I am very pleased to announce that our ACS In Focus digital primer, Molecular Representations for Machine Learning, has been released on Google Play and the ACS In Focus webpage. The overarching goal of this primer is to provide an overview of the basic categories of molecular representations and hands-on examples, using Python, so that the reader can readily apply these methods. Overall, this primer took a tremendous amount of work and I would like to thank my fellow coauthors: Brittany Story, Vasileios Maroulas, and Konstantinos Vogiatzis; the talented team of editors and graphic designers at the American Chemical Society, who provided valuable insights and figures throughout the writing process; and the reviewers and labmates, who provided useful feedback to help make this manuscript better.
One inspiration for this work was from when Konstantinos Vogiatzis (professor) and I (teaching assistant) taught Machine Learning for Chemical Applications in the Fall of 2021 at the University of Tennessee, Knoxville. During the course of that semester, we found many useful texts (textbooks, eBooks, articles, etc.), data repositories, and open source software packages that are included in this digital primer. With pedagogy and the principles of free and open-source software in mind, we have compiled our code examples in a GitHub repository which can be found here: Molecular Representations for Machine Learning Examples. All of these examples can either be downloaded and ran locally or can be launched using Google Colab, which can launch directly in your Google drive.
Grier Jones 