Probabilistic Generative Deep Learning for Molecular Design

TL;DR

This paper reviews probabilistic generative deep learning methods for molecular design, emphasizing how they utilize large datasets and deep models to discover and analyze new molecules and their properties.

Contribution

It provides a comprehensive overview of the key components and recent advances in probabilistic generative deep learning for molecular design.

Findings

Highlights the integration of large databases and quantum calculations

Summarizes recent successful applications of probabilistic models

Discusses molecular representations and latent space exploration

Abstract

Probabilistic generative deep learning for molecular design involves the discovery and design of new molecules and analysis of their structure, properties and activities by probabilistic generative models using the deep learning approach. It leverages the existing huge databases and publications of experimental results, and quantum-mechanical calculations, to learn and explore molecular structure, properties and activities. We discuss the major components of probabilistic generative deep learning for molecular design, which include molecular structure, molecular representations, deep generative models, molecular latent representations and latent space, molecular structure-property and structure-activity relationships, molecular similarity and molecular design. We highlight significant recent work using or applicable to this new approach.