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Потемкин, В. А.; Arslambekov, R. M.; Барташевич, Е. В.; Grishina, Maria; Belik, A. V.; Perspicace, Samantha; Guccione, Salvatore

doi:10.1023/a:1023615231976

Cited by 15 publications

(7 citation statements)

References 7 publications

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“…The IC 50 values were converted to pIC 50 , while 3D structures were generated using the GP_global module available at the chemosophia.com site [ 61 ]. Geometries were optimized by the MultiGen algorithm for global minimization with conserving initial stereochemistry [ 62 , 63 ]. Those molecules were used to reconstruct the molecular field of the model receptor using 3D-QSAR Cinderella’s Shoe (CiS) algorithm introduced in References [ 64 – 66 ].…”

Section: Methodsmentioning

confidence: 99%

“…Those contributions are calculated using the MERA force field [ 67 , 69 ]. The performance of the CiS algorithm was thoroughly tested using various small molecules datasets and for different kinds of bioactivities and proved as a high quality classification scheme, with cross-validation quality usually above 0.9 [ 62 , 65 , 70 – 72 ]. The neural network approach was used to model the relationship between bioactivity (pIC 50 ) and CiS descriptors.…”

Section: Methodsmentioning

confidence: 99%

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A new glimpse on the active site of SARS-CoV-2 3CLpro, coupled with drug repurposing study

Novak

Потемкин

2022

Mol Divers

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Coronavirus disease 2019 (COVID-19) is caused by novel severe acute respiratory syndrome coronavirus (SARS-CoV-2). Its main protease, 3C-like protease (3CLpro), is an attractive target for drug design, due to its importance in virus replication. The analysis of the radial distribution function of 159 3CLpro structures reveals a high similarity index. A study of the catalytic pocket of 3CLpro with bound inhibitors reveals that the influence of the inhibitors is local, perturbing dominantly only residues in the active pocket. A machine learning based model with high predictive ability against SARS-CoV-2 3CLpro is designed and validated. The model is used to perform a drug-repurposing study, with the main aim to identify existing drugs with the highest 3CLpro inhibition power. Among antiviral agents, lopinavir, idoxuridine, paritaprevir, and favipiravir showed the highest inhibition potential. Graphical abstract Enzyme – ligand interactions as a key ingredient for successful drug design Supplementary Information The online version contains supplementary material available at 10.1007/s11030-021-10355-8.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A new glimpse on the active site of SARS-CoV-2 3CLpro, coupled with drug repurposing study

Novak

Потемкин

2022

Mol Divers

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“…More than 500 geometric and quantum-chemical descriptors have been calculated. Calculations have been performed using the online laboratory Chemosophia [12][13][14][15][16][17][18][19][20]. The decision trees creation has been performed for all studied mechanisms and molecules and the descriptors determining the mechanism of drugs action have been found.…”

Section: Methodsmentioning

confidence: 99%

Decision Tree for Mechanism of Antitumor Drugs Action Prediction

Palko¹,

Потемкин²,

Grishina³

2019

Chem

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Classification by the mechanism of action of antitumor drugs using quantitative descriptors has been carried out in this study. The dataset of drugs includes 115 compounds with known activity and mechanism of action. The structures have been taken from the National Cancer Institute database. The dataset includes: 30 structures with the mechanism of alkylation action, 23 compounds are topoisomerase I inhibitors, 16 structures are topoisomerase II inhibitors, 17 compounds are DNA/RNA antimetabolites (dihydrofolate reductase inhibitors), 16 molecules are DNA antimetabolites, and 13 compounds are antimitotic drugs. A decision tree has been constructed for determination of each class of the compounds using 3D descriptors that have been computed by MERA software. Analysis of the results has shown that each mechanism of drug action is characterized by a set of descriptors. Important quantum-chemical descriptors of structures with mechanism for alkylation action, DNA/RNA antimetabolites, and DNA antimetabolites have been determined. Quantum-chemical and geometric descriptors for structures of topoisomerase II inhibitors and geometric descriptors for structures of topoisomerase I inhibitors have been established. An important energy descriptor for antimitotic drugs has been determined. The typical descriptor values for active and inactive structures for each mechanism of action have been determined. The quality recognition of active structures for each mechanism of drugs action has been determined. The highest recognition quality value of active and inactive compounds is observed in the decision tree of topoisomerase II inhibitors. The minimal recognition quality value of active and inactive compounds is observed in the decision tree of DNA/RNA-antimetabolites. The suggested decision trees can be used for determination of the action mechanism of antitumor drugs.

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“…The most widely used combining schemes are summation or averaging of descriptors of individual conformers or summation, weighted by a normalized Boltzmann distribution of conformers by energy. [29,[32][33] But application of such schemes looks somewhat artificial. First, only few conformers can fit a binding pocket.…”

Section: Introductionmentioning

confidence: 99%

Multiple Conformer Descriptors for QSAR Modeling

Nikonenko

Zankov

Baskin

et al. 2021

Molecular Informatics

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The most widely used QSAR approaches are mainly based on 2D molecular representation which ignores stereoconfiguration and conformational flexibility of compounds. 3D QSAR uses a single conformer of each compound which is difficult to choose reasonably. 4D QSAR uses multiple conformers to overcome the issues of 2D and 3D methods. However, many of existing 4D QSAR models suffer from the necessity to pre-align conformers, while alignment-independent approaches often ignore stereoconfiguration of compounds. In this study we propose a QSAR modeling approach based on transforming chiralityaware 3D pharmacophore descriptors of individual con-formers into a set of latent variables representing the whole conformer set of a molecule. This is achieved by clustering together all conformers of all training set compounds. The final representation of a compound is a bit string encoding cluster membership of its conformers. In our study we used Random Forest, but this representation can be used in combination with any machine learning method. We compared this approach with conventional 2D and 3D approaches using multiple data sets and investigated the sensitivity of the approach proposed to tuning parameters: number of conformers and clusters.

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Cited by 15 publications

References 7 publications

A new glimpse on the active site of SARS-CoV-2 3CLpro, coupled with drug repurposing study

A new glimpse on the active site of SARS-CoV-2 3CLpro, coupled with drug repurposing study

Decision Tree for Mechanism of Antitumor Drugs Action Prediction

Multiple Conformer Descriptors for QSAR Modeling

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