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

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 multitask learning framework that can predict drug-target binding affinities and simultaneously generate new target-aware drug variants, using common features for both tasks. In addition, we developed the FetterGrad algorithm to address the optimization challenges associated with multitask learning particularly those caused by gradient conflicts between distinct tasks. Comprehensive experiments on three real-world datasets demonstrate that the proposed model provides an effective mechanism for predicting drug-target binding affinities and generating novel drugs, thus greatly facilitating the drug discovery process. The authors proposed a multi-task deep learning model, to accurately predict drug-target affinity and generate target-aware drugs. In the proposed model, the authors developed the FetterGrad algorithm to mitigate gradient conflicts between both tasks. Through comprehensive experimentation, the authors have shown, that their proposed model effectively predicts affinity and generates target-aware drugs thus enhancing the drug discovery process.