Impact of the characteristics of quantum chemical databases on machine learning predictions of tautomerization energies

TL;DR

This study investigates how the content and structure of quantum chemical databases influence machine learning predictions of tautomerization energies, emphasizing the importance of chemical diversity and sampling strategies.

Contribution

It provides a quantitative analysis of database characteristics affecting ML prediction quality and introduces methods to identify and address data deficiencies.

Findings

Diverse databases improve prediction accuracy.

Conformational sampling can mitigate limited chemical coverage.

Undersampled bond types lead to poor predictions.

Abstract

An essential aspect for adequate predictions of chemical properties by machine learning models is the database used for training them. However, studies that analyze how the content and structure of the databases used for training impact the prediction quality are scarce. In this work, we analyze and quantify the relationships learned by a machine learning model (Neural Network) trained on five different reference databases (QM9, PC9, ANI-1E, ANI-1 and ANI-1x) to predict tautomerization energies from molecules in Tautobase. For this, characteristics such as the number of heavy atoms in a molecule, number of atoms of a given element, bond composition, or initial geometry on the quality of the predictions are considered. The results indicate that training on a chemically diverse database is crucial for obtaining good results but also that conformational sampling can partly compensate for limited coverage of chemical diversity. We explicitly demonstrate that when certain types of bonds need to be covered in the target database (Tautobase) but are undersampled in the reference databases the resulting predictions are poor. A quantitative measure for these deficiencies is the Kullback-Leibler divergence between reference and target distributions. Analysis of the results with a TreeMAP algorithm provides deeper understanding of specific deficiencies in the reference data sets. Capitalizing on this information can be used to either improve existing databases or to generate new databases of sufficient diversity for a range of ML applications in chemistry.