ArticleBMC medical informatics and decision making2020
A deep learning-based method for drug-target interaction prediction based on long short-term memory neural network.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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46 citing papers in PubMed.
- Sequence-based prediction of drug-target binding using machine learning, deep learning and ensemble models without 3D structural information.Scientific reports · 2026Article
- KansFormer network based on a multimodal cross-attention mechanism for drug-target interaction prediction.Molecular diversity · 2026Article
- LightDTA: lightweight drug-target affinity prediction via random-walk network embedding and knowledge distillation.Molecular diversity · 2026Article
- Artificial intelligence in drug discovery: from algorithmic foundations to clinical translation.Frontiers in pharmacology · 2026Review
- DCGAT-DTI: dynamic cross-graph attention network for drug-target interaction prediction.Bioinformatics advances · 2026Article
- Machine learning for drug-target interaction prediction: A comprehensive review of models, challenges, and computational strategies.Computational and structural biotechnology journal · 2026Review
- Two-stage sampling for better survival model performance.BMC medical research methodology · 2025Article
- BiMA-DTI: a bidirectional Mamba-Attention hybrid framework for enhanced drug-target interaction prediction.BMC biology · 2025Article
- Ensemble techniques for predictive modeling of leishmanial activity via molecular fingerprints.BMC medical informatics and decision making · 2025Article
- QKDTI A quantum kernel based machine learning model for drug target interaction prediction.Scientific reports · 2025Article
- DTIP-WINDGRU a novel drug-target interaction prediction with wind-enhanced gated recurrent unit.BMC bioinformatics · 2025Article
- EnGCI: enhancing GPCR-compound interaction prediction via large molecular models and KAN network.BMC biology · 2025Article
- Advanced Artificial Intelligence Technologies Transforming Contemporary Pharmaceutical Research.Bioengineering (Basel, Switzerland) · 2025Review
- Clinical and Operational Applications of Artificial Intelligence and Machine Learning in Pharmacy: A Narrative Review of Real-World Applications.Pharmacy (Basel, Switzerland) · 2025Article
- Mutational insights andFrontiers in bioinformatics · 2025Article
- Article
- A review of machine learning-based methods for predicting drug-target interactions.Health information science and systems · 2024Review
- Article
- Artificial Intelligence to Close the Gap between Pharmacokinetic/Pharmacodynamic Targets and Clinical Outcomes in Critically Ill Patients: A Narrative Review on Beta Lactams.Antibiotics (Basel, Switzerland) · 2024Review
- A review of deep learning methods for ligand based drug virtual screening.Fundamental research · 2024Review
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6 authors.
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Abstract
backgroundThe key to modern drug discovery is to find, identify and prepare drug molecular targets. However, due to the influence of throughput, precision and cost, traditional experimental methods are difficult to be widely used to infer these potential Drug-Target Interactions (DTIs). Therefore, it is urgent to develop effective computational methods to validate the interaction between drugs and target.
methodsWe developed a deep learning-based model for DTIs prediction. The proteins evolutionary features are extracted via Position Specific Scoring Matrix (PSSM) and Legendre Moment (LM) and associated with drugs molecular substructure fingerprints to form feature vectors of drug-target pairs. Then we utilized the Sparse Principal Component Analysis (SPCA) to compress the features of drugs and proteins into a uniform vector space. Lastly, the deep long short-term memory (DeepLSTM) was constructed for carrying out prediction.
resultsA significant improvement in DTIs prediction performance can be observed on experimental results, with AUC of 0.9951, 0.9705, 0.9951, 0.9206, respectively, on four classes important drug-target datasets. Further experiments preliminary proves that the proposed characterization scheme has great advantage on feature expression and recognition. We also have shown that the proposed method can work well with small dataset.
conclusionThe results demonstration that the proposed approach has a great advantage over state-of-the-art drug-target predictor. To the best of our knowledge, this study first tests the potential of deep learning method with memory and Turing completeness in DTIs prediction.
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