QALPA：属性引导的扩散建模用于高效探索柔性分子化学空间
QALPA: Property-guided diffusion modeling for efficient exploration of chemical spaces of flexible molecules
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中文总结 AI 辅助
QALPA结合E(3)-等变扩散模型与主动学习和量子力学方法，属性引导生成以高效探索柔性分子化学空间，扩充稀疏QM数据集，提升生成准确性和可迁移性。
中文摘要 AI 辅助
探索柔性分子的化学空间仍然具有挑战性，因为可能的化合物和构象数量庞大，同时用于更大和更复杂分子的3D生成模型成本增加且泛化能力有限，限制了对未开发化学领域的访问。在此，我们引入QALPA（“量子感知学习用于属性空间增强”），这是一个属性引导的生成框架，结合了E(3)-等变扩散模型与主动学习和高效的量子力学（QM）方法，以迭代探索目标QM属性流形。通过将生成与基于物理的评估相结合，QALPA在化学空间稀疏填充区域中提高了分子采样和模型可靠性。我们的结果表明，在涵盖小分子（QM7-X）和大分子（Aquamarine）类药化合物的互补QM数据集上训练，能够在广泛的尺寸范围内实现准确的分子生成，提高了超出训练分布的可迁移性，适用于涉及广延属性和强度属性的复杂属性流形。作为概念验证，QALPA与机器学习增强的紧束缚方法EquiDTB耦合，有效扩充了alloQM（本工作中引入的QM数据集，包含6,253个变构药物分子构象），通过填充由多体色散能和HOMO-LUMO能隙定义的属性景观中的稀疏区域。这些结果表明，生成式AI与高效ML/QM方法的集成为扩充稀疏QM数据集和可持续扩展分子发现的化学空间探索提供了一条实用途径。
英文摘要
Exploring the chemical space of flexible molecules remains challenging because the vast number of possible compounds and conformations, together with the increasing cost and limited generalization of 3D generative models for larger and more complex molecules, restrict access to unexplored chemistry. Here, we introduce QALPA ("Quantum-Aware Learning for Property-space Augmentation"), a property-guided generative framework that combines an E(3)-equivariant diffusion model with active learning and efficient quantum-mechanical (QM) methods to iteratively explore targeted QM property manifolds. By coupling generation with physics-based evaluation, QALPA improves molecular sampling and model reliability in sparsely populated regions of chemical space. Our results show that training on complementary QM datasets spanning both small (QM7-X) and large (Aquamarine) drug-like compounds enables accurate molecular generation across a broad size range, improving transferability beyond the training distribution for complex property manifolds involving both extensive and intensive properties. As a proof of concept, QALPA coupled with the machine learning-augmented tight-binding method EquiDTB efficiently augments alloQM, a QM dataset introduced in this work, comprising 6,253 conformers of allosteric drug molecules, by populating sparse regions of the property landscape defined by the many-body dispersion energy and HOMO-LUMO energy gap. These results demonstrate that the integration of generative AI with efficient ML/QM methods offers a practical pathway toward augmenting sparse QM datasets and sustainably expanding the exploration of chemical space for molecular discovery.
发表机构
- TUD Dresden University of Technology（德累斯顿工业大学）
- Pfizer Worldwide R&D（辉瑞全球研发中心）
- Center for Advanced Systems Understanding (CASUS)（高级系统理解中心（CASUS））
- Helmholtz Zentrum Dresden-Rossendorf（亥姆霍兹德累斯顿罗森多夫研究中心）
机构由 AI 辅助整理，请以论文原文为准。
