# Generative Models for Automatic Chemical Design

## TL;DR

This paper reviews how deep generative models are transforming chemical design by enabling inverse design approaches, categorizing different architectures, and discussing their potential and challenges in materials discovery.

## Contribution

It provides a comprehensive classification and review of generative models for molecular design, highlighting their evolution, performance, and future prospects.

## Key findings

- Generative models facilitate inverse chemical design.
- Different architectures impact molecular generation quality.
- Challenges include model interpretability and property optimization.

## Abstract

Materials discovery is decisive for tackling urgent challenges related to energy, the environment, health care and many others. In chemistry, conventional methodologies for innovation usually rely on expensive and incremental strategies to optimize properties from molecular structures. On the other hand, inverse approaches map properties to structures, thus expediting the design of novel useful compounds. In this chapter, we examine the way in which current deep generative models are addressing the inverse chemical discovery paradigm. We begin by revisiting early inverse design algorithms. Then, we introduce generative models for molecular systems and categorize them according to their architecture and molecular representation. Using this classification, we review the evolution and performance of important molecular generation schemes reported in the literature. Finally, we conclude highlighting the prospects and challenges of generative models as cutting edge tools in materials discovery.