Report
Open-Source Molecular Processing Pipeline for Generating Molecules
| العنوان: | Open-Source Molecular Processing Pipeline for Generating Molecules |
|---|---|
| المؤلفون: | Shreyas, V, Siguenza, Jose, Bania, Karan, Ramsundar, Bharath |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Quantitative Biology |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Quantitative Biology - Biomolecules |
| الوصف: | 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]. Comment: Presented at the Molecular Machine Learning Conference 2024 (MoML 2024), BayLearn 2024 and the Machine Learning and Physical Sciences (ML4PS) Workshop at NeurIPS 2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2408.06261 |
| رقم الانضمام: | edsarx.2408.06261 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |