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De novo molecular drug design benchmarking
RSC Medicinal Chemistry ( IF 4.1 ) Pub Date : 2021-6-3 , DOI: 10.1039/d1md00074h
Lauren L Grant 1 , Clarissa S Sit 1
Affiliation  

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.

中文翻译:

从头分子药物设计基准

用于药物发现的从头分子设计是一个不断发展的领域。深度神经网络 (DNN) 在机器学习模型中的应用越来越广泛。随着越来越多的 DNN 模型被提出用于分子设计,基准测试方法对于这些模型的比较和验证至关重要。本综述着眼于最近提出的基准测试方法 Fréchet ChemNet 距离、GuacaMol 和分子集 (MOSES),并就它们在从头分子药物设计中的未来潜在应用以及进一步验证这些基准测试方法的可能后续步骤提供了评论。
更新日期:2021-06-03
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