A Cloud Computing Platform for Scalable Relative and Absolute Binding Free Energy Prediction: New Opportunities and Challenges for Drug Discovery

16 October 2020, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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.


free energy perturbation
drug selectivity
core hopping
scaffold hopping
macrocycle design
R-group substitutions
drug resistance


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