Activity cliff-aware reinforcement learning for de novo drug design

Abstract

The integration of artificial intelligence (AI) in drug discovery offers promising opportunities to streamline and enhance the traditional drug development process. One core challenge in de novo molecular design is modeling complex structure-activity relationships (SAR), such as activity cliffs, where minor molecular changes yield significant shifts in biological activity. In response to the limitations of current models in capturing these critical discontinuities, we propose the Activity Cliff-Aware Reinforcement Learning (ACARL) framework. ACARL leverages a novel activity cliff index to identify and amplify activity cliff compounds, uniquely incorporating them into the reinforcement learning (RL) process through a tailored contrastive loss. This RL framework is designed to focus model optimization on high-impact regions within the SAR landscape, improving the generation of molecules with targeted properties. Experimental evaluations across multiple protein targets demonstrate ACARL’s superior performance in generating high-affinity molecules compared to existing state-of-the-art algorithms. These findings indicate that ACARL effectively integrates SAR principles into the RL-based drug design pipeline, offering a robust approach for de novo molecular design

Scientific contribution Our work introduces a machine learning-based drug design framework that explicitly models activity cliffs, a first in AI-driven molecular design. ACARL’s primary technical contributions include the formulation of an activity cliff index to detect these critical points, and a contrastive RL loss function that dynamically enhances the generation of activity cliff compounds, optimizing the model for high-impact SAR regions. This approach demonstrates the efficacy of combining domain knowledge with machine learning advances, significantly expanding the scope and reliability of AI in drug discovery.