Evaluating Machine Learning Affinity Prediction Methods on Novel Lead Optimization Data: A 15-PGDH Case Study.

Alexander S Rich¹, Leela S Dodda², Daniel J Price²

¹Inductive Bio, Brooklyn, New York, USA.

²Nimbus Therapeutics, Cambridge, Massachusetts, USA.

Abstract

Recent machine learning (ML) methods for protein-ligand affinity prediction, such as Boltz-2 and AEV-PLIG, have been reported to approach free energy perturbation (FEP) accuracy on well-known benchmarks. Even short of that accuracy, ML-based potency models that fall between FEP and common docking scoring functions in accuracy and speed offer clear potential value. Here, we report a case study usin…

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