Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities

TL;DR

This paper provides a comprehensive review of recent AI methods in chemistry, covering data characteristics, representations, models for key tasks, and future challenges, highlighting the rapid growth and potential of AI in chemical research.

Contribution

It offers an extensive survey of AI techniques in chemistry, summarizing current methods, applications, and identifying key challenges for future research.

Findings

AI accelerates chemical research and discovery

Diverse data sources influence model design

Key challenges include data quality and model interpretability

Abstract

The development of artificial intelligence (AI) techniques has brought revolutionary changes across various realms. In particular, the use of AI-assisted methods to accelerate chemical research has become a popular and rapidly growing trend, leading to numerous groundbreaking works. In this paper, we provide a comprehensive review of current AI techniques in chemistry from a computational perspective, considering various aspects in the design of methods. We begin by discussing the characteristics of data from diverse sources, followed by an overview of various representation methods. Next, we review existing models for several topical tasks in the field, and conclude by highlighting some key challenges that warrant further attention.