On the Virtues of Automated QSAR The New Kid on the Block

TL;DR

This paper presents an automated QSAR method that rapidly generates high-quality predictive models using machine learning, outperforming traditional manual approaches in efficiency and maintaining comparable accuracy.

Contribution

The paper introduces AutoQSAR, an automated approach for QSAR modeling that significantly reduces development time while achieving models of similar or better quality than manual methods.

Findings

AutoQSAR produces models of comparable or superior quality.

AutoQSAR significantly reduces modeling time.

AutoQSAR's potential has been largely undervalued.

Abstract

Quantitative Structure-Activity Relationship (QSAR) has proved an invaluable tool in medicinal chemistry. Data availability at unprecedented levels through various databases have collaborated to a resurgence in the interest for QSAR. In this context, rapid generation of quality predictive models is highly desirable for hit identification and lead optimization. We showcase the application of an automated QSAR approach, which randomly selects multiple training/test sets and utilizes machine-learning algorithms to generate predictive models. Results demonstrate that AutoQSAR produces models of improved or similar quality to those generated by practitioners in the field but in just a fraction of the time. Despite the potential of the concept to the benefit of the community, the AutoQSAR opportunity has been largely undervalued.