Rapid ADME Filters for Lead Discovery

Tudor I. Oprea, Paolo Benedetti, Ciuliano Berellini, Marius Olah, Kim Fejgin, and Scott Boyer 

MLI aims to bridge, at least in part, the innovation deficit [2] faced by the private sector that is leading to increased pressure to deliver new chemical entities (NCEs) and best-in-dass dmgs on the market. Dmg discovery scientists have been forced to develop computational tools that facilitate the identification of novel molecular scaffolds (diemotypes) moving across (local) chemical spaces. In the area of in silico drug discovery, the public sector is about to face the same challenges as the private sector. 

The entire drug discovery process, in particular at the cellular and animal level, has its own challenges [3] that contribute to the innovation deficif . It is thus imperative that computational tools deliver rapid and accurate models. At the molecular level, drug-receptor interactions continue to be too complex to provide failsafe in silico predictions [4] Entropy and the dielectric constant are but two examples of properties still under debate. The challenges of in silico drug discovery include the evaluation of multiple binding modes, accessible conformational 

1) The QSAR paradigm for structure-permeability expresses the passive permeability as 

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T.I. Oprea, P. Benedetti, G. Berellini, M. Olah, K. Fejgin, S. Boyer, Rapid ADME filters for lead discovery, in Molecular Interaction Fields, (Ed. ... 

The use of various methods and required prediction precision depends on the stage in the discovery process, hi the early stages of drug discovery, unsupervised molecular similarity methods are useful for compound selection and simple, rapid filters are useful for hbrary design and lead protihng. Supervised QSAR methods are widely apphed during LO to assist the medicinal chemist in optimizing potency, ADME... 

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