Employing Automated Machine Learning (AutoML) Methods to Facilitate the <i>In Silico</i> ADMET Properties Prediction.

ABSTRACT: The rationale for using ADMET prediction tools in the early drug discovery paradigm is to guide the design of new compounds with favorable ADMET properties and ultimately minimize the attrition rates of drug failures. Artificial intelligence (AI) in in silico ADMET modeling has gained momentum due to its high-throughput and low-cost attributes. In this study, we developed a machine learning model capable of predicting 11 ADMET properties of chemical compounds. Each model was constructed by combining one of 40 classification algorithms including random forest (RF), extreme gradient boosting (XGB), support vector machine (SVM), and gradient boosting (GB) with one of three predefined hyperparameter configurations. This process can be efficiently performed using automated machine learni

SUBMITTER: Han H 

PROVIDER: S-EPMC12004515 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature