Computational Approaches to Identify a Hidden Pharmacological Potential in Large Chemical Libraries

Druzhilovskiy, Dmitry S.; Stolbov, Leonid A.; Savosina, P I; Pogodin, Pavel V.; Filimonov, Dmitry; Veselovsky, A. V.; Stefanisko, Karen; Tarasova, Nadya I.; Nicklaus, Marc C.; Poroikov, Vladimir

doi:10.14529/jsfi200306

Cited by 8 publications

(6 citation statements)

References 54 publications

(58 reference statements)

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“…QNA descriptors characterize each atom in a molecule, taking into account the influence of all other atoms of the molecule on this atom. The algorithm for calculating similarity between structures represented by MNA descriptors uses the Tanimoto coefficient, and for structures represented by QNA descriptors, the Todeschini approach is used …”

Section: Methodsmentioning

confidence: 99%

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Phyto4Health: Database of Phytocomponents from Russian Pharmacopoeia Plants

Ионов

Druzhilovskiy

Филимонов

et al. 2023

J. Chem. Inf. Model.

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Medicinal plants growing in Russia are a rich source of biologically active compounds. However, the evaluation of the hidden pharmacological potential of these compounds by in silico methods is complicated by the lack of specialized databases. We have created a database of 3128 phytocomponents from 268 medical plants included in the Russian Pharmacopoeia. The information about the compounds was supplemented with their physicalchemical characteristics and biological activity profiles estimated using the PASS software. Comparison with phytocomponents of medicinal plants from five other countries showed that the similarity of phytocomponents in our database is rather small. The uniqueness of the contents significantly enriches and provides easy access to the necessary information. The Phyto4Health data are freely available at http://www.way2drug.com/p4h/.

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Section: Methodsmentioning

confidence: 99%

“…The algorithm for calculating similarity between structures represented by MNA descriptors uses the Tanimoto coefficient, and for structures represented by QNA descriptors, the Todeschini approach is used. 33…”

Section: Web Resource Development and Interfacementioning

confidence: 99%

Phyto4Health: Database of Phytocomponents from Russian Pharmacopoeia Plants

Ионов

Druzhilovskiy

Филимонов

et al. 2023

J. Chem. Inf. Model.

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“…The similarity is calculated using the Multilevel Neighborhoods of Atoms (MNA) and the Quantitative Neighborhoods of Atoms (QNA) descriptors. The similarity measure is calculated as the Tanimoto coefficient using MNA descriptors and as the Todeschini approach using the QNA descriptors …”

Section: Methodsmentioning

confidence: 99%

HGMMX: Host Gut Microbiota Metabolism Xenobiotics Database

Kolodnitsky,

Ionov,

Rudik

et al. 2023

J. Chem. Inf. Model.

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The metagenome of bacteria colonizing the human intestine is a set of genes that is almost 150 times greater than the set of host genes. Some of these genes encode enzymes whose functioning significantly expands the number of potential pathways for xenobiotic metabolism. The resulting metabolites can exhibit activity different from that of the parent compound. This can decrease the efficacy of pharmacotherapy as well as induce undesirable and potentially life-threatening side effects. Thus, analysis of the biotransformation of small drug-like compounds mediated by the gut microbiota is an important step in the development of new pharmaceutical agents and repurposing of the approved drugs. In vitro research, the interaction of drug-like compounds with the gut microbiota is a multistep and time-consuming process. Systematic testing of large sets of chemical structures is associated with a number of challenges, including the lack of standardized techniques and significant financial costs to identify the structure of the final metabolites. Estimation of the compounds' ability to be biotransformed by the gut microbiota and prediction of the structures of their metabolites are possible in silico. However, the development of computational approaches is limited by the lack of information about chemical structures metabolized by microbiota enzymes. The aim of this study is to create a database containing information on the metabolism of drug-like compounds by the gut microbiota. We created the data set containing information about 368 structures metabolized and 310 structures not metabolized by the human gut microbiota. The HGMMX database is freely available at https://www.way2drug.com/ hgmmx. The information presented will be useful in the development of computational approaches for analyzing the impact of the human microbiota on metabolism of drug-like molecules.

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“…In the early drug discovery process, without designing all the molecules and actually observing their interaction in vivo or in vitro assay, observing them in silico models has saved time and expenditure in many folds. 6) [61][62][63].…”

Section: Use Of Ai To Predict Pharmacological and Physicochemical Fea...mentioning

confidence: 99%

“…The machine is trained on different data points from various sources, i.e., DrugBank, Votano, PAMPA, etc. The AI processes the data using encoded ANN and exports the ADMET and physiological properties as graphs and charts for comparison purposes[61].…”

mentioning

confidence: 99%

A comprehensive review on the application of artificial intelligence in drug discovery.

Sahoo

Dar

2021

TABCJ

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The 21st century is witnessing immense achievements in human history, starting from home science to space science. Artificial Intelligence (AI) is a salient one among these feats, the critical factor of the 4th industrial revolution. Health is the primary and essential asset for the continuity of human civilization on this planet. Not only must we address the deadly existing diseases like Cancer, AIDS, Alzheimer's, heart diseases, gastrointestinal diseases, etc., but on top of that, we must effectively predict, prevent and respond to potential pathogens capable of causing havoc like the recent outbreak caused by SARS-CoV-2. AI-enabled technology with the computational capacity of a computer and reasoning ability of humans saves surplus labor and time that is majorly consumed in target validation, lead optimization, molecular representation, and designing reaction pathways, which traditionally is a decade-long way of searching, visualizing, studying, imagining, experimenting and maintaining a ton of data. This article would focus on how AI will help find the drug-like properties in the compound screening phase predicting the Structure-Activity Relationship (SAR) and ADMET properties in lead identification and optimization phases, sustainable development of chemicals in the synthesis phases up to AI's assistance in the successful conduct of clinical trials and repurposing.

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Computational Approaches to Identify a Hidden Pharmacological Potential in Large Chemical Libraries

Cited by 8 publications

References 54 publications

Phyto4Health: Database of Phytocomponents from Russian Pharmacopoeia Plants

Phyto4Health: Database of Phytocomponents from Russian Pharmacopoeia Plants

HGMMX: Host Gut Microbiota Metabolism Xenobiotics Database

A comprehensive review on the application of artificial intelligence in drug discovery.

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