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A Data-Driven Approach for Interpretable and Efficient Predictive Modeling: A Case Study in SARS-CoV-2 Protease
Branislav Stanković1, Sang-Yong Oh2, Dušan Ramljak2
1Department for Nuclear and Plasma Physics, Vinča Institute of Nuclear Sciences-National Institute of the Republic of Serbia, University of Belgrade, P.O. Box 522, 11001 Belgrade, Serbia.
Pharmaceuticals (Basel, Switzerland)
|March 28, 2026
Summary
A new chemoinformatics method using FeatureWiz and stepwise selection provides robust, interpretable, and efficient predictive models for drug discovery, particularly for SARS-CoV-2 protease inhibitors.
Area of Science:
- Chemoinformatics
- Computational Chemistry
- Drug Discovery
Background:
- State-of-the-art chemoinformatic models require feature selection methods that meet all evaluation criteria.
- Developing robust, interpretable, and computationally efficient methodologies is crucial for predictive modeling.
Purpose of the Study:
- To develop a robust methodology for interpretable and efficient predictive models.
- To apply this methodology to discover SARS-CoV-2 main protease inhibitors.
- To identify a transparent and reproducible descriptor selection approach.
Main Methods:
- Evaluation of various descriptor selection procedures.
- Training and testing models on CHEMBL database molecules.
- Validation on an external set of compounds.
- Utilizing the FeatureWiz algorithm combined with stepwise feature selection.
Main Results:
- The FeatureWiz and stepwise selection procedure uniquely satisfied all evaluation criteria for advanced chemoinformatic models.
- Models using 2D descriptors and Ordinary Least Squares regression yielded optimal performance.
- The developed framework demonstrated high interpretability and computational efficiency.
Conclusions:
- The proposed framework offers significant advantages for decision-making in drug discovery due to interpretability and efficiency.
- The derived models are effective, transparent tools for predicting biological activity.
- A validated framework for data-driven decisions in drug discovery and beyond was established.
