Collaborative Research: Frameworks: Interoperable High-Performance Classical, Machine Learning and Quantum Free Energy Methods in AMBER

合作研究：框架：AMBER 中可互操作的高性能经典、机器学习和量子自由能方法

• 批准号: 2209718
• 负责人: Darrin York
• 金额: $ 150万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2209718&HistoricalAwards=false
• 关键词: Collaborative; Research; Frameworks; Interoperable; Performance

With support from the Office of Advanced Infrastructure and the Division of Chemistry at NSF, Professor Merz and his group will work on molecular simulation cyberinfrastructure. Molecular simulations have become an invaluable tool for research and technology development in chemical, pharmaceutical, and materials sciences. With the availability of specialized hardware such as graphics processing units (GPUs), molecular dynamics simulations using classical or molecular mechanical force fields have reached the spatial and temporal scales needed to address important real-world problems in the chemical and biological sciences. Free energy simulations are a particularly important and challenging class of molecular simulations that are critical to gain a predictive understanding of chemical processes. For example, free energy methods can predict the barrier height and rates for chemical reactions, whether a reaction will occur, or how tightly a drug binds to a target. These predictions are extremely valuable for the design of new catalytic agents or drugs. However, the predictive capability of free energy simulations is sensitive to the underlying model that describes the inter-atomic potential energy and forces. Accurate free energy simulations of chemical processes require potential energy models that capture the essential physics and can respond to changes in the chemical environment, but conventional force field models are unsuitable for many processes involving bond breaking and formation as seen, for example, in catalyst design. Consequently, there is great need to extend the scope of free energy methods by enabling the use of a broader range of potential energy models that are more accurate as well as reactive and/or capable of quantum mechanical many-body polarization and charge transfer. The cyberinfrastructure created by this project allows for the routine application of free energy methods, using quantum mechanics, machine learning, reactive and classical potentials to a myriad of important problems that advance the state-of-the art in the biological and chemical sciences. The tools can be applied by a range of scientists to address fundamental problems of national interest, for example, in the design of drugs against zoonotic diseases (e.g., COVID-19), the design of materials with novel functions and in the design of improved batteries. Given the sophistication of the methods employed, education of a diverse pool of chemical, biological and computer scientists to advance this field is essential and is addressed in this project, thereby training the next generation of computational scientists that will form the backbone of the work force of the future. The project develops accurate and efficient free energy software within a powerful new multiscale modeling framework in the AMBER suite of programs for applications in chemistry, biology, and materials science. The multiscale framework enables the design and use of new classes of mixed-method force fields that involve interoperability between several existing and emerging reactive, machine learning and quantum many-body potentials. These potentials have enhanced accuracy, robustness, and predictive capability compared to classical molecular mechanical force fields and enable the study of chemical reactions and catalysis. The cyberinfrastructure supports innovative multi-layered hybrid potentials that can be customized to meet the needs of complex applications in biotechnology development, enzyme design and drug discovery. A robust endpoint "book-ending" approach that leverages the GPU-accelerated capability of the AMBER molecular dynamics engine is used to reach these goals. Specifically, the open-source high-performance software for free energy simulations is designed for multi-layered hybrid potentials using combinations of linear-scaling many-body quantum mechanical methods via the GPU-accelerated QUICK package, scalable reactive ReaxFF force fields via the PuReMD package, as well as the recently developed DeepMD-SE, ANAKIN-ME (ANI) and AP-Net families of machine learning potentials. The cyberinfrastructure is built upon the existing high-performance CUDA MD engine in AMBER and extends it to a broad range of GPU-accelerated architectures using industry-standard programming models. Scalability is ensured using innovative parallel algorithms. High impact is achieved by leveraging AMBER's broad user base to expand the scope and success of FE applications. In this way, the project leverages existing recognized capabilities and actively engages a diverse team of collaborators and the broader molecular simulations community. The cyberinfrastructure delivered by the project enables a wide range of new and enhanced applications for a broad community of users in academia, industry, and national laboratories. These applications include drug discovery, enzyme catalysis, and biomaterials design. The AMBER suite of programs has a long-standing extensive worldwide userbase, and is widely used on national production cyberinfrastructure. The enhancement of AMBER as an established, proven sustainable, and widely used package will ensure that the software has a broad impact well beyond the end of the project. The project will also train a diverse population of students and researchers in theory, programming, computational chemistry/biology, computer science, scientific writing, and communication.This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Chemistry within the NSF Directorate for Mathematical and Physical Sciences.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.

