Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects.
Christina E M SchindlerHannah BaumannAndreas BlumDietrich BöseHans-Peter BuchstallerLars BurgdorfDaniel CappelEugene CheklerPaul CzodrowskiDieter DorschMerveille K I EguidaBruce FollowsThomas FuchßUlrich GrädlerJakub GuneraTheresa JohnsonCatherine Jorand LebrunSrinivasa KarraMarkus KleinTim KnehansLisa KoetznerMireille KrierMatthias LeiendeckerBirgitta LeuthnerLiwei LiIgor MochalkinDjordje MusilConstantin NeaguFriedrich RippmannKai SchiemannRobert SchulzThomas SteinbrecherEva-Maria TanzerAndrea Unzue LopezAriele Viacava FollisAnsgar WegenerDaniel KuhnPublished in: Journal of chemical information and modeling (2020)
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.
Keyphrases
- drug discovery
- density functional theory
- molecular dynamics
- molecular dynamics simulations
- healthcare
- quality improvement
- mental health
- monte carlo
- binding protein
- electronic health record
- dna binding
- big data
- emergency department
- high resolution
- cross sectional
- protein protein
- amino acid
- mass spectrometry
- machine learning
- small molecule