ArticleScientific reports2022
KUALA: a machine learning-driven framework for kinase inhibitors repositioning.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- AGAPE (Computational G‑Quadruplex Stabilization Prediction): The First Machine Learning Workflow for G‑Quadruplex Stabilization Prediction.ACS omega · 2026Article
- Optimizing kinase and PARP inhibitor combinations through machine learning and in silico approaches for targeted brain cancer therapy.Molecular diversity · 2025Article
- Perturbation-Theory Machine Learning for Multi-Target Drug Discovery in Modern Anticancer Research.Current issues in molecular biology · 2025Review
- Ligand-Based Approach for Multi-Target Drug Discovery: PTML Modeling of Triple-Target Inhibitors.Current topics in medicinal chemistry · 2025Article
- Leveraging multiple data types for improved compound-kinase bioactivity prediction.Nature communications · 2024Article
- Drug repurposing: a nexus of innovation, science, and potential.Scientific reports · 2023Article
- AiKPro: deep learning model for kinome-wide bioactivity profiling using structure-based sequence alignments and molecular 3D conformer ensemble descriptors.Scientific reports · 2023Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The family of protein kinases comprises more than 500 genes involved in numerous functions. Hence, their physiological dysfunction has paved the way toward drug discovery for cancer, cardiovascular, and inflammatory diseases. As a matter of fact, Kinase binding sites high similarity has a double role. On the one hand it is a critical issue for selectivity, on the other hand, according to poly-pharmacology, a synergistic controlled effect on more than one target could be of great pharmacological interest. Another important aspect of binding similarity is the possibility of exploit it for repositioning of drugs on targets of the same family. In this study, we propose our approach called Kinase drUgs mAchine Learning frAmework (KUALA) to automatically identify kinase active ligands by using specific sets of molecular descriptors and provide a multi-target priority score and a repurposing threshold to suggest the best repurposable and non-repurposable molecules. The comprehensive list of all kinase-ligand pairs and their scores can be found at https://github.com/molinfrimed/multi-kinases .
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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.