Mapping and Classifying Molecules from a High-Throughput Structural Database

TL;DR

This paper demonstrates how machine learning can be used to analyze large structural databases of molecules, revealing structure-property relations, identifying outliers, and understanding the effects of perturbations.

Contribution

It introduces a novel metric-based approach combined with clustering and dimensionality reduction to analyze high-throughput molecular data sets.

Findings

Machine learning helps reveal structure-property relations.

Outliers and inconsistencies can be identified effectively.

Perturbations' effects on conformer stability are rationalized.

Abstract

High-throughput computational materials design promises to greatly accelerate the process of discovering new materials and compounds, and of optimizing their properties. The large databases of structures and properties that result from computational searches, as well as the agglomeration of data of heterogeneous provenance leads to considerable challenges when it comes to navigating the database, representing its structure at a glance, understanding structure-property relations, eliminating duplicates and identifying inconsistencies. Here we present a case study, based on a data set of conformers of amino acids and dipeptides, of how machine-learning techniques can help addressing these issues. We will exploit a recently developed strategy to define a metric between structures, and use it as the basis of both clustering and dimensionality reduction techniques showing how these can help reveal structure-property relations, identify outliers and inconsistent structures, and rationalise how perturbations (e.g. binding of ions to the molecule) affect the stability of different conformers.