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This paper reviews the application of machine learning to the inhibition of corrosion by organic molecules. The methodologies considered include quantitative structure‐property relationships (QSPR) and related data‐driven approaches. The characteristic features of their key components are considered as applied to corrosion inhibition, including datasets, response properties, molecular descriptors, machine learning methods, and structure‐property models. It is shown that the most important factors determining their choice and application features are: (1) the small or very small size of datasets, (2) the mechanism of corrosion inhibition associated with the adsorption of inhibitor molecules on the metal surface, and (3) multifactorial conditioning and noisiness of response property. On this basis, the application of machine learning to the inhibition of corrosion of materials based on iron, aluminum, and magnesium is considered. The main trends in the development of QSPR and related data‐driven modeling of corrosion inhibition are discussed, the shortcomings and common errors are considered, and the prospects for their further development are outlined. [ABSTRACT FROM AUTHOR]

Natural products have long been an important source of inspiration for medicinal chemistry and drug discovery. In the cosmetic field, they remain the major elements of the composition and serve as marketing asset. Recent research showed the implication of salt‐inducible kinases on the melanin production in skin via MITF regulation. Finding new potent modulators on such target could open the way to several cosmetic applications to attenuate visible signs of photoaging and improve the tan without sun. Since virtual screening can be a powerful tool for detecting hit compounds in the early stages of a drug discovery process, we applied this method on salt‐inducible kinase 2 to discover potential interesting compounds. Here, we present the different steps from the construction of a database of natural products, to the validation of a docking protocol and the results of the virtual screening. Hits from the screening were tested in vitro to confirm their efficiency and results are discussed. [ABSTRACT FROM AUTHOR]

Dimensionality reduction (DR) techniques are used for various purposes such as exploratory data analysis. A commonly employed linear DR technique is principal component analysis (PCA), which is one of the most popular methods for DR. Owing to its linear nature, PCA enables the determination of axes in a low‐dimensional space and the calculation of corresponding loading vectors. However, PCA cannot necessarily extract important features of non‐linearly distributed data. This study presents a technique aimed at aiding the interpretation of data reduced through non‐linear DR methods. In the proposed method, non‐linear dimensionally reduced data was clustered via a density‐based clustering method. Thereafter, the obtained cluster labels were classified by random forest (RF) classifiers. Further, feature importance (FI) of RF classifiers and Spearman's rank correlation coefficients between predictive probabilities to obtained clusters and original feature values were utilized for characterizing the visualized dimensionally reduced data. The results revealed that the proposed method can provide the interpretable FI‐based images of the handwritten digits dataset. Moreover, the proposed method was also applied to the polymer dataset. The study found that incorporating signed FI was advantageous in achieving a meaningful interpretation. Furthermore, Gaussian process regression was utilized to produce intuitive FI‐based heatmaps on a 2‐dimensional space for greater ease of understanding. Additionally, to enhance the interpretability of the obtained clusters, a feature selection technique called Boruta was applied. The Boruta feature selection method worked effectively to interpret the obtained clusters with limited and commonly important features. Additionally, the study suggested that computing FI solely from substructure‐based descriptors could further enhance the interpretability of the results. Finally, the automation of the proposed method was investigated, and through maximizing the target score based on the quality of both the DR and clustering, indicative results were automatically obtained for both the handwritten digits and polymer datasets. [ABSTRACT FROM AUTHOR]

Dissemination of novel research methods, especially in the form of chemoinformatics software, depends heavily on their ease of applicability for non‐expert users with only a little or no programming skills and knowledge in computer science. Visual programming has become widely popular over the last few years, also enabling researchers without in‐depth programming skills to develop tailored data processing pipelines using elements from a repository of predefined standard procedures. In this work, we present the development of a set of nodes for the KNIME platform implementing the QPhAR algorithm. We show how the developed KNIME nodes can be included in a typical workflow for biological activity prediction. Furthermore, we present best‐practice guidelines that should be followed to obtain high‐quality QPhAR models. Finally, we show a typical workflow to train and optimise a QPhAR model in KNIME for a set of given input compounds, applying the discussed best practices. [ABSTRACT FROM AUTHOR]

Maximum common substructures (MCS) have received a lot of attention in the chemoinformatics community. They are typically used as a similarity measure between molecules, showing high predictive performance when used in classification tasks, while being easily explainable substructures. In the present work, we applied the Pairwise Maximum Common Subgraph Feature Generation (PMCSFG) algorithm to automatically detect toxicophores (structural alerts) and to compute fingerprints based on MCS. We present a comparison between our MCS‐based fingerprints and 12 well‐known chemical fingerprints when used as features in machine learning models. We provide an experimental evaluation and discuss the usefulness of the different methods on mutagenicity data. The features generated by the MCS method have a state‐of‐the‐art performance when predicting mutagenicity, while they are more interpretable than the traditional chemical fingerprints. [ABSTRACT FROM AUTHOR]

Herein we report a virtual library of 1E+60 members, a common estimate for the size of the drug-like chemical space. The library consists of linear or cyclic oligomers forming molecules within the size range of peptide drugs. We demonstrate ligand-based virtual screening using a genetic algorithm., (© 2024 The Authors. Molecular Informatics published by Wiley-VCH GmbH.)

