| scholarly article | Q13442814 |
| P356 | DOI | 10.1038/S41587-019-0224-X |
| P698 | PubMed publication ID | 31477924 |
| P50 | author | Alex Zhavoronkov | Q16218945 |
| Alán Aspuru-Guzik | Q62109773 | ||
| Alexander Zhebrak | Q91285413 | ||
| Tao Guo | Q92412883 | ||
| Lennart H Lee | Q93065079 | ||
| Alex Aliper | Q125201186 | ||
| Arip Asadulaev | Q125201206 | ||
| P2093 | author name string | Li Xing | |
| Yan A Ivanenkov | |||
| Mark S Veselov | |||
| Vladimir A Aladinskiy | |||
| David Madge | |||
| Richard Soll | |||
| Yury Volkov | |||
| Victor A Terentiev | |||
| Bogdan A Zagribelnyy | |||
| Anastasiya V Aladinskaya | |||
| Artem Zholus | |||
| Daniil A Polykovskiy | |||
| Lidiya I Minaeva | |||
| Maksim D Kuznetsov | |||
| Rim R Shayakhmetov | |||
| P2860 | cites work | How to improve R&D productivity: the pharmaceutical industry's grand challenge | Q28314992 |
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| DNA-Encoded Library-Derived DDR1 Inhibitor Prevents Fibrosis and Renal Function Loss in a Genetic Mouse Model of Alport Syndrome | Q93210153 | ||
| P4510 | describes a project that uses | deep learning | Q197536 |
| P433 | issue | 9 | |
| P921 | main subject | deep learning | Q197536 |
| P304 | page(s) | 1038-1040 | |
| P577 | publication date | 2019-09-02 | |
| P1433 | published in | Nature Biotechnology | Q1893837 |
| P1476 | title | Deep learning enables rapid identification of potent DDR1 kinase inhibitors | |
| P478 | volume | 37 |
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| Q90433761 | MBLinhibitors.com, a Website Resource Offering Information and Expertise for the Continued Development of Metallo--Lactamase Inhibitors |
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