Quantum Chemical Molecular Representations for Machine Learning

机器学习的量子化学分子表示

• 批准号: 497190956
• 负责人: Professor Dr. Stefan Grimme
• 金额: --
• 依托单位: Mulliken Center for Theoretical Chemistry
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/497190956?language=en
• 关键词: Quantum; Chemical; Molecular; Representations; Machine

This project aims to develop new molecular representations for machine learning (ML) based on efficient tight-binding (TB) quantum chemistry ('quantum features') and to connect those representations to various new network architectures. The models will be applied to predict chemically relevant properties of pharmaceutical-type molecules, like conformational and tautomerization energies, pKa values, solubility or partition coefficients. It is a project of a world-wide leading theoretical chemistry group for the development and application of simplified quantum chemical (QC) methods with strong support from the science and technology company Merck with established competence in leveraging extensive chemical data. For computing the quantum features, a new model Hamiltonian (ShellQ) in an extended AO basis set (vDZP) will be developed that is able to reproduce accurately various properties (atomic charge, shell population, bond order, dipole moment, polarizability) from a reference DFT calculation and is still generally applicable to the whole periodic table including organometallic systems. It accounts for the first time in a semiempirical context for fundamental physical effects like orbital contraction and electronic polarization. It is combined with established continuum solvation theories to model solvated molecules. Further main aspects of the proposal are the optimization of the neural network architecture based on ShellQ features, development of feature representation, the automatized generation of molecular training data sets, and state-of-the-art multitask-learning inspired from image recognition algorithms. In general, we follow a Delta-ML strategy where a correction term to a fast QC calculation (typically the established GFN-xTB or GFN-FF methods) based on the available features is computed by the network. This entire approach is supposed to provide efficiency and accuracy for a potentially wide range of chemical properties.

该项目旨在基于高效的紧束缚（TB）量子化学（“量子特征”）为机器学习（ML）开发新的分子表示，并将这些表示连接到各种新的网络架构。这些模型将被应用于预测药物型分子的化学相关性质，如构象和互变异构能，pKa值，溶解度或分配系数。这是一个全球领先的理论化学小组的项目，旨在开发和应用简化的量子化学（QC）方法，并得到了在利用广泛化学数据方面拥有成熟能力的科技公司默克的大力支持。为了计算量子特征，将开发扩展AO基组（vDZP）中的新模型哈密顿量（ShellQ），其能够从参考DFT计算准确地再现各种性质（原子电荷、壳层布居、键级、偶极矩、极化率），并且仍然普遍适用于整个周期表，包括有机金属系统。它第一次在半经验的背景下解释了基本的物理效应，如轨道收缩和电子极化。它与已建立的连续溶剂化理论相结合来模拟溶剂化分子。该提案的其他主要方面是基于ShellQ特征的神经网络架构的优化，特征表示的开发，分子训练数据集的自动化生成，以及从图像识别算法中获得灵感的最先进的多任务学习。一般来说，我们遵循Delta-ML策略，其中基于可用特征的快速QC计算（通常是已建立的GFN-xTB或GFN-FF方法）的校正项由网络计算。整个方法应该为潜在的广泛的化学性质提供效率和准确性。