Multi-domain Distribution Learning for De Novo Drug Design

2025-08-25

AI Summary

This work addresses the challenge of 3D protein–ligand co-modeling in structure-based de novo drug design. We propose DrugFlow, a generative framework integrating continuous flow matching with discrete Markov bridges. DrugFlow jointly learns molecular chemical structures, 3D geometries, and physical interaction distributions; introduces uncertainty estimation—first in this domain—to reliably detect out-of-distribution samples; employs a joint preference alignment strategy to bias sampling toward regions with high binding affinity and drug-likeness; and extends to simultaneous ligand generation and protein side-chain conformational sampling, enabling exploration of the protein–ligand co-conformational space. On multiple benchmarks, DrugFlow achieves state-of-the-art performance, significantly improving generated molecules’ binding affinity, validity, and distributional fidelity.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Search and Optimization: Sampling/Simulation-based Search
• Natural Language Processing: Generation

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

We introduce DrugFlow, a generative model for structure-based drug design that integrates continuous flow matching with discrete Markov bridges, demonstrating state-of-the-art performance in learning chemical, geometric, and physical aspects of three-dimensional protein-ligand data. We endow DrugFlow with an uncertainty estimate that is able to detect out-of-distribution samples. To further enhance the sampling process towards distribution regions with desirable metric values, we propose a joint preference alignment scheme applicable to both flow matching and Markov bridge frameworks. Furthermore, we extend our model to also explore the conformational landscape of the protein by jointly sampling side chain angles and molecules.

Problem

• Generating novel drug molecules using protein-ligand structures
• Detecting out-of-distribution samples with uncertainty estimation
• Jointly sampling protein conformations and ligand molecules

Innovation

• Integrates continuous flow matching with discrete Markov bridges
• Proposes joint preference alignment scheme for sampling
• Extends model to jointly sample side chain angles and molecules