By Edgar Jacoby
This booklet specializes in purposes of compound library layout and digital screening to extend the bioactive chemical area, to focus on hopping of chemotypes to spot synergies inside of comparable drug discovery tasks or to repurpose identified medications, to suggest mechanism of motion of compounds, or to spot off-target results through cross-reactivity research. either ligand-based and structure-based in silico ways, as reviewed during this ebook, play vital roles for most of these functions. Computational chemogenomics is predicted to extend the standard and productiveness of drug discovery and result in the invention of latest medicines.
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Extra resources for Computational Chemogenomics
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Et al. Predicting new molecular targets for known drugs. Nature, 2009;462:175–181. 19. Weill, N. Chemogenomic approaches for the exploration of GPCR space. Curr. Top. Med. , 2011;11:1944–1955. 20. Jacoby, E. Computational chemogenomics. : Comput. Mol. , 2011;1:57–67. 21. , Schalon, C. and Rognan, D. How to measure the similarity between protein ligand-binding sites? Curr. -Aided Drug. , 4:209–220. 22. , Surgand, J. , Kellenberger, E. and Rognan, D. A simple and fuzzy method to align and compare druggable ligand-binding sites.
A ligand-based approach to mining the chemogenomic space of drugs. Comb. Chem. High. Throughput Screening, 2008;11:669–676. 87. , Gilles, M. and Alexandre, V. Predicting the predictability: a uniϐied approach to the applicability domain problem of QSAR models. J. Chem. Inf. , 2009;49:1762–1776. 88. van Westen, G. J. , Wegner, J. , et al. Which compound to select in lead optimization? Prospectively validated proteochemometric models guide preclinical development. PLoS One, 2011;6:e27518. 89. Receveur, J.
In other words, how different can a The Impact of Data Quality on Chemogenomics compound or a target be to still belong to the applicability domain of the model, and how can this difference be measured? There are not too many publications in chemogenomics that explicitly address that topic. Vidal and Mestres [74] used a set of different descriptors to predict the afϐinity of compounds for given targets based on the afϐinity landscape of similar compounds. They calibrated the similarity threshold for each descriptor by its ability to discriminate active from random compounds in public databases and used this threshold to deϐine an applicability domain for their chemogenomics studies.