Generative models for molecules have shown considerable promise for use in computational chemistry, but remain difficult to use for non-experts. For this reason, we introduce open-source infrastructure for easily building generative molecular models into the widely used DeepChem [Ramsundar et al., 2019] library with the aim of creating a robust and reusable molecular generation pipeline. In particular, we add high quality PyTorch [Paszke et al., 2019] implementations of the Molecular Generative Adversarial Networks (MolGAN) [Cao and Kipf, 2022] and Normalizing Flows [Papamakarios et al., 2021]. Our implementations show strong performance comparable with past work [Kuznetsov and Polykovskiy, 2021, Cao and Kipf, 2022].

翻译：分子生成模型在计算化学领域展现出巨大潜力，但其应用对非专业人士仍存在障碍。为此，我们在广泛使用的DeepChem库[Ramsundar等人，2019]中引入了开源基础设施，旨在构建易于集成的生成式分子模型，从而创建稳健且可复用的分子生成流程。具体而言，我们新增了分子生成对抗网络（MolGAN）[Cao与Kipf，2022]与标准化流模型[Normalizing Flows，Papamakarios等人，2021]的高质量PyTorch实现[Paszke等人，2019]。实验表明，我们的实现性能优异，与既往研究[Kuznetsov与Polykovskiy，2021；Cao与Kipf，2022]具有可比性。