用小分子数据生成大分子3D结构，无需大量训练数据
Chemistry-Enhanced Diffusion-Based Framework for Small-to-Large Molecular Conformation Generation
- 将分子拆解为化学有效片段，用扩散模型生成3D结构
- 在不依赖大分子数据情况下，生成多样化且符合化学规律的构象
- 适合药物分子设计、材料发现等需要快速生成大分子结构的场景
在量子化学精度下获取多原子分子的真实三维构象仍具挑战性。尽管机器学习带来希望，但预测大分子结构仍需大量计算资源。本文提出StoL——一种基于扩散模型的框架，可从少量小分子数据中快速生成大分子结构，无需在训练中接触目标分子或相似规模的结构。输入SMILES后，模型将其分解为化学有效的片段，利用在小分子上训练的扩散模型生成各片段的3D结构，并以拼乐高方式组装成多样构象。该片段策略避免了对大分子训练数据的需求，同时保持高可扩展性和迁移能力。通过嵌入化学原理，StoL实现更快收敛、更合理的化学结构和广泛的构象覆盖，经密度泛函理论（DFT）验证。
原文摘要 · Abstract (English)
Obtaining 3D conformations of realistic polyatomic molecules at the quantum chemistry level remains challenging, and although recent machine learning advances offer promise, predicting large-molecule structures still requires substantial computational effort. Here, we introduce StoL, a diffusion model-based framework that enables rapid and knowledge-free generation of large molecular structures from small-molecule data. Remarkably, StoL assembles molecules in a LEGO-style fashion from scratch, without seeing the target molecules or any structures of comparable size during training. Given a SMILES input, it decomposes the molecule into chemically valid fragments, generates their 3D structures with a diffusion model trained on small molecules, and assembles them into diverse conformations. This fragment-based strategy eliminates the need for large-molecule training data while maintaining high scalability and transferability. By embedding chemical principles into key steps, StoL ensures faster convergence, chemically rational structures, and broad configurational coverage, as confirmed against DFT calculations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。