Synthesis, biological evaluation, X-ray molecular structure and molecular docking studies of RGD mimetics containing 6-amino-2,3-dihydroisoindolin-1-one fragment as ligands of integrin αIIbβ3

AbstractA series of novel RGD mimetics containing phthalimidine fragment was designed and synthesized. Their antiaggregative activity determined by Born’s method was shown to be due to inhibition of fibrinogen binding to αIIbβ3. Molecular docking of RGD mimetics to αIIbβ3 receptor showed the key interactions in this complex, and also some correlations have been observed between values of biological activity and docking scores. The single crystal X-ray data were obtained for five mimetics

Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information

The Online Chemical Modeling Environment is a web-based platform that aims to automate and simplify the typical steps required for QSAR modeling. The platform consists of two major subsystems: the database of experimental measurements and the modeling framework. A user-contributed database contains a set of tools for easy input, search and modification of thousands of records. The OCHEM database is based on the wiki principle and focuses primarily on the quality and verifiability of the data. The database is tightly integrated with the modeling framework, which supports all the steps required to create a predictive model: data search, calculation and selection of a vast variety of molecular descriptors, application of machine learning methods, validation, analysis of the model and assessment of the applicability domain. As compared to other similar systems, OCHEM is not intended to re-implement the existing tools or models but rather to invite the original authors to contribute their results, make them publicly available, share them with other users and to become members of the growing research community. Our intention is to make OCHEM a widely used platform to perform the QSPR/QSAR studies online and share it with other users on the Web. The ultimate goal of OCHEM is collecting all possible chemoinformatics tools within one simple, reliable and user-friendly resource. The OCHEM is free for web users and it is available online at http://www.ochem.eu

QSAR Modeling: Where Have You Been? Where Are You Going To?

Quantitative Structure-Activity Relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss: (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists towards collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making

CATMoS: Collaborative Acute Toxicity Modeling Suite.

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

A community effort in SARS-CoV-2 drug discovery.

peer reviewedThe 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 an open science 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 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 biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.R-AGR-3826 - COVID19-14715687-CovScreen (01/06/2020 - 31/01/2021) - GLAAB Enric

Modèles multiples en QSAR/QSPR (Développement de nouvelles approches et leurs applications au design "in silico" de nouveaux extractants de métaux, aux propriétés ADMETox ainsi qu'à différentes activités biologiques de molécules organiques)

Cette thèse est consacrée à l amélioration des performances des modèles structure-propriété QSAR, grâce au développement de modèles multiples, basées sur les descripteurs fragmentaux, et à leurs applications à différents domaines de la chimie (complexation et extraction de métaux, propriétés ADMETox, activités biologiques). Dans une première partie, les principales notions et méthodes de la chemoinformatique sont récapitulées. Dans une seconde partie, la plateforme de logiciels ISIDA (In Silico Design and Data Analysis) est présentée. Lors de cette thèse, deux approches à modèles multiples ont été développées : la stratégie Diviser pour Conquérir et l approche Stepwise k-Nearest Neighbors. Dans une troisième partie, les méthodes d ISIDA ont été appliquées avec succès à la modélisation de différentes propriétés chimiques et biologiques. Les tests expérimentaux d extractants de métaux conçus grâce aux calculs ont confirmé les performances d ISIDA.This thesis work concerns the improvement of prediction performances of QSAR structureproperty models, using consensus modelling strategies based on fragment descriptors, and also, to their applications for in silico design of metal binders, ADMETox properties and different biological activities of organic compounds. In the first part, some important concepts and methodologies of chemoinformatics are described. In the second part, the ensemble of programs ISIDA (In Silico Design and Data Analysis) is introduced. During this thesis work, two consensus approaches have been suggested: the Divide and Conquer strategy and the Stepwise k- Nearest Neighbors approach. Applications of new strategies lead to significant improvement of predictions accuracy, compared to the conventional models. In the third part, all ISIDA methods have been successfully applied to model various chemical and biological properties. Experimentally proven predictions demonstrate the robustness of the methods.STRASBOURG-Sc. et Techniques (674822102) / SudocSudocFranceF

