Construction of An Oral Bioavailability Prediction Model Based on Machine Learning for Evaluating Molecular
Qi Yang1, Lili Fan1, Erwei Hao2
1School of Pharmacy, Guangxi University of Chinese Medicine, Nanning 530200, China.
Journal of Pharmaceutical Sciences
|March 2, 2024
Summary
Machine learning accurately predicts drug oral bioavailability (OB) by analyzing ADME properties. Modifying berberine and atenolol structures enhanced their OB, guiding future drug design for improved drug delivery.
Area of Science:
- Computational Chemistry
- Pharmacokinetics
- Machine Learning in Drug Discovery
Background:
- Oral bioavailability (OB) is a critical factor in drug efficacy.
- Understanding the impact of Absorption, Distribution, Metabolism, and Excretion (ADME) properties on OB is essential for drug development.
- Predictive modeling can accelerate the identification of drug candidates with favorable OB.
Purpose of the Study:
- To investigate the influence of ADME characteristics on drug oral bioavailability.
- To develop and validate a machine learning model for predicting OB.
- To apply the model to optimize the OB of berberine and atenolol derivatives.
Main Methods:
- Established a drug OB database (386 drugs) and collected ADME data.
- Employed machine learning algorithms (Random Forest, XGBoost, CatBoost, LightGBM) with Morgan fingerprints as molecular descriptors.
- Modified berberine and atenolol structures via mono- and di-substitution to predict OB changes.
Main Results:
- Identified that smaller molecular weight and more rotatable bonds (≤10) correlate with higher OB.
- Random Forest model demonstrated superior performance in OB prediction.
- Structural modifications significantly improved the oral bioavailability of berberine and atenolol.
Conclusions:
- Machine learning models, particularly Random Forest, can accurately predict drug OB.
- The established database and model provide a valuable tool for guiding drug design.
- Chemical modifications, specifically substitutions, can effectively enhance the oral bioavailability of drug molecules.
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