1
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Chen JJ, Schmucker LN, Visco DP. Identifying de-NEDDylation inhibitors: Virtual high-throughput screens targeting SENP8. Chem Biol Drug Des 2019; 93:590-604. [PMID: 30560590 DOI: 10.1111/cbdd.13457] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 11/21/2018] [Accepted: 11/24/2018] [Indexed: 12/16/2022]
Abstract
Protein modification can have far-reaching effects. NEDDylation, a protein modification process with the protein NEDD8, stabilizes and modifies how the targeted protein interacts with other proteins. Its role in system regulation makes it a prime therapeutic target, and virtual high-throughput screening has already identified new NEDD8 inhibitors. SENP8 matures the NEDD8 proenzyme into the active form and regulates NEDDylation by removing NEDD8 from over-NEDDylated proteins. In this work, SENP8 inhibitor candidates were identified in two rounds of virtual high-throughput screening. Of the ten candidates identified in the first round of screening, four were active in validation experiments to yield an experimental hit rate of 40%. Of the five candidates identified in the second round of screening, one was active in validation experiments to yield an experimental hit rate of 20%. Results indicate virtual high-throughput screening improved hit rates over traditional high-throughput screening. The SENP8 inhibitor candidates can be used to interrogate the NEDDylation regulation mechanism.
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Affiliation(s)
| | - Lyndsey N Schmucker
- Department of Chemical and Biomolecular Engineering, University of Akron, Akron, OH
| | - Donald P Visco
- Department of Chemical and Biomolecular Engineering, University of Akron, Akron, OH
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2
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Chen JJ, Schmucker LN, Visco DP. Virtual high-throughput screens identifying hPK-M2 inhibitors: Exploration of model extrapolation. Comput Biol Chem 2019; 78:317-329. [PMID: 30623877 DOI: 10.1016/j.compbiolchem.2018.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Revised: 12/11/2018] [Accepted: 12/13/2018] [Indexed: 10/27/2022]
Abstract
Glycolysis with PK-M2 occurs typically in anaerobic conditions and atypically in aerobic conditions, which is known as the Warburg effect. The Warburg effect is found in many oncogenic situations and is believed to provide energy and biomass for oncogenesis to persist. The work presented targets human PK-M2 (hPK-M2) in a virtual high-throughput screen to identify new inhibitors and leads for further study. In the initial screen, one of the 12 candidates selected for experimental validation showed biological activity (hit-rate = 8.13%). In the second screen with retrained models, six of 11 candidates selected for experimental validation showed biological activity (hit-rate: 54.5%). Additionally, four different scaffolds were identified for further analysis when examining the tested candidates and compounds in the training data. Finally, extrapolation was necessary to identify a sufficient number of candidates to test in the second screen. Examination of the results suggested stepwise extrapolation to maximize efficiency.
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Affiliation(s)
- Jonathan J Chen
- Department of Biology, The University of Akron, 302 Buchtel Common, Akron, OH 44325, USA.
| | - Lyndsey N Schmucker
- Department of Chemical and Biomolecular Engineering, The University of Akron, 302 Buchtel Common, Akron, OH 44325, USA.
| | - Donald P Visco
- Department of Chemical and Biomolecular Engineering, The University of Akron, 302 Buchtel Common, Akron, OH 44325, USA.
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3
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Chen JJ, Schmucker LN, Visco DP. Identifying new clotting factor XIa inhibitors in virtual high-throughput screens using PCA-GA-SVM models and signature. Biotechnol Prog 2018; 34:1553-1565. [PMID: 30009405 DOI: 10.1002/btpr.2693] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Revised: 05/08/2018] [Accepted: 06/28/2018] [Indexed: 12/17/2022]
Abstract
Blood Clotting Factor XI is an important actor in the clotting mechanism: it activates downstream zymogen involved in the clotting process. It can be targeted for activation or inhibition depending on treatment goals to enhance or inhibit clotting. In terms of antithrombosis treatment, Factor XI has emerged as a promising target to focus on. In this work, an iterative virtual high-throughput screening pipeline was proposed that can supplement current efforts to find inhibitors. The first iteration identified 11 compounds to test with 3 active for a hit-rate of 27.3%. The second iteration of the pipeline identified another 11 compounds to test with 7 active for a hit-rate of 63.6%. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:1553-1565, 2018.
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Affiliation(s)
- Jonathan J Chen
- Dept. of Biology, The University of Akron, 302 Buchtel Common, Akron, OH, 44325
| | - Lyndsey N Schmucker
- Dept. of Chemical and Biomolecular Engineering, The University of Akron, 302 Buchtel Common, Akron, OH, 44325
| | - Donald P Visco
- Dept. of Chemical and Biomolecular Engineering, The University of Akron, 302 Buchtel Common, Akron, OH, 44325
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4
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Chen JJ, Schmucker LN, Visco DP. Pharmaceutical Machine Learning: Virtual High-Throughput Screens Identifying Promising and Economical Small Molecule Inhibitors of Complement Factor C1s. Biomolecules 2018; 8:E24. [PMID: 29735903 PMCID: PMC6023033 DOI: 10.3390/biom8020024] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Revised: 04/26/2018] [Accepted: 04/27/2018] [Indexed: 12/17/2022] Open
Abstract
When excessively activated, C1 is insufficiently regulated, which results in tissue damage. Such tissue damage causes the complement system to become further activated to remove the resulting tissue damage, and a vicious cycle of activation/tissue damage occurs. Current Food and Drug Administration approved treatments include supplemental recombinant C1 inhibitor, but these are extremely costly and a more economical solution is desired. In our work, we have utilized an existing data set of 136 compounds that have been previously tested for activity against C1. Using these compounds and the activity data, we have created models using principal component analysis, genetic algorithm, and support vector machine approaches to characterize activity. The models were then utilized to virtually screen the 72 million compound PubChem repository. This first round of virtual high-throughput screening identified many economical and promising inhibitor candidates, a subset of which was tested to validate their biological activity. These results were used to retrain the models and rescreen PubChem in a second round vHTS. Hit rates for the first round vHTS were 57%, while hit rates for the second round vHTS were 50%. Additional structure⁻property analysis was performed on the active and inactive compounds to identify interesting scaffolds for further investigation.
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Affiliation(s)
- Jonathan J Chen
- Department of Biology, The University of Akron, 302 Buchtel Common, Akron, OH 44325, USA.
| | - Lyndsey N Schmucker
- Department of Chemical and Biomolecular Engineering, The University of Akron, 302 Buchtel Common, Akron, OH 44325, USA.
| | - Donald P Visco
- Department of Chemical and Biomolecular Engineering, The University of Akron, 302 Buchtel Common, Akron, OH 44325, USA.
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5
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Ekins S, Clark AM, Dole K, Gregory K, Mcnutt AM, Spektor AC, Weatherall C, Litterman NK, Bunin BA. Data Mining and Computational Modeling of High-Throughput Screening Datasets. Methods Mol Biol 2018; 1755:197-221. [PMID: 29671272 DOI: 10.1007/978-1-4939-7724-6_14] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
We are now seeing the benefit of investments made over the last decade in high-throughput screening (HTS) that is resulting in large structure activity datasets entering public and open databases such as ChEMBL and PubChem. The growth of academic HTS screening centers and the increasing move to academia for early stage drug discovery suggests a great need for the informatics tools and methods to mine such data and learn from it. Collaborative Drug Discovery, Inc. (CDD) has developed a number of tools for storing, mining, securely and selectively sharing, as well as learning from such HTS data. We present a new web based data mining and visualization module directly within the CDD Vault platform for high-throughput drug discovery data that makes use of a novel technology stack following modern reactive design principles. We also describe CDD Models within the CDD Vault platform that enables researchers to share models, share predictions from models, and create models from distributed, heterogeneous data. Our system is built on top of the Collaborative Drug Discovery Vault Activity and Registration data repository ecosystem which allows users to manipulate and visualize thousands of molecules in real time. This can be performed in any browser on any platform. In this chapter we present examples of its use with public datasets in CDD Vault. Such approaches can complement other cheminformatics tools, whether open source or commercial, in providing approaches for data mining and modeling of HTS data.
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Affiliation(s)
- Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA.
| | - Alex M Clark
- Collaborative Drug Discovery, Inc., Burlingame, CA, USA
- Molecular Materials Informatics, Inc., Montreal, QC, Canada
| | - Krishna Dole
- Collaborative Drug Discovery, Inc., Burlingame, CA, USA
| | | | | | | | | | | | - Barry A Bunin
- Collaborative Drug Discovery, Inc., Burlingame, CA, USA
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6
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Identifying novel factor XIIa inhibitors with PCA-GA-SVM developed vHTS models. Eur J Med Chem 2017; 140:31-41. [DOI: 10.1016/j.ejmech.2017.08.056] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2017] [Revised: 08/21/2017] [Accepted: 08/23/2017] [Indexed: 01/18/2023]
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7
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Backman TWH, Evans DS, Girke T. Large-scale bioactivity analysis of the small-molecule assayed proteome. PLoS One 2017; 12:e0171413. [PMID: 28178331 PMCID: PMC5298297 DOI: 10.1371/journal.pone.0171413] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2016] [Accepted: 01/20/2017] [Indexed: 12/12/2022] Open
Abstract
This study presents an analysis of the small molecule bioactivity profiles across large quantities of diverse protein families represented in PubChem BioAssay. We compared the bioactivity profiles of FDA approved drugs to non-FDA approved compounds, and report several distinct patterns characteristic of the approved drugs. We found that a large fraction of the previously reported higher target promiscuity among FDA approved compounds, compared to non-FDA approved bioactives, was frequently due to cross-reactivity within rather than across protein families. We identified 804 potentially novel protein target candidates for FDA approved drugs, as well as 901 potentially novel target candidates with active non-FDA approved compounds, but no FDA approved drugs with activity against these targets. We also identified 486348 potentially novel compounds active against the same targets as FDA approved drugs, as well as 153402 potentially novel compounds active against targets without active FDA approved drugs. By quantifying the agreement among replicated screens, we estimated that more than half of these novel outcomes are reproducible. Using biclustering, we identified many dense clusters of FDA approved drugs with enriched activity against a common set of protein targets. We also report the distribution of compound promiscuity using a Bayesian statistical model, and report the sensitivity and specificity of two common methods for identifying promiscuous compounds. Aggregator assays exhibited greater accuracy in identifying highly promiscuous compounds, while PAINS substructures were able to identify a much larger set of "middle range" promiscuous compounds. Additionally, we report a large number of promiscuous compounds not identified as aggregators or PAINS. In summary, the results of this study represent a rich reference for selecting novel drug and target protein candidates, as well as for eliminating candidate compounds with unselective activities.
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Affiliation(s)
- Tyler William H. Backman
- Department of Bioengineering, University of California Riverside, Riverside, California, United States of America
- Institute for Integrative Genome Biology, University of California Riverside, Riverside, California, United States of America
| | - Daniel S. Evans
- California Pacific Medical Center Research Institute, San Francisco, California, United States of America
| | - Thomas Girke
- Institute for Integrative Genome Biology, University of California Riverside, Riverside, California, United States of America
- * E-mail:
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8
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Developing an in silico pipeline for faster drug candidate discovery: Virtual high throughput screening with the Signature molecular descriptor using support vector machine models. Chem Eng Sci 2017. [DOI: 10.1016/j.ces.2016.02.037] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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9
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Abstract
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Despite a large and
rapidly growing body of small molecule bioactivity
screens available in the public domain, systematic leverage of the
data to assess target druggability and compound selectivity has been
confounded by a lack of suitable cross-target analysis software. We
have developed bioassayR, a computational tool that enables simultaneous
analysis of thousands of bioassay experiments performed over a diverse
set of compounds and biological targets. Unique features include support
for large-scale cross-target analyses of both public and custom bioassays,
generation of high throughput screening fingerprints (HTSFPs), and
an optional preloaded database that provides access to a substantial
portion of publicly available bioactivity data. bioassayR is implemented
as an open-source R/Bioconductor package available from https://bioconductor.org/packages/bioassayR/.
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Affiliation(s)
- Tyler William H Backman
- Institute for Integrative Genome Biology, University of California, Riverside , Riverside, California 92521, United States
| | - Thomas Girke
- Institute for Integrative Genome Biology, University of California, Riverside , Riverside, California 92521, United States
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10
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Zhai Y, Chen K, Zhong Y, Zhou B, Ainscow E, Wu YT, Zhou Y. An Automatic Quality Control Pipeline for High-Throughput Screening Hit Identification. ACTA ACUST UNITED AC 2016; 21:832-41. [PMID: 27313114 DOI: 10.1177/1087057116654274] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Accepted: 05/19/2016] [Indexed: 01/02/2023]
Abstract
The correction or removal of signal errors in high-throughput screening (HTS) data is critical to the identification of high-quality lead candidates. Although a number of strategies have been previously developed to correct systematic errors and to remove screening artifacts, they are not universally effective and still require fair amount of human intervention. We introduce a fully automated quality control (QC) pipeline that can correct generic interplate systematic errors and remove intraplate random artifacts. The new pipeline was first applied to ~100 large-scale historical HTS assays; in silico analysis showed auto-QC led to a noticeably stronger structure-activity relationship. The method was further tested in several independent HTS runs, where QC results were sampled for experimental validation. Significantly increased hit confirmation rates were obtained after the QC steps, confirming that the proposed method was effective in enriching true-positive hits. An implementation of the algorithm is available to the screening community.
