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arXiv · 2408.06261

Open-Source Molecular Processing Pipeline for Generating Molecules

V Shreyas·Jose Siguenza·Karan Bania·Bharath Ramsundar
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Abstract

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].

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V Shreyas, Jose Siguenza, Karan Bania, Bharath Ramsundar. 2024-08-12. Open-Source Molecular Processing Pipeline for Generating Molecules. https://arxiv.org/abs/2408.06261

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