J Chem Inf Model - Applying medicinal chemistry transformations and multiparameter optimization to guide the search for high-quality leads and candidates.

Tópicos

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Resumo

In this article we describe a computational method that automatically generates chemically relevant compound ideas from an initial molecule, closely integrated with in silico models, and a probabilistic scoring algorithm to highlight the compound ideas most likely to satisfy a user-defined profile of required properties. The new compound ideas are generated using medicinal chemistry 'transformation rules' taken from examples in the literature. We demonstrate that the set of 206 transformations employed is generally applicable, produces a wide range of new compounds, and is representative of the types of modifications previously made to move from lead-like to drug-like compounds. Furthermore, we show that more than 94% of the compounds generated by transformation of typical drug-like molecules are acceptable to experienced medicinal chemists. Finally, we illustrate an application of our approach to the lead that ultimately led to the discovery of duloxetine, a marketed serotonin reuptake inhibitor.

Resumo Limpo

articl describ comput method automat generat chemic relev compound idea initi molecul close integr silico model probabilist score algorithm highlight compound idea like satisfi userdefin profil requir properti new compound idea generat use medicin chemistri transform rule taken exampl literatur demonstr set transform employ general applic produc wide rang new compound repres type modif previous made move leadlik druglik compound furthermor show compound generat transform typic druglik molecul accept experienc medicin chemist final illustr applic approach lead ultim led discoveri duloxetin market serotonin reuptak inhibitor

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