CASTELO: clustered atom subtypes aided lead optimization—a combined machine learning and molecular modeling method

Background Drug discovery is a multi-stage process that comprises two costly major steps: pre-clinical research and clinical trials. Among its stages, lead optimization easily consumes more than half of the pre-clinical budget. We propose a combined machine learning and molecular modeling approach t...

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Podrobná bibliografia
Vydané v:	BMC bioinformatics Ročník 22; číslo 1; s. 1 - 21
Hlavní autori:	Zhang, Leili, Domeniconi, Giacomo, Yang, Chih-Chieh, Kang, Seung-gu, Zhou, Ruhong, Cong, Guojing
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London BioMed Central 22.06.2021 BioMed Central Ltd Springer Nature B.V BMC
Predmet:	Algorithms Analysis BASIC BIOLOGICAL SCIENCES Bioinformatics Biomedical and Life Sciences Chemists Clinical trials Clustering Computational Biology/Bioinformatics Computer Appl. in Life Sciences Data collection Drug discovery Drugs Feature extraction Information management Lead optimization Learning algorithms Life Sciences Machine learning Methods Microarrays Modelling Molecular dynamics Molecular dynamics simulation Molecular modelling Optimization Pharmaceutical industry Proteins Simulation Sucrose Variational autoencoder Workflow United States Lead optimization Variational autoencoder Drug discovery Molecular dynamics simulation Clustering Machine learning
ISSN:	1471-2105, 1471-2105
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Popis
Shrnutí:	Background Drug discovery is a multi-stage process that comprises two costly major steps: pre-clinical research and clinical trials. Among its stages, lead optimization easily consumes more than half of the pre-clinical budget. We propose a combined machine learning and molecular modeling approach that partially automates lead optimization workflow in silico, providing suggestions for modification hot spots. Results The initial data collection is achieved with physics-based molecular dynamics simulation. Contact matrices are calculated as the preliminary features extracted from the simulations. To take advantage of the temporal information from the simulations, we enhanced contact matrices data with temporal dynamism representation, which are then modeled with unsupervised convolutional variational autoencoder (CVAE). Finally, conventional and CVAE-based clustering methods are compared with metrics to rank the submolecular structures and propose potential candidates for lead optimization. Conclusion With no need for extensive structure-activity data, our method provides new hints for drug modification hotspots which can be used to improve drug potency and reduce the lead optimization time. It can potentially become a valuable tool for medicinal chemists.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 USDOE IBM AC05-00OR22725
ISSN:	1471-2105 1471-2105
DOI:	10.1186/s12859-021-04214-4