Accurate Pocket Identification for Binding-Site-Agnostic Docking Article Swipe
Yaroslav Balytskyi
,
Inna Hubenko
,
Alina Balytska
,
Christopher V. Kelly
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202502.0352.v2
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202502.0352.v2
Accurate identification of druggable pockets and their features is essential for structure-based drug design and effective downstream docking. Here, we present RAPID-Net, a deep learning-based algorithm designed for the accurate prediction of binding pockets and seamless integration with docking pipelines. On the PoseBusters benchmark, RAPID-Net–guided AutoDock Vina achieves 54.9% of Top-1 poses with RMSD <2 A and satisfying the PoseBusters chemical‐validity criterion, compared to 49.1% for DiffBindFR. On the most challenging time split of PoseBusters aiming to assess generalization ability (structures submitted after September 30, 2021), RAPID-Net-guided AutoDock Vina achieves 53.1% of Top-1 poses with RMSD < 2 A and PB-valid, versus 59.5% for AlphaFold 3. Notably, in 92.2% of cases, RAPID-Net-guided Vina samples at least one pose with RMSD < 2 A (regardless of its rank), indicating that pose ranking, rather than sampling, is the primary accuracy bottleneck. The lightweight inference, scalability, and competitive accuracy of RAPID-Net position it as a viable option for large-scale virtual screening campaigns. Across diverse benchmark datasets, RAPID-Net outperforms other pocket prediction tools, including PUResNet and Kalasanty, in both docking accuracy and pocket–ligand intersection rates. Furthermore, we demonstrate the potential of RAPID-Net to accelerate the development of novel therapeutics by highlighting its performance on pharmacologically relevant targets. RAPID-Net accurately identifies distal functional sites, offering new opportunities for allosteric inhibitor design. In the case of the RNA-dependent RNA polymerase of SARS-CoV-2, RAPID-Net uncovers a wider array of potential binding pockets than existing predictors, which typically annotate only the orthosteric pocket and overlook secondary cavities.
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Metadata
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- Language
- en
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- https://doi.org/10.20944/preprints202502.0352.v2
- OA Status
- green
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- OpenAlex ID
- https://openalex.org/W4407186348
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4407186348Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.20944/preprints202502.0352.v2Digital Object Identifier
- Title
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Accurate Pocket Identification for Binding-Site-Agnostic DockingWork 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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2025-07-24Full publication date if available
- Authors
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Yaroslav Balytskyi, Inna Hubenko, Alina Balytska, Christopher V. KellyList of authors in order
- Landing page
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https://doi.org/10.20944/preprints202502.0352.v2Publisher landing page
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://doi.org/10.20944/preprints202502.0352.v2Direct OA link when available
- Concepts
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Docking (animal), Identification (biology), Binding site, Computer science, Computational biology, Chemistry, Medicine, Biology, Biochemistry, Botany, NursingTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.accuracy | 137, 145, 176 |
| abstract_inverted_index.accurate | 29 |
| abstract_inverted_index.achieves | 47, 89 |
| abstract_inverted_index.annotate | 240 |
| abstract_inverted_index.compared | 62 |
| abstract_inverted_index.designed | 26 |
| abstract_inverted_index.docking. | 17 |
| abstract_inverted_index.existing | 236 |
| abstract_inverted_index.features | 7 |
| abstract_inverted_index.offering | 209 |
| abstract_inverted_index.overlook | 246 |
| abstract_inverted_index.position | 148 |
| abstract_inverted_index.ranking, | 130 |
| abstract_inverted_index.relevant | 201 |
| abstract_inverted_index.seamless | 35 |
| abstract_inverted_index.targets. | 202 |
| abstract_inverted_index.uncovers | 227 |
| abstract_inverted_index.&lt;2 | 54 |
| abstract_inverted_index.AlphaFold | 104 |
| abstract_inverted_index.PB-valid, | 100 |
| abstract_inverted_index.RAPID-Net | 147, 163, 187, 203, 226 |
| abstract_inverted_index.September | 83 |
| abstract_inverted_index.algorithm | 25 |
| abstract_inverted_index.benchmark | 161 |
| abstract_inverted_index.cavities. | 248 |
| abstract_inverted_index.datasets, | 162 |
| abstract_inverted_index.druggable | 3 |
| abstract_inverted_index.effective | 15 |
| abstract_inverted_index.essential | 9 |
| abstract_inverted_index.including | 169 |
| abstract_inverted_index.inhibitor | 214 |
| abstract_inverted_index.potential | 185, 232 |
| abstract_inverted_index.sampling, | 133 |
| abstract_inverted_index.screening | 157 |
| abstract_inverted_index.secondary | 247 |
| abstract_inverted_index.submitted | 81 |
| abstract_inverted_index.typically | 239 |
| abstract_inverted_index.Kalasanty, | 172 |
| abstract_inverted_index.RAPID-Net, | 21 |
| abstract_inverted_index.accelerate | 189 |
| abstract_inverted_index.accurately | 204 |
| abstract_inverted_index.allosteric | 213 |
| abstract_inverted_index.benchmark, | 43 |
| abstract_inverted_index.campaigns. | 158 |
| abstract_inverted_index.criterion, | 61 |
| abstract_inverted_index.downstream | 16 |
| abstract_inverted_index.functional | 207 |
| abstract_inverted_index.identifies | 205 |
| abstract_inverted_index.indicating | 127 |
| abstract_inverted_index.inference, | 141 |
| abstract_inverted_index.pipelines. | 39 |
| abstract_inverted_index.polymerase | 223 |
| abstract_inverted_index.prediction | 30, 167 |
| abstract_inverted_index.satisfying | 57 |
| abstract_inverted_index.(regardless | 123 |
| abstract_inverted_index.(structures | 80 |
| abstract_inverted_index.DiffBindFR. | 66 |
| abstract_inverted_index.PoseBusters | 42, 59, 74 |
| abstract_inverted_index.SARS-CoV-2, | 225 |
| abstract_inverted_index.bottleneck. | 138 |
| abstract_inverted_index.challenging | 70 |
| abstract_inverted_index.competitive | 144 |
| abstract_inverted_index.demonstrate | 183 |
| abstract_inverted_index.development | 191 |
| abstract_inverted_index.integration | 36 |
| abstract_inverted_index.large-scale | 155 |
| abstract_inverted_index.lightweight | 140 |
| abstract_inverted_index.orthosteric | 243 |
| abstract_inverted_index.outperforms | 164 |
| abstract_inverted_index.performance | 198 |
| abstract_inverted_index.predictors, | 237 |
| abstract_inverted_index.Furthermore, | 181 |
| abstract_inverted_index.highlighting | 196 |
| abstract_inverted_index.intersection | 179 |
| abstract_inverted_index.scalability, | 142 |
| abstract_inverted_index.therapeutics | 194 |
| abstract_inverted_index.RNA-dependent | 221 |
| abstract_inverted_index.opportunities | 211 |
| abstract_inverted_index.generalization | 78 |
| abstract_inverted_index.identification | 1 |
| abstract_inverted_index.learning-based | 24 |
| abstract_inverted_index.pocket–ligand | 178 |
| abstract_inverted_index.structure-based | 11 |
| abstract_inverted_index.RAPID-Net-guided | 86, 111 |
| abstract_inverted_index.pharmacologically | 200 |
| abstract_inverted_index.RAPID-Net–guided | 44 |
| abstract_inverted_index.chemical‐validity | 60 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.04638292 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |