Late-Stage Ligand Prioritization by Combining Rapid-Dissociation Filtering with MM/GBSA
6 affiliations
- 1Genetics, Bioinformatics, and Computational Biology
- 2Virginia Tech
- 3Department of Chemistry
- 4Roanoke College
- 5Department of Computer Science
- 6Department of Physics
Abstract
Accurate and efficient prediction of binding free energies remains a central challenge in computational chemistry, largely due to the complexity of solvent effects and the trade-off between accuracy and computational cost. Explicit solvent molecular dynamics (MD) simulations provide detailed thermodynamic and kinetic insight, now routinely reaching microsecond timescales, while end-point methods such as MM/GBSA offer high efficiency but are sensitive to model parameters. In particular, the accuracy of MM/GBSA strongly depends on the atomic radii used to define the dielectric boundary within the implicit solvent framework. Here, we introduce an explicit–implicit hybrid workflow for late-stage ligand prioritization that integrates the strengths of both approaches. In this framework, microsecond-scale explicit solvent MD simulations act as a rapid-dissociation filter, flagging weak or unstable binders through spontaneous dissociation within the simulation window. MM/GBSA is then used to rank the remaining candidates. To improve the accuracy of the implicit solvent stage, we employ optimized atomic radii sets (OPTBIND), specifically designed for binding free-energy calculations. On the protein–ligand complexes of Curated32-4 set , a new curated benchmark of 32 complexes including 4 congeneric series, the entropy-free MM/GBSA stage with OPT- BIND 6D achieved Pearson r = 0.82 and Spearman ρ = 0.84, with 100% recovery of the experimental rank ordering across all four congeneric series, while a single-replica consistency test of the rapid-dissociation filter yielded an AUC of 0.88 and retained all 15 sub-micromolar binders. Finally, as a real-world drug design application, we apply the workflow to halogen substitutions in SARS-CoV-2 main protease inhibitors, demonstrating its ability to identify favorable chemical modifications. Notably, the approach prospectively identifies F→Cl and F→I as affinity-enhancing substitutions; the predicted enhancement for the chlorine analog is subsequently confirmed experimentally, while the iodine analog remains a testable prediction, highlighting its predictive power in guiding chemical optimization. Together, these results establish a practical framework for detailed studies of receptor–ligand binding and for late-stage ligand prioritization in drug discovery.
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BibTeX
@misc{ghafouriplate2026latestage,
title = {{Late-Stage Ligand Prioritization by Combining Rapid-Dissociation Filtering with MM/GBSA}},
author = {Fatemeh Ghafouri-Plate and Timothy W. Johann and Luke H. Elder and Alexey V. Onufriev},
howpublished = {ChemRxiv},
year = {2026},
month = oct,
doi = {10.26434/chemrxiv.15010255/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010255/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Late-Stage Ligand Prioritization by Combining Rapid-Dissociation Filtering with MM/GBSA
AU - Fatemeh Ghafouri-Plate
AU - Timothy W. Johann
AU - Luke H. Elder
AU - Alexey V. Onufriev
PY - 2026
DA - 2026/10/12
PB - ChemRxiv
DO - 10.26434/chemrxiv.15010255/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010255/v1
AB - Accurate and efficient prediction of binding free energies remains a central challenge in computational chemistry, largely due to the complexity of solvent effects and the trade-off between accuracy and computational cost. Explicit solvent molecular dynamics (MD) simulations provide detailed thermodynamic and kinetic insight, now routinely reaching microsecond timescales, while end-point methods such as MM/GBSA offer high efficiency but are sensitive to model parameters. In particular, the accuracy of MM/GBSA strongly depends on the atomic radii used to define the dielectric boundary within the implicit solvent framework. Here, we introduce an explicit–implicit hybrid workflow for late-stage ligand prioritization that integrates the strengths of both approaches. In this framework, microsecond-scale explicit solvent MD simulations act as a rapid-dissociation filter, flagging weak or unstable binders through spontaneous dissociation within the simulation window. MM/GBSA is then used to rank the remaining candidates. To improve the accuracy of the implicit solvent stage, we employ optimized atomic radii sets (OPTBIND), specifically designed for binding free-energy calculations. On the protein–ligand complexes of Curated32-4 set , a new curated benchmark of 32 complexes including 4 congeneric series, the entropy-free MM/GBSA stage with OPT- BIND 6D achieved Pearson r = 0.82 and Spearman ρ = 0.84, with 100% recovery of the experimental rank ordering across all four congeneric series, while a single-replica consistency test of the rapid-dissociation filter yielded an AUC of 0.88 and retained all 15 sub-micromolar binders. Finally, as a real-world drug design application, we apply the workflow to halogen substitutions in SARS-CoV-2 main protease inhibitors, demonstrating its ability to identify favorable chemical modifications. Notably, the approach prospectively identifies F→Cl and F→I as affinity-enhancing substitutions; the predicted enhancement for the chlorine analog is subsequently confirmed experimentally, while the iodine analog remains a testable prediction, highlighting its predictive power in guiding chemical optimization. Together, these results establish a practical framework for detailed studies of receptor–ligand binding and for late-stage ligand prioritization in drug discovery.
ER -