De novo molecular drug design benchmarking
Literature Information
Publication Date
2021-06-03
DOI
10.1039/D1MD00074H
Impact Factor
0
Authors
Lauren L. Grant, Clarissa S. Sit
View Original
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.
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