作者
Christina Schindler,Hannah M. Baumann,Andreas Blum,Dietrich Böse,Hans‐Peter Buchstaller,Lars T. Burgdorf,Daniel Cappel,Eugene L. Piatnitski Chekler,Paul Czodrowski,Dieter Dorsch,Merveille Eguida,Bruce Follows,Thomas Fuchß,Ulrich Grädler,Jakub Gunera,Theresa Johnson,Catherine Jorand Lebrun,Srinivasa R. Karra,Markus Klein,Tim Knehans,Lisa Koetzner,Mireille Krier,Matthias Leiendecker,Birgitta Leuthner,Liwei Li,Igor Mochalkin,Djordje Müsil,Constantin Neagu,Friedrich Rippmann,Kai Schiemann,Robert Schulz,Thomas Steinbrecher,Eva‐Maria Tanzer,Andrea Unzue Lopez,Ariele Viacava Follis,Ansgar Wegener,Daniel Kühn
摘要
Accurate ranking of compounds with regards to their binding affinity to a protein using computational methods is of great interest to pharmaceutical research. Physics-based free energy calculations are regarded as the most rigorous way to estimate binding affinity. In recent years, many retrospective studies carried out both in academia and industry have demonstrated its potential. Here, we present the results of large-scale prospective application of the FEP+ method in active drug discovery projects in an industry setting at Merck KGaA, Darmstadt, Germany. We compare these prospective data to results obtained on a new diverse, public benchmark of eight pharmaceutically relevant targets. Our results offer insights into the challenges faced when using free energy calculations in real-life drug discovery projects and identify limitations that could be tackled by future method development. The new public data set we provide to the community can support further method development and comparative benchmarking of free energy calculations.