Free energy perturbation (FEP) has become widely used in drug discovery programs for binding affinity prediction between candidate compounds and their biological targets. Simultaneously limitations of FEP applications also exist, including but not limited to, the high cost, long waiting time, limited scalability and application scenarios. To overcome these problems, we have developed a scalable cloud computing platform (XFEP) for both relative and absolute free energy predictions with refined simulation protocols. XFEP enables large-scale FEP calculations in a more efficient, scalable and affordable way, e.g. the evaluation of 5,000 compounds can be performed in one week using 50-100 GPUs with a computing cost approximately corresponding to the cost for one new compound synthesis. Together with artificial intelligence (AI) techniques for goal-directed molecule generation and evaluation, new opportunities can be explored for FEP applications in the drug discovery stages of hit identification, hit-to-lead, and lead optimization with R-group substitutions, scaffold hopping, and completely different molecule evaluation. We anticipate scalable FEP applications will become widely used in more drug discovery projects to speed up the drug discovery process from hit identification to pre-clinical candidate compound nomination.

中文翻译：

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用于可伸缩的相对和绝对结合自由能预测的云计算平台：药物发现的新机遇和挑战

自由能摄动（FEP）已广泛用于药物发现程序中，以预测候选化合物与其生物学靶标之间的结合亲和力。同时也存在FEP应用程序的局限性，包括但不限于成本高，等待时间长，可伸缩性和应用程序场景有限。为了克服这些问题，我们已经开发了可扩展的云计算平台（XFEP），用于具有精确模拟协议的相对和绝对自由能预测。XFEP能够以更有效，可扩展和可负担的方式进行大规模FEP计算，例如，可以使用50-100个GPU在一周内对5,000种化合物进行评估，其计算成本大约相当于一种新化合物合成的成本。结合用于目标分子生成和评估的人工智能（AI）技术，可以在命中识别，命中潜在顾客和使用R-基团替代，支架进行潜在顾客优化的药物发现阶段探索FEP应用的新机会跳跃和完全不同的分子评估。我们预计可扩展的FEP应用程序将在更多的药物发现项目中广泛使用，以加快从命中鉴定到临床前候选化合物提名的药物发现过程。