RESUMEN
High Throughput Screening (HTS) is a successful strategy for finding hits and leads that have the opportunity to be converted into drugs. In this paper we highlight novel computational methods used to select compounds to build a new screening file at Pfizer and the analytical methods we used to assess their quality. We also introduce the novel concept of molecular redundancy to help decide on the density of compounds required in any region of chemical space in order to be confident of running successful HTS campaigns.
Asunto(s)
Algoritmos , Descubrimiento de Drogas , Bibliotecas de Moléculas Pequeñas/química , Simulación por Computador , Diseño de Fármacos , Modelos Moleculares , Probabilidad , Relación Estructura-Actividad CuantitativaRESUMEN
Since the advent of modern-day screening collections in the early 2000s, various aspects of our knowledge of good handling practices have continued to evolve. Some early practices, however, continue to prevail due to the absence of defining data that would bust the myths of tradition. The lack of defining data leads to a gap between plate-based screeners, on the one hand, and compound sample handling groups, on the other, with the latter being the default party to blame when an assay goes awry.In this paper, we highlight recommended practices that ensure sample integrity and present myth busting data that can help determine the root cause of an assay gone bad. We show how a strong and collaborative relationship between screening and sample handling groups is the better state that leads to the accomplishment of the common goal of finding breakthrough medicines.