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

TL;DR

This paper introduces an uncertainty-aware reinforcement learning framework to guide diffusion models in generating 3D molecules with optimized multiple properties, improving quality and drug-like characteristics for molecular design.

Contribution

It presents a novel RL-guided diffusion approach with uncertainty estimation for multi-objective 3D molecular generation, outperforming existing methods.

Findings

Enhanced molecular quality and property optimization across benchmarks.

Generated molecules exhibit promising drug-like behavior and stability.

Framework effectively balances multiple complex objectives.

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.