# MolFORM: Multi-modal Flow Matching for Structure-Based Drug Design

arXiv: https://arxiv.org/abs/2507.05503 · HTML: https://mlsanity.com/paper/2507.05503/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2025.

## Built on by (most cited first, 1 of 1)

- [MolChord: Structure-Sequence Alignment for Protein-Guided Drug Design](https://mlsanity.com/paper/2510.27671/) (2025)

## Abstract (arXiv)

Structure-based drug design (SBDD) seeks to generate molecules that bind effectively to protein targets by leveraging their 3D structural information. While diffusion-based generative models have become the predominant approach for SBDD, alternative non-autoregressive frameworks remain relatively underexplored. In this work, we introduce MolFORM, a novel generative framework that jointly models discrete (atom types) and continuous (3D coordinates) molecular modalities using multi-flow matching. To further enhance generation quality, we incorporate a preference-guided fine-tuning stage based on Direct Preference Optimization (DPO), using Vina score as a reward signal. We propose a multi-modal flow DPO co-modeling strategy that simultaneously aligns discrete and continuous modalities, leading to consistent improvements across multiple evaluation metrics. The source code for MolFORM is publicly available at https://github.com/huang3170/MolForm.git.