Relations structure-activité pour le métabolisme et la toxicité

Prédire à l avance quels composés seront toxiques chez l homme ou non représente un réel challenge dans le monde pharmaceutique. En effet, les mécanismes à l origine de la toxicité ne sont pas toujours bien connus, et à cela s ajoute le fait qu un composé peut devenir néfaste seulement après qu il ait été métabolisé. Nous proposons ici une approche originale utilisant les graphes condensés de réactions afin de modéliser les réactions métaboliques et prédire le devenir des xénobiotiques dans l organisme humain. Différentes formes de toxicité sont aussi prédites : la mutagénicité et l hépatotoxicité. Pour cette seconde toxicité, l approche utilisée est la première à notre connaissance à prédire avec succès les molécules toxiques décrites par des données autres que résultant d observations in vivo.Predict in advance which compounds will be toxic in humans or not is a real challenge in the pharmaceutical world. Indeed, the mechanisms responsible for toxicity are not always well known, and in some case a compound become toxic only after it has been metabolized. We propose here a novel approach using condensed graphs of reactions to model and predict the metabolic fate of xenobiotics in the human body. Various forms of toxicity are also predicted : mutagenicity and hepatotoxicity. For this second toxicity, the approach proposed is the first to our knowledge to successfully predict the toxic molecules described by data other than resulting from observations in vivo.STRASBOURG-Bib.electronique 063 (674829902) / SudocSudocFranceF

L' approche par transfert inductif des connaissances (Application à la modélisation QSAR des propriétés pharmacocinétiques)

STRASBOURG-Sc. et Techniques (674822102) / SudocSudocFranceF

Graphes condensés de réactions, applications à la recherche par similarité, la classification et la modélisation

Ce travail est consacré au développement de nouvelles méthodes de fouille de données dans le domaine des réactions en utilisant le concept de Graphe Condensé de Réaction (CGR). Le CGR est un graphe en 2D qui condense l information contenue dans les réactifs et les produits d une réaction. Grâce à la présence des liaisons conventionnelles (simples, doubles, etc.) et dynamiques (coupure d une liaison simple, transformation d une double en simple etc.), le CGR permet de condenser une réaction (incluant plusieurs molécules) en une pseudo-molécule. Ainsi, le CGR permettra d appliquer des approches de chemoinformatique déjà développées pour les molécules. Trois applications possibles des CGRs ont été exploréees : la classification non supervisée des réactions basées sur des algorithmes de clustering, la recherche de réactions par similarité, la modélisation structure-réactivité (QSRR, Quantitative Structure Reactivity Relationships). Ces méthodes, testées sur quatre bases de données contenant entre 1 000 et 200 000 réactions, ont démontré l efficacité de l approche et des logiciels développés. Un système d optimisation de conditions réactionnelles a ainsi été implémenté et un brevet a été déposé aux États-Unis.This work is devoted to the developpement of new methods of mining of chemical reactions based on the Condensed Graph of Reaction (CGR) approach. A CGR integrates an information about all reactants and products of a given chemical reaction into one 2D molecular graph. Due to the application of both conventional (simple, double, etc.) and dynamical (single to double, broken single, etc.) bond types, a CGR condenses a reaction (involving many molecules) into one pseudo-molecule. This formally allows one to apply to CGRs the chemoinformatics approaches earlier developed for individual compounds. Three possible applications of CGRs were considered: unsupervised classification of reactions based on clustering algorithms; reactions similarity search, and Quantitative Structure Reactivity Relationships (QSRR). Model calculations performed on four databases containing from 1 000 to 200 000 reactions demonstrated high efficiency of the developed approaches and software tools. An system for optimizing reactions condition has been designed, and patented in the USA.STRASBOURG-Sc. et Techniques (674822102) / SudocSudocFranceF