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Oncology exploration: charting cancer medicinal chemistry space
We have computationally compared cancer medicinal chemistry space to orally bioavailable drug space. Applying traditional cheminformatic filtering techniques to potential oncology targeted drugs acutely limits the amount of chemical space explored. Approaches for the experimental determination of pr...
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Published in: | Drug discovery today 2006-02, Vol.11 (3-4), p.149-159 |
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container_title | Drug discovery today |
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creator | Lloyd, David G. Golfis, Georgia Knox, Andrew J.S. Fayne, Darren Meegan, Mary J. Oprea, Tudor I. |
description | We have computationally compared cancer medicinal chemistry space to orally bioavailable drug space. Applying traditional cheminformatic filtering techniques to potential oncology targeted drugs acutely limits the amount of chemical space explored.
Approaches for the experimental determination of protein–ligand molecular interactions are reliant on the quality of the compounds being tested. The application of large, randomly designed combinatorial libraries has given way to the creation of more-focused ‘drug-like’ libraries. Prior to synthesis, we wish to screen the potential compounds to remove undesired chemical moieties and to be within a required range of physiochemical properties. We have used a principal-component analysis (PCA) computational approach to analyze the 3D descriptor space of active and non-active (hit-like) cancer medicinal chemistry compounds. We define hit-like those molecules passing the unmodified OpenEye FILTER program. Our analysis indicates that these compounds occupy quite different regions in space. Cancer-active compounds exist in a much greater volume of space than generic hit-like space and most of them fail the commonly applied filters for orally bioavailable drugs. This is of great significance when designing orally bioavailable cancer target drugs. |
doi_str_mv | 10.1016/S1359-6446(05)03688-3 |
format | article |
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Approaches for the experimental determination of protein–ligand molecular interactions are reliant on the quality of the compounds being tested. The application of large, randomly designed combinatorial libraries has given way to the creation of more-focused ‘drug-like’ libraries. Prior to synthesis, we wish to screen the potential compounds to remove undesired chemical moieties and to be within a required range of physiochemical properties. We have used a principal-component analysis (PCA) computational approach to analyze the 3D descriptor space of active and non-active (hit-like) cancer medicinal chemistry compounds. We define hit-like those molecules passing the unmodified OpenEye FILTER program. Our analysis indicates that these compounds occupy quite different regions in space. Cancer-active compounds exist in a much greater volume of space than generic hit-like space and most of them fail the commonly applied filters for orally bioavailable drugs. This is of great significance when designing orally bioavailable cancer target drugs.</description><subject>Antineoplastic Agents</subject><subject>bioavailability</subject><subject>Biological and medical sciences</subject><subject>cancer</subject><subject>chemistry space</subject><subject>Combinatorial Chemistry Techniques</subject><subject>Drug Design</subject><subject>General pharmacology</subject><subject>Medical sciences</subject><subject>medicinal chemistry</subject><subject>Pharmaceutical technology. Pharmaceutical industry</subject><subject>Pharmacology. 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Pharmaceutical industry</topic><topic>Pharmacology. Drug treatments</topic><topic>Principal Component Analysis</topic><topic>protein-ligand interactions</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lloyd, David G.</creatorcontrib><creatorcontrib>Golfis, Georgia</creatorcontrib><creatorcontrib>Knox, Andrew J.S.</creatorcontrib><creatorcontrib>Fayne, Darren</creatorcontrib><creatorcontrib>Meegan, Mary J.</creatorcontrib><creatorcontrib>Oprea, Tudor I.</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Drug discovery today</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lloyd, David G.</au><au>Golfis, Georgia</au><au>Knox, Andrew J.S.</au><au>Fayne, Darren</au><au>Meegan, Mary J.</au><au>Oprea, Tudor I.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Oncology exploration: charting cancer medicinal chemistry space</atitle><jtitle>Drug discovery today</jtitle><addtitle>Drug Discov Today</addtitle><date>2006-02-01</date><risdate>2006</risdate><volume>11</volume><issue>3-4</issue><spage>149</spage><epage>159</epage><pages>149-159</pages><issn>1359-6446</issn><eissn>1878-5832</eissn><abstract>We have computationally compared cancer medicinal chemistry space to orally bioavailable drug space. Applying traditional cheminformatic filtering techniques to potential oncology targeted drugs acutely limits the amount of chemical space explored.
Approaches for the experimental determination of protein–ligand molecular interactions are reliant on the quality of the compounds being tested. The application of large, randomly designed combinatorial libraries has given way to the creation of more-focused ‘drug-like’ libraries. Prior to synthesis, we wish to screen the potential compounds to remove undesired chemical moieties and to be within a required range of physiochemical properties. We have used a principal-component analysis (PCA) computational approach to analyze the 3D descriptor space of active and non-active (hit-like) cancer medicinal chemistry compounds. We define hit-like those molecules passing the unmodified OpenEye FILTER program. Our analysis indicates that these compounds occupy quite different regions in space. Cancer-active compounds exist in a much greater volume of space than generic hit-like space and most of them fail the commonly applied filters for orally bioavailable drugs. This is of great significance when designing orally bioavailable cancer target drugs.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><pmid>16533713</pmid><doi>10.1016/S1359-6446(05)03688-3</doi><tpages>11</tpages></addata></record> |
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subjects | Antineoplastic Agents bioavailability Biological and medical sciences cancer chemistry space Combinatorial Chemistry Techniques Drug Design General pharmacology Medical sciences medicinal chemistry Pharmaceutical technology. Pharmaceutical industry Pharmacology. Drug treatments Principal Component Analysis protein-ligand interactions |
title | Oncology exploration: charting cancer medicinal chemistry space |
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