Open data-driven infrastructure for building biomolecular force fields for predictive biophysics and drug design

项目摘要/摘要 当代分子模拟模型非常准确，是构建当代分子模拟模型的重要工具 这些模式是有限的，而不是自动化的，而且是基于老化的基础设施。 我们的原创R01《用于为预测性生物物理构建生物分子力场的开放数据驱动的基础设施》 和药物设计，“旨在解决这些问题，产生一个现代化的基础设施建设，应用和 提高精确的分子力学作用力fi场。作为我们NIH资助的项目的一部分，我们合作了 与分子科学软件研究所(MolSSI)密切合作，利用QCArchive生态系统生成- 创建并不断扩展与生物分子系统相关的超大型量子化学数据集 在各种超级计算资源上。QC存档现在包含超过4200万个量子化学计算 超过3900万个分子，并且变得非常受欢迎，每月有超过179万次访问。 与生物分子系统相关的大型量子化学数据集对AI/ML具有难以置信的价值 社区。数据是对ML体系结构进行基础研究和构建所需的关键元素 下游使用的预测模型。不幸的是，量子化学数据集的成本令人难以置信 生成，限制内部生成将AI/ML研究推向少数大型所需的大型、有用的数据集 能够访问fi有效计算资源的公司和研究人员。而AI/ML量子化学 这些方法已经显示出生物分子系统的巨大前景，即有限地获得大的、经过管理的 数据集极大地阻碍了研究人员在这一领域取得快速进展。 我们的目标是通过与MolSSI QCArchive开发人员密切合作来弥合这一差距，以解决健壮性、可扩展性和 能力和数据交付挑战，以满足需要访问的生物分子AI/ML社区的需求 到大型量子化学数据集(目标1)。更多的软件开发人员将改进 QCArchive基础设施，以满足AI/ML社区快速增长的需求。因为QC档案主要是 由一名MolSSI软件科学家维护，完全启用AI/ML需要额外的开发人员 社区充分利用我们由NIH资助的项目所产生的丰富数据，以及 由我们的项目设计的工具主动生成的数据，以实现分布式容错量程 化学正在迅速普及到QC档案中。我们还将开发界面和仪表板，以实现 在流行的机器学习框架内方便地发现、检索和导入量子化学数据集 (目标2)。为了确保我们的工具特别适用于最有前途的AI/ML应用程序，我们将合作 与OpenMM、TorchMD和SchNetPack社区中的AI研究人员直接合作，积极开发和 利用量子机器学习(QML)潜力进行生物分子模拟，目标是产生 一般适用于更广泛的社区的有用工具，但能够驱动这些高优先级应用程序。

PROJECT SUMMARY/ABSTRACT Current generation molecular simulation models are insufficiently accurate, and current generation tools for building those models are limited, not automated, and based on aging infrastructure. Our original R01, “Open Data-driven Infrastructure for Building Biomolecular Force Fields for Predictive Biophysics and Drug Design,” aims to solve these problems, producing a modern infrastructure for building, applying, and improving accurate molecular mechanics force fields. As part of our NIH-funded project, we have collaborated closely with the Molecular Sciences Software Institute (MolSSI) to use the QCArchive ecosystem to gen- erate and continuously expand very large quantum chemical datasets relevant to biomolecular systems on a variety of supercomputing resources. QCArchive now contains over 42M quantum chemical calculations for over 39M molecules, and has become incredibly popular, with over 1.79M accesses/month. Large quantum chemical datasets relevant to biomolecular systems are incredibly valuable to the AI/ML community. Data is the key element needed for both fundamental research into ML architectures and constructing predictive models for downstream use. Unfortunately, quantum chemical datasets are incredibly expensive to generate, limiting in-house generation of large, useful datasets needed to drive AI/ML research to a few large companies and researchers with access to sufficient computing resources. While AI/ML quantum chemical methods have shown immense promise for biomolecular systems, the limited access to large, curated datasets has greatly hindered researchers from making rapid progress in this area. We aim to bridge this gap by working closely with MolSSI QCArchive developers to address robustness, scal- ability, and data delivery challenges to meet the needs of the biomolecular AI/ML community requiring access to large quantum chemistry datasets (Aim 1). Additional software developers will enable improvements to the QCArchive infrastructure to meet the rapidly growing demands of the AI/ML community. As QCArchive is primarily maintained by a single MolSSI Software Scientist, additional developers are necessary for fully enabling the AI/ML community to take full advantage of the wealth of data generated by our NIH-funded project directly, as well as the data actively being generated by the tools our project has engineered to enable distributed, fault-tolerant quantum chemistry that is rapidly populating QCArchive. We will additionally develop interfaces and dashboards to enable facile discovery, retrieval, and import of quantum chemical datasets within popular machine learning frameworks (Aim 2). To ensure our tools are specifically useful for the most promising AI/ML applications, we will collaborate directly with AI researchers in the OpenMM, TorchMD, and SchNetPack communities actively developing and deploying quantum machine learning (QML) potentials for biomolecular simulation, with the goal of producing generally useful tools suitable for the wider community yet capable of driving these high-priority applications.