validity, uniqueness, and reconstruction rates. Compared to the training set compounds, HyFactor generates more similar structures than ReFactor. This could be explained by the fact that the latter generates many open-chain analogues of cyclic structures in the training set. It has been demonstrated that the reconstruction error of heavy molecules can be significantly reduced using the data augmentation technique. The codes of HyFactor and ReFactor as well as all models obtained in this study are publicly available from our GitHub repository: https://github.com/Laboratoire-de-Chemoinformatique/HyFactor.

Molecular similarity is an impressively broad topic with many implications in several areas of chemistry. Its roots lie in the paradigm that 'similar molecules have similar properties'. For this reason, methods for determining molecular similarity find wide application in pharmaceutical companies, e.g., in the context of structure-activity relationships. The similarity evaluation is also used in the field of chemical legislation, specifically in the procedure to judge if a new molecule can obtain the status of orphan drug with the consequent financial benefits. For this procedure, the European Medicines Agency uses experts' judgments. It is clear that the perception of the similarity depends on the observer, so the development of models to reproduce the human perception is useful. In this paper, we built models using both 2D fingerprints and 3D descriptors, i.e., molecular shape and pharmacophore descriptors. The proposed models were also evaluated by constructing a dataset of pairs of molecules which was submitted to a group of experts for the similarity judgment. The proposed machine-learning models can be useful to reduce or assist human efforts in future evaluations. For this reason, the new molecules dataset and an online tool for molecular similarity estimation have been made freely available.

COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression. Massive experimental and computational research effort to understand and characterize the disease and rapidly develop diagnostics, vaccines, and drugs has emerged in response to this devastating pandemic and more than 130 000 COVID-19-related research papers have been published in peer-reviewed journals or deposited in preprint servers. Much of the research effort has focused on the discovery of novel drug candidates or repurposing of existing drugs against COVID-19, and many such projects have been either exclusively computational or computer-aided experimental studies. Herein, we provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature. We further outline that, after the first year the COVID-19 pandemic, it appears that drug repurposing has not produced rapid and global solutions. However, several known drugs have been used in the clinic to cure COVID-19 patients, and a few repurposed drugs continue to be considered in clinical trials, along with several novel clinical candidates. We posit that truly impactful computational tools must deliver actionable, experimentally testable hypotheses enabling the discovery of novel drugs and drug combinations, and that open science and rapid sharing of research results are critical to accelerate the development of novel, much needed therapeutics for COVID-19., We cover diverse methodologies, computational approaches, and case studies illustrating the ongoing efforts to develop viable drug candidates for treatment of COVID-19.

International audience; The days when medicinal chemistry was limited to a few series of compounds of therapeutic interest are long gone. Nowadays, no human may succeed to acquire a complete overview of more than a billion existing or feasible compounds within which the potential “blockbuster drugs” are well hidden and yet only a few mouse clicks away. To reach these “hidden treasures”, we adapted the generative topographic mapping method to enable efficient navigation through the chemical space, from a global overview to a structural pattern detection, covering, for the first time, the complete ZINC library of purchasable compounds, relative to 1.6 million biologically relevant ChEMBL molecules. About 40 000 hierarchical maps of the chemical space were constructed. Structural motifs inherent to only one library were identified. Roughly 20 000 off-market ChEMBL compound families represent incentives to enrich commercial catalogs. Alternatively, 125 000 ZINC-specific compound classes, absent in structure–activity bases, are novel paths to explore in medicinal chemistry. The complete list of these chemotypes can be downloaded using the link https://forms.gle/B6bUJj82t9EfmttV6.

