Durai, Prasannavenkatesh Ko, Young-Joon Pan, Cheol-Ho Park, Keunwan 2024-01-19T17:03:13Z 2024-01-19T17:03:13Z 2021-08-31 2020-07-14 1471-2105 https://pubs.kist.re.kr/handle/201004/118384 BackgroundDespite continued efforts using chemical similarity methods in virtual screening, currently developed approaches suffer from time-consuming multistep procedures and low success rates. We recently developed a machine learning-based chemical binding similarity model considering common structural features from molecules binding to the same, or evolutionarily related targets. The chemical binding similarity measures the resemblance of chemical compounds in terms of binding site similarity to better describe functional similarities that arise from target binding. In this study, we have shown how the chemical binding similarity could be used in virtual screening together with the conventional structure-based methods.ResultsThe chemical binding similarity, receptor-based pharmacophore, chemical structure similarity, and molecular docking methods were evaluated to identify an effective virtual screening procedure for desired target proteins. When we tested the chemical binding similarity method with test sets of 51 kinases, it outperformed the traditional structural similarity-based methods as well as structure-based methods, such as molecular docking and receptor-based pharmacophore modeling, in terms of finding active compounds. We further validated the results by performing virtual screening (using the chemical binding similarity and receptor-based pharmacophore methods) against a completely blind dataset for mitogen-activated protein kinase kinase 1 (MEK1), ephrin type-B receptor 4 (EPHB4) and wee1-like protein kinase (WEE1). The in vitro kinase binding assay confirmed that 6 out of 13 (46.2%) for MEK1 and 2 out of 12 (16.7%) for EPHB4 were newly identified only by the chemical binding similarity model.ConclusionsWe report that the virtual screening results could further be improved by combining the chemical binding similarity model with 3D-QSAR pharmacophore and molecular docking models. Not only the new inhibitors are identified in this study, but also many of the identified molecules have low structural similarity scores against already reported inhibitors and that show the revelation of novel scaffolds. English BMC DRUG DISCOVERY WEB SERVER SC-PDB MAP CLASSIFICATION PHARMACOLOGY GENERATION INHIBITORS SOFTWARE DATABASE Evolutionary chemical binding similarity approach integrated with 3D-QSAR method for effective virtual screening Article 10.1186/s12859-020-03643-x 1 BMC BIOINFORMATICS, v.21, no.1 BMC BIOINFORMATICS 21 1 scie scopus 000553152900003 2-s2.0-85088014428 Biochemical Research Methods Biotechnology & Applied Microbiology Mathematical & Computational Biology Biochemistry & Molecular Biology Biotechnology & Applied Microbiology Mathematical & Computational Biology Article DRUG DISCOVERY WEB SERVER SC-PDB MAP CLASSIFICATION PHARMACOLOGY GENERATION INHIBITORS SOFTWARE DATABASE Evolutionary chemical binding similarity Virtual screening 3D-QSAR Machine learning Ligand similarity Pharmacophore Molecular docking