Do Language Models Dream of Binding Molecules? Benchmarking LLMs under Spatial Constraints
Structure-based drug design (SBDD) leverages the 3D structure of protein targets, often complemented by other spatial constraints, to generate candidate binding molecules. While diffusion models have dominated as a leading paradigm for high-quality 3D molecule generation, LLM-based methods are rapidly emerging in molecular design and have shown competitive performance in pocket-conditioned molecular generation. However, their ability to reason about physics and 3D spatial environments is largely underexplored. In this work, we systematically analyze whether current general-purpose LLMs are cap
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- PossiblePossibly related (embedding) · 61%Bridging three-dimensional molecular structures and artificial intelligence with a conformation description language →
- PossiblePossibly related (embedding) · 57%Reshaping biomolecular structure prediction through strategic conformational exploration with HelixFold-S1 →
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- PossiblePossibly related (embedding) · 51%Efficient and valid large molecule generation via self-supervised generative models - Nature →
- LinkedLinked via arxiv author · 85%Thomas MacDougall →
“Do Language Models Dream of Binding Molecules? Benchmarking LLMs under Spatial Constraints”
- LinkedLinked via arxiv author · 85%Maksim Kuznetsov →
“Do Language Models Dream of Binding Molecules? Benchmarking LLMs under Spatial Constraints”
- LinkedLinked via arxiv author · 85%Roman Schutski →
“Do Language Models Dream of Binding Molecules? Benchmarking LLMs under Spatial Constraints”
- LinkedLinked via arxiv author · 85%Rim Shayakhmetov →
“Do Language Models Dream of Binding Molecules? Benchmarking LLMs under Spatial Constraints”