International audience; The Generalized Born (GB) solvent model is offering the best accuracy/computing effort ratio yet requires drastic simplifications to estimate of the Effective Born Radii (EBR) in bypassing a too expensive volume integration step. EBRs are a measure of the degree of burial of an atom and not very sensitive to small changes of geometry: in molecular dynamics, the costly EBR update procedure is not mandatory at every step. This work however aims at implementing a GB model into the Sampler for Multiple Protein−Ligand Entities (S4MPLE) evolutionary algorithm with mandatory EBR updates at each step triggering arbitrarily large geometric changes. Therefore, a quantitative structure−property relationship has been developed in order to express the EBRs as a linear function of both the topological neighborhood and geometric occupancy of the space around atoms. A training set of 810 molecular systems, starting from fragment-like to drug-like compounds, proteins, host−guest systems, and ligand−protein complexes, has been compiled. For each species, S4MPLE generated several hundreds of random conformers. For each atom in each geometry of each species, its "standard" EBR was calculated by numeric integration and associated to topological and geometric descriptors of the atom neighborhood. This training set (EBR, atom descriptors) involving >5 M entries was subjected to a boot-strapping multilinear regression process with descriptor selection. In parallel, the strategy was repurposed to also learn atomic solvent-accessible areas (SA) based on the same descriptors. Resulting linear equations were challenged to predict EBR and SA values for a similarly compiled external set of >2000 new molecular systems. Solvation energies calculated with estimated EBR and SA match "standard" energies within the typical error of a force-field-based approach (a few kilocalories per mole). Given the extreme diversity of molecular systems covered by the model, this simple EBR/SA estimator covers a vast applicability domain.

Carbon capture and storage technologies are projected to increasingly contribute to cleaner energy transitions by significantly reducing CO2 emissions from fossil fuel-driven power and industrial plants. The industry standard technology for CO2 capture is chemical absorption with aqueous alkanolamines, which are often being mixed with an activator, piperazine, to increase the overall CO2 absorption rate. Inefficiency of the process due to the parasitic energy required for thermal regeneration of the solvent drives the search for new tertiary amines with better kinetics. Improving the efficiency of experimental screening using computational tools is challenging due to the complex nature of chemical absorption. We have developed a novel computational approach that combines kinetic experiments, molecular simulations and machine learning for the in silico screening of hundreds of prospective candidates and identify a class of tertiary amines that absorbs CO2 faster than a typical commercial solvent when mixed with piperazine, which was confirmed experimentally.

BACKGROUND: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg). METHODS: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.

Dynamic combinatorial libraries (DCLs) display adaptive behavior, enabled by the reversible generation of their molecular constituents from building blocks, in response to external effectors, e.g., protein receptors. So far, chemoinformatics has not yet been used for the design of DCLs-which comprise a radically different set of challenges compared to classical library design. Here, we propose a chemoinformatic model for theoretically assessing the composition of DCLs in the presence and the absence of an effector. An imine-based DCL in interaction with the effector human carbonic anhydrase II (CA II) served as a case study. Support vector regression models for the imine formation constants and imine-CA II binding were derived from, respectively, a set of 276 imines synthesized and experimentally studied in this work and 4350 inhibitors of CA II from ChEMBL. These models predict constants for all DCL constituents, to feed software assessing equilibrium concentrations. They are publicly available on the dedicated website. Models rationally selected two amines and two aldehydes predicted to yield stable imines with high affinity for CA II and provided a virtual illustration on how effector affinity regulates DCL members.

The ability to efficiently synthesize desired compounds can be a limiting factor for chemical space exploration in drug discovery. This ability is conditioned not only by the existence of well-studied synthetic protocols but also by the availability of corresponding reagents, so-called building blocks (BB). In this work, we present a detailed analysis of the chemical space of 400K purchasable BB. The chemical space was defined by corresponding synthons – fragments contributed to the final molecules upon reaction. They allow an analysis of BB physicochemical properties and diversity, unbiased by the leaving and protective groups in actual reagents. The main classes of BB were analyzed in terms of their availability, rule-of-two-defined quality, and diversity. Available BBs were eventually compared to a reference set of biologically relevant synthons derived from ChEMBL fragmentation, in order to illustrate how well they cover the actual medicinal chemistry needs. This was performed on a newly constructed universal generative topographic map of synthon chemical space, allowing to visualize both libraries and analyze their overlapping and library-specific regions.

Graph-based architectures are becoming increasingly popular as a tool for structure generation. Here, we introduce a novel open-source architecture HyFactor which is inspired by previously reported DEFactor architecture and based on the hydrogen labeled graphs. Since the original DEFactor code was not available, its new implementation (ReFactor) was prepared in this work for the benchmarking purpose. HyFactor demonstrates its high performance on the ZINC 250K MOSES and ChEMBL data set and in molecular generation tasks, it is considerably more effective than ReFactor. The code of HyFactor and all models obtained in this study are publicly available from our GitHub repository: https://github.com/Laboratoire-de-Chemoinformatique/hyfactor

The removal of CO

In this paper, we compare the most popular Atom-to-Atom Mapping (AAM) tools: ChemAxon,([1]) Indigo,([2]) RDTool,([3]) NameRXN (NextMove),([4]) and RXNMapper([5]) which implement different AAM algorithms. An open-source RDTool program was optimized, and its modified version ("new RDTool") was considered together with several consensus mapping strategies. The Condensed Graph of Reaction approach was used to calculate chemical distances and develop the "AAM fixer" algorithm for an automatized correction of erroneous mapping. The benchmarking calculations were performed on a Golden dataset containing 1851 manually mapped and curated reactions. The best performing RXNMapper program together with the AMM Fixer was applied to map the USPTO database. The Golden dataset, mapped USPTO and optimized RDTool are available in the GitHub repository https://github.com/Laboratoire-de-Chemoinformatique.

Modern QSAR approaches have wide practical applications in drug discovery for designing potentially bioactive molecules. If such models are based on the use of 2D descriptors, important information contained in the spatial structures of molecules is lost. The major problem in constructing models using 3D descriptors is the choice of a putative bioactive conformation, which affects the predictive performance. The multi-instance (MI) learning approach considering multiple conformations in model training could be a reasonable solution to the above problem. In this study, we implemented several multi-instance algorithms, both conventional and based on deep learning, and investigated their performance. We compared the performance of MI-QSAR models with those based on the classical single-instance QSAR (SI-QSAR) approach in which each molecule is encoded by either 2D descriptors computed for the corresponding molecular graph or 3D descriptors issued for a single lowest energy conformation. The calculations were carried out on 175 data sets extracted from the ChEMBL23 database. It is demonstrated that (i) MI-QSAR outperforms SI-QSAR in numerous cases and (ii) MI algorithms can automatically identify plausible bioactive conformations.

Motivation Human immunodeficiency virus (HIV) drug resistance is a global healthcare issue. The emergence of drug resistance influenced the efficacy of treatment regimens, thus stressing the importance of treatment adaptation. Computational methods predicting the drug resistance profile from genomic data of HIV isolates are advantageous for monitoring drug resistance in patients. However, existing computational methods for drug resistance prediction are either not suitable for emerging HIV strains with complex mutational patterns or lack interpretability, which is of paramount importance in clinical practice. The approach reported here overcomes these limitations and combines high accuracy of predictions and interpretability of the models. Results In this work, a new methodology based on generative topographic mapping (GTM) for biological sequence space representation and quantitative genotype–phenotype relationships prediction purposes was introduced. The GTM-based resistance landscapes allowed us to predict the resistance of HIV strains based on sequencing and drug resistance data for three viral proteins [integrase (IN), protease (PR) and reverse transcriptase (RT)] from Stanford HIV drug resistance database. The average balanced accuracy for PR inhibitors was 0.89 ± 0.01, for IN inhibitors 0.85 ± 0.01, for non-nucleoside RT inhibitors 0.73 ± 0.01 and for nucleoside RT inhibitors 0.84 ± 0.01. We have demonstrated in several case studies that GTM-based resistance landscapes are useful for visualization and analysis of sequence space as well as for treatment optimization purposes. Here, GTMs were applied for the in-depth analysis of the relationships between mutation pattern and drug resistance using mutation landscapes. This allowed us to predict retrospectively the importance of the presence of particular mutations (e.g. V32I, L10F and L33F in HIV PR) for the resistance development. This study highlights some perspectives of GTM applications in clinical informatics and particularly in the field of sequence space exploration. Availability and implementation https://github.com/karinapikalyova/ISIDASeq. Supplementary information Supplementary data are available at Bioinformatics online.

New iron(II) complexes with 2,6-bis(benzimidazol-2-yl)pyridine (L), in particular, [FeL2]A2·nH2O (A = Br− (I), NO3− (II), C2N3− (III); n = 1 (I), 0.5 (II), 2 (III)) and [NiL2]Br2·1.23H2O·3.33EtOH (IV), have been synthesized and studied using single-crystal and powder X-ray diffraction techniques, UV-vis (diffuse reflection), IR and Mossbauer spectroscopy, as well as static magnetic susceptibility measurements. According to the experimental μeff(T) curves all the studied iron(II) compounds exhibit 1A1 ↔ 5T2 spin-crossover.

