posted on 2024-01-25, 16:15authored byCara E. Brocklehurst, Eva Altmann, Corentin Bon, Holly Davis, David Dunstan, Peter Ertl, Carol Ginsburg-Moraff, Jonathan Grob, Daniel J. Gosling, Guillaume Lapointe, Alexander N. Marziale, Heinrich Mues, Marco Palmieri, Sophie Racine, Richard I. Robinson, Clayton Springer, Kian Tan, William Ulmer, René Wyler
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.