Open-Source Molecular Processing Pipeline for Generating Molecules
<|reference_start|>Open-Source Molecular Processing Pipeline for Generating Molecules: 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].<|reference_end|>
@article{shreyas2024open-source,
    title={Open-Source Molecular Processing Pipeline for Generating Molecules},
    author={V Shreyas, Jose Siguenza, Karan Bania, Bharath Ramsundar},
    journal={arXiv preprint arXiv:2408.06261},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06261},
    primaryClass={cs.LG cs.AI q-bio.BM}
}