在NSF高级基础设施办公室和化学部的支持下，Merz教授和他的团队将致力于分子模拟网络基础设施。分子模拟已成为化学、制药和材料科学研究和技术开发的宝贵工具。随着图形处理单元（GPU）等专用硬件的出现，使用经典或分子机械力场的分子动力学模拟已经达到了解决化学和生物科学中重要现实问题所需的空间和时间尺度。自由能模拟是一类特别重要和具有挑战性的分子模拟，对于获得化学过程的预测性理解至关重要。例如，自由能方法可以预测化学反应的势垒高度和速率，反应是否会发生，或者药物与靶标结合的紧密程度。这些预测对于设计新的催化剂或药物是非常有价值的。然而，自由能模拟的预测能力对描述原子间势能和力的基础模型敏感。化学过程的精确自由能模拟需要势能模型，该势能模型捕获基本物理并且可以响应化学环境的变化，但是传统的力场模型不适合于许多涉及键断裂和形成的过程，例如在催化剂设计中。因此，非常需要通过使得能够使用更广泛的势能模型来扩展自由能方法的范围，所述势能模型更准确以及反应性和/或能够量子力学多体极化和电荷转移。该项目创建的网络基础设施允许自由能方法的常规应用，使用量子力学，机器学习，反应和经典势能来解决无数重要问题，这些问题推动了生物和化学科学的发展。一系列科学家可以应用这些工具来解决国家利益的基本问题，例如，设计防治人畜共患病的药物（例如，COVID-19），设计具有新功能的材料以及设计改进的电池。考虑到所采用的方法的复杂性，对化学、生物和计算机科学家的多样化人才库进行教育以推进这一领域是至关重要的，并在本项目中得到解决，从而培养下一代计算科学家，他们将成为未来劳动力的骨干。该项目在一个强大的新的多尺度建模框架内开发准确，高效的自由能软件，该框架在AMBER程序套件中用于化学，生物学和材料科学的应用。多尺度框架使设计和使用新的混合方法力场，涉及几个现有的和新兴的反应，机器学习和量子多体势之间的互操作性。与经典的分子力学力场相比，这些势能具有更高的准确性，鲁棒性和预测能力，并使化学反应和催化的研究成为可能。网络基础设施支持创新的多层混合潜力，可以定制以满足生物技术开发，酶设计和药物发现等复杂应用的需求。 利用AMBER分子动力学引擎的GPU加速能力，一种强大的端点“书结束”方法被用来实现这些目标。 具体来说，用于自由能模拟的开源高性能软件是为多层混合势而设计的，它使用了通过GPU加速的QUICK包的线性缩放多体量子力学方法，通过PuReMD包的可扩展反应ReaxFF力场，以及最近开发的DeepMD-SE，ANAKIN-ME（ANI）和AP-Net系列机器学习势的组合。网络基础设施建立在AMBER中现有的高性能CUDA MD引擎之上，并使用行业标准编程模型将其扩展到广泛的GPU加速架构。使用创新的并行算法确保可扩展性。通过利用AMBER广泛的用户基础来扩大FE应用的范围和成功，实现了高影响力。通过这种方式，该项目利用了现有的公认能力，并积极吸引了不同的合作者团队和更广泛的分子模拟社区。该项目提供的网络基础设施为学术界、工业界和国家实验室的广大用户提供了广泛的新的和增强的应用程序。这些应用包括药物发现，酶催化和生物材料设计。AMBER程序套件在全球范围内拥有长期广泛的用户群，并广泛用于国家生产网络基础设施。 作为一个成熟的、经过验证的可持续的和广泛使用的软件包，AMBER的增强将确保该软件在项目结束后产生广泛的影响。该项目还将在理论，编程，计算化学/生物学，计算机科学，科学写作，该奖项由高级网络基础设施办公室颁发，由NSF数学和物理科学理事会化学部共同支持。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的学术价值和更广泛的影响审查标准。

项目成果

AmberTools.

• DOI: 10.1021/acs.jcim.3c01153
• 发表时间: 2023-10-23
• 期刊: JOURNAL OF CHEMICAL INFORMATION AND MODELING
• 影响因子: 5.6
• 作者: Case, David A.;Aktulga, Hasan Metin;Belfon, Kellon;Cerutti, David S.;Cisneros, G. Andres;Cruzeiro, Vinicus Wilian D.;Forouzesh, Negin;Giese, Timothy J.;Gotz, Andreas W.;Gohlke, Holger;Izadi, Saeed;Kasavajhala, Koushik;Kaymak, Mehmet C.;King, Edward;Kurtzman, Tom;Lee, Tai-Sung;Li, Pengfei;Liu, Jian;Luchko, Tyler;Luo, Ray;Manathunga, Madushanka;Machado, Matias R.;Nguyen, Hai Minh;O'Hearn, Kurt A.;Onufriev, Alexey V.;Pan, Feng;Pantano, Sergio;Qi, Ruxi;Rahnamoun, Ali;Risheh, Ali;Schott-Verdugo, Stephan;Shajan, Akhil;Swails, Jason;Wang, Junmei;Wei, Haixin;Wu, Xiongwu;Wu, Yongxian;Zhang, Shi;Zhao, Shiji;Zhu, Qiang;Cheatham, I. I. I. Thomas E.;Roe, Daniel R.;Roitberg, Adrian;Simmerling, Carlos;York, Darrin M.;Nagan, Maria C.;Merz, Jr Kenneth M.
• 通讯作者: Merz, Jr Kenneth M.

Modern semiempirical electronic structure methods and machine learning potentials for drug discovery: Conformers, tautomers, and protonation states

现代半经验电子结构方法和机器学习在药物发现方面的潜力：构象异构体、互变异构体和质子化态

• DOI: 10.1063/5.0139281
• 发表时间: 2023
• 期刊: The Journal of Chemical Physics
• 作者: Zeng, Jinzhe;Tao, Yujun;Giese, Timothy J.;York, Darrin M.
• 通讯作者: York, Darrin M.