A Multi-Task Self-Supervised Strategy for Predicting Molecular Properties and FGFR1 Inhibitors.

ABSTRACT: Studying the molecular properties of drugs and their interactions with human targets aids in better understanding the clinical performance of drugs and guides drug development. In computer-aided drug discovery, it is crucial to utilize effective molecular feature representations for predicting molecular properties and designing ligands with high binding affinity to targets. However, designing an effective multi-task and self-supervised strategy remains a significant challenge for the pretraining framework. In this study, a multi-task self-supervised deep learning framework is proposed, MTSSMol, which utilizes ≈10 million unlabeled drug-like molecules for pretraining to identify potential inhibitors of fibroblast growth factor receptor 1 (FGFR1). During the pretraining of MTSSMol, molecular

SUBMITTER: Yang X 

PROVIDER: S-EPMC11967764 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature