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<p>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.</p>

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Unveiling molecular signatures for precision drug design: machine learning insights from trypanothione reductase, PKC-θ, and CB1

  • 期刊论文

  • 发表日期：

    2025年9月10日

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  • Sunil Sahu,
  • Adarsh Anmol,
  • Tushar Nishad,
  • Satya Eswari Jujjavarapu

摘要

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.