Jul 2026· IEEE Transactions on Neural Networks and Learning Systems· Vol PP, pp. 1-15· 0 citations
Medicine
TL;DR
GoMA-DTA is proposed, a framework integrating gene ontology (GO) functional annotations with protein semantic features with channelwise gating mechanism that uses functional semantics as anchors to dynamically recalibrate ESM-2embeddings, achieving adaptive semantic filtering.
Abstract
Accurate prediction of drug-target affinity (DTA) is essential for accelerating drug discovery. Although pretrained protein language models have achieved significant progress, existing methods predominantly focus on bottom-up sequence patterns and lack explicit constraints from high-level biological functions. We propose GoMA-DTA, a framework integrating gene ontology (GO) functional annotations with protein semantic features. GoMA-DTA introduces a channelwise gating mechanism that uses functional semantics as anchors to dynamically recalibrate ESM-2embeddings, achieving adaptive semantic filtering. For drugs, the model integrates Molformer-based semantic and TransConv-derived structural features. These dual-modality drug representations interact with calibrated protein features through a parallel synergistic architecture of cross-attention and Mamba modules, ensuring precise cross-modal alignment and efficient long-range dependency modeling. Evaluations on PDBBind, BindingDB, and ChEMBL benchmarks demonstrate that GoMA-DTA significantly outperforms state-of-the-art models across various evaluation scenarios. Its superior screening power is further validated on CASF-2016. Moreover, virtual screening of 200 000compounds against the SARS-CoV-2Spike protein, supported by experimental evidence (ZINC2111387), underscores its practical utility as a robust and biologically reliable tool. The datasets and codes are publicly available at https://github.com/xa-123955/GoMA-DTA.
Experiments demonstrate that HSAF-DDI achieves superior overall performance compared to state-of-the-art methods, indicating the critical role of fine-grained biological features in improving the DDI prediction performance.
Xiaoli Lin, Si-Yuan Zhang, Bo Li et al.· Journal of Chemical Informat...· 0 citations
Current computational approaches for drug design typically focus on generating molecules conditioned on specific targets or general molecular properties, often neglecting the influence of disease context on target behavior and therapeutic outcomes. To address this gap, we introduce DrugGen-2, a novel generative model that designs small molecules conditioned on both disease ontology and target protein sequences. DrugGen-2 was developed by fine-tuning a pre-trained GPT-2 model on a curated dataset of approved drugs linked to their diseases and targets, using a two-step strategy of supervised fine-tuning followed by reinforcement learning via group relative policy optimization (GRPO). This process was guided by reward functions optimizing for chemical validity, novelty, diversity, and high predicted binding affinity. When evaluated on five protein targets relevant to diabetic nephropathy, DrugGen-2 significantly outperformed baseline models (DrugGPT and DrugGen). It demonstrated a superior capacity to generate unique molecules, exhibited greater structural similarity to approved drugs, and achieved improved predicted binding affinities across all targets. Molecular docking analyses further supported these findings, identifying candidate ligands with strong binding potential, including compounds with predicted affinities (-9.917, -9.485, and -9.367) exceeding those of reference drugs such as enalapril for angiotensin-converting enzyme (-8.283). By integrating disease-specific context into molecular generation, DrugGen-2 advances AI-assisted drug discovery, offering a powerful tool for de novo design and drug repurposing that accounts for the complex interplay between diseases and molecular targets.
Ali Motahharynia, Mohammadreza Ghaffarzadeh-Esfahani, Mahsa Sheikholeslami et al.· 0 citations
Accurately predicting binding affinities between drugs and targets is crucial for drug discovery but remains challenging due to the complexity of modeling interactions between small drug and large targets. This research presents Dual modality feature fused-drug target affinity (DMFF-DTA), a model for drug-target affinity anticipation using dual-modality neural networks that considers both the sequence and graph structure of medicines and proteins. To facilitate more exact and efficient drug-target interaction modeling, the model incorporates a binding site-focused graph generation method for extracting binding information. Experimental results show that DMFF-DTA is far more effective than current state-of-the-art approaches. By outperforming state-of-the-art approaches by more than 8%, the model demonstrates remarkable generalizability to hitherto unexplored medicines and targets. The model's biological relevance is confirmed by the model interpretability analysis. This paper presents a reliable and understandable method for improving computational drug discovery by integrating multi-view protein and drug properties.
Ghazala Sultan, J. Vincent, Ratna Sahaya et al.· International Conference Com...· 0 citations
Empirical evaluation and robustness experiments show that M2DDI maintains high predictive accuracy even when modality-specific information is partially missing, outperforming existing methods under similar conditions and establish M2DDI as an effective and mechanism-aware solution for comprehensive DDI prediction.
Runqing Xu, Siyi Liu, Haoyang Li et al.· Proceedings of the 32nd ACM...· 0 citations
A multi-modal deep learning framework to predict drug-target affinity by integrating sequence semantics with graph structural information and design a new symmetric dual cross-attention fusion mechanism for drugs and targets.
Wei Lan, Tian Huang, Guohang He et al.· IEEE journal of biomedical a...· 0 citations
Experiments show that GraESM-FuseDTA achieves competitive overall performance and consistent advantages in ranking-oriented and variance-explanation metrics across warm start, drug cold start, target cold start, and strict pair cold start settings.