Generative chemistry: drug discovery with deep learning generative models

TL;DR

This paper reviews recent advances in generative deep learning models for molecular design, highlighting their potential to accelerate drug discovery and discussing current challenges and future directions.

Contribution

It provides a comprehensive overview of generative architectures and tools used in chemistry for drug discovery, emphasizing recent progress and challenges.

Findings

Generative models can create novel molecular structures efficiently.

Deep learning architectures like RNNs, VAEs, and GANs are effectively used in compound generation.

Challenges include data quality, model interpretability, and integration into drug development pipelines.

Abstract

The de novo design of molecular structures using deep learning generative models introduces an encouraging solution to drug discovery in the face of the continuously increased cost of new drug development. From the generation of original texts, images, and videos, to the scratching of novel molecular structures, the incredible creativity of deep learning generative models surprised us about the height machine intelligence can achieve. The purpose of this paper is to review the latest advances in generative chemistry which relies on generative modeling to expedite the drug discovery process. This review starts with a brief history of artificial intelligence in drug discovery to outline this emerging paradigm. Commonly used chemical databases, molecular representations, and tools in cheminformatics and machine learning are covered as the infrastructure for the generative chemistry. The detailed discussions on utilizing cutting-edge generative architectures, including recurrent neural network, variational autoencoder, adversarial autoencoder, and generative adversarial network for compound generation are focused. Challenges and future perspectives follow.