eLife

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1. We couldn't get details of this article at this time:
   10.1101/2023.07.17.549348

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   10.1101/2023.07.23.550248

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   10.1101/2023.06.14.544869

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   10.1101/2023.06.02.542626

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   10.1101/2023.06.01.543194

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   10.1101/2023.06.03.542675

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   10.1101/2023.06.29.547072

8. Deep Batch Active Learning for Drug Discovery

   This article has 14 authors:

   1. Michael Bailey
   2. Saeed Moayedpour
   3. Ruijiang Li
   4. Alejandro Corrochano-Navarro
   5. Alexander Kötter
   6. Lorenzo Kogler-Anele
   7. Saleh Riahi
   8. Christoph Grebner
   9. Gerhard Hessler
   10. Hans Matter
   11. Marc Bianciotto
   12. Pablo Mas
   13. Ziv Bar-Joseph
   14. Sven Jager

   This article has been curated by 1 group:

   • Curated by eLife

     eLife assessment

     This valuable study reports novel active learning batch selection methods that have been applied to optimization tasks related to ADMET and affinity properties relevant within the drug discovery field. While the evidence is solid, the paper could have benefited from a clearer and deeper description of methods as well as interpretation of the obtained models, and a wider comparison to existing methods. The article will be of general interest to scientist working in the field of drug discovery and, in general, to researchers within the fields of machine learning and data analysis.

   Reviewed by eLife

9. We couldn't get details of this article at this time:
   10.1101/2023.03.30.534918

10. We couldn't get details of this article at this time:
    10.1101/2023.04.02.535252