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Park M, Baek SJ, Park SM, Yi JM, Cha S. Comparative study of the mechanism of natural compounds with similar structures using docking and transcriptome data for improving in silico herbal medicine experimentations. Brief Bioinform 2023; 24:bbad344. [PMID: 37798251 PMCID: PMC10555731 DOI: 10.1093/bib/bbad344] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 08/31/2023] [Accepted: 09/12/2023] [Indexed: 10/07/2023] Open
Abstract
Natural products have successfully treated several diseases using a multi-component, multi-target mechanism. However, a precise mechanism of action (MOA) has not been identified. Systems pharmacology methods have been used to overcome these challenges. However, there is a limitation as those similar mechanisms of similar components cannot be identified. In this study, comparisons of physicochemical descriptors, molecular docking analysis and RNA-seq analysis were performed to compare the MOA of similar compounds and to confirm the changes observed when similar compounds were mixed and used. Various analyses have confirmed that compounds with similar structures share similar MOA. We propose an advanced method for in silico experiments in herbal medicine research based on the results. Our study has three novel findings. First, an advanced network pharmacology research method was suggested by partially presenting a solution to the difficulty in identifying multi-component mechanisms. Second, a new natural product analysis method was proposed using large-scale molecular docking analysis. Finally, various biological data and analysis methods were used, such as in silico system pharmacology, docking analysis and drug response RNA-seq. The results of this study are meaningful in that they suggest an analysis strategy that can improve existing systems pharmacology research analysis methods by showing that natural product-derived compounds with the same scaffold have the same mechanism.
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Affiliation(s)
- Musun Park
- Korean Medicine (KM) Data Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea
| | - Su-Jin Baek
- Korean Medicine (KM) Data Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea
| | - Sang-Min Park
- College of Pharmacy, Chungnam National University, Daejeon, Republic of Korea
| | - Jin-Mu Yi
- KM Convergence Research Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea
| | - Seongwon Cha
- Korean Medicine (KM) Data Division, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea
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2
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Fujimori I, Wakabayashi T, Murakami M, Okabe A, Ishii T, McGrath A, Zou H, Saikatendu KS, Imoto H. Discovery of Novel and Highly Selective Cyclopropane ALK Inhibitors through a Fragment-Assisted, Structure-Based Drug Design. ACS OMEGA 2020; 5:31984-32001. [PMID: 33344853 PMCID: PMC7745413 DOI: 10.1021/acsomega.0c04900] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 11/13/2020] [Indexed: 06/12/2023]
Abstract
Fragment screening is frequently used for hit identification. However, there was no report starting from a small fragment for the development of an anaplastic lymphoma kinase (ALK) inhibitor, despite the number of ALK inhibitors reported. We began our research with the fragment hit F-1 and our subsequent linker design, and its docking analysis yielded novel cis-1,2,2-trisubstituted cyclopropane 1. The fragment information was integrated with a structure-based approach to improve upon the selectivity over tropomyosin receptor kinase A, leading to the potent and highly selective ALK inhibitor, 4-trifluoromethylphenoxy-cis-1,2,2-trisubstituted cyclopropane 12. This work shows that fragments become a powerful tool for both lead generation and optimization, such as the improvement of selectivity, by combining them with a structure-based drug design approach, resulting in the fast and efficient development of a novel, potent, and highly selective compound.