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Affiliation(s)
- Yufeng Zhai
- Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Kaisheng Chen
- Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Yang Zhong
- Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Bin Zhou
- Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Edward Ainscow
- Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
| | - Ying-Ta Wu
- Genomics Research Center, Academia Sinica, Nankang, Taipei, Taiwan
| | - Yingyao Zhou
- Genomics Institute of the Novartis Research Foundation, San Diego, CA, USA
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11
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Abstract
The human kinome is made up of 518 distinctive serine/threonine and tyrosine kinases, which are key components of virtually every mammalian signal transduction pathway. Consequently, kinases provide a compelling target family for the development of small molecule inhibitors, which could be used as tools to delineate the mechanism of action for biological processes and potentially be used as therapeutics to treat human diseases such as cancer. A myriad of recent technological advances have accelerated our understanding of kinome function, its relationship to tumorigenic development, and have contributed to the progression of small molecule kinase inhibitors into the clinic. Essential to the continued growth of the field are informatics tools that can assist in interpreting disparate and voluminous data sets and correctly guide decision making processes. These advances are expected to have a dramatic impact on kinase drug development and clinical diagnoses and treatment in the near future.:
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12
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Bird CL, Frey JG. Chemical information matters: an e-Research perspective on information and data sharing in the chemical sciences. Chem Soc Rev 2014; 42:6754-76. [PMID: 23686012 DOI: 10.1039/c3cs60050e] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Recently, a number of organisations have called for open access to scientific information and especially to the data obtained from publicly funded research, among which the Royal Society report and the European Commission press release are particularly notable. It has long been accepted that building research on the foundations laid by other scientists is both effective and efficient. Regrettably, some disciplines, chemistry being one, have been slow to recognise the value of sharing and have thus been reluctant to curate their data and information in preparation for exchanging it. The very significant increases in both the volume and the complexity of the datasets produced has encouraged the expansion of e-Research, and stimulated the development of methodologies for managing, organising, and analysing "big data". We review the evolution of cheminformatics, the amalgam of chemistry, computer science, and information technology, and assess the wider e-Science and e-Research perspective. Chemical information does matter, as do matters of communicating data and collaborating with data. For chemistry, unique identifiers, structure representations, and property descriptors are essential to the activities of sharing and exchange. Open science entails the sharing of more than mere facts: for example, the publication of negative outcomes can facilitate better understanding of which synthetic routes to choose, an aspiration of the Dial-a-Molecule Grand Challenge. The protagonists of open notebook science go even further and exchange their thoughts and plans. We consider the concepts of preservation, curation, provenance, discovery, and access in the context of the research lifecycle, and then focus on the role of metadata, particularly the ontologies on which the emerging chemical Semantic Web will depend. Among our conclusions, we present our choice of the "grand challenges" for the preservation and sharing of chemical information.
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Affiliation(s)
- Colin L Bird
- Chemistry, Faculty of Natural and Environmental Sciences, University of Southampton, University Road, Highfield, Southampton SO17 1BJ, UK
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13
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Phatak SS, Stephan CC, Cavasotto CN. High-throughput and in silico screenings in drug discovery. Expert Opin Drug Discov 2013; 4:947-59. [PMID: 23480542 DOI: 10.1517/17460440903190961] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
BACKGROUND In the current situation of weak drug pipelines, impending patent expiration of several blockbuster drugs, industry consolidation and changing business models that target special diseases like cancer, diabetes, Alzheimer's and obesity, the pharmaceutical industry is under intense pressure to generate a strong drug pipeline distinguished by better productivity, diversity and cost effectiveness. The goal is discovering high-quality leads in the initial stages of the development cycle, to minimize the costs associated with failures at later ones. OBJECTIVE Thus, there is a great amount of interest in further developing and optimizing high-throughput screening and in silico screening, the two methods responsible for generating most of the lead compounds. Although high-throughput screening is the predominant starting point for discovery programs, in silico methods have gradually made inroads by their more rational approach, to expedite the drug discovery and development process. CONCLUSION Modern drug discovery strategies include both methods in tandem or in an iterative way. This review primarily provides a succinct overview and comparison of experimental and in silico screening techniques, selected case studies where both methods were used in concert to investigate their performance and complementary nature and a statement on the developments in experimental and in silico approaches in the near future.
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Affiliation(s)
- Sharangdhar S Phatak
- The University of Texas Health Science Center at Houston, School of Health Information Sciences, 7000 Fannin, Suite 860B, Houston, TX 77030, USA +1 713 500 3934 ; +1 713 500 3907 ;
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14
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Hermann JC, Chen Y, Wartchow C, Menke J, Gao L, Gleason SK, Haynes NE, Scott N, Petersen A, Gabriel S, Vu B, George KM, Narayanan A, Li SH, Qian H, Beatini N, Niu L, Gan QF. Metal impurities cause false positives in high-throughput screening campaigns. ACS Med Chem Lett 2013; 4:197-200. [PMID: 24900642 DOI: 10.1021/ml3003296] [Citation(s) in RCA: 71] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2012] [Accepted: 12/12/2012] [Indexed: 11/29/2022] Open
Abstract
Organic impurities in compound libraries are known to often cause false-positive signals in screening campaigns for new leads, but organic impurities do not fully account for all false-positive results. We discovered inorganic impurities in our screening library that can also cause positive signals for a variety of targets and/or readout systems, including biochemical and biosensor assays. We investigated in depth the example of zinc for a specific project and in retrospect in various HTS screens at Roche and propose a straightforward counter screen using the chelator TPEN to rule out inhibition caused by zinc.
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Affiliation(s)
- Johannes C. Hermann
- Discovery
Chemistry, ‡Discovery Technologies, and §Inflammation Discovery, Roche pRED, 340 Kingsland Street, Nutley, New Jersey
07110, United States
| | - Yingsi Chen
- Discovery
Chemistry, ‡Discovery Technologies, and §Inflammation Discovery, Roche pRED, 340 Kingsland Street, Nutley, New Jersey
07110, United States
| | - Charles Wartchow
- Discovery
Chemistry, ‡Discovery Technologies, and §Inflammation Discovery, Roche pRED, 340 Kingsland Street, Nutley, New Jersey
07110, United States
| | - John Menke
- Discovery
Chemistry, ‡Discovery Technologies, and §Inflammation Discovery, Roche pRED, 340 Kingsland Street, Nutley, New Jersey
07110, United States
| | - Lin Gao
- Discovery
Chemistry, ‡Discovery Technologies, and §Inflammation Discovery, Roche pRED, 340 Kingsland Street, Nutley, New Jersey
07110, United States
| | - Shelley K. Gleason
- Discovery
Chemistry, ‡Discovery Technologies, and §Inflammation Discovery, Roche pRED, 340 Kingsland Street, Nutley, New Jersey
07110, United States
| | - Nancy-Ellen Haynes
- Discovery
Chemistry, ‡Discovery Technologies, and §Inflammation Discovery, Roche pRED, 340 Kingsland Street, Nutley, New Jersey
07110, United States
| | - Nathan Scott
- Discovery
Chemistry, ‡Discovery Technologies, and §Inflammation Discovery, Roche pRED, 340 Kingsland Street, Nutley, New Jersey
07110, United States
| | - Ann Petersen
- Discovery
Chemistry, ‡Discovery Technologies, and §Inflammation Discovery, Roche pRED, 340 Kingsland Street, Nutley, New Jersey
07110, United States
| | - Stephen Gabriel
- Discovery
Chemistry, ‡Discovery Technologies, and §Inflammation Discovery, Roche pRED, 340 Kingsland Street, Nutley, New Jersey
07110, United States
| | - Binh Vu
- Discovery
Chemistry, ‡Discovery Technologies, and §Inflammation Discovery, Roche pRED, 340 Kingsland Street, Nutley, New Jersey
07110, United States
| | - Kelly M. George
- Discovery
Chemistry, ‡Discovery Technologies, and §Inflammation Discovery, Roche pRED, 340 Kingsland Street, Nutley, New Jersey
07110, United States
| | - Arjun Narayanan
- Discovery
Chemistry, ‡Discovery Technologies, and §Inflammation Discovery, Roche pRED, 340 Kingsland Street, Nutley, New Jersey
07110, United States
| | - Shirley H. Li
- Discovery
Chemistry, ‡Discovery Technologies, and §Inflammation Discovery, Roche pRED, 340 Kingsland Street, Nutley, New Jersey
07110, United States
| | - Hong Qian
- Discovery
Chemistry, ‡Discovery Technologies, and §Inflammation Discovery, Roche pRED, 340 Kingsland Street, Nutley, New Jersey
07110, United States
| | - Nanda Beatini
- Discovery
Chemistry, ‡Discovery Technologies, and §Inflammation Discovery, Roche pRED, 340 Kingsland Street, Nutley, New Jersey
07110, United States
| | - Linghao Niu
- Discovery
Chemistry, ‡Discovery Technologies, and §Inflammation Discovery, Roche pRED, 340 Kingsland Street, Nutley, New Jersey
07110, United States
| | - Qing-Fen Gan
- Discovery
Chemistry, ‡Discovery Technologies, and §Inflammation Discovery, Roche pRED, 340 Kingsland Street, Nutley, New Jersey
07110, United States
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15
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Che J, King FJ, Zhou B, Zhou Y. Chemical and Biological Properties of Frequent Screening Hits. J Chem Inf Model 2012; 52:913-26. [DOI: 10.1021/ci300005y] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Jianwei Che
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San
Diego, California 92121, United States
| | - Frederick J. King
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San
Diego, California 92121, United States
- Developmental and Molecular Pathways, Novartis Institutes for BioMedical Research, 250 Massachusetts
Avenue, Cambridge, Massachusetts 02139, United States
| | - Bin Zhou
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San
Diego, California 92121, United States
| | - Yingyao Zhou
- Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San
Diego, California 92121, United States
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16
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Varin T, Didiot MC, Parker CN, Schuffenhauer A. Latent Hit Series Hidden in High-Throughput Screening Data. J Med Chem 2012; 55:1161-70. [DOI: 10.1021/jm201328e] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Thibault Varin
- Novartis Institutes for BioMedical Research, Forum
1, Novartis Campus, CH-4056 Basel, Switzerland
| | - Marie-Cecile Didiot
- Novartis Institutes for BioMedical Research, Forum
1, Novartis Campus, CH-4056 Basel, Switzerland
| | - Christian N. Parker
- Novartis Institutes for BioMedical Research, Forum
1, Novartis Campus, CH-4056 Basel, Switzerland
| | - Ansgar Schuffenhauer
- Novartis Institutes for BioMedical Research, Forum
1, Novartis Campus, CH-4056 Basel, Switzerland
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17
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Meister S, Plouffe DM, Kuhen KL, Bonamy GMC, Wu T, Barnes SW, Bopp SE, Borboa R, Bright AT, Che J, Cohen S, Dharia NV, Gagaring K, Gettayacamin M, Gordon P, Groessl T, Kato N, Lee MCS, McNamara CW, Fidock DA, Nagle A, Nam TG, Richmond W, Roland J, Rottmann M, Zhou B, Froissard P, Glynne RJ, Mazier D, Sattabongkot J, Schultz PG, Tuntland T, Walker JR, Zhou Y, Chatterjee A, Diagana TT, Winzeler EA. Imaging of Plasmodium liver stages to drive next-generation antimalarial drug discovery. Science 2011; 334:1372-7. [PMID: 22096101 PMCID: PMC3473092 DOI: 10.1126/science.1211936] [Citation(s) in RCA: 252] [Impact Index Per Article: 19.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Most malaria drug development focuses on parasite stages detected in red blood cells, even though, to achieve eradication, next-generation drugs active against both erythrocytic and exo-erythrocytic forms would be preferable. We applied a multifactorial approach to a set of >4000 commercially available compounds with previously demonstrated blood-stage activity (median inhibitory concentration < 1 micromolar) and identified chemical scaffolds with potent activity against both forms. From this screen, we identified an imidazolopiperazine scaffold series that was highly enriched among compounds active against Plasmodium liver stages. The orally bioavailable lead imidazolopiperazine confers complete causal prophylactic protection (15 milligrams/kilogram) in rodent models of malaria and shows potent in vivo blood-stage therapeutic activity. The open-source chemical tools resulting from our effort provide starting points for future drug discovery programs, as well as opportunities for researchers to investigate the biology of exo-erythrocytic forms.
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Affiliation(s)
- Stephan Meister
- Department of Genetics, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - David M Plouffe
- Genomics Institute of the Novartis Research Foundation, San Diego, CA 92121, USA
| | - Kelli L Kuhen
- Genomics Institute of the Novartis Research Foundation, San Diego, CA 92121, USA
| | - Ghislain MC Bonamy
- Genomics Institute of the Novartis Research Foundation, San Diego, CA 92121, USA
| | - Tao Wu
- Genomics Institute of the Novartis Research Foundation, San Diego, CA 92121, USA
| | - S Whitney Barnes
- Genomics Institute of the Novartis Research Foundation, San Diego, CA 92121, USA
| | - Selina E Bopp
- Department of Genetics, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Rachel Borboa
- Genomics Institute of the Novartis Research Foundation, San Diego, CA 92121, USA
| | - A Taylor Bright
- Department of Genetics, The Scripps Research Institute, La Jolla, CA 92037, USA
- Biomedical Sciences Graduate Program, UC San Diego, La Jolla, CA 92093, USA
| | - Jianwei Che
- Genomics Institute of the Novartis Research Foundation, San Diego, CA 92121, USA
| | - Steve Cohen
- Genomics Institute of the Novartis Research Foundation, San Diego, CA 92121, USA
| | - Neekesh V Dharia
- Department of Genetics, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Kerstin Gagaring
- Genomics Institute of the Novartis Research Foundation, San Diego, CA 92121, USA
| | | | - Perry Gordon
- Genomics Institute of the Novartis Research Foundation, San Diego, CA 92121, USA
| | - Todd Groessl
- Genomics Institute of the Novartis Research Foundation, San Diego, CA 92121, USA
| | - Nobutaka Kato
- Genomics Institute of the Novartis Research Foundation, San Diego, CA 92121, USA
| | - Marcus CS Lee
- Department of Microbiology & Immunology, Columbia University Medical Center, New York, NY 10032, USA
| | - Case W McNamara
- Genomics Institute of the Novartis Research Foundation, San Diego, CA 92121, USA
| | - David A Fidock
- Department of Microbiology & Immunology, Columbia University Medical Center, New York, NY 10032, USA
- Department of Medicine (Division of Infectious Diseases), Columbia University Medical Center, New York, NY 10032, USA
| | - Advait Nagle
- Genomics Institute of the Novartis Research Foundation, San Diego, CA 92121, USA
| | - Tae-gyu Nam
- Department of Chemistry, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Wendy Richmond
- Genomics Institute of the Novartis Research Foundation, San Diego, CA 92121, USA
| | - Jason Roland
- Genomics Institute of the Novartis Research Foundation, San Diego, CA 92121, USA
| | - Matthias Rottmann
- Swiss Tropical and Public Health Institute, Parasite Chemotherapy, CH-4002 Basel, Switzerland
- University of Basel, CH-4003 Basel, Switzerland
| | - Bin Zhou
- Genomics Institute of the Novartis Research Foundation, San Diego, CA 92121, USA
| | - Patrick Froissard
- INSERM, U945, Paris, France
- Université Pierre et Marie Curie-Paris, UMR S511 Paris, France
