Problem
Research questions and friction points this paper is trying to address.
flexible molecules
chemical space exploration
generative models
quantum-mechanical properties
QALPA: Property-guided diffusion modeling for efficient exploration of chemical spaces of flexible molecules
🤖 AI Summary
QALPA通过结合E(3)等变扩散模型、主动学习和量子力学方法，有效探索柔性分子化学空间中的稀疏区域，提高分子采样和模型可靠性。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
property-guided generative framework
E(3)-equivariant diffusion model
active learning
quantum-mechanical methods
molecular discovery
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