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Affiliation(s)
- Ikuo Fujimori
- Pharmaceutical Research Division, Takeda
Pharmaceutical Company Limited, 26-1, Muraoka-Higashi 2-chome, Fujisawa, Kanagawa 251-8555, Japan
| | - Takeshi Wakabayashi
- Pharmaceutical Research Division, Takeda
Pharmaceutical Company Limited, 26-1, Muraoka-Higashi 2-chome, Fujisawa, Kanagawa 251-8555, Japan
| | - Morio Murakami
- Pharmaceutical Research Division, Takeda
Pharmaceutical Company Limited, 26-1, Muraoka-Higashi 2-chome, Fujisawa, Kanagawa 251-8555, Japan
| | - Atsutoshi Okabe
- Pharmaceutical Research Division, Takeda
Pharmaceutical Company Limited, 26-1, Muraoka-Higashi 2-chome, Fujisawa, Kanagawa 251-8555, Japan
| | - Tsuyoshi Ishii
- Pharmaceutical Research Division, Takeda
Pharmaceutical Company Limited, 26-1, Muraoka-Higashi 2-chome, Fujisawa, Kanagawa 251-8555, Japan
| | - Aaron McGrath
- Takeda California, Inc., 10410 Science Center Drive, San Diego, California 92121, United States
| | - Hua Zou
- Takeda California, Inc., 10410 Science Center Drive, San Diego, California 92121, United States
| | - Kumar Singh Saikatendu
- Takeda California, Inc., 10410 Science Center Drive, San Diego, California 92121, United States
| | - Hiroshi Imoto
- Pharmaceutical Research Division, Takeda
Pharmaceutical Company Limited, 26-1, Muraoka-Higashi 2-chome, Fujisawa, Kanagawa 251-8555, Japan
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3
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Liu WS, Yang B, Wang RR, Li WY, Ma YC, Zhou L, Du S, Ma Y, Wang RL. Design, synthesis and biological evaluation of pyridine derivatives as selective SHP2 inhibitors. Bioorg Chem 2020; 100:103875. [DOI: 10.1016/j.bioorg.2020.103875] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 03/31/2020] [Accepted: 04/21/2020] [Indexed: 10/24/2022]
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4
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Lautié E, Russo O, Ducrot P, Boutin JA. Unraveling Plant Natural Chemical Diversity for Drug Discovery Purposes. Front Pharmacol 2020; 11:397. [PMID: 32317969 PMCID: PMC7154113 DOI: 10.3389/fphar.2020.00397] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 03/16/2020] [Indexed: 12/11/2022] Open
Abstract
The screening and testing of extracts against a variety of pharmacological targets in order to benefit from the immense natural chemical diversity is a concern in many laboratories worldwide. And several successes have been recorded in finding new actives in natural products, some of which have become new drugs or new sources of inspiration for drugs. But in view of the vast amount of research on the subject, it is surprising that not more drug candidates were found. In our view, it is fundamental to reflect upon the approaches of such drug discovery programs and the technical processes that are used, along with their inherent difficulties and biases. Based on an extensive survey of recent publications, we discuss the origin and the variety of natural chemical diversity as well as the strategies to having the potential to embrace this diversity. It seemed to us that some of the difficulties of the area could be related with the technical approaches that are used, so the present review begins with synthetizing some of the more used discovery strategies, exemplifying some key points, in order to address some of their limitations. It appears that one of the challenges of natural product-based drug discovery programs should be an easier access to renewable sources of plant-derived products. Maximizing the use of the data together with the exploration of chemical diversity while working on reasonable supply of natural product-based entities could be a way to answer this challenge. We suggested alternative ways to access and explore part of this chemical diversity with in vitro cultures. We also reinforced how important it was organizing and making available this worldwide knowledge in an "inventory" of natural products and their sources. And finally, we focused on strategies based on synthetic biology and syntheses that allow reaching industrial scale supply. Approaches based on the opportunities lying in untapped natural plant chemical diversity are also considered.
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Affiliation(s)
- Emmanuelle Lautié
- Centro de Valorização de Compostos Bioativos da Amazônia (CVACBA)-Instituto de Ciências Biológicas, Universidade Federal do Pará (UFPA), Belém, Brazil
| | - Olivier Russo
- Institut de Recherches Internationales SERVIER, Suresnes, France
| | - Pierre Ducrot
- Molecular Modelling Department, 'PEX Biotechnologie, Chimie & Biologie, Institut de Recherches SERVIER, Croissy-sur-Seine, France
| | - Jean A Boutin
- Institut de Recherches Internationales SERVIER, Suresnes, France
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5
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O'Hagan S, Kell DB. Analysis of drug-endogenous human metabolite similarities in terms of their maximum common substructures. J Cheminform 2017; 9:18. [PMID: 28316656 PMCID: PMC5344883 DOI: 10.1186/s13321-017-0198-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2016] [Accepted: 02/09/2017] [Indexed: 12/21/2022] Open
Abstract
In previous work, we have assessed the structural similarities between marketed drugs (‘drugs’) and endogenous natural human metabolites (‘metabolites’ or ‘endogenites’), using ‘fingerprint’ methods in common use, and the Tanimoto and Tversky similarity metrics, finding that the fingerprint encoding used had a dramatic effect on the apparent similarities observed. By contrast, the maximal common substructure (MCS), when the means of determining it is fixed, is a means of determining similarities that is largely independent of the fingerprints, and also has a clear chemical meaning. We here explored the utility of the MCS and metrics derived therefrom. In many cases, a shared scaffold helps cluster drugs and endogenites, and gives insight into enzymes (in particular transporters) that they both share. Tanimoto and Tversky similarities based on the MCS tend to be smaller than those based on the MACCS fingerprint-type encoding, though the converse is also true for a significant fraction of the comparisons. While no single molecular descriptor can account for these differences, a machine learning-based analysis of the nature of the differences (MACCS_Tanimoto vs MCS_Tversky) shows that they are indeed deterministic, although the features that are used in the model to account for this vary greatly with each individual drug. The extent of its utility and interpretability vary with the drug of interest, implying that while MCS is neither ‘better’ nor ‘worse’ for every drug–endogenite comparison, it is sufficiently different to be of value. The overall conclusion is thus that the use of the MCS provides an additional and valuable strategy for understanding the structural basis for similarities between synthetic, marketed drugs and natural intermediary metabolites.
