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

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

Abstract 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 a...

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Bibliographic Details
Main Authors: Leili Zhang, Giacomo Domeniconi, Chih-Chieh Yang, Seung-gu Kang, Ruhong Zhou, Guojing Cong
Format: Article
Language:English
Published: BMC 2021-06-01
Series:BMC Bioinformatics
Subjects:
Lead optimization
Drug discovery
Molecular dynamics simulation
Machine learning
Variational autoencoder
Clustering
Online Access:https://doi.org/10.1186/s12859-021-04214-4

Internet

https://doi.org/10.1186/s12859-021-04214-4

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