<i>N,N</i>-bis-(dimethylfluorosilylmethyl)amides of <i>N</i>-organosulfonylproline and sarcosine: synthesis, structure, stereodynamic behaviour and <i>in silico</i> studies

(O→Si)-Chelate difluorides R3R2NCH(R1)C(O)N(CH2SiMe2F)2 (9a–c, R1R2 = (CH2)3, R3 = Ms (a), Ts (b); R1 = H, R2 = Me, R3 = Ms (c)), containing one penta- and one tetracoordinate silicon atoms were synthesized by silylmethylation of amides R3R2NCH(R1)C(O)NH2, subsequent hydrolysis of unstable intermediates R3R2NCH(R1)C(O)N(CH2SiMe2Cl)2 (7a–c) into 4-acyl-2,6-disilamorpholines R3R2NCH(R1)C(O)N(CH2SiMe2O)2 (8a–c) and the reaction of the latter compounds with BF3·Et2O. The structures of disilamorpholines 8a,c and difluoride 9a were confirmed by an X-ray diffraction study. According to the IR and NMR data, the O→Si coordination in solutions of these compounds was weaker than that in the solid state due to effective solvation of the Si–F bond. A permutational isomerisation involving an exchange of equatorial Me groups at the pentacoordinate Si atom in complexes 9a–c was detected, and its activational parameters were determined by 1H DNMR. In silico estimation of possible pharmacological effects and acute rat toxicity by PASS Online and GUSAR Online services showed a potential for their further pharmacological study

PREDICTION OF THE METABOLIC NETWORK FOR XENOBIOTICS IN THE HUMAN ORGANISM

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HOW SHOULD ONE USE AVAILABLE DATA TO BE SUCCESSFUL IN COMPUTER-AIDED SEARCH FOR NOVEL KINASE INHIBITORS?

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WAY2DRUG CHEMINFORMATICS PLATFORM FOR DRUG REPURPOSING

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Towards Novel Potential Molecular Targets for Antidepressant and Antipsychotic Pharmacotherapies

Depression and schizophrenia are two highly prevalent and severely debilitating neuropsychiatric disorders. Both conventional antidepressant and antipsychotic pharmacotherapies are often inefficient clinically, causing multiple side effects and serious patient compliance problems. Collectively, this calls for the development of novel drug targets for treating depressed and schizophrenic patients. Here, we discuss recent translational advances, research tools and approaches, aiming to facilitate innovative drug discovery in this field. Providing a comprehensive overview of current antidepressants and antipsychotic drugs, we also outline potential novel molecular targets for treating depression and schizophrenia. We also critically evaluate multiple translational challenges and summarize various open questions, in order to foster further integrative cross-discipline research into antidepressant and antipsychotic drug development. © 2023 by the authors.075-15-2021-684; 857600; 122030100170-5; Suzhou University of Science and Technology; State Committee of Science, SCS: N 10-14/23-I/YSMUThis work was supported by the Republic of Armenia State Committee of Science (N 10-14/23-I/YSMU) and the European Union-funded H2020 COBRAIN project (857600). The funders had no role in the design, analyses and interpretation of the submitted study, or decision to publish.Computer-aided prediction of biological activity in this pharmacotherapeutic field (A.A.L. and V.V.P.) was overviewed within the framework of the Program for Basic Research in the Russian Federation for a long-term period (2021–2030) (project 122030100170-5). The research partially used the facilities and equipment of the Resource Fund of Applied Genetics MIPT (support grant 075-15-2021-684). A.V.K. is supported by St. Petersburg State University 2023 budget assignment funds. T.O.K. is supported by Neurobiology program of Sirius University of Science and Technology 2023 research budget funds. The authors thank Hasmik Harutyunyan (COBRAIN Center, Yerevan State Medical University) for help with graphical illustrations

Functional classification of proteins based on projection of amino acid sequences: application for prediction of protein kinase substrates

<p>Abstract</p> <p>Background</p> <p>The knowledge about proteins with specific interaction capacity to the protein partners is very important for the modeling of cell signaling networks. However, the experimentally-derived data are sufficiently not complete for the reconstruction of signaling pathways. This problem can be solved by the network enrichment with predicted protein interactions. The previously published <it>in silico </it>method PAAS was applied for prediction of interactions between protein kinases and their substrates.</p> <p>Results</p> <p>We used the method for recognition of the protein classes defined by the interaction with the same protein partners. 1021 protein kinase substrates classified by 45 kinases were extracted from the Phospho.ELM database and used as a training set. The reasonable accuracy of prediction calculated by leave-one-out cross validation procedure was observed in the majority of kinase-specificity classes. The random multiple splitting of the studied set onto the test and training set had also led to satisfactory results. The kinase substrate specificity for 186 proteins extracted from TRANSPATH^®database was predicted by PAAS method. Several kinase-substrate interactions described in this database were correctly predicted. Using the previously developed ExPlain™ system for the reconstruction of signal transduction pathways, we showed that addition of the newly predicted interactions enabled us to find the possible path between signal trigger, TNF-alpha, and its target genes in the cell.</p> <p>Conclusions</p> <p>It was shown that the predictions of protein kinase substrates by PAAS were suitable for the enrichment of signaling pathway networks and identification of the novel signaling pathways. The on-line version of PAAS for prediction of protein kinase substrates is freely available at <url>http://www.ibmc.msk.ru/PAAS/</url>.</p

Calculation of substructural analysis weights using a genetic algorithm

This paper describes a genetic algorithm for the calculation of substructural analysis for use in ligand-based virtual screening. The algorithm is simple in concept and effective in operation, with simulated virtual screening experiments using the MDDR and WOMBAT datasets showing it to be superior to substructural analysis weights based on a naive Bayesian classifier

Collaborative development of predictive toxicology applications

OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying the chemical safety assessment requirements of the REACH legislation as it supports access to experimental data, (Quantitative) Structure-Activity Relationship models, and toxicological information through an integrating platform that adheres to regulatory requirements and OECD validation principles. Initial research defined the essential components of the Framework including the approach to data access, schema and management, use of controlled vocabularies and ontologies, architecture, web service and communications protocols, and selection and integration of algorithms for predictive modelling. OpenTox provides end-user oriented tools to non-computational specialists, risk assessors, and toxicological experts in addition to Application Programming Interfaces (APIs) for developers of new applications. OpenTox actively supports public standards for data representation, interfaces, vocabularies and ontologies, Open Source approaches to core platform components, and community-based collaboration approaches, so as to progress system interoperability goals