Aug 2026· Journal of Medicinal Chemistry· 0 citations· 38 references
TL;DR
CoBind is presented, a multitask deep learning framework that jointly predicts RNA–compound interactions and nucleotide-level binding-site probabilities within a unified architecture and provides complementary nucleotide-level binding-site localization, supporting a site-aware view of RNA–ligand recognition under distribution shift.
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.
The results demonstrate the effectiveness of integrating multi-scale and multi-modal representations with cross-scale alignment for protein–RNA affinity prediction, and suggest that M2-PRNet can highlight relevant RNA-binding regions and support preliminary discrimination between strong and weak binders when plausible complex structures are available.
Junkai Wang, G. Luo, Yun-Song Yang et al.· Bioinformatics· 0 citations
CONTEXT
RNA-ligand molecular recognition is important for RNA-targeted drug discovery and candidate small-molecule prioritization. However, RNA-ligand binding datasets often contain biased associations between RNA sequences and binding labels, which may cause models to rely on sequence-driven cues rather than ligand-dependent binding features. Such sequence-bias-driven reliance can reduce model reliability in challenging prediction scenarios, especially when evaluating unseen RNA targets, structurally dissimilar ligands, or hard decoy molecules.
METHODS
We propose CABA-Bind, a causal debiasing framework for RNA-ligand binding prediction. CABA-Bind encodes RNA sequences and ligand SMILES using RNA-FM and ChemBERTa, constructs confounder centers by K-means clustering to represent recurrent RNA prior patterns, and combines RNA-Confounder Alignment with backdoor-adjusted prediction to reduce the influence of RNA sequence-driven bias. The model was evaluated on the Robin and Biosensor datasets using four data-splitting strategies, prior-dependency metrics, ablation studies, hard decoy ranking, and structure-guided interpretation. CABA-Bind reduced the Score-prior |ρ| by 40.8% compared with the baseline model, achieved an MRR of 0.65 in hard decoy evaluation, and provided computational evidence suggesting that model-highlighted RNA regions around G17 and A53/A54 may contribute to ligand-associated recognition. These results suggest that CABA-Bind improves the reliability and ligand-specific interpretability of RNA-ligand molecular recognition modeling.
Xiaoqin Wang, Xingyu Liu, Zhiwei Zhang et al.· Computational biology and ch...· 0 citations
Accurately predicting the effects of mutations on protein-RNA binding is crucial for elucidating disease mechanisms. Yet, exhaustively exploring the space of all possible variants is prohibitively expensive, motivating computational methods that can quantify mutation-induced changes in binding affinity (aka ΔΔG) accurately and efficiently. We present iSCALE, an interpretable and generalizable deep learning method that adopts an implicit Spatial Coupling-Aware Ligand Encoding strategy to predict mutation-induced binding affinity changes. By injecting this implicit multiscale encoding scheme into a bidirectional state space modeling architecture, iSCALE learns a generalizable multiscale coupling pattern that achieves superior performances on not only the protein-RNA binding ΔΔG, but also the protein stability ΔΔG and protein-protein binding ΔΔG predictions. Detailed analyses demonstrate that the model attention scores align well with structural characteristics. In addition, iSCALE shows good discriminative ability when predicting close samples such as complexes of same mutation but with different ligands or the same complex but with different mutation sites. In summary, iSCALE serves as an effective in silico tool for large-scale protein-RNA binding ΔΔG prediction, which pushes the border of understanding in mutation-induced pathological outcomes.
Ribo-LENS is a geometric deep-learning framework for detecting small-molecule binding sites in RNA structures. It is designed to exploit two properties of RNA base-pairing networks: their robustness to conformational fluctuation and the functional signatures they encode. By reasoning directly in the space of base-pairing subgraphs, Ribo-LENS assembles coherent binding sites, in contrast to methods that score residues independently. In extensive experiments, Ribo-LENS is competitive with, and often outperforms, large all-atom co-folding models (AlphaFold3, Chai-1), fine-tuned language models (GerNA-Bind), and structure-based tools (RNAsite), raising mean MCC to 0.380 (versus 0.321 for the state-of-the-art GerNA-Bind). It is strongly robust to apo/holo rearrangement, with its accuracy tracking the base-pairing graph (Spearman ρ = 0.82 with binding-site graph edit distance) rather than backbone displacement (ρ = −0.15 with RMSD), and depends far less on sequence homology than competing predictors. In an end-to-end, sequence-based virtual screen of the ROBIN assay (∼25,000 compounds), Ribo-LENS guides docking to a small predicted pocket, matching a blind all-atom cavity search (enrichment factors up to 6.1) at a fraction of the search cost; on two SARS-CoV-2 targets its predicted sites align with NMR chemical-shift perturbations. Ribo-LENS turns coarse base-pairing structure into a practical entry point for screening the vast, largely unexplored RNA target space.
David Nitchi, J. Waldispühl, C. Oliver· bioRxiv· 0 citations
By combining protein language model embeddings with topology-adaptive geometric reasoning, DiConSite offers a reusable framework for residue-level protein interaction analysis and achieves consistently strong and often best-performing results, while improving robustness to structural uncertainty and cross-modal variation.
Shou-Zhi Chen, Zhenchao Tang, Linlin You et al.· IEEE Transactions on Pattern...· 1 citation
Abstract Motivation To enable real-world protein-ligand affinity prediction, not only out-of-distribution generalization but also robustness to variable structural availability and quality should be considered in model design. Results We present AlignNet, a hierarchical representation alignment framework that mitigates intra- and inter-molecular heterogeneity to learn robust protein-ligand embeddings for generalizable affinity prediction, even from sequence-level inputs. Its intra-molecular module projects unimodal and multimodal features into a unified space, aligning augmented multimodal views for feature fusion and unimodal with multimodal embeddings to distill multimodal priors for structure-agnostic inference. Its inter-molecular module aligns protein and ligand embeddings for cross-molecular integration. Extensive experiments show that AlignNet (i) achieves highly competitive performance, with up to a 20.4% gain in SCC on the challenging LBA 30% split under sequence-only settings, suggesting improved out-of-distribution generalization; and (ii) learns well-separated affinity-related clusters, supporting reliable structure-independent prediction. Availability and implementation AlignNet is available at https://github.com/altriavin/AlignNet.
Xiaowen Hu, Hongyi Huang, Hao Sun et al.· Bioinformatics· 0 citations