CSD&E: Expanding Efficient Conformer Sampling to Diverse Charged and Neutral Molecules

在化学系化学理论、模型和计算方法项目的支持下，匹兹堡大学的Geoffrey Hutchison和大卫·科斯博士正在开发数据驱动技术，以研究中性和带电分子的基本几何形状和灵活性。本研究旨在通过逆向性质驱动的设计方法，提高分子计算化学在聚合物，分子材料和药物发现领域的预测能力。大多数分子，从蛋白质到药物和塑料，都是柔性的，在室温下可以以多种几何形状存在，称为构象异构体。随着分子大小、复杂性和灵活性的增加，可能的稳定构象的数量呈指数级增加。此外，虽然中性分子存在常见几何图案的规则，但带电状态，无论是带正电还是带负电，都可能具有非常不同的结构。该项目利用数据科学，机器学习和优化理论的专业知识来构建实验和计算数据资源，以预测可能的构象。这些技术将在多个实验和计算基准中得到验证，并应用于化学的关键领域，包括寻找药物设计和塑料电子材料的新目标。该项目将为高中生、本科生和研究生提供数据科学、机器学习和化学方面的教育和积极培训机会。该项目借鉴了统计热力学和贝叶斯统计之间的联系。利用实验和计算数据，人们可以估计大多数分子的二面角分布。从这样的概率，贝叶斯优化可以准确地探索和采样势能面的玻尔兹曼加权系综。通过构建中性和带电物种的数据存储库，人们可以有效地训练新的循环机器学习方法，以及设计新的“少数”几何优化方法。反过来，这些方法将利用精确量子化学方法的改进来预测大量中性和带电有机物种的热化学和自由能。通过生成这些大型数据存储库，哈钦森和Koes及其同事将为数据科学课程、机器学习研讨会和化学课程创建新的课程。该项目还将为高中生、本科生和研究生提供数据科学、统计学、机器学习和计算化学跨学科结合方面的实质性教育培训。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

With support from the Chemical Theory, Models, and Computational Methods program in the Division of Chemistry, Drs. Geoffrey Hutchison and David Koes at the University of Pittsburgh are developing data-driven techniques to study the fundamental geometries and flexibility of neutral and charged molecules. This research seeks to advance the predictive ability of molecular computational chemistry in the areas of polymers, molecular materials, and drug discovery via an inverse property-driven design approach. Most molecules, ranging from proteins to pharmaceuticals and plastics, are flexible and at room temperature can exist in multiple geometries called conformers. As a molecule increases in size, complexity, and flexibility, the number of possible stable conformers increases exponentially. Moreover, while rules for common geometric motifs exist for neutral molecules, charged states, whether positively or negatively charged, may have very different structures. The project draws on expertise in data science, machine learning, and optimization theory to build both experimental and computational data resources to predict likely conformers. Techniques will be validated across multiple experimental and computational benchmarks and applied to key areas of chemistry, including finding new targets for drug design and plastic electronic materials. The project will provide opportunities for the education and active training of high school, undergraduate, and graduate students in data science, machine learning, and chemistry.The project draws on a connection between statistical thermodynamics and Bayesian statistics. Using experimental and computational data, one can estimate distributions of dihedral angles for most molecules. From such probabilities, Bayesian optimization can accurately explore and sample Boltzmann-weighted ensembles of the potential energy surface. By building data repositories of neutral and charged species, one can efficiently train new recurrent machine learning methods, as well as design new “few-shot” geometry optimization methods. In turn, these methods will draw on improvements in accurate quantum chemical methods to predict thermochemistry and free energies for a large selection of neutral and charged organic species. Through generating these large data repositories, Hutchinson and Koes and their co-workers will create a new curriculum for data science lessons, machine learning workshops, and classes in chemistry. The project will also provide substantial educational training to high school, undergraduate, and graduate students in the interdisciplinary combination of data science, statistics, machine learning, and computational chemistry.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.