1. Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks
- Author
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Michael Widrich, Markus Hofmarcher, Andreas Mayr, Johannes Schimunek, Günter Klambauer, Sepp Hochreiter, Elisabeth Rumetshofer, Philipp Renz, Peter Ruch, Andreu Vall, and Philipp Seidl
- Subjects
FOS: Computer and information sciences ,Virtual screening ,Computer Science - Machine Learning ,Computer science ,business.industry ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Deep learning ,Biomolecules (q-bio.BM) ,Machine Learning (stat.ML) ,Computational biology ,Zinc database ,Ligand (biochemistry) ,medicine.disease_cause ,Quantitative Biology - Quantitative Methods ,Machine Learning (cs.LG) ,Quantitative Biology - Biomolecules ,Statistics - Machine Learning ,FOS: Biological sciences ,medicine ,Deep neural networks ,Artificial intelligence ,business ,Quantitative Methods (q-bio.QM) ,Coronavirus - Abstract
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., Additional results added. Various corrections to formulations and typos
- Published
- 2020