AI Article Synopsis
- * The article examines various biological descriptors and their applications, showing that biological data can remain valuable even beyond their initial intended use.
- * The comparison of 150 high-throughput screening campaigns at Novartis reveals insights into the benefits and difficulties of learning across different drug discovery projects.
Article Abstract
Vast amounts of bioactivity data have been generated for small molecules across public and corporate domains. Biological signatures, either derived from systematic profiling efforts or from existing historical assay data, have been successfully employed for small molecule mechanism-of-action elucidation, drug repositioning, hit expansion and screening subset design. This article reviews different types of biological descriptors and applications, and we demonstrate how biological data can outlive the original purpose or project for which it was generated. By comparing 150 HTS campaigns run at Novartis over the past decade on the basis of their active and inactive chemical matter, we highlight the opportunities and challenges associated with cross-project learning in drug discovery.
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| http://dx.doi.org/10.1016/j.drudis.2014.11.004 | DOI Listing |
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