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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2606.01816 |
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| _version_ | 1866911739515240448 |
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| author | Kim, Taehan Leung, Sarrah Rose Mikhail Mekala, Bharat Park, Jeongbin |
| author_facet | Kim, Taehan Leung, Sarrah Rose Mikhail Mekala, Bharat Park, Jeongbin |
| contents | Selecting where to intervene on a protein (i.e., choosing a targetable site) is often a more ambiguous and failure-prone bottleneck than selecting what binds, especially for membrane proteins where accessibility, topology, and post-translational modifications (PTMs) constrain actionable regions. We present Site4Drug, a modality-aware site-finding agent that outputs a ranked list of targetable regions with explicit constraints, evidence summaries, risk flags, and a traceable decision log.
Rather than requiring users to specify the drug modality upfront, Site4Drug can recommend a binding modality (e.g., antibody/peptide-like vs small-molecule) from the same evidence used for site discovery, including topology, hydropathy, PTM propensity, disulfides, domain context, and sequence. Importantly, this evidence is applied consistently across modalities, including small-molecule pocket discovery, to avoid selecting chemically plausible but biologically occluded sites. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_01816 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Site4Drug: Predicting Drug-Binding Target Sites with an AI Agent Kim, Taehan Leung, Sarrah Rose Mikhail Mekala, Bharat Park, Jeongbin Biomolecules Machine Learning Selecting where to intervene on a protein (i.e., choosing a targetable site) is often a more ambiguous and failure-prone bottleneck than selecting what binds, especially for membrane proteins where accessibility, topology, and post-translational modifications (PTMs) constrain actionable regions. We present Site4Drug, a modality-aware site-finding agent that outputs a ranked list of targetable regions with explicit constraints, evidence summaries, risk flags, and a traceable decision log. Rather than requiring users to specify the drug modality upfront, Site4Drug can recommend a binding modality (e.g., antibody/peptide-like vs small-molecule) from the same evidence used for site discovery, including topology, hydropathy, PTM propensity, disulfides, domain context, and sequence. Importantly, this evidence is applied consistently across modalities, including small-molecule pocket discovery, to avoid selecting chemically plausible but biologically occluded sites. |
| title | Site4Drug: Predicting Drug-Binding Target Sites with an AI Agent |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2606.01816 |