# Probabilistic Generative Deep Learning for Molecular Design

arXiv: https://arxiv.org/abs/1902.05148 · HTML: https://mlsanity.com/paper/1902.05148/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2019.

## Built on by (most cited first, 1 of 1)

- [Visual design intuition: Predicting dynamic properties of beams from raw cross-section images](https://mlsanity.com/paper/2111.09701/) (2021)

## Abstract (arXiv)

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.