New coordination compounds of iron(II) sulfate, hexafluorosilicate, and perrhenate with 2,6-bis(benzimidazole-2-yl)pyridine (L), namely, [FeL2]SO4 ∙ H2O (I), [FeL2]SiF6 ∙ H2O (II), and [FeL2](ReO4)2 ∙ 1.5H2O (III) have been synthesized. The complexes have been studied by static magnetic susceptibility, electronic (diffuse reflectance), IR, and Mossbauer spectroscopy, and X-ray diffraction. The μeff(T) study of dehydrated complexes I–III (1–3) within the range 80–520 K has shown the spin transition 1А1 ⇔ 5Т2. A two-stage transition is observed in complex 1, whereas one-stage transitions occur in complexes 2 and 3. The forward spin transition temperatures (Тс↑) of complexes 1–3 are 423 and 503, 476, 362 K, respectively. Complex 3 sustains an abrupt spin transition with a hysteresis in the μeff(T) curve (ΔT = 21 K).

This is a review article of Generative Topographic Mapping (GTM) – a non-linear dimensionality reduction technique producing generative 2D maps of high-dimensional vector spaces – and its specific applications in Drug Design (chemical space cartography, compound library design and analysis, virtual screening, pharmacological profiling, de novo drug design, conformational space & docking interaction cartography, etc.) Written by chemoinformaticians for potential users among medicinal chemists and biologists, the article purposely avoids all underlying mathematics. First, the GTM concept is intuitively explained, based on the strong analogies with the rather popular Self-Organizing Maps (SOMs), which are well established library analysis tools. GTM is basically a fuzzy-logics-based generalization of SOMs. The second part of the review, some of published GTM applications in drug design are briefly revisited.

International audience; This work proposes a unified approach to predict glass transition temperatures (Tgs) of linear homo/hetero-polymers and cross-linked epoxy resins by machine-learning approaches based on descriptors of reagents undergoing polymerization, represented in a formal way such as to encompass all the three scenarios: linear homo-and heteropolymers, plus network heteropolymers. The "formal" representation of reagents is a problem-specific, herein designed standardization protocol of compounds, sometimes differing from typical structure curation rules in chemoinformatics. For example, heteropolymers are represented by the two partner reagents, while homopolymers are depicted as formal "heteropolymers" with identical partners. The key rule proposed here is to choose "formal" monomers such as to minimize the number of marked atoms, involved in bonds being formed or changing bond order. Accordingly, carbonyl compounds are rendered as the less stable vinyl alcohol tautomer, following the same formalism as in olefin polymerization, in order to minimize the total number of formal polymerization mechanisms and herewith provide the most general framework encompassing a maximum of polymerization processes. ISIDA (In Silico design and Data Analysis) fragment counts with special status given to the "marked atoms" participating in the polymerization process were combined using "mixture" strategies to generate the final polymer descriptors. Three predictive models based on SVR (Support Vector Regression) are discussed here. After reproducing results of Katritzky et al. with a local model applicable only to linear homo/hetero-polymers, an epoxy resin-specific model applicable to both linear and network forms was built. Eventually, the general model applicable to all these families was constructed. In 12  repeated 3-fold cross-validation challenges, it displayed the highest accuracy of Q 2 = 0.920, RMSE = 34.3 K over the training set of 270 polymers, and R 2 = 0.779, RMSE 35.9 K for an external test set of 119 polymers. GTM (Generative Topographic Mapping) analysis produced a 2D map of "polymer chemical space", highlighting the various classes of polymers included in the study and their relationship with respect to Tg values. The epoxy-specific and general models are publicly available on our web server: http://infochim.u-strasbg.fr/webserv/VSEngine.html. 2

CGRtools is an open-source Python library aimed to handle molecular and reaction information. It is the sole library developed so far which can process condensed graph of reaction (CGR) handling. CGR provides the possibility for advanced operations with reaction information and could be used for reaction descriptor calculation, structure-reactivity modeling, atom-to-atom mapping comparison and correction, reaction center extraction, reaction balancing, and some other related tasks. Unlike other popular libraries, CGRtools is fully written in Python with minor dependencies on other libraries and cross-platform. Reaction, molecule, and CGR objects in CGRtools support native Python methods and are comparable with the help of operations "equal to", "less than", and "bigger than". CGRtools supports common structural formats. CGRtools is distributed via an L-GPL license and available on https://github.com/cimm-kzn/CGRtools .

