ArticleACS omega2024
ImageDTA: A Simple Model for Drug-Target Binding Affinity Prediction.
Article in ACS omega, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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
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Who cites it
10 citing papers in PubMed.
- Comprehensive overview of AI methodologies in nano-drug delivery Optimization and Design.NPJ precision oncology · 2026Review
- GraESM-FuseDTA: adaptive gated multimodal fusion of graph neural networks and protein language models for robust drug-target affinity prediction.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- DynamicDTA: Drug-Target Binding Affinity Prediction Using Dynamic Descriptors and Graph Representation.Interdisciplinary sciences, computational life sciences · 2026Article
- LKE-DTA: predicting drug-target binding affinity with large language model representations and knowledge graph embeddings.Molecular diversity · 2026Article
- Identification of small-molecule inhibitors for GluN1/GluN3A NMDA receptors via a multiscale CNN-based prediction model.Acta pharmacologica Sinica · 2026Article
- A Systematic Review of Drug-Related InteractionsUtilizing Deep Learning and LLMs for Prediction and Mitigation.ACS omega · 2025Review
- All That Glitters Is Not Gold: Importance of Rigorous Evaluation of Proteochemometric Models.Journal of chemical information and modeling · 2025Article
- Deep Drug-Target Binding Affinity Prediction Base on Multiple Feature Extraction and Fusion.ACS omega · 2025Article
- Improving drug-target affinity prediction by adaptive self-supervised learning.PeerJ. Computer science · 2025Article
- AI driven network pharmacology: Multi-scale mechanisms of traditional Chinese medicine from molecular to patient analysis.Computational and structural biotechnology journal · 2025Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting the drug-target binding affinity (DTA) is crucial in drug discovery, and an increasing number of researchers are using artificial intelligence techniques to make such predictions. Many effective deep neural network prediction models have been proposed. However, current methods need improvement in accuracy, complexity, and efficiency. In this study, we propose a method based on a multiscale 2-dimensional convolutional neural network (CNN), namely ImageDTA. Many studies have shown that CNN achieves good learning effects with limited data. Therefore, we take a unique perspective by treating the word vector encoded with a simplified molecular input line entry system (SMILES) string as an "image" and processing it like handling images, fully leveraging the efficient processing capabilities of CNN for image data. Furthermore, we show that ImageDTA has higher training and inference efficiency than pretrained large models and outperforms attention-based graph neural network models in accuracy and interpretability. We also use visualization techniques to select appropriate convolutional kernel sizes, thereby increasing the network's interpretability.
Identifiers
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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.