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. While modern experimental techniques have made measuring compound kinetics data more routine, there is a lack of accurate, high-throughput simulation techniques to guide compound prioritization by residence time. 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 physics-based residence time screening at the atomistic level in ≈1 GPU minute per complex using inexpensive hardware, a speed-up of at least 3-5 orders of magnitude compared to current state-of-the-art simulation approaches. Koffee Unbinding Kinetics can enhance compound selection to mitigate costly future program failures by adding fast, predictive residence time simulations to early-stage computational drug discovery pipelines.

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Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2026-08-18
DOI
https://doi.org/10.1021/acs.jcim.6c01781
Primary Topic
Protein Degradation and Inhibitors
Type
article
Field-Weighted Citation Impact
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article

Unlocking Scalable Ligand Residence Time Predictions with Koffee Unbinding Kinetics Simulations

N. T. Zinner, David E. Gloriam, Nils Berglund, Albert J. Kooistra et al.
Journal of Chemical Information and Modeling
Protein Degradation and Inhibitors
article

Unlocking Scalable Ligand Residence Time Predictions with Koffee Unbinding Kinetics Simulations

N. T. Zinner, David E. Gloriam, Nils Berglund, Albert J. Kooistra, Klaus Michelsen, Daniela Dolciami, Robert M. Ziolek, Mikael H. Christensen, Niels Kristian Madsen, Lise Husted, Daniel Kongsgaard, Anders Dyhr Norlov
article en

Abstract

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. While modern experimental techniques have made measuring compound kinetics data more routine, there is a lack of accurate, high-throughput simulation techniques to guide compound prioritization by residence time. 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 physics-based residence time screening at the atomistic level in ≈1 GPU minute per complex using inexpensive hardware, a speed-up of at least 3-5 orders of magnitude compared to current state-of-the-art simulation approaches. Koffee Unbinding Kinetics can enhance compound selection to mitigate costly future program failures by adding fast, predictive residence time simulations to early-stage computational drug discovery pipelines.

Journal of Chemical Information and Modeling
University of Copenhagen (DK), Io Therapeutics (United States) (US), Covance (United States) (US), IT University of Copenhagen (DK)
Openalex Percentile: Top 99%
Protein Degradation and Inhibitors
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