| | - Richard J Glynne
- Genomics Institute of the Novartis Research Foundation, San Diego, CA 92121, USA
| | - Dominique Mazier
- INSERM, U945, Paris, France
- Université Pierre et Marie Curie-Paris, UMR S511 Paris, France
- AP-HP, Groupe hospitalier Pitié-Salpêtrière, Service Parasitologie-Mycologie, Paris, France
| | | | - Peter G Schultz
- Department of Chemistry, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Tove Tuntland
- Genomics Institute of the Novartis Research Foundation, San Diego, CA 92121, USA
| | - John R Walker
- Genomics Institute of the Novartis Research Foundation, San Diego, CA 92121, USA
| | - Yingyao Zhou
- Genomics Institute of the Novartis Research Foundation, San Diego, CA 92121, USA
| | - Arnab Chatterjee
- Genomics Institute of the Novartis Research Foundation, San Diego, CA 92121, USA
| | | | - Elizabeth A Winzeler
- Department of Genetics, The Scripps Research Institute, La Jolla, CA 92037, USA
- Genomics Institute of the Novartis Research Foundation, San Diego, CA 92121, USA
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18
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Abstract
The aim of this chapter is to describe the stages of early drug discovery that can be assisted by techniques commonly used in the field of cheminformatics. In fact, cheminformatics tools can be applied all the way from the design of compound libraries and the analysis of HTS results, to the discovery of functional relationships between compounds and their targets.
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Affiliation(s)
- Anne Kümmel
- Novartis Institutes for BioMedical Research, Basel, Switzerland
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19
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Label-free screening assays: a strategy for finding better drug candidates. Future Med Chem 2010; 2:1703-16. [DOI: 10.4155/fmc.10.246] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
The last 10 years have seen advances in automation and high-throughput biochemistry in the drug-discovery arena. However, these advances have not led to improvements in drug-discovery success. Drug programs must find new ways to identify superior compounds. Advances in label-free assay technologies may provide advantages needed for improved drug discovery. In this article, we will discuss high-throughput MS, a technology that allows screening with native substrates and with targets inaccessible to standard assay formats. We will then discuss cell-based label-free biosensors, focusing on the increased information content available when using these platforms. We will conclude with speculation on the future and ways to obtain relevant biological information early in development to ensure the best compounds are promoted to medicinal chemistry campaigns.
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20
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Khlebnikov AI, Schepetkin IA, Quinn MT. Computational structure-activity relationship analysis of small-molecule agonists for human formyl peptide receptors. Eur J Med Chem 2010; 45:5406-19. [PMID: 20870313 DOI: 10.1016/j.ejmech.2010.09.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2010] [Revised: 08/30/2010] [Accepted: 09/01/2010] [Indexed: 11/25/2022]
Abstract
N-formyl peptide receptors (FPRs) are important in host defense. Because of the potential for FPRs as therapeutic targets, recent efforts have focused on identification of non-peptide agonists for two FPR subtypes, FPR1 and FPR2. Given that a number of specific small-molecule agonists have recently been identified, we hypothesized that computational structure-activity relationship (SAR) analysis of these molecules could provide new information regarding molecular features required for activity. We used a training set of 71 compounds, including 10 FPR1-specific agonists, 36 FPR2-specific agonists, and 25 non-active analogs. A sequence of (1) one-way analysis of variance selection, (2) cluster analysis, (3) linear discriminant analysis, and (4) classification tree analysis led to the derivation of SAR rules with high (95.8%) accuracy for correct classification of compounds. These SAR rules revealed key features distinguishing FPR1 versus FPR2 agonists. To verify predictive ability, we evaluated a test set of 17 additional FPR agonists, and found that the majority of these agonists (>94%) were classified correctly as agonists. This study represents the first successful application of classification tree methodology based on atom pairs to SAR analysis of FPR agonists. Importantly, these SAR rules represent a relatively simple classification approach for virtual screening of FPR1/FPR2 agonists.
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Affiliation(s)
- Andrei I Khlebnikov
- Department of Chemistry, Altai State Technical University, Barnaul 656038, Russia.
