ArticleComputational and structural biotechnology journal2025
DrugForm-DTA: Towards real-world drug-target binding affinity model.
Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- SCAD-DTA: spherical concept alignment with dynamic multi-modal fusion for drug-target affinity prediction.Molecular diversity · 2026Article
- Structure-Aware Compound-Protein Affinity Prediction via Graph Neural Networks with Group Lasso Regularization.Computational and structural biotechnology journal · 2026Article
- Artemis: Harnessing Knowledge Graphs for Next-Generation Drug Target Prioritization.Computational and structural biotechnology journal · 2026Article
- DrugForm-TAS: Target-Agnostic Selectivity as Proteome-wide Binding Propensity Estimation.Computational and structural biotechnology journal · 2026Article
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9 authors.
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Abstract
Drug-target affinity (DTA) prediction is a fundamental challenge in drug discovery. Computational methods for predicting DTA can greatly assist drug design by narrowing the search space and reducing the number of protein-ligand complexes with low affinity. Currently DTA approaches often do not require three-dimensional (3D) structural information of proteins, which is frequently unavailable. In this study we present the DrugForm-DTA model, which uses only structure-less representations of ligand and protein. It is a Transformer-based neural network with protein encoding based on ESM-2, and small molecule ligand encoding obtained with Chemformer. We evaluated the model on the standard benchmarks Davis and KIBA, and revealed superior performance of DrugForm-DTA with the best result for KIBA. Moreover, we developed a ready-to-use model trained on the BindingDB dataset which was subjected to high-quality filtering and transformation. Overall, our method predicts drug-target affinity values with a confidence level comparable to that of a single
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