Molecular diversity management strategies for building and enhancement of diverse and focused lead discovery compound screening collections.
This publication describes processes for the selection of chemical compounds for the building of a high-throughput screening (HTS) collection for drug discovery, using the currently implemented process in the Discovery Technologies Unit of the Novartis Institute for Biomedical Research, Basel Switzerland as reference. More generally, the currently existing compound acquisition models and practices are discussed. Our informatics, chemistry and biology-driven compound selection consists of two steps: 1) The individual compounds are filtered and grouped into three priority classes on the basis of their individual structural properties. Substructure filters are used to eliminate or penalize compounds based on unwanted structural properties. The similarity of the structures to reference ligands of the main proven druggable target families is computed, and drug-similar compounds are prioritized for the following diversity analysis. 2) The compounds are compared to the archive compounds and a diversity analysis is performed. This is done separately for the prioritized, regular and penalized compounds with increasingly stringent dissimilarity criterion. The process includes collecting vendor catalogues and monitoring the availability of samples together with the selection and purchase decision points. The development of a corporate vendor catalogue database is described. In addition to the selection methods on a per single molecule basis, selection criteria for scaffold and combinatorial chemistry projects in collaboration with compound vendors are discussed.
Schuffenhauer
A
A
Novartis Institutes for Biomedical Research Basel, Discovery Technologies, Compound Logistics and Properties Unit, Molecular and Library Informatics Program, CH-4002 Basel, Switzerland. ansgar.schuffenhauer@pharma.novartis.com
Popov
M
M
Schopfer
U
U
Acklin
P
P
Stanek
J
J
Jacoby
E
E
D019295:Computational Biology; D016208:Databases, Factual; D015195:Drug Design; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D013329:Structure-Activity Relationship