The Materials Genome Initiative (MGI) was conceived as a unified effort to capture, curate, and exploit materials structure/property information on a grand scale to enable rapid, cost-effective development of novel materials with predictable properties. While the use of “genomic” methods to facilitate property prediction, virtual design, and discovery of materials is relatively new, the concepts driving the development of materials informatics are based, solidly, on the lessons learned during the development history of cheminformatics and bioinformatics. This chapter describes some of the ways in which cheminformatics and machine learning methods have been adapted for, and utilized in, materials science and engineering applications. Examples of how materials quantitative structure–property relationship (MQSPR) models are created, validated, and utilized are presented.