تقرير
Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks
| العنوان: | Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks |
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| المؤلفون: | Hofmarcher, Markus, Mayr, Andreas, Rumetshofer, Elisabeth, Ruch, Peter, Renz, Philipp, Schimunek, Johannes, Seidl, Philipp, Vall, Andreu, Widrich, Michael, Hochreiter, Sepp, Klambauer, Günter |
| سنة النشر: | 2020 |
| المجموعة: | Computer Science Quantitative Biology Statistics |
| مصطلحات موضوعية: | Quantitative Biology - Biomolecules, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods, Statistics - Machine Learning |
| الوصف: | Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs. We conducted a large-scale virtual screening for small molecules that are potential CoV-2 inhibitors. To this end, we utilized "ChemAI", a deep neural network trained on more than 220M data points across 3.6M molecules from three public drug-discovery databases. With ChemAI, we screened and ranked one billion molecules from the ZINC database for favourable effects against CoV-2. We then reduced the result to the 30,000 top-ranked compounds, which are readily accessible and purchasable via the ZINC database. Additionally, we screened the DrugBank using ChemAI to allow for drug repurposing, which would be a fast way towards a therapy. We provide these top-ranked compounds of ZINC and DrugBank as a library for further screening with bioassays at https://github.com/ml-jku/sars-cov-inhibitors-chemai. Comment: Additional results added. Various corrections to formulations and typos |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2004.00979 |
| رقم الأكسشن: | edsarx.2004.00979 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |