Integrating Everything: The Molecule Selection Toolkit, a System for Compound Prioritization in Drug Discovery

摘要

In recent years there have been numerous papers on the topic of multiattribute optimization in pharmaceutical discovery chemistry, applied to compound prioritization. Many solutions proposed are static in nature; fixed functions are proposed for general purpose use. As needs change, these are modified and proposed as the latest enhancement. Rather than producing one more set of static functions, this work proposes a flexible approach to prioritizing compounds. Most published approaches also lack a design component. This work describes a comprehensive implementation that includes predictive modeling, multiattribute optimization, and modern statistical design. This gives a complete package for effectively prioritizing compounds for lead generation and lead optimization. The approach described has been used at our company in various stages of discovery since 2001. An adaptable system alleviates the need for different static solutions, each of which inevitably must be updated as the needs of a project change.