Molecular Representations for AI in Chemistry and Materials Science: An NLP Perspective

TL;DR

This paper reviews NLP-inspired molecular representations used in AI applications for chemistry and materials science, highlighting their importance and providing guidance for researchers new to chemical informatics.

Contribution

It offers a comprehensive overview of popular NLP-inspired molecular representations and discusses their applications in AI-driven chemistry and materials science.

Findings

Summarizes key molecular representations used in AI for chemistry.

Highlights applications of these representations in chemical informatics.

Provides guidance for researchers new to chemical representations.

Abstract

Deep learning, a subfield of machine learning, has gained importance in various application areas in recent years. Its growing popularity has led it to enter the natural sciences as well. This has created the need for molecular representations that are both machine-readable and understandable to scientists from different fields. Over the years, many chemical molecular representations have been constructed, and new ones continue to be developed as computer technology advances and knowledge of molecular complexity increases. This paper presents some of the most popular digital molecular representations inspired by natural language processing (NLP) and used in chemical informatics. In addition, the paper discusses some notable AI-based applications that use these representations. This paper aims to provide a guide to structural representations that are important for the application of AI in chemistry and materials science from the perspective of an NLP researcher. This review is a reference tool for researchers with little experience working with chemical representations who wish to work on projects at the interface of these fields.