MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design

TL;DR

This paper provides a comprehensive survey of machine learning models used for molecule design, covering representation methods, generative techniques, problem setups, and future challenges in the field.

Contribution

It systematically reviews existing ML approaches for molecule design, highlighting current methods, problem classifications, and identifying open challenges and future directions.

Findings

Summarizes main molecule featurization techniques (1D, 2D, 3D)

Categorizes molecule design problems by setup and goals

Discusses open challenges and future opportunities

Abstract

Molecule design is a fundamental problem in molecular science and has critical applications in a variety of areas, such as drug discovery, material science, etc. However, due to the large searching space, it is impossible for human experts to enumerate and test all molecules in wet-lab experiments. Recently, with the rapid development of machine learning methods, especially generative methods, molecule design has achieved great progress by leveraging machine learning models to generate candidate molecules. In this paper, we systematically review the most relevant work in machine learning models for molecule design. We start with a brief review of the mainstream molecule featurization and representation methods (including 1D string, 2D graph, and 3D geometry) and general generative methods (deep generative and combinatorial optimization methods). Then we summarize all the existing molecule design problems into several venues according to the problem setup, including input, output types and goals. Finally, we conclude with the open challenges and point out future opportunities of machine learning models for molecule design in real-world applications.