DeepDTAGen: a multitask deep learning framework for drug-target affinity prediction and target-aware drugs generation.

ABSTRACT: Identifying novel drugs that can interact with target proteins is a highly challenging, time-consuming, and costly task in drug discovery and development. Numerous machine learning-based models have recently been utilized to accelerate the drug discovery process. However, these existing methods are primarily uni-tasking, either designed to predict drug-target interaction (DTI) or generate new drugs. Through the lens of pharmacological research, these tasks are intrinsically interconnected and play a critical role in effective drug development. Therefore, the learning models must be utilized in such a manner to learn the structural properties of drug molecules, the conformational dynamics of proteins, and the bioactivity between drugs and targets. To this end, this paper develops a novel mu

SUBMITTER: Shah PM 

PROVIDER: S-EPMC12125237 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature