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1.
J Chem Inf Model ; 52(5): 1328-36, 2012 May 25.
Artículo en Inglés | MEDLINE | ID: mdl-22509999

RESUMEN

In modern day drug discovery campaigns, computational chemists have to be concerned not only about improving the potency of molecules but also reducing any off-target ADMET activity. There are a plethora of antitargets that computational chemists may have to consider. Fortunately many antitargets have crystal structures deposited in the PDB. These structures are immediately useful to our Autocorrelator: an automated model generator that optimizes variables for building computational models. This paper describes the use of the Autocorrelator to construct high quality docking models for cytochrome P450 2C9 (CYP2C9) from two publicly available crystal structures. Both models result in strong correlation coefficients (R² > 0.66) between the predicted and experimental determined log(IC50) values. Results from the two models overlap well with each other, converging on the same scoring function, deprotonated charge state, and predicted the binding orientation for our collection of molecules.


Asunto(s)
Hidrocarburo de Aril Hidroxilasas/antagonistas & inhibidores , Simulación por Computador , Descubrimiento de Drogas , Modelos Moleculares , Citocromo P-450 CYP2C9 , Activación Enzimática/efectos de los fármacos , Inhibidores Enzimáticos/farmacología , Flurbiprofeno/química , Flurbiprofeno/farmacología , Humanos , Concentración 50 Inhibidora , Estructura Molecular , Unión Proteica/efectos de los fármacos , Warfarina/química , Warfarina/farmacología
2.
J Chem Inf Model ; 48(4): 811-6, 2008 Apr.
Artículo en Inglés | MEDLINE | ID: mdl-18338845

RESUMEN

Computer aided drug design is progressing and playing an increasingly important role in drug discovery. Computational methods are being used to evaluate larger and larger numbers of real and virtual compounds. New methods based on molecular simulations that take protein and ligand flexibility into account also contribute to an ever increasing need for compute time. Computational grids are therefore becoming a critically important tool for modern drug discovery, but can be expensive to deploy and maintain. Here, we describe the low cost implementation of a 165 node, computational grid at Anadys Pharmaceuticals. The grid makes use of the excess computing capacity of desktop computers deployed throughout the company and of outdated desktop computers which populate a central computing grid. The performance of the grid grows automatically with the size of the company and with advances in computer technology. To ensure the uniformity of the nodes in the grid, all computers are running the Linux operating system. The desktop computers run Linux inside MS Windows using coLinux as virtualization software. HYDRA has been used to optimize computational models, for virtual screening and for lead optimization.


Asunto(s)
Diseño Asistido por Computadora , Diseño de Fármacos
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