CoCoBind: Consistency-ContrastiveMultitask Learningfor RNA–Ligand Interaction and Binding Site Prediction
Abstract RNA-targeted small-molecule therapeutics are of growing clinical interest, but computational modeling of RNA–ligand recognition remains challenging because of limited curated data, conformational heterogeneity, and the close coupling between interaction prediction and binding-site localization. We present CoCoBind, a multitask deep learning framework that jointly predicts RNA–compound interactions and nucleotide-level binding-site probabilities within a unified architecture. CoCoBind integrates pretrained RNA and molecular representation models with cross-modal cross-attention, a Noisy-OR-based consistency constraint, and contrastive alignment. On the DeepRNA-DTI benchmark, CoCoBind shows its clearest interaction-prediction gain in the most stringent Unseen Both setting and provides complementary nucleotide-level binding-site localization, supporting a site-aware view of RNA–ligand recognition under distribution shift. Structure-level analyses further support the localization of ligand-proximal RNA pocket neighborhoods. CoCoBind thus provides a site-aware computational framework for RNA–ligand interaction modeling and AI-enabled hit prioritization in RNA-targeted small-molecule discovery.
Authors
- Yike Shen (ORCID: https://orcid.org/0000-0001-8480-8258)
- Yuanlin He (ORCID: https://orcid.org/0000-0001-7019-6756)
- Lin Huang (ORCID: https://orcid.org/0000-0002-2121-365X)
- Silong Zhai (ORCID: https://orcid.org/0000-0001-5102-3446)
- Huanxiang Liu (ORCID: https://orcid.org/0000-0002-9284-3667)
- Zhecheng Zhou (ORCID: https://orcid.org/0009-0004-3175-4478)
- Yang Zhang (ORCID: https://orcid.org/0000-0002-2739-1916)
- Shaolong Lin
- Xiaojun Yao (ORCID: https://orcid.org/0000-0001-9958-8438)
- Mutian He (ORCID: https://orcid.org/0000-0002-8939-4207)
- Lingzhi Hu
- Kai Xu
- Yuxuan Jiang
- Daohong Gong
- Jing Li
- Yuquan Li
- Lin Wang
- Shihang Wang
- Wei Zhao
Institutions
- National Sun Yat-sen University (TW)
- Sun Yat-sen University (CN)
- National University of Singapore (SG)
- Guizhou University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Sun Yat-sen Memorial Hospital (CN)
- Chinese Academy of Medical Sciences Dermatology Hospital (CN)
- Suzhou Institute of Systems Medicine (CN)
- Macao Polytechnic University (MO)
Publication Details
- Journal
- Journal of Medicinal Chemistry
- Published
- 2026-08-17
- DOI
- https://doi.org/10.1021/acs.jmedchem.6c00902
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Agency for Science, Technology and Research
- National Research Foundation Singapore
- Ministry of Education - Singapore
- Fundo para o Desenvolvimento das Ciências e da Tecnologia
- Peking Union Medical College