Uncertainty-Aware Multi-Objective Reinforcement Learning-Guided Diffusion Models for 3D De Novo Molecular Design

Lianghong Chen, Dongkyu Eugene Kim, Mike Domaratzki, Pingzhao Hu

Keywords: 3D De Novo Molecular Design, Diffusion Model, Multi-Objective Reinforcement Learning, Uncertainty Quantification, Deep Learning

TL;DR: We propose an uncertainty-aware multi-objective reinforcement learning framework to guide diffusion models for 3D molecular generation, providing high-quality candidate molecules for drug discovery.

Abstract: Designing de novo 3D molecules with desirable properties remains a fundamental challenge in drug discovery and molecular engineering. While diffusion models have demonstrated remarkable capabilities in generating high-quality 3D molecular structures, they often struggle to effectively control complex multi-objective constraints critical for real-world applications. In this study, we propose an uncertainty-aware Reinforcement Learning (RL) framework to guide the optimization of 3D molecular diffusion models toward multiple property objectives while enhancing the overall quality of the generated molecules. Our method leverages surrogate models with predictive uncertainty estimation to dynamically shape reward functions, facilitating balance across multiple optimization objectives. We comprehensively evaluate our framework across three benchmark datasets and multiple diffusion model architectures, consistently outperforming baselines for molecular quality and property optimization. Additionally, Molecular Dynamics (MD) simulations and ADMET profiling of top generated candidates indicate promising drug-like behavior and binding stability, comparable to known Epidermal Growth Factor Receptor (EGFR) inhibitors. Our results demonstrate the strong potential of RL-guided generative diffusion models for advancing automated molecular design.

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Submission Number: 16815