Structure-based in silico screening

In nonfavorable cases, HTS, meaning the screening of the full compound deck, remains the only option. In favorable cases, both ligand- and structure-based in silico hits lists can be compiled using a variety of VS methods (24-26). 

If structural information of the protein target is available, e.g., a crystal structure, in silico screening of huge virtual compound libraries can be conducted by the use of docking simulations. Based on identified primary hits, structural variations of the ligand can be evaluated by computational modeling of the ligand-protein complex. 

The abundance of crystallographic information on the 3D structure of protein kinases, including knowledge of receptor-ligand complexes, has seen protein kinases feature consistently in datasets employed to both develop and evaluate methods for receptor-based screening. Indeed, the vast majority of literature surrounding protein kinases and receptor-based in silico... 

In collaboration with University of Trieste, we have developed rational approaches for the design and synthesis of peptidomimetic and non-peptidic inhibitors of HIV PR, utilizing structure-based [12-15], as well as combinatorial, library design methods [16, 17]. In this paper, we survey computer-assisted studies on the design, focusing and in silico screening of virtual combinatorial libraries of peptidomimetics and cyclic ureas, as potential anti-HIV agents, that were carried out in our laboratory. 

We have developed a two-step procedure for the in silico screening of compound libraries based on biopharmaceutical property estimation linked to a mechanistic simulation of GI absorption. The first step involves biopharmaceutical property estimation by application of machine learning procedures to empirical data modeled with a set of molecular descriptors derived from 2D and 3D molecular structures. In silico methods were used to estimate such biopharmaceutical properties as effective human jejunal permeability, cell culture permeability, aqueous solubility, and molecular diffusivity. In the second step, differential equations for the advanced compartmental absorption and transit model were numerically integrated to determine the rate, extent, and approximate GI location of drug liberation (for controlled release), dissolution, and absorption. Figure 17.3 shows the schematic diagram of the ACAT model in which each one of the arrows represents an ordinary differential equation (ODE). 

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