Structure-guided molecular design with contrastive 3D protein-ligand learning

• URL: http://arxiv.org/abs/2604.19562v1
• Date: Tue, 21 Apr 2026 15:13:41 GMT
• Title: Structure-guided molecular design with contrastive 3D protein-ligand learning
• Abstract summary: Structure-based drug discovery faces the dual challenge of accurately capturing 3D protein-ligand interactions and navigating ultra-large chemical spaces.<n>We present a unified framework that addresses these challenges by combining contrastive 3D structure encoding with autoregressive molecular generation conditioned on commercial compound spaces.
• Score: 4.938502760488423
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Structure-based drug discovery faces the dual challenge of accurately capturing 3D protein-ligand interactions while navigating ultra-large chemical spaces to identify synthetically accessible candidates. In this work, we present a unified framework that addresses these challenges by combining contrastive 3D structure encoding with autoregressive molecular generation conditioned on commercial compound spaces. First, we introduce an SE(3)-equivariant transformer that encodes ligand and pocket structures into a shared embedding space via contrastive learning, achieving competitive results in zero-shot virtual screening. Second, we integrate these embeddings into a multimodal Chemical Language Model (MCLM). The model generates target-specific molecules conditioned on either pocket or ligand structures, with a learned dataset token that steers the output toward targeted chemical spaces, yielding candidates with favorable predicted binding properties across diverse targets.

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