tmQM-RDF Dataset: a Knowledge Graph Representing Transition Metal Complexes

TL;DR

This paper introduces tmQM-RDF, a comprehensive knowledge graph dataset of around 50,000 transition metal complexes, designed to facilitate machine learning and computational analysis in chemistry.

Contribution

The paper presents a novel RDF-based knowledge graph dataset for TMCs, integrating detailed qualitative and quantitative data to support advanced analysis and manipulation tasks.

Findings

Effective representation of TMCs in RDF format

Promising results in TMC manipulation tasks using simple models

Rich dataset enabling machine learning applications in chemistry

Abstract

Transition Metal Complexes (TMCs) have wide-ranging practical utility in chemistry, with possible applications that range from catalysis to medicinal chemistry. The study of TMCs and their properties is thus a field rich with potential, one in which machine learning and computational approaches can offer a substantial aid. For this reason, appropriate and accessible datasets, collecting a wide range of information, are required in order to facilitate the effective analysis and investigation of such compounds. This paper contributes to the data modelling effort via the introduction of the transition metal quantum mechanics RDF (tmQM-RDF) dataset, a knowledge graph constructed using the Resource Description Framework (RDF) vocabulary which collects rich and detailed descriptions of approximately 50k TMCs. These descriptions are both qualitative and quantitative in nature, encompassing the compositional nature of TMCs in terms of their constituting ligands, as well as the entirety of their molecular graphs. An example of the power of the proposed representation is presented, showcasing how the information available in tmQM-RDF can be exploited for TMC manipulation tasks, achieving promising performance even with relatively simple probabilistic models.