The importance of epigenetic drug and probe discovery is on the rise. This is not only paramount to identify and develop therapeutic treatments associated with epigenetic processes but also to understand the underlying epigenetic mechanisms involved in biological processes. To this end, chemical vendors have been developing synthetic compound libraries focused on epigenetic targets to increase the probabilities of identifying promising starting points for drug or probe candidates. However, the chemical contents of these data sets, the distribution of their physicochemical properties, and diversity remain unknown. To fill this gap and make this information available to the scientific community, we report a comprehensive analysis of eleven libraries focused on epigenetic targets containing more than 50,000 compounds. We used well‐validated chemoinformatics approaches to characterize these sets, including novel methods such as automated detection of analog series and visual representations of the chemical space based on Constellation Plots and Chemical Library Networks. This work will guide the efforts of experimental groups working on high‐throughput and medium‐throughput screening of epigenetic‐focused libraries. The outcome of this work can also be used as a reference to design and describe novel focused epigenetic libraries. [ABSTRACT FROM AUTHOR]

Current pandemics propelled research efforts in unprecedented fashion, primarily triggering computational efforts towards new vaccine and drug development as well as drug repurposing. There is an urgent need to design novel drugs with targeted biological activity and minimum adverse reactions that may be useful to manage viral outbreaks. Hence an attempt has been made to develop Machine Learning based predictive models that can be used to assess whether a compound has the potency to be antiviral or not. To this end, a set of 2358 antiviral compounds were compiled from the CAS COVID‐19 antiviral SAR dataset whose activity was reported based on IC50 value. A total 1157 two‐dimensional molecular descriptors were computed among which, the most highly correlated descriptors were selected using Tree‐based, Correlation‐based and Mutual information‐based feature selection methods. Seven Machine Learning algorithms i. e., Random Forest, XGBoost, Support Vector Machine, KNN, Decision Tree, MLP Classifier and Logistic Regression were benchmarked. The best performance was achieved by the models developed using Random Forest and XGBoost algorithms in all the feature selection methods. The maximum predictive accuracy of both these models was 88 % with internal validation. Whereas, with an external dataset, a maximum accuracy of 93.10 % for XGBoost and 100 % for Random Forest based model was achievable. Furthermore, the study demonstrated scaffold analysis of the molecules as a pragmatic approach to explore the importance of structurally diverse compounds in data driven studies. [ABSTRACT FROM AUTHOR]

AlphaScreen is one of the most widely used assay technologies in drug discovery due to its versatility, dynamic range and sensitivity. However, a presence of false positives and frequent hitters contributes to difficulties with an interpretation of measured HTS data. Although filters do exist to identify frequent hitters for AlphaScreen, they are frequently based on privileged scaffolds. The development of such filters is time consuming and requires deep domain knowledge. Recently, machine learning and artificial intelligence methods are emerging as important tools to advance drug discovery and chemoinformatics, including their application to identification of frequent hitters in screening assays. However, the relative performance and complementarity of the Machine Learning and scaffold‐based techniques has not yet been comprehensively compared. In this study, we analysed filters based on the privileged scaffolds with filters built using machine learning. Our results demonstrate that machine‐learning methods provide more accurate filters for identification of frequent hitters in AlphaScreen assays than scaffold‐based methods and can be easily redeveloped once new data are measured. We present highly accurate models to identify frequent hitters in AlphaScreen assays. [ABSTRACT FROM AUTHOR]

The characterization of physical hazards of substances is a key information to manage the risks associated to their use, storage and transport. With decades of work in this area, Ineris develops and implements cutting‐edge experimental facilities allowing such characterizations at different scales and under various conditions to study all of the dreaded accident scenarios. This review presents the efforts engaged by Ineris more recently in the field of chemoinformatics to develop and use new predictive methods for the anticipation and management of industrials risks associated to energetic and reactive materials as a complement to experiments. An overview of the methods used for the development of Quantitative Structure‐Property Relationships for physical hazards are presented and discussed regarding the specificities associated to this class of properties. A review of models developed at Ineris is also provided from the first tentative models on the explosivity of nitro compounds to the successful application to the flammability of organic mixtures. Then, a discussion is proposed on the use of QSPR models. Good practices for robust use for QSPR models are recalled with specific comments related to physical hazards, notably for regulatory purpose. Dissemination and training efforts engaged by Ineris are also presented. The potential offered by these predictive methods in terms of in silico design and for the development of new intrinsically safer technologies in safety‐by‐design strategies is finally discussed. At last, challenges and perspectives to extend the application of chemoinformatics in the field of safety and in particular for the physical hazards of energetic and reactive substances are proposed. [ABSTRACT FROM AUTHOR]

Compound databases of natural products play a crucial role in drug discovery and development projects and have implications in other areas, such as food chemical research, ecology and metabolomics. Recently, we put together the first version of the Latin American Natural Product database (LANaPDB) as a collective effort of researchers from six countries to ensemble a public and representative library of natural products in a geographical region with a large biodiversity. The present work aims to conduct a comparative and extensive profiling of the natural product-likeness of an updated version of LANaPDB and the individual ten compound databases that form part of LANaPDB. The natural product-likeness profile of the Latin American compound databases is contrasted with the profile of other major natural product databases in the public domain and a set of small-molecule drugs approved for clinical use. As part of the extensive characterization, we employed several chemoinformatics metrics of natural product likeness. The results of this study will capture the attention of the global community engaged in natural product databases, not only in Latin America but across the world., (© 2024 The Authors. Molecular Informatics published by Wiley-VCH GmbH.)

SLN (SYBYL Line Notation) is the most comprehensive and rich linear notation for representation of chemical objects of various kinds facilitating a wide range of cheminformatics algorithms. Though, it is not the most popular linear notation nowadays, SLN has capabilities for supporting the most challenging tasks of the present day cheminformatics research. We present Ambit‐SLN, a new software library for cheminformatics processing of chemical objects via linear notation SLN. Ambit‐SLN is developed as a part of the cheminformatics platform AMBIT. It is an open‐source tool, distributed under LGPL license, written in Java and based on the Chemistry Development Kit. Ambit‐SLN includes a parser for the full SLN syntax of chemical structures and substructure search queries including support for macro and Markush atoms, global and local dictionaries and user defined properties which can be stored and used by the Ambit data model. The Ambit‐SLN library includes functionalities for substructure matching based on SLN query strings and utilities for conversion of SLN objects to other chemical formats such as SMILES and SMARTS. The functionality for Markush atom expansion can be used for generation of combinatorial structure sets. [ABSTRACT FROM AUTHOR]

Similarity measures are widely used in various areas from taxonomy to cheminformatics. To this end, a large number of similarity and distance measures (or, collectively, comparative measures) have been introduced, with only a few studies directed to revealing their inner relationships. We present a thorough analytical study of the conditions leading to two comparative measures providing equivalent results over a given set of molecules. A key part of this work is the introduction of a novel way to study the consistency between comparative measures: the differential consistency analysis (DCA). This tool reveals how the consistency can be established in an analytical way with minimal (or no) assumptions. We found that the consensus between Tanimoto and the Cosine coefficients improved by choosing a reference whose similarity to the rest of the molecules varies less, or by representing the molecules in a way that does not depend strongly on their size (i. e. bit frequency in the chosen fingerprint representation). The presented derivations are just some generic examples; DCA can be applied widely and for all binary similarity coefficients introduced so far, independently from the molecular representations. [ABSTRACT FROM AUTHOR]

Drug‐resistant bacteria are a worldwide public health concern. As the prevalence of multi‐drug resistant pathogens outpaces the discovery of new antibacterials, it is of importance to explore the structure‐activity relationships for series of known bactericides with proven scaffolds. Herein, we assembled a set of 507 fluoroquinolone analogues all experimentally tested for their inhibition potency against four pathogens: Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and Streptococcus pneumoniae. We relied on cheminformatics techniques to characterize and cluster them based on their structural similarity and analyzed the structure‐activity relationships identified for each cluster of fluoroquinolones. Then, we utilized machine learning techniques to develop and validate predictive QSAR models for computing the inhibition potencies (pMIC) of analogues for each pathogen. These QSAR models afforded reasonable external prediction performances (R2≥0.6, MAE∼0.4). This study confirmed that (i) there are both global and local inter‐pathogen concordance regarding the antibacterial potency of fluoroquinolones, (ii) small clusters of fluoroquinolone analogues are characterized by unique patterns of strain selectivity and potency, the latter being potentially useful to design new analogues with enhanced potency and/or selectivity towards a given pathogen, and (iii) robust QSAR models were obtained allowing for future design of new bioactive fluoroquinolones. [ABSTRACT FROM AUTHOR]

The main protease (Mpro) of the SARS‐CoV‐2 has been proposed as one of the major drug targets for COVID‐19. We have identified the experimental data on the inhibitory activity of compounds tested against the closely related (96 % sequence identity, 100 % active site conservation) Mpro of SARS‐CoV. We developed QSAR models of these inhibitors and employed these models for virtual screening of all drugs in the DrugBank database. Similarity searching and molecular docking were explored in parallel, but docking failed to correctly discriminate between experimentally active and inactive compounds, so it was not relied upon for prospective virtual screening. Forty‐two compounds were identified by our models as consensus computational hits. Subsequent to our computational studies, NCATS reported the results of experimental screening of their drug collection in SARS‐CoV‐2 cytopathic effect assay (https://opendata.ncats.nih.gov/covid19/). Coincidentally, NCATS tested 11 of our 42 hits, and three of them, cenicriviroc (AC50 of 8.9 μM), proglumetacin (tested twice independently, with AC50 of 8.9 μM and 12.5 μM), and sufugolix (AC50 12.6 μM), were shown to be active. These observations support the value of our modeling approaches and models for guiding the experimental investigations of putative anti‐COVID‐19 drug candidates. All data and models used in this study are publicly available via Supplementary Materials, GitHub (https://github.com/alvesvm/sars‐cov‐mpro), and Chembench web portal (https://chembench.mml.unc.edu/). [ABSTRACT FROM AUTHOR]

This review seeks to provide a timely survey of the scope and limitations of cheminformatics methods in natural product‐based drug discovery. Following an overview of data resources of chemical, biological and structural information on natural products, we discuss, among other aspects, in silico methods for (i) data curation and natural products dereplication, (ii) analysis, visualization, navigation and comparison of the chemical space, (iii) quantification of natural product‐likeness, (iv) prediction of the bioactivities (virtual screening, target prediction), ADME and safety profiles (toxicity) of natural products, (v) natural products‐inspired de novo design and (vi) prediction of natural products prone to cause interference with biological assays. Among the many methods discussed are rule‐based, similarity‐based, shape‐based, pharmacophore‐based and network‐based approaches, docking and machine learning methods. [ABSTRACT FROM AUTHOR]