International audience; The Virtual Screening (VS) study described herein aimed at detecting novel Bromodomain BRD4 binders and relied on knowledge from public databases (ChEMBL, REAXYS) to establish a battery of predictive models of BRD activity for in silico selection of putative ligands. Beyond the actual discovery of new BRD ligands, this represented an opportunity to practically estimate the actual usefulness of public domain "Big Data" for robust predictive model building. Obtained models were used to virtually screen a collection of 2 million compounds from the Enamine company collection. This industrial partner then experimentally screened a subset of 2992 molecules selected by the VS procedure for their high likelihood to be active. Twenty nine confirmed hits were detected after experimental testing, representing 1% of the selected candidates. As a general conclusion, this study emphasizes once more that public structure-activity databases are nowadays key assets in drug discovery. Their usefulness is however limited by the state-of-the-art knowledge harvested so far by published studies. Target-specific structure activity information is rarely rich enough, and its heterogeneity makes it extremely difficult to exploit in rational drug design. Furthermore, published affinity measures serving to build models selecting compounds to be experimentally screened may not be well correlated with the experimental hit selection criterion (in practice, often imposed by equipment constraints). Nevertheless, a robust 2.6-fold increase in hit rate with respect to an equivalent, random screening campaign showed that machine learning is able to extract some real knowledge in spite of all the noise in structure-activity data.

New models for ACE2 receptor binding, based on QSAR and docking algorithms were developed, using XRD structural data and ChEMBL 26 database hits as training sets. The selectivity of the potential ACE2-binding ligands towards Neprilysin (NEP) and ACE was evaluated. The Enamine screening collection (3.2 million compounds) was virtually screened according to the above models, in order to find possible ACE2-chemical probes, useful for the study of SARS-CoV2-induced neurological disorders. An enzymology inhibition assay for ACE2 was optimized, and the combined diversified set of predicted selective ACE2-binding molecules from QSAR modeling, docking, and ultrafast docking was screened in vitro. The in vitro hits included two novel chemotypes suitable for further optimization.

Most of the existing computational tools for de novo library design are focused on the generation, rational selection, and combination of promising structural motifs to form members of the new library. However, the absence of a direct link between the chemical space of the retrosynthetically generated fragments and the pool of available reagents makes such approaches appear as rather theoretical and reality-disconnected. In this context, here we present Synthons Interpreter (SynthI), a new open-source toolkit for de novo library design that allows merging those two chemical spaces into a single synthons space. Here synthons are defined as actual fragments with valid valences and special labels, specifying the position and the nature of reactive centers. They can be issued from either the "breakup" of reference compounds according to 38 retrosynthetic rules or real reagents, after leaving group withdrawal or transformation. Such an approach not only enables the design of synthetically accessible libraries and analog generation but also facilitates reagents (building blocks) analysis in the medicinal chemistry context. SynthI code is publicly available at https://github.com/Laboratoire-de-Chemoinformatique/SynthI.

Here we present a new open-source toolkit for library design, called Synthons Interpreter or SynthI that allows merging those chemical space of the retrosynthetically generated fragments and the pool of available reagents into a single synthons space. Here synthons are defined as the actual fragments with valid valences and special labels, defining the position and nature of reactive centers. They can be issued from either the “break-up” of reference compounds according to retrosynthetic rules, or real reagents/BBs, after leaving groups transformation. Such an approach not only enables the design of synthetically accessible libraries and analogs generation but also facilitates BB analysis in the medicinal chemistry context. SynthI code is available in https://github.com/Laboratoire-de-Chemoinformatique/SynthI

Natural products (NPs), being evolutionary selected over millions of years to bind to biological macromolecules, remain an important source of inspiration for medicinal chemists even after the advent of efficient drug discovery technologies such as combinatorial chemistry and high-throughput screening. Thus, there is a strong demand for efficient and user-friendly computational tools that allow to analyze large libraries of NPs. In this context, we present NP Navigator – a free, intuitive online tool for visualization and navigation through the chemical space of NPs and NP-like molecules[1] (https://infochm.chimie.unistra.fr/npnav/chematlas_userspace ). It is based on the hierarchical ensemble of more than 200 Generative Topographic Maps(GTM)[2], featuring NPs from the COlleCtion of Open NatUral producTs (COCONUT), bioactive compounds from ChEMBL and commercially available molecules from ZINC. NP Navigator allows to efficiently analyze different aspects of NPs - chemotype distribution, physicochemical properties, reported and/or predicted biological activity and commercial availability of NPs. Users are welcome not only to browse through hundreds of thousands of compounds from ZINC, ChEMBL and COCONUT but also project a several external molecules that play the role of “chemical trackers” allowing to trace particular chemotypes in the NP chemical space and detect analogs of the compound of interest. [1] Y. Zabolotna, P. Ertl, D. Horvath, F. Bonachera, G. Marcou, A. Varnek, NP Navigator: a New Look at the Natural Product Chemical Space, 2021.[2] C. M. Bishop, M. Svensen, C. K. I. Williams, Neural Comput. 1998, 10, 215-234.