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Affiliation(s)
- Steve O'Hagan
- School of Chemistry, The University of Manchester, 131 Princess St, Manchester, M1 7DN UK.,Manchester Institute of Biotechnology, The University of Manchester, 131 Princess St, Manchester, M1 7DN UK
| | - Douglas B Kell
- School of Chemistry, The University of Manchester, 131 Princess St, Manchester, M1 7DN UK.,Manchester Institute of Biotechnology, The University of Manchester, 131 Princess St, Manchester, M1 7DN UK.,Centre for the Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM), The University of Manchester, 131 Princess St, Manchester, M1 7DN UK
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6
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Abstract
Fragment hopping is a fragment-based approach to designing biologically active small molecules. The key of this approach is the determination of the minimal pharmacophoric elements in the three-dimensional space. Based on the derived minimal pharmacophoric elements, new fragments with different chemotypes can be generated and positioned to the active site of the target protein. Herein, we detail a protocol for performing fragment hopping. This approach can not only explore a wide chemical space to produce new ligands with novel scaffolds but also characterize and utilize the delicate differences in the active sites between isofunctional proteins to produce new ligands with high target selectivity/specificity.
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Affiliation(s)
- Kevin B Teuscher
- Department of Chemistry, Center for Cell and Genome Science, University of Utah, 315 South 1400 East, Salt Lake City, Utah, 84112-0850, USA
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7
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Schneider G. De novo design - hop(p)ing against hope. DRUG DISCOVERY TODAY. TECHNOLOGIES 2014; 10:e453-60. [PMID: 24451634 DOI: 10.1016/j.ddtec.2012.06.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Current trends in computational de novo design provide a fresh approach to 'scaffold-hopping' in drug discovery. The methodological repertoire is no longer limited to receptor-based methods, but specifically ligand-based techniques that consider multiple properties in parallel, including the synthetic feasibility of the computer-generated molecules and their polypharmacology, provide innovative ideas for the discovery of new chemical entities. The concept of fragment-based and virtual reaction-driven design enables rapid compound optimization from scratch with a manageable complexity of the search. Starting from known drugs as a reference, such algorithms suggest drug-like molecules with motivated scaffold variations, and advanced mathematical models of structure-activity landscapes and multi-objective design techniques have created new opportunities for hit and lead finding.
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'Fuzziness' in pharmacophore-based virtual screening and de novo design. DRUG DISCOVERY TODAY. TECHNOLOGIES 2013; 7:e203-70. [PMID: 24103799 DOI: 10.1016/j.ddtec.2010.10.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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9
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Fernald GH, Altman RB. Using molecular features of xenobiotics to predict hepatic gene expression response. J Chem Inf Model 2013; 53:2765-73. [PMID: 24010729 PMCID: PMC3810861 DOI: 10.1021/ci3005868] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Despite recent advances in molecular medicine and rational drug design, many drugs still fail because toxic effects arise at the cellular and tissue level. In order to better understand these effects, cellular assays can generate high-throughput measurements of gene expression changes induced by small molecules. However, our understanding of how the chemical features of small molecules influence gene expression is very limited. Therefore, we investigated the extent to which chemical features of small molecules can reliably be associated with significant changes in gene expression. Specifically, we analyzed the gene expression response of rat liver cells to 170 different drugs and searched for genes whose expression could be related to chemical features alone. Surprisingly, we can predict the up-regulation of 87 genes (increased expression of at least 1.5 times compared to controls). We show an average cross-validation predictive area under the receiver operating characteristic curve (AUROC) of 0.7 or greater for each of these 87 genes. We applied our method to an external data set of rat liver gene expression response to a novel drug and achieved an AUROC of 0.7. We also validated our approach by predicting up-regulation of Cytochrome P450 1A2 (CYP1A2) in three drugs known to induce CYP1A2 that were not in our data set. Finally, a detailed analysis of the CYP1A2 predictor allowed us to identify which fragments made significant contributions to the predictive scores.