Natural products (NPs) have evolved over a very long natural selection process to form optimal interactions with biologically relevant macromolecules. NPs are therefore a very useful source of inspiration for the design of new drugs. In the present study we report the results of a cheminformatics analysis of a large database of NP structures focusing on their scaffolds. First, general differences between NP scaffolds and scaffolds from synthetic molecules are discussed, followed by a comparison of the properties of scaffolds produced by different types of organisms. Scaffolds produced by plants are the most complex and those produced by bacteria differ in many structural features from scaffolds produced by other organisms. The results presented here may be used as a guidance in selection of scaffolds for the design of novel NP-like bioactive structures or NP-inspired libraries. [ABSTRACT FROM AUTHOR]

Computational approaches to analyze various drug/ compound centered analysis often present a need to map attributes from multiple drug databases. In this study, we provide a Neo4j repository that integrates two of the most prominent open source drug databases, DrugBank and ChEMBL, with a goal of establishing an integrated data visualization and analysis tool for drug discovery studies. The drugs present in DrugBank are mapped to their counterparts in ChEMBL. The integration of these resources and the harmonization using knowledge graph serialization using Neo4j lead to identification of relationships between drugs and other related features that are otherwise spread across two different resources. A common data format, a prerequisite to populate the Neo4j database, enables users to identify new relationships central to drug discovery research, like Drug Target Interactions (DTI). The resource is freely available at: https://github.com/ambf0632/CompoundDB4j [ABSTRACT FROM AUTHOR]

The use of computer tools to solve chemistry‐related problems has given rise to a large and increasing number of publications these last decades. This new field of science is now well recognized and labelled Chemoinformatics. Among all chemoinformatics techniques, the use of statistical based approaches for property predictions has been the subject of numerous research reflecting both new developments and many cases of applications. The so obtained predictive models relating a property to molecular features – descriptors – are gathered under the acronym QSPR, for Quantitative Structure Property Relationships. Apart from the obvious use of such models to predict property values for new compounds, their use to virtually synthesize new molecules – de novo design – is currently a high‐interest subject. Inverse‐QSPR (i‐QSPR) methods have hence been developed to accelerate the discovery of new materials that meet a set of specifications. In the proposed manuscript, we review existing i‐QSPR methodologies published in the open literature in a way to highlight developments, applications, improvements and limitations of each. [ABSTRACT FROM AUTHOR]

GUIDEMOL is a Python computer program based on the RDKit software to process molecular structures and calculate molecular descriptors with a graphical user interface using the tkinter package. It can calculate descriptors already implemented in RDKit as well as grid representations of 3D molecular structures using the electrostatic potential or voxels. The GUIDEMOL app provides easy access to RDKit tools for chemoinformatics users with no programming skills and can be adapted to calculate other descriptors or to trigger other procedures. A command line interface (CLI) is also provided for the calculation of grid representations. The source code is available at https://github.com/jairesdesousa/guidemol., (© 2023 Wiley-VCH GmbH.)

Abstract: Androgen receptor (AR) is a master regulator of prostate cancer (PCa), and therefore is a pivotal drug target for the treatment of PCa including its castration‐resistance form (CRPC). The development of acquired resistance is a major challenge in the use of the current antiandrogens. The recent advancements in inhibiting AR activity with small molecules specifically designed to target areas distinct from the receptor's androgen binding site are carefully discussed. Our new classes of AR inhibitors of AF2 and BF3 functional sites and DBD domains designed using cheminformatics techniques are promising to circumvent various AR‐dependent resistance mechanisms. [ABSTRACT FROM AUTHOR]

Abstract: Cheminformatics has established itself as a core discipline within large scale drug discovery operations. It would be impossible to handle the amount of data generated today in a small molecule drug discovery project without persons skilled in cheminformatics. In addition, due to increased emphasis on “Big Data”, machine learning and artificial intelligence, not only in the society in general, but also in drug discovery, it is expected that the cheminformatics field will be even more important in the future. Traditional areas like virtual screening, library design and high‐throughput screening analysis are highlighted in this review. Applying machine learning in drug discovery is an area that has become very important. Applications of machine learning in early drug discovery has been extended from predicting ADME properties and target activity to tasks like de novo molecular design and prediction of chemical reactions. [ABSTRACT FROM AUTHOR]

Abstract: The With‐No‐Lysine (WNK) serine/threonine kinase family constitutes a unique and distinctive branch of the kinome. The four proteins of this family (WNK1/2/3/4) are involved in blood pressure regulation, body fluid, and electrolyte homeostasis. Herein, we modeled and analyzed the binding modes of all publicly‐available small orthosteric and allosteric binders (including WNK463 and WNK467) experimentally tested towards any of the WNK family member. To do so, we relied on state‐of‐the‐art cheminformatics approaches including structure‐based molecular docking and molecular dynamics simulations. In particular, we computed and analyzed the (i) molecular selectivity of known inhibitors when docked in the binding site of each WNK family member, (ii) the dynamic WNK‐inhibitor interactions at both orthosteric and allosteric sites to derive new structure‐activity relationships, and (iii) the key specific interactions present in each binding site. This study reports on the first, cheminformatics‐powered analysis of the entire chemical space of known WNK inhibitors. We discuss the conservation of critical WNK‐inhibitor interactions and the existence of isoform‐specific interactions that could enable the rational design of more potent and selective WNK binders. [ABSTRACT FROM AUTHOR]

