D3SC: CDS&E: Conformer Toolkit: Generating Accurate Small Molecule Conformer Ensembles

匹兹堡大学的Geoffrey Hutchison和David Koes在化学系化学理论、模型和计算方法项目的支持下，开发和应用新的统计机器学习或“人工智能”技术来分析分子的结构和灵活性。大多数分子都是柔性的，在室温下可以以多种相互转换的几何形状存在，称为构象。随着分子的大小、复杂性和灵活性的增加，可能的稳定构象的数量呈指数增长。为了准确地预测分子性质，重要的是要考虑到所有的几何形状——即使是那些概率较低的构象——如果它们在给定的温度下有一定的自发形成的可能性。因此，尽管存在极其复杂的多维几何搜索空间，但如何有效地识别这些构象是一个挑战。Hutchison教授和Koes教授采用了一种独特的基于网格的神经网络方法来预测构象，该方法是在使用NSF XSEDE超级计算资源生成的高质量计算数据库上进行训练的。通过实验和计算基准验证了机器学习技术。它们还应用于开发识别药物靶标的改进方法，以及优化塑料电子材料的设计。为这个项目开发的数据库和软件是公开分发的。为阿伏伽德罗和3DMol.js软件工具开发了新的教程资源，并将编程，可视化和统计机器学习的新教育组件纳入了Hutchison教授教授的“化学家数学”课程。两位调查人员都向公众和当地高中生公开讲授数据科学和化学。他们还通过美国化学学会项目种子、匹兹堡公立学校科学技术学院和高中生药物发现、系统和计算生物学暑期学院积极参与到代表性不足的群体中。这个项目的第一部分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，可以估计大多数分子的二面角分布。根据这些概率，贝叶斯优化可以精确地探测和采样势能面玻尔兹曼加权系综。第二个目标是利用最近量子化学方法的改进来预测有机分子的热化学。利用这些精确的能量与实验和其他计算数据库相结合，可以为不同大小和包含各种元素的分子生成循环神经网络热化学模型。由此产生的大型数据存储库和开源软件工具被传播到社区，并用作化学新课程教育材料的基础。他们正在为高中生和本科生提供持续扩展和扩大参与活动的基础，涉及最先进的跨学科数据科学，机器学习和计算化学。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Geoffrey Hutchison and David Koes of the University of Pittsburgh are supported by the Chemical Theory, Models and Computational Methods Program in the Division of Chemistry, to develop and apply novel statistical machine learning or "artificial intelligence" techniques to analyze the structure and flexibility of molecules. Most molecules are flexible, and at room temperature can exist in multiple interconverting geometries called conformers. As molecules increase in size, complexity, and flexibility, the number of possible stable conformers increases exponentially. To accurately predict molecular properties, it is important to take into account all geometries---even those lower-probability conformers---if they have some probability of spontaneously forming at a given temperature. Thus, a challenge is to efficiently identify these conformers despite an extremely complex, multi-dimensional geometric search spaces. Professors Hutchison and Koes implement a unique grid-based neural network approach to predict conformers, trained on databases of high-quality calculations generated using NSF XSEDE supercomputing resources. The machine learning techniques are validated against experimental and computational benchmarks. They are also applied to developing improved methods for identifying drug targets, and for optimizing the design of plastic electronic materials. The databases and software developed for this project are publicly disseminated. New tutorial resources are developed for Avogadro and 3DMol.js software tools, and new educational components on programming, visualization, and statistical machine learning areincorporated into the "Mathematics for Chemists" course taught by Professor Hutchison. Both investigators give open lectures on data science and chemistry to the public and to local high school students. They are also actively engaged in outreach to underrepresented groups through the American Chemical Society Project Seed, Pittsburgh Public Schools Science and Technology Academy, and the Drug Discovery, Systems, and Computational Biology Summer Academy for high school students.The first part of this project draws upon 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 be used to accurately explore and sample Boltzmann-weighted ensembles of the potential energy surface. The second aim takes advantage of recent improvements in quantum chemical methods for predicting thermochemistry for organic molecules. Using these accurate energies in tandem with experimental and other computational databases, recurrent neural-network thermochemical models are produced for molecules of different sizes and containing a wide range of elements. The resulting large data repositories and open source software tools are disseminated to the community, and used as the foundation for educational materials in a new curriculum for chemistry. They are providing the basis for ongoing outreach and broadening participation activities to high school and undergraduate students, involving a state-of-the-art, interdisciplinary mix of data science, 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.