تقرير
Machine learning for the prediction of safe and biologically active organophosphorus molecules
| العنوان: | Machine learning for the prediction of safe and biologically active organophosphorus molecules |
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| المؤلفون: | Hu, Hang, Ooi, Hsu Kiang, Ghaemi, Mohammad Sajjad, Hu, Anguang |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Quantitative Biology |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Quantitative Biology - Biomolecules |
| الوصف: | Drug discovery is a complex process with a large molecular space to be considered. By constraining the search space, the fragment-based drug design is an approach that can effectively sample the chemical space of interest. Here we propose a framework of Recurrent Neural Networks (RNN) with an attention model to sample the chemical space of organophosphorus molecules using the fragment-based approach. The framework is trained with a ZINC dataset that is screened for high druglikeness scores. The goal is to predict molecules with similar biological action modes as organophosphorus pesticides or chemical warfare agents yet less toxic to humans. The generated molecules contain a starting fragment of PO2F but have a bulky hydrocarbon side chain limiting its binding effectiveness to the targeted protein. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2302.10952 |
| رقم الأكسشن: | edsarx.2302.10952 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |