Cost-Effective Quantum-Mechanical Workflows for Molecular Machine Learning

Abstract

The current digital revolution has driven a rapid adoption of machine learning (ML) to support decision-making in chemistry. In this context, many studies now incorporate ML models based on quantum-mechanical (QM) descriptors to accelerate the discovery of candidates across a wide range of chemical problems. While density functional theory (DFT) has traditionally served as the primary source of such descriptors, recent advances in cost-effective QM methods offer an opportunity to reduce computational cost and facilitate faster integration of ML into chemical workflows. Herein, we present an automated and user-friendly workflow that generates 39 electronic and steric cost-effective descriptors and their integration into ML models. These models are broadly applicable to fields involving finite molecular systems, such as homogeneous catalysis and drug discovery. We evaluated the acceptance of these cost-effective descriptors in ML-driven catalysis through a blinded survey of 52 participants, which indicated that experts consider the resulting ML models to be as reliable and interpretable as publication-quality DFT-based models. Finally, we introduced a fully automated protocol that generates descriptors and ML predictors from SMILES strings, enabling rapid predictor development accessible to the broader chemistry community. This cost-effective framework holds the potential to accelerate the integration of ML into everyday chemical research and contribute to a more inclusive digital transformation of the field.