Pharma
AI and AutoML in Drug Discovery: Multi-Target Machine Learning Models for Asthma and COPD Therapeutics
- AI in drug discovery
- AutoML pharmaceutical research
- machine learning QSAR modeling
- multi-target drug design
- PDE4B inhibitors
- PDE8A inhibitors
- TRPA1 antagonists
- virtual screening ZINC database
- computational drug discovery
- artificial intelligence in healthcare
MLJAR tools were used in the following publication.
Application of automated machine learning in the identification of multi-target-directed ligands blocking PDE4B, PDE8A, and TRPA1 with potential use in the treatment of asthma and COPD
Alicja Gawalska, Natalia Czub, Michał Sapa, Marcin Kołaczkowski, Adam Bucki, Aleksander Mendyk
Department of Medicinal Chemistry, Jagiellonian University Medical College, Kraków, Poland | Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, Kraków, Poland
This study demonstrates how artificial intelligence and Automated Machine Learning (AutoML) can accelerate multi-target drug discovery for complex respiratory diseases such as asthma and COPD. Using mljar-supervised, QSAR modeling, symbolic regression, and large-scale virtual screening of the ZINC15 database, the researchers developed predictive machine learning models for PDE4B, PDE8A, and TRPA1 inhibition. The AutoML-driven pipeline enabled the identification of novel multi-target-directed ligands (MTDLs) with favorable ADME properties and strong predicted nanomolar activity. This work highlights the growing role of AI-powered molecular modeling, feature engineering, and automated hyperparameter optimization in modern pharmaceutical research and computational drug design.
Molecular Informatics • June 16, 2023
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