<p>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. </p>
A Cloud Computing Platform for Scalable Relative and Absolute Binding Free Energy Prediction: New Opportunities and Challenges for Drug Discovery
