Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II.

ABSTRACT: Assessing the hERG liability in the early stages of drug discovery programs is important. The recent increase of hERG-related information in public databases enabled various successful applications of machine learning techniques to predict hERG inhibition. However, most of these researches constructed the datasets from only one database, limiting the predictability and scope of the models. In this study, a hERG classification model was constructed using the largest dataset for hERG inhibition built by integrating multiple databases. The integrated dataset consisted of more than 291,000 structurally diverse compounds derived from ChEMBL, GOSTAR, PubChem, and hERGCentral. The prediction model was built by support vector machine (SVM) with descriptor selection based on Non-dominated Sorting G

SUBMITTER: Ogura K 

PROVIDER: S-EPMC6704061 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature