Open AccessDe novo molecular drug design benchmarking
Author(s) -
Lauren L Grant,
Clarissa S. Sit
Publication year - 2021
Publication title -
rsc medicinal chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.754
H-Index - 55
ISSN - 2632-8682
DOI - 10.1039/d1md00074h
Subject(s) - benchmarking , computer science , artificial intelligence , field (mathematics) , deep learning , machine learning , computational biology , data science , biology , mathematics , business , marketing , pure mathematics
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.