Traditionally many fragment-based de novo tools focused on optimization of affinity to a desired target deferring optimization of other properties  (synthetic accessibility, ADME-TOX, drug-likeness) to  later stage of development. While such approaches scored a number of successes they involve  risk of failure in further development. Here I will discuss an alternative route based on initial generation of library of compounds biased towards desired properties. I will discuss two algorithms developed in my lab, FOG and TopClass. FOG is a knowledge-based algorithm to generate diverse libraries of compounds biased towards enrichment in certain properties such as synthetic ease, drug-likeness or chemical diversity of natural compounds. TopClass is a ranking algorithm, which goes beyond Lipinski rules to score compounds by their drug-likeness. FOG generates sizeable libraries of high quality compounds which can be then focused towards affinity to desired targets by high throughput screening.

FOG: A knowledge-based algorithm to generate optimized libraries of compounds for drug discovery

243rd National Meeting (2012)

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