Unlocking Scalable Ligand Residence Time Predictions with Koffee Unbinding Kinetics Simulations

A great number of drug discovery programs fail due to poor in vivo efficacy and ADMET liabilities. On- and off-target ligand residence times can act as important drivers of these problems. However, the kinetics of ligand-protein residence times has historically been largely overlooked during early-stage drug discovery with a strong focus on binding affinity. While modern experimental techniques have made measuring compound kinetics data more routine, there is a lack of accurate, high-throughput computational techniques to guide compound prioritization by residence time as already exist for binding affinity. In this work, we introduce Koffee ™ Unbinding Kinetics as a solution to the hitherto unanswered problem of scalable ligand-protein residence time prediction. By bypassing conventional approaches based on molecular dynamics simulations, Koffee Unbinding Kinetics performs computational residence time screening in ≈ 1 GPU minute per complex using inexpensive hardware. This represents a speed-up of between 3 − 5 orders of magnitude compared to present state-of-the-art computational methods based on molecular dynamics simulations. By adding fast, predictive RT simulations to computational drug discovery pipelines, Koffee Unbinding Kinetics can enhance compound selection to mitigate costly future program failures.