Abstract: We consider lead discovery as active search in a space of labelled graphs. In particular, we extend our recent data‐driven adaptive Markov chain approach, and evaluate it on a focused drug design problem, where we search for an antagonist of an α v integrin, the target protein that belongs to a group of Arg−Gly−Asp integrin receptors. This group of integrin receptors is thought to play a key role in idiopathic pulmonary fibrosis, a chronic lung disease of significant pharmaceutical interest. As an in silico proxy of the binding affinity, we use a molecular docking score to an experimentally determined α v β 6 protein structure. The search is driven by a probabilistic surrogate of the activity of all molecules from that space. As the process evolves and the algorithm observes the activity scores of the previously designed molecules, the hypothesis of the activity is refined. The algorithm is guaranteed to converge in probability to the best hypothesis from an a priori specified hypothesis space. In our empirical evaluations, the approach achieves a large structural variety of designed molecular structures for which the docking score is better than the desired threshold. Some novel molecules, suggested to be active by the surrogate model, provoke a significant interest from the perspective of medicinal chemistry and warrant prioritization for synthesis. Moreover, the approach discovered 19 out of the 24 active compounds which are known to be active from previous biological assays. [ABSTRACT FROM AUTHOR]

Abstract: In this article we consider the application of the Transductive Ridge Regression (TRR) approach to structure‐activity modeling. An original procedure of the TRR parameters optimization is suggested. Calculations performed on 3 different datasets involving two types of descriptors demonstrated that TRR outperforms its non‐transductive analogue (Ridge Regression) in more than 90 % of cases. The most significant transductive effect was observed for small datasets. This suggests that transduction may be particularly useful when the data are expensive or difficult to collect. [ABSTRACT FROM AUTHOR]

Abstract: In medicinal chemistry, the molecular scaffolds commonly found in compounds with preferable biological activities are called bioactive scaffolds. They are important because if present in a structure, it is more likely that the compound will be bioactive. Traditionally, medicinal chemists use their knowledge to identify bioactive scaffolds from a given data set after systematic extraction of all candidate scaffolds. However, manually sorting all the scaffolds is not practical as the number of compounds in a data set is often very large. Herein, we propose a method to systematically identify bioactive scaffolds based on a structure generator and a QSAR model. Two proof‐of‐concept studies showed that known bioactive scaffolds as well as scaffolds containing important substructures were extracted. The proposed method does not depend on scaffold frequencies in a data set, which is different from currently used methods for bioactive scaffold identification. [ABSTRACT FROM AUTHOR]

Active molecules among numerous chemical structures in a chemical database can be searched easily by statistical prediction of compound-protein interactions. However, constructing a simple prediction model against one protein does not aid drug design, because detecting chemical structures that act similarly against multiple proteins is necessary for preventing side effects of the potential drug. To tackle this problem, we propose a new method that visualizes chemical and protein spaces. For simultaneous visualization of both spaces, we employ a counterpropagation neural network (CPNN) and develop a new visualization method named multi-input CPNN (MICPNN). In a case study of the kinase protein family, the MICPNN model predicted accurately the complex relationships between compounds and proteins. The proposed method identified chemical structures with promising activity against kinases. Our proposed method is also applicable to other protein families, such as G-protein coupled receptors, ion channels and transporters. [ABSTRACT FROM AUTHOR]

Promiscuity is an interesting concept in fragment-based drug design as fragments with low specificity can be advantageous for finding many screening hits. We present a PDB-wide analysis of multi-target fragments and their binding mode conservation. Focussing on multi-target fragments, we found that the majority shows non-conserved binding modes, even if they bind in a similar conformation or similar protein targets. Surprisingly, fragment properties alone are not able to predict whether a fragment will exhibit a versatile or conserved binding mode, emphasizing the interplay between protein and fragment features during a binding event and the importance of structure-based modelling. [ABSTRACT FROM AUTHOR]

Our research and teaching group called MTi (Molécules Thérapeutiques in silico) has developed numerous applications available online, thanks to the RPBS platform (Ressource Parisienne en Bioinformatique Structurale), in the field of chemoinformatics, structural bioinformatics and drug design. Since its opening in 2009, over 200 articles/reviews have been reported and involve virtual screening studies, prediction of druggability, analysis of protein-protein interaction inhibitors, development of databases, data mining and knowledge discovery, as well as combined in silico-in vitro work to search for new hits and chemical probes acting on original targets in several therapeutic areas. An international training program has also been developed pertaining to the field of in silico drug design. In this review, we present some tools developed in our laboratory with a special emphasis on the prediction of some ADMET properties, compound collection preparation and 3D-ADMET computations. [ABSTRACT FROM AUTHOR]

Nuclear receptors (NRs) constitute an important class of therapeutic targets. During the last 4 years, we tackled the pharmacological profile assessment of NR ligands for which we constructed the NRLiSt BDB. We evaluated and compared the performance of different virtual screening approaches: mean of molecular descriptor distribution values, molecular docking and 3D pharmacophore models. The simple comparison of the distribution profiles of 4885 molecular descriptors between the agonist and antagonist datasets didn′t provide satisfying results. We obtained an overall good performance with the docking method we used, Surflex-Dock which was able to discriminate agonist from antagonist ligands. But the availability of PDB structures in the 'pharmacological-profile-to-predict-bound-state' (agonist-bound or antagonist-bound) and the availability of enough ligands of both pharmacological profiles constituted limits to generalize this protocol for all NRs. Finally, the 3D pharmacophore modeling approach, allowed us to generate selective agonist pharmacophores and selective antagonist pharmacophores that covered more than 99 % of the whole NRLiSt BDB. This study allowed a better understanding of the pharmacological modulation of NRs with small molecules and could be extended to other therapeutic classes. [ABSTRACT FROM AUTHOR]

