ArticleNature machine intelligence2023
Calibrated geometric deep learning improves kinase-drug binding predictions.
Article in Nature machine intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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
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.
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Who cites it
33 citing papers in PubMed.
- An interpretable framework applying protein words to predict protein-small molecule complementary pairing rules.Chemical science · 2026Article
- A Unified Hierarchical Multiscale Fusion Framework for Drug-Target Affinity Prediction: From Benchmark Performance to Nanomolar Inhibitor Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Operando state monitoring of diversified lithium-ion batteries via laser-excited ultrasonic sensing with transformer networks.Science advances · 2026Article
- Deep Contrastive Learning for High-Throughput Prediction of Drug Resistance Mutations from Sequences.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Improving Generalizability in Whole-Cell Antibiotic Discovery Through Active Learning.bioRxiv : the preprint server for biology · 2026Article
- DrugDL: dual-modal deep learning framework for multi-property drug prediction and targeted therapy discovery.Bioinformatics (Oxford, England) · 2026Article
- Implementing trust in non-small cell lung cancer diagnosis with a conformalized uncertainty-aware AI framework.Nature biomedical engineering · 2026Article
- A Node-Adaptive Feature Fusion Network for Drug-Target Interaction Prediction Based on Multi-View Graphs.Biomolecules · 2026Article
- An electron-density point-cloud framework for robust protein-ligand interaction prediction.Nature communications · 2026Article
- Review
- Query Matters: How Selection Strategies Influence Active Learning in Drug Discovery.Journal of chemical information and modeling · 2026Article
- Collision-free morgan fingerprints: a principled approach to enhance machine learning performance and interpretability in chemistry.Journal of cheminformatics · 2026Article
- Artificial intelligence in drug discovery from advanced molecular representation to pipeline applications.Frontiers in bioinformatics · 2026Review
- Research progress of artificial intelligence in high-throughput drug screening.Frontiers in pharmacology · 2026Review
- Computational approaches to druggable site identification: Current status and future perspective.Acta pharmaceutica Sinica. B · 2026Review
- Semi-inductive dataset construction and framework optimization for practical drug target interaction prediction with ScopeDTI.Nature communications · 2025Article
- Enhancing kinase-inhibitor activity and selectivity prediction through contrastive learning.Nature communications · 2025Article
- DeepRNA-DTI: a deep learning approach for RNA-compound interaction prediction with binding site interpretability.Journal of cheminformatics · 2025Article
- Hierarchical affinity landscape navigation through learning a shared pocket-ligand space.Patterns (New York, N.Y.) · 2025Article
- Evidential deep learning-based drug-target interaction prediction.Nature communications · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Protein kinases regulate various cellular functions and hold significant pharmacological promise in cancer and other diseases. Although kinase inhibitors are one of the largest groups of approved drugs, much of the human kinome remains unexplored but potentially druggable. Computational approaches, such as machine learning, offer efficient solutions for exploring kinase-compound interactions and uncovering novel binding activities. Despite the increasing availability of three-dimensional (3D) protein and compound structures, existing methods predominantly focus on exploiting local features from one-dimensional protein sequences and two-dimensional molecular graphs to predict binding affinities, overlooking the 3D nature of the binding process. Here we present KDBNet, a deep learning algorithm that incorporates 3D protein and molecule structure data to predict binding affinities. KDBNet uses graph neural networks to learn structure representations of protein binding pockets and drug molecules, capturing the geometric and spatial characteristics of binding activity. In addition, we introduce an algorithm to quantify and calibrate the uncertainties of KDBNet's predictions, enhancing its utility in model-guided discovery in chemical or protein space. Experiments demonstrated that KDBNet outperforms existing deep learning models in predicting kinase-drug binding affinities. The uncertainties estimated by KDBNet are informative and well-calibrated with respect to prediction errors. When integrated with a Bayesian optimization framework, KDBNet enables data-efficient active learning and accelerates the exploration and exploitation of diverse high-binding kinase-drug pairs.
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.