ArticleJournal of cheminformatics2024
DLM-DTI: a dual language model for the prediction of drug-target interaction with hint-based learning.
Article in Journal of cheminformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 38 citations in OpenAlex.
- Multiscale cross modal fusion and cascaded signal propagation for drug target interaction prediction.iScience · 2026Article
- DLRNA-BERTa: a transformer approach for predicting molecule-RNA binding affinities.Briefings in bioinformatics · 2026Article
- IIC-DTI: A Contrastive Learning Enhanced Inter-Intra Molecular Fusing Framework for Drug-Target Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- A Scalable and Robust Ensemble Deep Learning Method for Predicting Drug-Target Interactions.Interdisciplinary sciences, computational life sciences · 2026Article
- How Advanced Artificial Intelligence Technologies Shape Drug-Drug and Drug-Target Interaction Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- ASCENT: an active transfer learning paradigm for efficient drug-target interaction prediction.Science China. Life sciences · 2026Article
- Article
- Graph-based drug-target interaction modeling: from representation learning to output-driven drug discovery.Briefings in bioinformatics · 2026Review
- 3DICE: interpretable 3D cross-modal learning for drug-target interaction prediction and large-scale drug discovery.Bioinformatics (Oxford, England) · 2026Article
- Review
- Out-of-distribution generalization enhances protein function annotation for low-homology sequences.Briefings in bioinformatics · 2026Article
- TriDTI: tri-modal representation learning with cross-modal alignment for drug-target interaction prediction.Briefings in bioinformatics · 2026Article
- A Systematic Review of Drug-Related InteractionsUtilizing Deep Learning and LLMs for Prediction and Mitigation.ACS omega · 2025Review
- PLiSAGE: enhancing protein-ligand interaction prediction with multimodal surface and geometry encoding.Bioinformatics (Oxford, England) · 2025Article
- A survey on deep learning for drug-target binding prediction: models, benchmarks, evaluation, and case studies.Briefings in bioinformatics · 2025Review
- GS-DTI: a graph-structure-aware framework leveraging large language models for drug-target interaction prediction.Bioinformatics (Oxford, England) · 2025Article
- Evidential deep learning-based drug-target interaction prediction.Nature communications · 2025Article
- Top-DTI: integrating topological deep learning and large language models for drug-target interaction prediction.Bioinformatics (Oxford, England) · 2025Article
- DTBA-net: Drug-Target Binding Affinity prediction using feature selection in hybrid CNN model.Journal of computer-aided molecular design · 2025Article
- Knowledge Distillation for Molecular Property Prediction: A Scalability Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 4 institutions in 1 country.
Funding
Abstract
The drug discovery process is demanding and time-consuming, and machine learning-based research is increasingly proposed to enhance efficiency. A significant challenge in this field is predicting whether a drug molecule's structure will interact with a target protein. A recent study attempted to address this challenge by utilizing an encoder that leverages prior knowledge of molecular and protein structures, resulting in notable improvements in the prediction performance of the drug-target interactions task. Nonetheless, the target encoders employed in previous studies exhibit computational complexity that increases quadratically with the input length, thereby limiting their practical utility. To overcome this challenge, we adopt a hint-based learning strategy to develop a compact and efficient target encoder. With the adaptation parameter, our model can blend general knowledge and target-oriented knowledge to build features of the protein sequences. This approach yielded considerable performance enhancements and improved learning efficiency on three benchmark datasets: BIOSNAP, DAVIS, and Binding DB. Furthermore, our methodology boasts the merit of necessitating only a minimal Video RAM (VRAM) allocation, specifically 7.7GB, during the training phase (16.24% of the previous state-of-the-art model). This ensures the feasibility of training and inference even with constrained computational resources.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.