Aug 2026· Journal of Chemical Information and Modeling· 0 citations· 151 references
TL;DR
A systematic reference for future algorithm design, mechanism exploration, and real-world drug discovery applications for AI-driven DTI prediction methodologies, covering binding theories, task formulations, data representation, model design, translational applications, and unresolved challenges.
Abstract
Drug–target interaction (DTI) prediction is central to drug discovery, target identification, and drug repurposing. With the rapid growth of biomedical data and advances in artificial intelligence (AI), DTI prediction has shifted from docking, similarity-based inference, and hand-crafted features toward data-driven representation learning and interaction modeling. This review examines AI-driven DTI prediction methodologies, covering binding theories, task formulations, data representation, model design, translational applications, and unresolved challenges. First, we introduce classical molecular binding theories, including the lock-and-key model, induced fit, and conformational selection, highlighting the transition from static matching to dynamic interaction. Second, we summarize major DTI task settings, including binary interaction classification, binding affinity regression, and multitask prediction with uncertainty assessment. We then discuss data resources and multimodal representation approaches for drugs, target proteins, interaction labels, and auxiliary biomedical data, including molecular sequences, graph structures, 3D conformations, physicochemical properties, biological perturbation profiles, protein sequences and structures, and biomedical knowledge networks. Representative approaches are compared across orthogonal methodological dimensions, including input representation, encoder architecture, interaction-modeling mechanism, representation learning and pretraining, learning objective, prediction output, data acquisition or optimization strategy, and generalization setting. Finally, we outline DTI applications in disease target mining, compound virtual screening, affinity and selectivity optimization, complex structure prediction, and industrial drug discovery pipelines and discuss key challenges such as data distribution shifts, dynamic protein conformational variability, and insufficient experimental validation. This survey aims to provide a systematic reference for future algorithm design, mechanism exploration, and real-world drug discovery applications.
This review systematically summarizes the latest developments in heterogeneous graph neural networks, protein language models, and generative artificial intelligence, pointing out the problems currently being addressed in research such as data sparsity and cold start as well as the manifestations of general machine learning challenges.
Qi-Zhong Yang· ITM Web of Conferences· 0 citations
Abstract Motivation Identifying drug targets is fundamental in drug development, both for discovering new therapies and for ensuring effective and safe treatments. Drug-target interactions (DTIs) have been predicted using machine learning approaches that integrate heterogeneous data; however, often these models are complex and lack interpretability. Results We investigate whether a simple, fully interpretable linear model can achieve competitive performance for DTI prediction. We propose Linear Interpretable Drug-Target Interaction (LI-DTI), a prediction model inspired by recommender systems. LI-DTI learns from different drug-drug and target-target similarity matrices and provides interpretable predictions as a linear combination of these similarity measures. We show that LI-DTI can recover DTIs even when drugs or targets have no previously known interactions, across multiple cross-validation settings. We further evaluate performance while mitigating potential bias arising from high chemical similarity between drugs or sequence similarity between targets. Finally, we assess LI-DTI in a prospective evaluation, training on DTIs present in DrugBank from 2011 and testing on interactions added through 2022. Across all evaluations, LI-DTI achieves state-of-the-art performance while producing interpretable predictions. For practical use, we provide a web-based tool that enables users to visualize individual LI-DTI predictions for DrugBank (2025) and inspect the biological evidence underlying them. Our results indicate that simple linear models with well-curated similarity features can deliver robust and interpretable DTI predictions, facilitating hypothesis generation and downstream experimental prioritization. Availability and implementation Code and data available at https://github.com/paccanarolab/LI-DTI. Web tool available at https://paccanarolab.org/lidtiweb/.
Santiago Noto, Santiago Ferreyra, Rubén Jiménez et al.· Bioinformatics· 0 citations
Drug-drug interaction (DDI) event prediction is critical for ensuring patient safety and optimizing therapeutic outcomes. Existing computational approaches are limited by their inability to jointly model the heterogeneous mechanisms underlying DDIs, which span molecular structure, pharmacodynamic function, and network-mediated relations. To address this limitation, we introduce M2DDI, a unified framework for dynamic multimodal fusion in DDI prediction. M2DDI utilizes a Mixture-of-Experts architecture, with each expert dedicated to a distinct pharmacological modality. A novel prior-enhanced dual-path gating strategy adaptively selects relevant experts for each drug pair by integrating mechanism-matched feature queries and ATC-based biomedical priors, thereby aligning expert selection with underlying pharmacological mechanisms and addressing the challenge of data incompleteness. Empirical evaluation on benchmark datasets demonstrates that M2DDI achieves state-of-the-art performance, particularly in new drug scenarios. Additional robustness experiments show that M2DDI maintains high predictive accuracy even when modality-specific information is partially missing, outperforming existing methods under similar conditions. Analysis of expert selection patterns further confirms alignment with established pharmacological mechanisms. These results establish M2DDI as an effective and mechanism-aware solution for comprehensive DDI prediction. The code is available at: https://github.com/RunqingXuCn/M2DDI.
Runqing Xu, Siyi Liu, Haoyang Li et al.· Proceedings of the 32nd ACM...· 0 citations
Drug-target affinity (DTA) prediction is an important task in computer-aided drug discovery. Existing methods usually use a fixed drug-protein interaction strategy. This design is hard to adapt to heterogeneous samples. It is also limited in cold-start and low-similarity scenarios. This study proposes SIGMA-DTA, a similarity-guided adaptive interaction modeling framework for DTA prediction. The framework uses drug and protein similarities as explicit reasoning priors. It introduces a similarity-driven routing mechanism. The mechanism assigns weights to different interaction paths according to the distance between a sample and the training distribution. This enables sample-specific interaction modeling. Unlike conventional unified interaction schemes, SIGMA-DTA adjusts information propagation under different similarity levels. It can model complex drug-target relationships more effectively. Experiments on the Davis and KIBA datasets verify the effectiveness of the method. The results show that integrating similarity information into the interaction modeling process improves robustness and generalization in DTA prediction.
Unknown authors· Journal of Biomedical Inform...· 0 citations
Declining research and development productivity is a structural challenge in the
pharmaceutical industry, where the discovery, optimization, and clinical evaluation of a
single medicine require long timelines, high cost, and repeated decision-making under
uncertainty. Artificial intelligence (AI), big data analysis, and computational simulation are
expected to accelerate drug discovery, but isolated applications cannot by themselves
transform the whole process. This invited narrative review and platform perspective
summarizes the concept and current implementation of an integrated Drug Discovery DX
Platform (DXPF) that connects disease information and patient omics data to target
gene/protein discovery, lead compound generation, and simulation-based evaluation. In the
target-discovery component, Bayesian network and graph-based methods infer diseasespecific regulatory networks from omics data and support identification of mechanisms and
candidate targets. In the drug-design component, graph convolutional neural network-based
compound profile prediction is coupled with ChemTS molecular generation, docking, and
molecular dynamics simulation to address multi-objective optimization of potency,
absorption, distribution, metabolism, excretion (ADME), toxicity, and structural binding
behavior. The platform also incorporates integrated databases and federated learning
developed through academia-industry collaboration, enabling model improvement while
preserving confidential company data. By connecting these modules through a browserbased interface, DXPF aims to shorten an end-to-end computational cycle from analysisready patient data to prioritized candidate structures; the approximately one-week objective
is an engineering target under favorable input and computing conditions and excludes
compound synthesis and biological, pharmacokinetic, toxicological, and clinical validation.
Current evidence is mainly module-level, retrospective, or computational, and prospective
benchmarking, uncertainty assessment, and experimental validation are required before
effects on attrition or regulatory decision-making can be established. Future expansion to
biologics, disease-prevention AI, and preclinical and clinical data could broaden the
platform's scope, but these extensions remain development goals.
Unknown authors· Journal of Asian Association...· 0 citations
Artificial intelligence (AI) has become an important tool in drug discovery by enabling the analysis of large-scale molecular dynamics (MD) simulation data and improving the understanding of protein-ligand interactions. However, identifying functionally relevant ligand-binding conformations from highly dynamic MD trajectories remains a major challenge. We propose a spectral analysis-based AI/machine learning (ML) framework to improve the identification of ligand-binding protein conformations. The framework compares the Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) by transforming protein feature time series into frequency-domain and time-frequency-domain representations, respectively. These spectral features capture global conformational dynamics and localized structural changes. A probabilistic majority-voting decision-fusion strategy integrates predictions from multiple AI/ML models, while the spectral feature space is used to mitigate class imbalance, improve the signalto-noise ratio, and enhance discriminative learning. The framework was evaluated on three G protein-coupled receptors (GPCRs): ADORA2A, OPRD1, and OPRK1. Compared with baseline models trained on raw time-series data, the proposed approach achieved improved predictive performance. FFT-based features effectively captured high-frequency conformational signatures, whereas DWT-based features identified localized high-energy patterns associated with ligand-binding events. The decision-fusion strategy further improved the sensitivity, the area under the receiver operating characteristic curve (AUC), and the overall consistency of the prediction across all targets. Spectral-domain analysis substantially enhances AI-driven identification of ligand-binding protein conformations by providing complementary representations of protein dynamics and improving classification performance. This framework offers a robust approach for analyzing MD simulations in the discovery of structure-based drugs and can facilitate the identification of biologically relevant conformations. Future work will validate the predicted conformations through molecular docking and extend the framework to additional therapeutic protein targets.
Shivangi Gupta, V. Menon, Jerome Baudry· Frontiers in Drug Discovery· 0 citations
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