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21
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Agrafiotis DK, Wiener JJM. Scaffold Explorer: An Interactive Tool for Organizing and Mining Structure−Activity Data Spanning Multiple Chemotypes. J Med Chem 2010; 53:5002-11. [DOI: 10.1021/jm1004495] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Dimitris K. Agrafiotis
- Johnson & Johnson Pharmaceutical Research & Development, LLC, Welsh & McKean Roads, Spring House, Pennsylvania 19477
| | - John J. M. Wiener
- Johnson & Johnson Pharmaceutical Research & Development, LLC, 3210 Merryfield Road, San Diego, California 92121
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22
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Coverage and bias in chemical library design. Curr Opin Chem Biol 2008; 12:359-65. [DOI: 10.1016/j.cbpa.2008.03.015] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2008] [Revised: 03/25/2008] [Accepted: 03/25/2008] [Indexed: 11/22/2022]
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23
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Gillet VJ. New directions in library design and analysis. Curr Opin Chem Biol 2008; 12:372-8. [PMID: 18331851 DOI: 10.1016/j.cbpa.2008.02.015] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2008] [Accepted: 02/06/2008] [Indexed: 10/22/2022]
Abstract
The high costs associated with high-throughput screening (HTS) coupled with the limited coverage and bias of current screening collections is such that diversity analysis continues to be an important criterion in lead generation. Whereas early approaches to diversity analysis were based on traditional descriptors such as two-dimensional fingerprints a recent emphasis has been on assessing scaffold coverage to ensure that a variety of different chemotypes are represented. Moreover, whether designing diverse or focused libraries, it is widely recognised that designs should aim to achieve a balance in a number of different properties and multiobjective optimisation provides an effective way of achieving such designs.
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Affiliation(s)
- Valerie J Gillet
- Department of Information Studies, University of Sheffield, Sheffield, UK.
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24
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Koyama M, Hasegawa K, Arakawa M, Funatsu K. Application of Rough Set Theory to High Throughput Screening Data for Rational Selection of Lead Compounds. CHEM-BIO INFORMATICS JOURNAL 2008. [DOI: 10.1273/cbij.8.85] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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25
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Sakata T, Winzeler EA. Genomics, systems biology and drug development for infectious diseases. MOLECULAR BIOSYSTEMS 2007; 3:841-8. [PMID: 18000561 DOI: 10.1039/b703924g] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Although a variety of drugs are available for many infectious diseases that predominantly affect the developing world reasons remain for continuing to search for new chemotherapeutics. First, the development of microbial resistance has made some of the most effective and inexpensive drug regimes unreliable and dangerous to use on severely ill patients. Second, many existing antimicrobial drugs show toxicity or are too expensive for countries where the per capita income is in the order of hundreds of dollars per year. In recognition of this, new publicly and privately financed drug discovery efforts have been established to identify and develop new therapies for diseases such as tuberculosis, malaria and AIDS. This in turn, has intensified the need for tools to facilitate drug identification for those microbes whose molecular biology is poorly understood, or which are difficult to grow in the laboratory. While much has been written about how functional genomics can be used to find novel protein targets for chemotherapeutics this review will concentrate on how genome-wide, systems biology approaches may be used following whole organism, cell-based screening to understand the mechanism of drug action or to identify biological targets of small molecules. Here we focus on protozoan parasites, however, many of the approaches can be applied to pathogenic bacteria or parasitic helminths, insects or disease-causing fungi.
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Affiliation(s)
- Tomoyo Sakata
- The Genomics Institute of the Novartis Research Foundation, 10660 John Jay Hopkins Dr., San Diego, CA 92121, USA
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26
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Ekins S, Mestres J, Testa B. In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br J Pharmacol 2007; 152:9-20. [PMID: 17549047 PMCID: PMC1978274 DOI: 10.1038/sj.bjp.0707305] [Citation(s) in RCA: 397] [Impact Index Per Article: 23.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Pharmacology over the past 100 years has had a rich tradition of scientists with the ability to form qualitative or semi-quantitative relations between molecular structure and activity in cerebro. To test these hypotheses they have consistently used traditional pharmacology tools such as in vivo and in vitro models. Increasingly over the last decade however we have seen that computational (in silico) methods have been developed and applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, pharmacophores, homology models and other molecular modeling approaches, machine learning, data mining, network analysis tools and data analysis tools that use a computer. In silico methods are primarily used alongside the generation of in vitro data both to create the model and to test it. Such models have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The aim of this review is to illustrate some of the in silico methods for pharmacology that are used in drug discovery. Further applications of these methods to specific targets and their limitations will be discussed in the second accompanying part of this review.
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Affiliation(s)
- S Ekins
- ACT LLC, 1 Penn Plaza, New York, NY 10119, USA.
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