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

• URL: http://arxiv.org/abs/2510.21153v1
• Date: Fri, 24 Oct 2025 04:49:23 GMT
• Title: Uncertainty-Aware Multi-Objective Reinforcement Learning-Guided Diffusion Models for 3D De Novo Molecular Design
• Authors: Lianghong Chen, Dongkyu Eugene Kim, Mike Domaratzki, Pingzhao Hu,
• Abstract summary: We propose an uncertainty-aware Reinforcement Learning framework to guide the optimization of 3D molecular diffusion models.<n>Our results demonstrate the strong potential of RL-guided generative diffusion models for advancing automated molecular design.
• Score: 0.8749675983608171
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Can Molecular Foundation Models Know What They Don't Know? A Simple Remedy with Preference Optimization [54.22711328577149]
  We introduce Molecular-Aligned Preference Instance Ranking (Mole-PAIR), a plug-and-play module that can be flexibly integrated with existing foundation models.<n>We show that our approach significantly improves the OOD detection capabilities of existing molecular foundation models.
  arXiv  Detail & Related papers  (2025-09-29T21:06:52Z)
• Guiding Diffusion Models with Reinforcement Learning for Stable Molecule Generation [16.01877423456416]
  Reinforcement Learning with Physical Feedback (RLPF) is a novel framework that extends Denoising Diffusion Policy Optimization to 3D molecular generation.<n>RLPF introduces reward functions derived from force-field evaluations to guide the generation toward energetically stable and physically meaningful structures.<n> Experiments on the QM9 and GEOM-drug datasets demonstrate that RLPF significantly improves molecular stability compared to existing methods.
  arXiv  Detail & Related papers  (2025-08-22T16:44:55Z)
• Generative molecule evolution using 3D pharmacophore for efficient Structure-Based Drug Design [8.652951659846838]
  We propose an evolutionary framework named MEVO, which bridges the gap between billion-scale small molecule dataset and the scarce protein-ligand complex dataset.<n>MEVO is composed of three key components: a high-fidelity VQ-VAE for molecule representation in latent space, a diffusion model for pharmacophore-guided molecule generation, and a pocket-aware evolutionary strategy for molecule optimization.
  arXiv  Detail & Related papers  (2025-07-27T04:58:11Z)
• Iterative Distillation for Reward-Guided Fine-Tuning of Diffusion Models in Biomolecular Design [58.8094854658848]
  We address the problem of fine-tuning diffusion models for reward-guided generation in biomolecular design.<n>We propose an iterative distillation-based fine-tuning framework that enables diffusion models to optimize for arbitrary reward functions.<n>Our off-policy formulation, combined with KL divergence minimization, enhances training stability and sample efficiency compared to existing RL-based methods.
  arXiv  Detail & Related papers  (2025-07-01T05:55:28Z)
• DEMO:Diffusion-based Evolutionary Optimization for 3D Multi-Objective Molecular Generation [25.82864719790724]
  optimizing multiple objective properties while satisfying structural constraints is a major challenge in 3D molecular discovery.<n>We propose a novel 3D molecular multi-objective evolutionary algorithm that leverages the generative power of a pretrained diffusion model.<n>Our method performs crossover operations in the noise space defined by the diffusion model's forward process, thereby enabling parental features or desired fragments to be fused into offspring.
  arXiv  Detail & Related papers  (2025-05-16T09:32:40Z)
• UniGenX: a unified generative foundation model that couples sequence, structure and function to accelerate scientific design across proteins, molecules and materials [62.72989417755985]
  We present UniGenX, a unified generative model for function in natural systems.<n>UniGenX represents heterogeneous inputs as a mixed stream of symbolic and numeric tokens.<n>It achieves state-of-the-art or competitive performance for the function-aware generation across domains.
  arXiv  Detail & Related papers  (2025-03-09T16:43:07Z)
• Conditional Synthesis of 3D Molecules with Time Correction Sampler [58.0834973489875]
  Time-Aware Conditional Synthesis (TACS) is a novel approach to conditional generation on diffusion models. It integrates adaptively controlled plug-and-play "online" guidance into a diffusion model, driving samples toward the desired properties.
  arXiv  Detail & Related papers  (2024-11-01T12:59:25Z)
• Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization [147.7899503829411]
  AliDiff is a novel framework to align pretrained target diffusion models with preferred functional properties. It can generate molecules with state-of-the-art binding energies with up to -7.07 Avg. Vina Score.
  arXiv  Detail & Related papers  (2024-07-01T06:10:29Z)
• Diffusion Models in $\textit{De Novo}$ Drug Design [0.0]
  Diffusion models have emerged as powerful tools for molecular generation, particularly in the context of 3D molecular structures. This review focuses on the technical implementation of diffusion models tailored for 3D molecular generation.
  arXiv  Detail & Related papers  (2024-06-07T06:34:13Z)
• 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction [9.67574543046801]
  The inclusion of 3D structures during targeted drug design shows superior performance to other target-free models. We develop a 3D equivariant diffusion model to solve the above challenges. Our model could generate molecules with more realistic 3D structures and better affinities towards the protein targets, and improve binding affinity ranking and prediction without retraining.
  arXiv  Detail & Related papers  (2023-03-06T23:01:43Z)
• Molecular Attributes Transfer from Non-Parallel Data [57.010952598634944]
  We formulate molecular optimization as a style transfer problem and present a novel generative model that could automatically learn internal differences between two groups of non-parallel data. Experiments on two molecular optimization tasks, toxicity modification and synthesizability improvement, demonstrate that our model significantly outperforms several state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-11-30T06:10:22Z)
• Learning Neural Generative Dynamics for Molecular Conformation Generation [89.03173504444415]
  We study how to generate molecule conformations (textiti.e., 3D structures) from a molecular graph. We propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph.
  arXiv  Detail & Related papers  (2021-02-20T03:17:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.