ArticleMolecular diversity2026
Unveiling molecular signatures for precision drug design: machine learning insights from trypanothione reductase, PKC-θ, and CB1.
Article in Molecular diversity, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Cracking ERα Y537S Resistance: Explainable Machine Learning-Guided Discovery and Molecular Dynamics Validation of Stable Candidate Ligands.Pharmaceuticals (Basel, Switzerland) · 2026Article
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4 authors.
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Abstract
Traditional drug discovery methods like high-throughput screening and molecular docking are slow and costly. This study introduces a machine learning framework to predict bioactivity (pIC₅₀) and identify key molecular properties and structural features for targeting Trypanothione reductase (TR), Protein kinase C theta (PKC-θ), and Cannabinoid receptor 1 (CB1) using data from the ChEMBL database. Molecular fingerprints, generated via PaDEL-Descriptor and RDKit, encoded structural features as binary vectors. Three models-Random Forest (RF), Gradient Boosting (GB), and a stacking ensemble with Ridge Regression-predicted pIC₅₀, with the ensemble achieving the lowest RMSE. Results highlight heteroatom-containing rings for TR, multiple ring systems for PKC-θ, and aromatic rings for CB1 as critical for high bioactivity. This adaptable framework accelerates drug design by pinpointing optimizable structures, enhancing efficiency in therapeutic development.
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