Over the past decades, virtual screening has proved itself to be a valuable asset to identify new bioactive compounds. The vast majority of commonly used techniques can be described in three steps: pre-processing the dataset i. e. small (ligands) and eventually larger (receptors) molecules, execute the method and finally analyse the results. Hence, the preparation of ligands is a critical step for success of commonly used virtual screening approaches such as protein-ligand docking, similarity or pharmacophore search. We present here a new workflow, VSPrep, for the pre-processing of small molecules; it is based on freely accessible tools for academics and is integrated within the KNIME platform. It can be used to perform several chemoinformatics tasks such as molecular database cleaning, tautomer and stereoisomer enumeration, focused library design and conformer generation. Additionally, graphical reports of the results are provided to the user as a convenient analysis tool. [ABSTRACT FROM AUTHOR]

The objective of the present paper is to summarize chemoinformatics based research, and more precisely, the development of quantitative structure property relationships performed at IFP Energies nouvelles (IFPEN) during the last decade. A special focus is proposed on research activities performed in the 'Thermodynamics and Molecular Simulation' department, i. e. the use of multiscale molecular simulation methods in responses to projects. Molecular simulation techniques can be envisaged to supplement dataset when experimental information lacks, thus the review includes a section dedicated to molecular simulation codes, development of intermolecular potentials, and some of their possible applications. Know-how and feedback from our experiences in terms of machine learning application for thermophysical property predictions are included in a section dealing with methodological aspects. The generic character of chemoinformatics is emphasized through applications in the fields of energy, transport, and environment, with illustrations for three IFPEN business units: 'Transports', 'Energy Resources', and 'Processes'. More precisely, the review focus on different challenges such as the prediction of properties for alternative fuels, the prediction of fuel compatibility with polymeric materials, the prediction of properties for surfactants usable in chemical enhanced oil recovery, and the prediction of guest-host interactions between gases and nanoporous materials in the frame of carbon dioxide capture or gas separation activities. [ABSTRACT FROM AUTHOR]

Similarity searching using molecular fingerprints has a long history in chemoinformatics and continues to be a popular approach for virtual screening. Typically, known active reference molecules are used to search databases for new active compounds. However, this search has black box character because similarity value distributions are dependent on fingerprints and compound classes. Consequently, no generally applicable similarity threshold values are available as reliable indicators of activity relationships between reference and database compounds. Therefore, it is generally uncertain where new active compounds might appear in database rankings, if at all. In this contribution, methods are discussed for modeling similarity value distributions of fingerprint search calculations using Tanimoto coefficients and estimating rank positions of active compounds. To our knowledge, these are the first approaches for predicting the results of fingerprint-based similarity searching. [ABSTRACT FROM AUTHOR]

As stricter regulations on CO2 emissions are adopted worldwide, identifying efficient chemical processes to capture and recycle CO2 is of critical importance for industry. The most common process known as amine scrubbing suffers from the lack of available amine solutions capable of capturing CO2 efficiently. Tertiary amines characterized by low heats of reaction are considered good candidates but their absorption properties can significantly differ from one analogue to another despite high structural similarity. Herein, after collecting and curating experimental data from the literature, we have built a modeling set of 41 amine structures with their absorption properties. Then we analyzed their chemical composition using molecular descriptors and non-supervised clustering. Furthermore, we developed a series of quantitative structure-property relationships (QSPR) to assess amines' CO2 absorption properties from their structural characteristics. These models afforded reasonable prediction performances (e. g., Q2LOO=0.63 for CO2 absorption amount) even though they are solely based on 2D chemical descriptors and individual machine learning techniques (random forest and neural network). Overall, we believe the chemical analysis and the series of QSPR models presented in this proof-of-concept study represent new knowledge and innovative tools that could be very useful for screening and prioritizing hypothetical amines to be synthesized and tested experimentally for their CO2 absorption properties. [ABSTRACT FROM AUTHOR]

Molecular descriptors capture diverse structural information of molecules and are a prerequisite for ligand-based similarity searching. In this study, we introduce topological matrix-based descriptors to virtual screening for hit discovery. We evaluated the usefulness of matrix-based descriptors in a retrospective setting and compared them with topological pharmacophore descriptors. Special attention was given to the influence of data pre-processing and the applied similarity metric on the virtual screening performance. Overall, the MB descriptors showed a competitive and complementary performance to other descriptors. A prospective screen of a commercial compound library led to the discovery of a novel natural-product-derived cyclooxygenase-2 inhibitor predicted to interact differently with the target protein compared to the query compound ibuprofen. The results of our study motivate the use of matrix-based descriptors for molecular similarity-based virtual screening and scaffold hopping. [ABSTRACT FROM AUTHOR]

Computational de novo molecular design and macromolecular target prediction have become routine in applied cheminformatics. In this study, we have generated populations of drug template-derived designs using ligand-based building block assembly, and predicted their potential targets. The results of our analysis show that the reaction-based de novo design generated new chemical entities with similar properties and pharmacophores as that of the template drugs as well as up to 44 % of the de novo compounds receiving the correct target predictions. Keeping in mind the probabilistic nature of the methods, such a combination of fast and meaningful computational structure generation by reaction-based design and product scoring by target class prediction may be appropriate for prospective application in medicinal chemistry. [ABSTRACT FROM AUTHOR]

Soft sensor is statistical model as an essential tool for controlling pharmaceutical, chemical and industrial plants. I introduce soft sensor, the roles, the applications, the problems and the research examples such as adaptive soft sensor, database monitoring and efficient process control. The use of soft sensor enables chemical industrial plants to be operated more effectively and stably. [ABSTRACT FROM AUTHOR]

A brief overview of the work in the research group of the present author on extracting knowledge from chemical reaction data is presented. Methods have been developed to calculate physicochemical effects at the reaction site. It is shown that these physicochemical effects can quite favourably be used to derive equations for the calculation of data on gas phase reactions and on reactions in solution such as aqueous acidity of alcohols or carboxylic acids or the hydrolysis of amides. Furthermore, it is shown that these physicochemical effects are quite effective for assigning reactions into reaction classes that correspond to chemical knowledge. Biochemical reactions constitute a particularly interesting and challenging task for increasing our understanding of living species. The BioPath.Database is a rich source of information on biochemical reactions and has been used for a variety of applications of chemical, biological, or medicinal interests. Thus, it was shown that biochemical reactions can be assigned by the physicochemical effects into classes that correspond to the classification of enzymes by the EC numbers. Furthermore, 3D models of reaction intermediates can be used for searching for novel enzyme inhibitors. It was shown in a combined application of chemoinformatics and bioinformatics that essential pathways of diseases can be uncovered. Furthermore, a study showed that bacterial flavor-forming pathways can be discovered. [ABSTRACT FROM AUTHOR]

Substructure search (SSS) is a fundamental technique supported by various chemical information systems. Many users apply it in an iterative manner: they modify their queries to shape the composition of the retrieved hit sets according to their needs. We propose and evaluate two heuristic extensions of SSS aimed at simplifying these iterative query modifications by collecting additional information during query processing and visualizing this information in an intuitive way. This gives the user a convenient feedback on how certain changes to the query would affect the retrieved hit set and reduces the number of trial-and-error cycles needed to generate an optimal search result. The proposed heuristics are simple, yet surprisingly effective and can be easily added to existing SSS implementations. [ABSTRACT FROM AUTHOR]

Classification rules are often used in chemoinformatics to predict categorical properties of drug candidates related to bioactivity from explanatory variables, which encode the respective molecular structures (i.e. molecular descriptors). To avoid predictions with an unduly large error probability, the domain the classifier is applied to should be restricted to the domain covered by the training set objects. This latter domain is commonly referred to as applicability domain in chemoinformatics. Conceptually, the applicability domain defines the region in space where the 'normal' objects are located. Defining the border of the applicability domain may then be viewed as detecting anomalous or novel objects or as detecting outliers. Currently two different types of measures are in use. The first one defines the applicability domain solely in terms of the molecular descriptor space, which is referred to as novelty detection. The second type defines the applicability domain in terms of the expected reliability of the predictions which is referred to as confidence estimation. Both types are systematically differentiated here and the most popular measures are reviewed. It will be shown that all common chemoinformatic classifiers have built-in confidence scores. Since confidence estimation uses information of the class labels for computing the confidence scores, it is expected to be more efficient in reducing the error rate than novelty detection, which solely uses the information of the explanatory variables. [ABSTRACT FROM AUTHOR]

In this work the idea was investigated whether calculated hydration energy (ΔGhyd) can be used as a molecular descriptor in defining promising regions of chemical space for drug design. Calculating ΔGhyd using the Density Solvation Model (SMD) in conjunction with the density functional theory (DFT) gave an excellent correlation with experimental values. Furthermore, calculated ΔGhyd correlates reasonably well with experimental water solubility (r2=0.545) and also log P (r2=0.530). Three compound collections were used: Known drugs (n=150), drug-like compounds (n=100) and simple organic compounds (n=140). As an approximation only molecules, which do not de/protonate at physiological pH were considered. A relatively broad distribution was seen for the known drugs with an average at −15.3 kcal/mol and a standard deviation of 7.5 kcal/mol. Interestingly, much lower averages were found for the drug-like compounds (−7.5 kcal/mol) and the simple organic compounds (−3.1 kcal/mol) with tighter distributions; 4.3 and 3.2 kcal/mol, respectively. This trend was not observed for these collections when calculated log P and log S values were used. The considerable greater exothermic ΔGhyd average for the known drugs clearly indicates in order to develop a successful drug candidate value of ΔGhyd<−5 kcal/mol or less is preferable. [ABSTRACT FROM AUTHOR]

We created a computational method to identify allosteric sites using a machine learning method trained and tested on protein structures containing bound ligand molecules. The Random Forest machine learning approach was adopted to build our three-way predictive model. Based on descriptors collated for each ligand and binding site, the classification model allows us to assign protein cavities as allosteric, regular or orthosteric, and hence to identify allosteric sites. 43 structural descriptors per complex were derived and were used to characterize individual protein-ligand binding sites belonging to the three classes, allosteric, regular and orthosteric. We carried out a separate validation on a further unseen set of protein structures containing the ligand 2-(N-cyclohexylamino) ethane sulfonic acid (CHES). [ABSTRACT FROM AUTHOR]

Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical research by borrowing from the field of 'deep learning'. Compared with some of the other life sciences, their application in drug discovery is still limited. Here, we provide an overview of this emerging field of molecular informatics, present the basic concepts of prominent deep learning methods and offer motivation to explore these techniques for their usefulness in computer-assisted drug discovery and design. We specifically emphasize deep neural networks, restricted Boltzmann machine networks and convolutional networks. [ABSTRACT FROM AUTHOR]

