| Literature DB >> 32813975 |
Christina E M Schindler1, Hannah Baumann1, Andreas Blum1, Dietrich Böse1, Hans-Peter Buchstaller1, Lars Burgdorf1, Daniel Cappel2, Eugene Chekler3, Paul Czodrowski1, Dieter Dorsch1, Merveille K I Eguida1, Bruce Follows3, Thomas Fuchß1, Ulrich Grädler1, Jakub Gunera1, Theresa Johnson3, Catherine Jorand Lebrun3, Srinivasa Karra3, Markus Klein1, Tim Knehans1, Lisa Koetzner1, Mireille Krier1, Matthias Leiendecker1, Birgitta Leuthner1, Liwei Li3, Igor Mochalkin3, Djordje Musil1, Constantin Neagu3, Friedrich Rippmann1, Kai Schiemann1, Robert Schulz1,4, Thomas Steinbrecher2, Eva-Maria Tanzer3, Andrea Unzue Lopez1, Ariele Viacava Follis3, Ansgar Wegener1, Daniel Kuhn1.
Abstract
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.Year: 2020 PMID: 32813975 DOI: 10.1021/acs.jcim.0c00900
Source DB: PubMed Journal: J Chem Inf Model ISSN: 1549-9596 Impact factor: 4.956