Issue 8, 2021

De novo molecular drug design benchmarking

Abstract

De novo molecular design for drug discovery is a growing field. Deep neural networks (DNNs) are becoming more widespread in their use for machine learning models. As more DNN models are proposed for molecular design, benchmarking methods are crucial for the comparision and validation of these models. This review looks at recently proposed benchmarking methods Fréchet ChemNet Distance, GuacaMol and Molecular Sets (MOSES), and provides a commentary on their future potential applications in de novo molecular drug design and possible next steps for further validation of these benchmarking methods.

Graphical abstract: De novo molecular drug design benchmarking

Article information

Article type
Review Article
Submitted
07 Մրտ 2021
Accepted
24 Մյս 2021
First published
03 Հնս 2021

RSC Med. Chem., 2021,12, 1273-1280

De novo molecular drug design benchmarking

L. L. Grant and C. S. Sit, RSC Med. Chem., 2021, 12, 1273 DOI: 10.1039/D1MD00074H

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements