Towards automated physics-based absolute drug residence time predictions Article Swipe
Zachary Smith
,
Davide Branduardi
,
Dmitry Lupyan
,
G. D’Arrigo
,
Pratyush Tiwary
,
Lingle Wang
,
Goran Krilov
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2025-wg75c
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2025-wg75c
The residence time (τ) of a drug bound to a receptor target is increasingly recognized as a key property to control during ligand-optimization campaigns and provides a useful dimension for modulating compound profiles in addition to binding affinity. For this reason, many computational approaches have been developed to predict this quantity. Several methods employ an empirical correlation between the residence time and the measured simulation time for a ligand to escape the binding pocket during biased molecular dynamics (MD), while others rely on more formal approaches that require a substantially larger computational effort and/or setup times often impractical in a fast-paced drug-discovery setting. Here we propose a new scheme to calculate absolute residence times by using two enhanced sampling approaches, consisting of an exploration phase followed by an exploitation phase that estimates the residence time: Random Acceleration Molecular Dynamics (RAMD) to harvest plausible egress pathways, and then Infrequent Metadynamics (iMetaD) to estimate residence time. This protocol caters to drug discovery programs, where a key aspect is the compromise between accuracy, throughput, and ease of use. We benchmark this approach by computing residence times for a congeneric series of ligands binding to several diverse drug targets and show that we can achieve good accuracy (RMSE of 1.22 and R2 of 0.80 in log10(τ)) without manually tuning the enhanced sampling parameters.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.26434/chemrxiv-2025-wg75c
- OA Status
- gold
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https://openalex.org/W4407537181Canonical identifier for this work in OpenAlex
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https://doi.org/10.26434/chemrxiv-2025-wg75cDigital Object Identifier
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Towards automated physics-based absolute drug residence time predictionsWork title
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preprintOpenAlex work type
- Language
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enPrimary language
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2025Year of publication
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2025-02-13Full publication date if available
- Authors
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Zachary Smith, Davide Branduardi, Dmitry Lupyan, G. D’Arrigo, Pratyush Tiwary, Lingle Wang, Goran KrilovList of authors in order
- Landing page
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https://doi.org/10.26434/chemrxiv-2025-wg75cPublisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.26434/chemrxiv-2025-wg75cDirect OA link when available
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Absolute (philosophy), Residence time (fluid dynamics), Statistical physics, Physics, Computer science, Engineering, Philosophy, Epistemology, Geotechnical engineeringTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.predict | 48 |
| abstract_inverted_index.propose | 105 |
| abstract_inverted_index.reason, | 40 |
| abstract_inverted_index.require | 87 |
| abstract_inverted_index.several | 191 |
| abstract_inverted_index.targets | 194 |
| abstract_inverted_index.without | 212 |
| abstract_inverted_index.(iMetaD) | 149 |
| abstract_inverted_index.Dynamics | 138 |
| abstract_inverted_index.absolute | 111 |
| abstract_inverted_index.accuracy | 202 |
| abstract_inverted_index.addition | 34 |
| abstract_inverted_index.approach | 178 |
| abstract_inverted_index.compound | 31 |
| abstract_inverted_index.dynamics | 77 |
| abstract_inverted_index.enhanced | 117, 216 |
| abstract_inverted_index.estimate | 151 |
| abstract_inverted_index.followed | 125 |
| abstract_inverted_index.manually | 213 |
| abstract_inverted_index.measured | 63 |
| abstract_inverted_index.profiles | 32 |
| abstract_inverted_index.property | 18 |
| abstract_inverted_index.protocol | 155 |
| abstract_inverted_index.provides | 25 |
| abstract_inverted_index.receptor | 10 |
| abstract_inverted_index.sampling | 118, 217 |
| abstract_inverted_index.setting. | 102 |
| abstract_inverted_index.Molecular | 137 |
| abstract_inverted_index.accuracy, | 169 |
| abstract_inverted_index.affinity. | 37 |
| abstract_inverted_index.benchmark | 176 |
| abstract_inverted_index.calculate | 110 |
| abstract_inverted_index.campaigns | 23 |
| abstract_inverted_index.computing | 180 |
| abstract_inverted_index.developed | 46 |
| abstract_inverted_index.dimension | 28 |
| abstract_inverted_index.discovery | 159 |
| abstract_inverted_index.empirical | 55 |
| abstract_inverted_index.estimates | 131 |
| abstract_inverted_index.molecular | 76 |
| abstract_inverted_index.pathways, | 144 |
| abstract_inverted_index.plausible | 142 |
| abstract_inverted_index.programs, | 160 |
| abstract_inverted_index.quantity. | 50 |
| abstract_inverted_index.residence | 1, 59, 112, 133, 152, 181 |
| abstract_inverted_index.Infrequent | 147 |
| abstract_inverted_index.approaches | 43, 85 |
| abstract_inverted_index.compromise | 167 |
| abstract_inverted_index.congeneric | 185 |
| abstract_inverted_index.consisting | 120 |
| abstract_inverted_index.fast-paced | 100 |
| abstract_inverted_index.log10(τ)) | 211 |
| abstract_inverted_index.modulating | 30 |
| abstract_inverted_index.recognized | 14 |
| abstract_inverted_index.simulation | 64 |
| abstract_inverted_index.approaches, | 119 |
| abstract_inverted_index.correlation | 56 |
| abstract_inverted_index.exploration | 123 |
| abstract_inverted_index.impractical | 97 |
| abstract_inverted_index.parameters. | 218 |
| abstract_inverted_index.throughput, | 170 |
| abstract_inverted_index.Acceleration | 136 |
| abstract_inverted_index.Metadynamics | 148 |
| abstract_inverted_index.exploitation | 128 |
| abstract_inverted_index.increasingly | 13 |
| abstract_inverted_index.computational | 42, 91 |
| abstract_inverted_index.substantially | 89 |
| abstract_inverted_index.drug-discovery | 101 |
| abstract_inverted_index.ligand-optimization | 22 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.93867322 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |