ArticleNature communications2021
Crowdsourced mapping of unexplored target space of kinase inhibitors.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 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
40 citing papers in PubMed.
- Enhancing kinase-inhibitor activity and selectivity prediction through contrastive learning.Nature communications · 2025Article
- Tracking protein kinase targeting advances: integrating QSAR into machine learning for kinase-targeted drug discovery.Future science OA · 2025Review
- AΙ-Driven Drug Repurposing: Applications and Challenges.Medicines (Basel, Switzerland) · 2025Review
- Automated Iterative N─C and C─C Bond Formation.Angewandte Chemie (International ed. in English) · 2025Article
- MONSTROUS: a web-based chemical-transporter interaction profiler.Frontiers in pharmacology · 2025Article
- Structure and dynamics in drug discovery.npj drug discovery · 2024Review
- Article
- Leveraging multiple data types for improved compound-kinase bioactivity prediction.Nature communications · 2024Article
- Machine learning in preclinical drug discovery.Nature chemical biology · 2024Review
- Developing a Semi-Supervised Approach Using a PU-Learning-Based Data Augmentation Strategy for Multitarget Drug Discovery.International journal of molecular sciences · 2024Article
- EMPDTA: An End-to-End Multimodal Representation Learning Framework with Pocket Online Detection for Drug-Target Affinity Prediction.Molecules (Basel, Switzerland) · 2024Article
- Article
- De novo generation of multi-target compounds using deep generative chemistry.Nature communications · 2024Article
- Best practices for managing and disseminating resources and outreach and evaluating the impact of the IDG Consortium.Drug discovery today · 2024Article
- Kinome-Wide Virtual Screening by Multi-Task Deep Learning.International journal of molecular sciences · 2024Article
- Article
- Calibrated geometric deep learning improves kinase-drug binding predictions.Nature machine intelligence · 2023Article
- Poor Generalization by Current Deep Learning Models for Predicting Binding Affinities of Kinase Inhibitors.bioRxiv : the preprint server for biology · 2023Article
- Predicting the target landscape of kinase inhibitors using 3D convolutional neural networks.PLoS computational biology · 2023Article
- Computational Advancements in Cancer Combination Therapy Prediction.JCO precision oncology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
28 authors.
Funding
Abstract
Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome.
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.