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

BMC Bioinformatics

Chih-Chieh Yang¹  Seung-gu Kang¹  Giacomo Domeniconi¹  Leili Zhang¹  Guojing Cong²  Ruhong Zhou³ 

[1]IBM Thomas J. Watson Research Center, 1101 Kitchawan Rd, 10598, Yorktown Heights, NY, USA

[2]Oak Ridge national laboratory, 1 Bethel Valley Rd, 37830, Oak Ridge, TN, USA

[3]ZheJiang University, 688 Yuhangtang Road, 310027, Hangzhou, China

关键词: Lead optimization;    Drug discovery;    Molecular dynamics simulation;    Machine learning;    Variational autoencoder;    Clustering;   

DOI  :  10.1186/s12859-021-04214-4

来源: Springer

【 摘 要 】

BackgroundDrug 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.ResultsThe 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.ConclusionWith 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.

【 授权许可】

CC BY