Evotec (UK) Ltd., Dorothy Crowfoot Hodgkin Campus, Abingdon, Oxfordshire, UK.
Digital Futures Institute, University of Suffolk, Ipswich, United Kingdom.
Methods Mol Biol. 2024;2716:181-202. doi: 10.1007/978-1-0716-3449-3_8.
The high-performance computing (HPC) platform for large-scale drug discovery simulation demands significant investment in speciality hardware, maintenance, resource management, and running costs. The rapid growth in computing hardware has made it possible to provide cost-effective, robust, secure, and scalable alternatives to the on-premise (on-prem) HPC via Cloud, Fog, and Edge computing. It has enabled recent state-of-the-art machine learning (ML) and artificial intelligence (AI)-based tools for drug discovery, such as BERT, BARD, AlphaFold2, and GPT. This chapter attempts to overview types of software architectures for developing scientific software or application with deployment agnostic (on-prem to cloud and hybrid) use cases. Furthermore, the chapter aims to outline how the innovation is disrupting the orthodox mindset of monolithic software running on on-prem HPC and provide the paradigm shift landscape to microservices driven application programming (API) and message parsing interface (MPI)-based scientific computing across the distributed, high-available infrastructure. This is coupled with agile DevOps, and good coding practices, low code and no-code application development frameworks for cost-efficient, secure, automated, and robust scientific application life cycle management.
高性能计算 (HPC) 平台用于大规模药物发现模拟,需要在专用硬件、维护、资源管理和运行成本方面进行大量投资。计算硬件的快速发展使得通过云计算、雾计算和边缘计算为内部部署 (on-prem) HPC 提供具有成本效益、强大、安全和可扩展的替代方案成为可能。它使最近的基于机器学习 (ML) 和人工智能 (AI) 的药物发现工具,如 BERT、BARD、AlphaFold2 和 GPT 成为可能。本章试图概述用于开发具有部署不可知论(从内部部署到云以及混合)用例的科学软件或应用程序的软件架构类型。此外,本章旨在概述创新如何打破在内部部署 HPC 上运行的单片软件的正统思维模式,并提供面向微服务的应用程序编程 (API) 和基于消息解析接口 (MPI) 的科学计算的范例转变,跨越分布式、高可用基础设施。这与敏捷 DevOps 和良好的编码实践、低代码和无代码应用程序开发框架相结合,用于高效、安全、自动化和强大的科学应用程序生命周期管理。