ACLPred: an explainable machine learning and tree-based ensemble model for anticancer ligand prediction.

ABSTRACT: Several small molecules have been approved for cancer treatment, but the continuously growing cancer cases have further encouraged the identification of new anticancer drug compounds. Experimental methods are costly and time-consuming, thus rapid and cost-effective alternative method is much required. The effective identification of anticancer compounds using machine learning (ML) offers a promising solution, reducing both time and cost. In this study, small molecules with known inhibitory activities, both anticancer and non-anticancer were considered to train classification models. Molecular descriptors were calculated, and multistep feature selection was applied to identify significant features. Multiple ML algorithms were employed to build classification models and evaluated their perfo

SUBMITTER: Yadav AK 

PROVIDER: S-EPMC12378186 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature