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.

在高级基础设施办公室和NSF化学司的支持下，梅尔斯教授及其小组将在分子模拟网络基础设施上工作。分子模拟已成为化学，药物和材料科学研究和技术开发的宝贵工具。借助专业硬件（例如图形处理单元（GPU）），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学科学中重要的现实世界问题所需的空间和时间尺度。自由能模拟是一类特别重要且具有挑战性的分子模拟，对于获得化学过程的预测理解至关重要。例如，自由能方法可以预测化学反应的屏障高度和速率，是否会发生反应或药物与靶标的紧密结合。这些预测对于新的催化剂或药物的设计非常有价值。但是，自由能模拟的预测能力对描述原子间势能和力的基本模型敏感。化学过程的准确自由能模拟需要捕获基本物理的势能模型并可以响应化学环境的变化，但是常规力场模型不适合许多涉及键断裂和形成的过程，如催化剂设计所示。因此，非常需要通过实现更广泛的势能模型来扩展自由能法的范围，这些模型更准确，反应性和/或能够量子机械多体性极化和电荷传递。该项目创建的网络基础设施允许使用量子力学，机器学习，反应性和经典潜力来常规应用自由能法，以推动生物学和化学科学的最先进的一系列重要问题。这些工具可以由一系列科学家应用来解决国家兴趣的基本问题，例如，在针对人畜共患病的药物设计时（例如，Covid-19），具有新功能的材料的设计以及改进的电池。鉴于所采用的方法的复杂性，对推进该领域的化学，生物学和计算机科学家的多样化教育至关重要，并且在该项目中得到了解决，从而培训了下一代的计算科学家，这些计算科学家将构成未来劳动力的骨干。该项目在功能强大的新的多尺度建模框架内开发了准确有效的自由能量软件，以用于化学，生物学和材料科学应用程序的琥珀色套件。多尺度框架可以设计和使用新的混合方法场，涉及几种现有和新兴的反应性，机器学习和量子多体电位之间的互操作性。与经典的分子机械力场相比，这些电势具有增强的准确性，鲁棒性和预测能力，并能够研究化学反应和催化。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，酶设计和药物发现中复杂应用的需求。 使用琥珀分子动力学发动机的GPU加速功能的强大端点“终止”方法用于实现这些目标。 具体而言，用于自由能模拟的开源高性能软件是为多层混合电位而设计的，该软混合电位通过GPU加速快速套件，可伸缩的反应性reaxff力领域的线性尺度多体量子机械方法的组合，以及最近开发的deepMd-se，anaakin-se，anaakin-ni and an ani andi and apni and an an an an an an an an an an an an an an an an an an an an an an an an an an an an an an an an an an an an an an an an an an an a anakin-ni。网络基础设施建立在琥珀现有的高性能CUDA MD引擎上，并使用行业标准的编程模型将其扩展到广泛的GPU加速体系结构。使用创新的并行算法确保可伸缩性。通过利用琥珀的广泛用户群来扩大FE应用程序的范围和成功来实现高影响。通过这种方式，该项目利用了现有的公认功能，并积极参与了一个多样化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，工业和国家实验室的广泛用户社区提供了广泛的新应用和增强的应用。这些应用包括药物发现，酶催化和生物材料设计。 Amber套房的计划具有长期的全球用户群，并广泛用于国家生产网络基础设施。 琥珀色作为已建立，可证明的可持续和广泛使用的软件包的增强将确保该软件具有广泛的影响，远远超出了项目的尽头。该项目还将在理论，编程，计算化学/生物学，计算机科学，科学写作和沟通方面培训各种各样的学生和研究人员。该奖项由高级网络基础设施办公室颁发，由NSF内的化学局共同支持NSF的化学局，以反映了NSF的数学和物理奖。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.