Malaria remains a dreadful disease by putting every year about 3.4 billion people at risk and resulting into mortality of 627 thousand people worldwide. Existing therapies based upon Quinines and Artemisinin-based combination therapies have started showing resistance, pressing the need for search of anti-malarials with different mechanisms of action. In this respect erythrocyte invasion by Plasmodium is immensely crucial, as being obligate intracellular parasite it must invade host cells. This process is mediated by interaction between conserved Apical Membrane Antigen (AMA1) and Rhoptry Neck (RON2) protein, which is compulsory for successful invasion of erythrocyte by Plasmodium and manifestation of the disease Malaria. Here, using the physicochemical properties of the compounds available from a confirmatory high throughput screening, which were tested for their disruption capability of this crucial molecular interaction, we trained supervised classifiers and validated their robustness by various statistical parameters. Best model was used for screening new compounds from Traditional Chinese Medicine Database. Some of the best hits already find their use as anti-malarials and the model predicts that an essential part of their effectiveness is likely due to inhibition of AMA1-RON2 interaction. Pharmacophoric features have also been identified to ease further designing of possible leads in an effective way. [ABSTRACT FROM AUTHOR]

The Centre for Molecular Informatics, formerly Unilever Centre for Molecular Science Informatics (UCMSI), at the University of Cambridge is a world-leading driving force in the field of cheminformatics. Since its opening in 2000 more than 300 scientific articles have fundamentally changed the field of molecular informatics. The Centre has been a key player in promoting open chemical data and semantic access. Though mainly focussing on basic research, close collaborations with industrial partners ensured real world feedback and access to high quality molecular data. A variety of tools and standard protocols have been developed and are ubiquitous in the daily practice of cheminformatics. Here, we present a retrospective of cheminformatics research performed at the UCMSI, thereby highlighting historical and recent trends in the field as well as indicating future directions. [ABSTRACT FROM AUTHOR]

This paper summarises work in chemoinformatics carried out in the Information School of the University of Sheffield during the period 2002-2014. Research studies are described on fingerprint-based similarity searching, data fusion, applications of reduced graphs and pharmacophore mapping, and on the School's teaching in chemoinformatics. [ABSTRACT FROM AUTHOR]

In this paper we take a historical view of e-Science and e-Research developments within the Chemical Sciences at the University of Southampton, showing the development of several stages of the evolving data ecosystem as Chemistry moves into the digital age of the 21st Century. We cover our research on aspects of the representation of chemical information in the context of the world wide web (WWW) and its semantic enhancement (the Semantic Web) and illustrate this with the example of the representation of quantities and units within the Semantic Web. We explore the changing nature of laboratories as computing power becomes increasing powerful and pervasive and specifically look at the function and role of electronic or digital notebooks. Having focussed on the creation of chemical data and information in context, we finish the paper by following the use and reuse of this data as facilitated by the features provided by digital repositories and their importance in facilitating the exchange of chemical information touching on the issues of open and or intelligent access to the data. [ABSTRACT FROM AUTHOR]

The application of Machine Learning to cheminformatics is a large and active field of research, but there exist few papers which discuss whether ensembles of different Machine Learning methods can improve upon the performance of their component methodologies. Here we investigated a variety of methods, including kernel-based, tree, linear, neural networks, and both greedy and linear ensemble methods. These were all tested against a standardised methodology for regression with data relevant to the pharmaceutical development process. This investigation focused on QSPR problems within drug-like chemical space. We aimed to investigate which methods perform best, and how the 'wisdom of crowds' principle can be applied to ensemble predictors. It was found that no single method performs best for all problems, but that a dynamic, well-structured ensemble predictor would perform very well across the board, usually providing an improvement in performance over the best single method. Its use of weighting factors allows the greedy ensemble to acquire a bigger contribution from the better performing models, and this helps the greedy ensemble generally to outperform the simpler linear ensemble. Choice of data preprocessing methodology was found to be crucial to performance of each method too. [ABSTRACT FROM AUTHOR]

Nuclear magnetic resonance (NMR) spectroscopy and molecular docking are regularly being employed as helpful tools of drug discovery research. Molecular docking is an extremely rapid method to evaluate possible binders from a large chemical library in a fast and cheap manner. NMR techniques can directly detect a protein-ligand interaction, can determine the corresponding association constant, and can consistently identify the ligand binding cavity. Consequently, molecular docking and NMR techniques are naturally complementary techniques where the combination of the two has the potential to improve the overall efficiency of drug discovery process. In this review, we would like to summarize the state of the art of docking methods which have been recently bridged to NMR experiments to identify novel and effective therapeutic drug candidates. [ABSTRACT FROM AUTHOR]

Sets of scaffolds with conserved molecular topology are abundant among drugs and bioactive compounds. Core structure topology is one of the determinants of biological activity. Heteroatom replacements and/or bond order variation render topologically equivalent scaffolds chemically distinct and also contribute to differences in the biological activity of compounds containing these scaffolds. Relationships between core structure topology, chemical modifications, and observed activity profiles are difficult to analyze. A computational method is introduced to consistently assess chemical transformations that distinguish scaffolds with conserved topology. The methodology is applied to quantify chemical differences in conserved topological environments and systematically relate chemical changes in topologically equivalent scaffolds to associated activity profiles. [ABSTRACT FROM AUTHOR]