MultiGeo is a DTA prediction framework that explicitly leverages multiple protein conformations rather than a single snapshot, and introduces a disagreement-aware gating mechanism that adaptively fuses this ensemble representation with the dominant structure only when the additional conformers provide complementary information.
Accurate prediction of drug-target binding affinity (DTA) is a key task in virtual screening. However, current computational methods face a key challenge: sequence-based approaches often fail to capture critical spatial information, while structure-based models rely on computationally expensive 3D coordinates, which restrict their scalability. To address this issue, we propose StructuraDTA, a novel multimodal framework that adopts an implicit structure modeling strategy. Instead of using static protein folding data, our method encodes drug molecular graphs via Graph Isomorphism Networks (GINs) to capture fine-grained topological features. Meanwhile, we optimize protein representations by integrating probabilistic structural priors into a pretrained language model, which effectively simulates thermodynamic conformational flexibility without relying on explicit 3D structural data. A bidirectional cross-attention mechanism is then used to dynamically align these heterogeneous feature modalities. Comprehensive evaluations on the Davis and KIBA benchmark datasets show that StructuraDTA stably outperforms state of-the-art comparison methods. Importantly, the model exhibits strong robustness in cold-start scenarios, and can accurately predict binding affinities for previously unseen drugs and targets. By retaining the predictive performance of structure based models while maintaining the high inference efficiency of sequence-based methods, we provide an accurate and scalable solution to accelerate genome-scale drug discovery research.
Junlin Xu, Ye Yuan, Menglong Hu et al.· IEEE journal of biomedical a...· 0 citations
A category-stratified, statistically powered benchmark comparing pose prediction from receptor conformational ensembles against AlphaFold2, used as a matched static-structure baseline, across 29 protein–ligand systems spanning cryptic-pocket, induced-fit, water-mediated, and autoimmune-indication target classes is presented.
Ryan Varghese, Pooja Tiwary, Krishil Oswal· bioRxiv· 0 citations
Virtual screening (VS) is an essential tool in drug discovery to prioritize potential drug candidates from vast chemical space. One key challenge limiting its performance is accounting for protein conformational flexibility. While ensemble docking methods have been developed to address this challenge by incorporating multiple protein conformations, these methods often rely on computationally intensive physics-based simulations to sample the relevant conformational space. Generative machine learning models offer a highly promising, scalable, and high-throughput alternative to overcome the limitations of these traditional approaches. We therefore investigate whether conformational ensembles generated by BioEmu, a recently developed generative model, can improve VS performance for kinase targets. Using the DUD-E benchmark data set and a validated AutoDock-GPU protocol, we generated and analyzed nearly 1300 structures across 26 kinases (approximately 50 structures each). BioEmu produces structurally diverse ensembles with substantial performance variation among individual structures. However, ensemble methods employing consensus or best-score selection fail to improve upon, and often degrade, VS performance compared to crystal structure baselines. To investigate the source of this limitation, we quantified the relationship between KinCoRe-based conformational state classification and screening performance. By calculating the coefficient of determination (R2) across the kinase subset, we found that the structural features governing VS performance differ substantially from those defining standard conformational states, with KinCoRe classifications leaving over 84% of performance variance unexplained. This critical gap demonstrates that structural diversity alone is insufficient to guarantee screening success. We show that prospective structure selection, rather than structure generation, represents the primary bottleneck in ensemble-based VS, highlighting an urgent need for novel structural descriptors to identify high-performing conformations.
Jaeoh Shin, K. Joo, Jejoong Yoo· Journal of Chemical Informat...· 0 citations
MOTIVATION
Predicting protein-RNA binding affinity is crucial for understanding cellular regulation and advancing RNA-targeted drug discovery. However, this task remains challenging due to structural complexity, limited labeled data, and insufficient modeling of fine-grained interactions.
RESULTS
We propose M2-PRNet, a multi-scale and multi-modal framework that integrates atom-level graphs, residue-level graphs, and tri-view molecular representations to capture complementary structural information. A cross-scale contrastive learning objective is introduced to align representations across different structural resolutions of the same complex. Under a clustering-based five-fold cross-validation setting on benchmark datasets, M2-PRNet achieves state-of-the-art performance. To further assess generalization under reduced sequence homology, we construct homology-aware RNA-cold, protein-cold, and dual-cold evaluations under a stricter 40% sequence identity threshold, where M2-PRNet maintains competitive performance. To account for conformational flexibility, we evaluate the model on MD150-1ns and an extended MD75-10ns subset, demonstrating stable performance under MD-derived structural perturbations. In addition, representative case studies 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. These results demonstrate the effectiveness of integrating multi-scale and multi-modal representations with cross-scale alignment for protein-RNA affinity prediction.
AVAILABILITY AND IMPLEMENTATION
The source code and datasets for M2-PRNet are freely available at https://github.com/CSUBioGroup/M2-PRNet.
Junkai Wang, G. Luo, Yunsong Yang et al.· Bioinformatics· 0 citations
The resulting model, HydrAffinity, is an interaction-free, dynamic sparse model that uses pre-trained encoders and MoE for parameter-efficient learning and outperforms all interaction-free methods and matches state-of-the-art interaction-based methods on CASF-2016.
Accurate prediction of drug-target interactions (DTI) is essential for drug discovery. Despite the success of pre-trained language models (PLMs) in learning robust molecular and protein representations, a fundamental challenge remains in characterizing the fine-grained, localized biochemical interactions between drug substructures and protein binding sites. Such critical interaction patterns are often underrepresented in conventional global embedding approaches, thereby limiting both predictive accuracy and biological interpretability. To address this challenge, we propose CoAff-DTI, an end-to-end deep learning framework designed to enhance multi-scale interaction modeling for DTI prediction. The model introduces three key components. First, a token-level decomposition strategy is employed to transform global embeddings into pharmacophore- and residue-level representations, facilitating the capture of localized features. Second, an Affinity-Guided Cross-Attention (AGCA) module is designed to explicitly model fine-grained interactions between ligand substructures and protein residues. Third, an Affinity-Gating Fusion (AGF) module is proposed to enhance cross-modal feature integration by dynamically modeling element-wise interactions. Extensive experiments on multiple benchmark datasets demonstrate that CoAff-DTI consistently outperforms state-of-the-art methods. In addition, attention-based visualization results suggest improved interpretability, as the model's learned attention patterns align effectively with experimentally verified binding regions.
Jia Peng, Xiaoyu Liu, Lei Wang et al.· Journal of Biomedical Inform...· 0 citations