Journal of Medicinal Chemistry
We herein describe the development and application of a modular technology platform which incorporates recent advances in plate-based microscale chemistry, automated purification, in situ quantification, and robotic liquid handling to enable rapid access to high-quality chemical matter already formatted for assays. In using microscale chemistry and thus consuming minimal chemical matter, the platform is not only efficient but also follows green chemistry principles. By reorienting existing high-throughput assay technology, the platform can generate a full package of relevant data on each set of compounds in every learning cycle. The multiparameter exploration of chemical and property space is hereby driven by active learning models. The enhanced compound optimization process is generating knowledge for drug discovery projects in a time frame never before possible.
For details:
MicroCycle: An Integrated and Automated Platform to Accelerate Drug Discovery
Cara E. Brocklehurst a, Eva Altmann a, Corentin Bon a, Holly Davis a, David Dunstan b, Peter Ertl a, Carol Ginsburg-Moraff b, Jonathan Grob b, Daniel J. Gosling a, Guillaume Lapointe a, Alexander N. Marziale a, Heinrich Mues a, Marco Palmieri a, Sophie Racine a, Richard I. Robinson b, Clayton Springer b, Kian Tan b, William Ulmer b, and René Wyler a
a. Global Discovery Chemistry, Novartis Biomedical Research, Novartis Pharma AG, Basel 4033, Switzerland
b. Global Discovery Chemistry, Novartis Biomedical Research, Cambridge, Massachusetts 02139, United States
DOI: https://doi.org/10.1021/acs.jmedchem.3c02029
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