This paper is devoted to the analysis of available experimental data and preparation of predictive models for binding affinity of molecules with respect to two nuclear receptors involved in endocrine disruption (ED): the oestrogen (ER) and the androgen (AR) receptors. The ED-relevant data were retrieved from multiple sources, including the CERAPP, CoMPARA, and the Tox21 projects as well as ChEMBL and PubChem databases. Data analysis performed with the help of generative topographic mapping revealed the problem of low agreement between experimental values from different sources. Collected data were used to train both classification models for ER and AR binding activities and regression models for relative binding affinity (RBA) and median inhibition concentration (IC50). These models displayed relatively poor performance in classification (sensitivities ER = 0.34, AR = 0.49) and in regression (determination coefficient r2 for the RBA and IC50 models in external validation varied from 0.44 to 0.76). Our analysis demonstrates that low models’ performance resulted from misinterpreted experimental endpoints or wrongly reported values, thus confirming the observations reported in CERAPP and CoMPARA studies. Developed models and collected data sets included of 6215 (ER) and 3789 (AR) unique compounds, which are freely available.

In this article, we consider cross-validation of the quantitative structure-property relationship models for reactions and show that the conventional k-fold cross-validation (CV) procedure gives an `optimistically' biased assessment of prediction performance. To address this issue, we suggest two strategies of model cross-validation, `transformation-out' CV, and `solvent-out' CV. Unlike the conventional k-fold cross-validation approach that does not consider the nature of objects, the proposed procedures provide an unbiased estimation of the predictive performance of the models for novel types of structural transformations in chemical reactions and reactions going under new conditions. Both the suggested strategies have been applied to predict the rate constants of bimolecular elimination and nucleophilic substitution reactions, and Diels-Alder cycloaddition. All suggested cross-validation methodologies and tutorial are implemented in the open-source software package CIMtools (https://github.com/cimm-kzn/CIMtools).

Modern QSAR approaches have wide practical applications in drug discovery for screening potentially bioactive molecules before their experimental testing. Most models predicting the bioactivity of compounds are based on molecular descriptors derived from 2D structure losing explicit information about the spatial structure of molecules which is important for protein-ligand recognition. The major problem in constructing models using 3D descriptors is the choice of a probable bioactive conformation that affects the predictive performance. Multi-instance (MI) learning approach considering multiple conformations upon the model training can be a reasonable solution to the above problem. Here, we compared MI-QSAR with the classical single-instance QSAR (SI-QSAR) approach, where each molecule was encoded by either 2D descriptors or 3D descriptors issued from the single lowest-energy conformation. The calculations were carried out on a sample of 175 datasets extracted from the ChEMBL23 database. It was demonstrated that (i) MI-QSAR outperforms SI-QSAR in numerous cases and (ii) MI algorithms can automatically identify plausible bioactive conformations. Instance-attention based network can be applied for most important conformer selection which was shown to correspond PDB conformer in 50-84% of molecules.

Modern QSAR approaches have wide practical applications in drug discovery for screening potentially bioactive molecules before their experimental testing. Most models predicting the bioactivity of compounds are based on molecular descriptors derived from 2D structure losing explicit information about the spatial structure of molecules which is important for protein-ligand recognition. The major problem in constructing models using 3D descriptors is the choice of a probable bioactive conformation that affects the predictive performance. Multi-instance (MI) learning approach considering multiple conformations upon the model training can be a reasonable solution to the above problem. Here, we compared MI-QSAR with the classical single-instance QSAR (SI-QSAR) approach, where each molecule was encoded by either 2D descriptors or 3D descriptors issued from the single lowest-energy conformation. The calculations were carried out on a sample of 175 datasets extracted from the ChEMBL23 database. It was demonstrated that (i) MI-QSAR outperforms SI-QSAR in numerous cases and (ii) MI algorithms can automatically identify plausible bioactive conformations. Instance-attention based network can be applied for most important conformer selection which was shown to correspond PDB conformer in 50-84% of molecules.