Grant Lauren L, Sit Clarissa S
Saint Mary's University Halifax NS Canada
RSC Med Chem. 2021 Jun 3;12(8):1273-1280. doi: 10.1039/d1md00074h. eCollection 2021 Aug 18.
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 molecular drug design and possible next steps for further validation of these benchmarking methods.
用于药物发现的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。本综述探讨了最近提出的基准测试方法——弗雷歇化学网络距离、瓜卡莫尔和分子集（MOSES），并对它们在分子药物设计中的未来潜在应用以及这些基准测试方法进一步验证的可能后续步骤进行了评论。
