Learning a CoNCISE language for small-molecule binding Article Swipe
Mert Erden
,
Kapil Devkota
,
Lia Varghese
,
Lenore Cowen
,
Rohit Singh
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.01.08.632039
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.01.08.632039
Rapid advances in deep learning have improved in silico methods for drug-target interaction (DTI) prediction. However, current methods do not scale to the massive catalogs that list millions or billions of commercially-available small molecules. Here, we introduce CoNCISE, a method that accelerates drug-target interaction (DTI) prediction by 2-3 orders of magnitude while maintaining high accuracy. CoNCISE uses a novel vector-quantized codebook approach and a residual-learning based training of hierarchical codes. Strikingly, we find that much of binding-specificity information in the small molecule space can be compressed into just 15 bits of information per compound, characterizing all small molecules into 32,768 hierarchically-organized binding categories. Our DTI architecture, which combines these compact ligand representations with fixed-length protein embeddings in a cross-attention framework, achieves state-of-the-art prediction accuracy at unprecedented speed. We demonstrate CoNCISE’s practical utility by indexing 6.4 billion ligands in the Enamine dataset, enabling researchers to query vast chemical libraries against a protein target in seconds. A “CoNCISE + docking” pipeline screened Enamine to propose strong binders (predicted K D ≈ 10-20 µ M) of three difficult-to-drug targets, each within two hours. CoNCISE’s advance could democratize access to largescale computational drug discovery, potentially enabling rapid identification of promising molecules for therapeutic targets and cellular perturbations.
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- Type
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- Language
- en
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- https://doi.org/10.1101/2025.01.08.632039
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https://doi.org/10.1101/2025.01.08.632039Digital Object Identifier
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Learning a CoNCISE language for small-molecule bindingWork title
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preprintOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-01-13Full publication date if available
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Mert Erden, Kapil Devkota, Lia Varghese, Lenore Cowen, Rohit SinghList of authors in order
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https://doi.org/10.1101/2025.01.08.632039Publisher landing page
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greenOpen access status per OpenAlex
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https://doi.org/10.1101/2025.01.08.632039Direct OA link when available
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1Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.prediction | 46, 123 |
| abstract_inverted_index.“CoNCISE | 156 |
| abstract_inverted_index.CoNCISE’s | 130, 181 |
| abstract_inverted_index.Strikingly, | 71 |
| abstract_inverted_index.accelerates | 42 |
| abstract_inverted_index.categories. | 103 |
| abstract_inverted_index.democratize | 184 |
| abstract_inverted_index.demonstrate | 129 |
| abstract_inverted_index.drug-target | 12, 43 |
| abstract_inverted_index.information | 78, 92 |
| abstract_inverted_index.interaction | 13, 44 |
| abstract_inverted_index.maintaining | 53 |
| abstract_inverted_index.potentially | 191 |
| abstract_inverted_index.prediction. | 15 |
| abstract_inverted_index.researchers | 143 |
| abstract_inverted_index.therapeutic | 199 |
| abstract_inverted_index.fixed-length | 114 |
| abstract_inverted_index.hierarchical | 69 |
| abstract_inverted_index.architecture, | 106 |
| abstract_inverted_index.computational | 188 |
| abstract_inverted_index.unprecedented | 126 |
| abstract_inverted_index.characterizing | 95 |
| abstract_inverted_index.identification | 194 |
| abstract_inverted_index.perturbations. | 203 |
| abstract_inverted_index.cross-attention | 119 |
| abstract_inverted_index.representations | 112 |
| abstract_inverted_index.state-of-the-art | 122 |
| abstract_inverted_index.vector-quantized | 60 |
| abstract_inverted_index.difficult-to-drug | 175 |
| abstract_inverted_index.residual-learning | 65 |
| abstract_inverted_index.binding-specificity | 77 |
| abstract_inverted_index.commercially-available | 32 |
| abstract_inverted_index.hierarchically-organized | 101 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5081688191 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I170897317 |
| citation_normalized_percentile.value | 0.84537559 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |