MolChord: Structure-Sequence Alignment for Protein-Guided Drug Design

• URL: http://arxiv.org/abs/2510.27671v1
• Date: Fri, 31 Oct 2025 17:35:53 GMT
• Title: MolChord: Structure-Sequence Alignment for Protein-Guided Drug Design
• Authors: Wei Zhang, Zekun Guo, Yingce Xia, Peiran Jin, Shufang Xie, Tao Qin, Xiang-Yang Li,
• Abstract summary: MolChord aims to align protein and molecule structures with their textual descriptions and sequential representations.<n>We leverage autoregressive model unifying text, small molecules, and proteins, as the molecule generator, alongside a diffusion-based structure encoder.<n>We curate a property-aware dataset by integrating preference data and refine the alignment process using Direct Preference Optimization.
• Score: 25.550555350063366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Structure-based drug design (SBDD), which maps target proteins to candidate molecular ligands, is a fundamental task in drug discovery. Effectively aligning protein structural representations with molecular representations, and ensuring alignment between generated drugs and their pharmacological properties, remains a critical challenge. To address these challenges, we propose MolChord, which integrates two key techniques: (1) to align protein and molecule structures with their textual descriptions and sequential representations (e.g., FASTA for proteins and SMILES for molecules), we leverage NatureLM, an autoregressive model unifying text, small molecules, and proteins, as the molecule generator, alongside a diffusion-based structure encoder; and (2) to guide molecules toward desired properties, we curate a property-aware dataset by integrating preference data and refine the alignment process using Direct Preference Optimization (DPO). Experimental results on CrossDocked2020 demonstrate that our approach achieves state-of-the-art performance on key evaluation metrics, highlighting its potential as a practical tool for SBDD.

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