Uncertainty-aware hybrid deep generative framework for robust and explainable drug discovery

Conventional techniques in drug discovery are time-consuming and less accurate due to the vast chemical space and associated uncertainty. Artificial intelligence and machine learning have introduced new methods to improve the drug discovery process. But still, some challenges need to be overcome, like higher attrition rates and the need to optimize multiple molecular properties simultaneously. The proposed study overcomes these problems by introducing a novel hybrid computational framework that integrates Graph Convolutional Networks, Variational Autoencoders, and Uncertainty Aware Adaptive Multi-Objective Optimization-based Reinforcement Learning (UAAMOO-RL) for generating drug molecules with needed pharmacological properties. By combining uncertainty awareness and explainability, the proposed method generates new drug molecules, prioritizing reliability and reducing false positives, thereby addressing two challenges in drug discovery, such as robustness and interpretability. The proposed framework is trained and validated using the ChEMBL dataset, consisting of 1.5 million bioactive molecules with known properties. The results indicate that the proposed model achieves a QED pass rate of 88.8% at a threshold of [Formula: see text] = 0.60, which outperforms the state-of-the-art techniques while maintaining structural diversity, uniqueness, and validity. Results also show significant improvement on the Zinc250k (82.7%) and PDBbind (85.9%) datasets. The proposed approach enhances early-stage drug discovery by reducing exhaustive experimental screening.