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Affiliation(s)
- Guy Haskin Fernald
- Biomedical Informatics Training Program, Stanford University School of Medicine and ‡Departments of Bioengineering and Genetics, Stanford University , Stanford, California 94305, United States
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10
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Sheng C, Zhang W. Fragment Informatics and Computational Fragment-Based Drug Design: An Overview and Update. Med Res Rev 2012; 33:554-98. [DOI: 10.1002/med.21255] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Chunquan Sheng
- Department of Medicinal Chemistry; School of Pharmacy; Second Military Medical University; 325 Guohe Road Shanghai 200433 People's Republic of China
| | - Wannian Zhang
- Department of Medicinal Chemistry; School of Pharmacy; Second Military Medical University; 325 Guohe Road Shanghai 200433 People's Republic of China
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11
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Schneider G. Designing the molecular future. J Comput Aided Mol Des 2011; 26:115-20. [DOI: 10.1007/s10822-011-9485-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2011] [Accepted: 11/03/2011] [Indexed: 10/15/2022]
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12
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Choi J, He N, Kim N, Yoon S. Enrichment of virtual hits by progressive shape-matching and docking. J Mol Graph Model 2011; 32:82-8. [PMID: 22088763 DOI: 10.1016/j.jmgm.2011.10.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2011] [Revised: 10/01/2011] [Accepted: 10/03/2011] [Indexed: 11/17/2022]
Abstract
The main applications of virtual chemical screening include the selection of a minimal receptor-relevant subset of a chemical library with a maximal chemical diversity. We have previously reported that the combination of ligand-centric and receptor-centric virtual screening methods may provide a compromise between computational time and accuracy during the hit enrichment process. In the present work, we propose a "progressive distributed docking" method that improves the virtual screening process using an iterative combination of shape-matching and docking steps. Known ligands with low docking scores were used as initial 3D templates for the shape comparisons with the chemical library. Next, new compounds with good template shape matches and low receptor docking scores were selected for the next round of shape searching and docking. The present iterative virtual screening process was tested for enriching peroxisome proliferator-activated receptor and phosphoinositide 3-kinase relevant compounds from a selected subset of the chemical libraries. It was demonstrated that the iterative combination improved the lead-hopping practice by improving the chemical diversity in the selected list of virtual hits.
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Affiliation(s)
- Jiwon Choi
- Department of Biological Sciences, Research Center for Women's Diseases, Sookmyung Women's University, Hyochangwongil 52, Yongsan-gu, Seoul 140-742, Republic of Korea
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13
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Khanna V, Ranganathan S. Structural diversity of biologically interesting datasets: a scaffold analysis approach. J Cheminform 2011; 3:30. [PMID: 21824432 PMCID: PMC3179739 DOI: 10.1186/1758-2946-3-30] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2011] [Accepted: 08/08/2011] [Indexed: 11/25/2022] Open
Abstract
Background The recent public availability of the human metabolome and natural product datasets has revitalized "metabolite-likeness" and "natural product-likeness" as a drug design concept to design lead libraries targeting specific pathways. Many reports have analyzed the physicochemical property space of biologically important datasets, with only a few comprehensively characterizing the scaffold diversity in public datasets of biological interest. With large collections of high quality public data currently available, we carried out a comparative analysis of current day leads with other biologically relevant datasets. Results In this study, we note a two-fold enrichment of metabolite scaffolds in drug dataset (42%) as compared to currently used lead libraries (23%). We also note that only a small percentage (5%) of natural product scaffolds space is shared by the lead dataset. We have identified specific scaffolds that are present in metabolites and natural products, with close counterparts in the drugs, but are missing in the lead dataset. To determine the distribution of compounds in physicochemical property space we analyzed the molecular polar surface area, the molecular solubility, the number of rings and the number of rotatable bonds in addition to four well-known Lipinski properties. Here, we note that, with only few exceptions, most of the drugs follow Lipinski's rule. The average values of the molecular polar surface area and the molecular solubility in metabolites is the highest while the number of rings is the lowest. In addition, we note that natural products contain the maximum number of rings and the rotatable bonds than any other dataset under consideration. Conclusions Currently used lead libraries make little use of the metabolites and natural products scaffold space. We believe that metabolites and natural products are recognized by at least one protein in the biosphere therefore, sampling the fragment and scaffold space of these compounds, along with the knowledge of distribution in physicochemical property space, can result in better lead libraries. Hence, we recommend the greater use of metabolites and natural products while designing lead libraries. Nevertheless, metabolites have a limited distribution in chemical space that limits the usage of metabolites in library design.
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Affiliation(s)
- Varun Khanna
- Department of Chemistry and Biomolecular Sciences and ARC Centre of Excellence in Bioinformatics, Macquarie University, Sydney, Australia.
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Nicolotti O, Pisani L, Catto M, Leonetti F, Giangreco I, Stefanachi A, Carotti A. Discovery of a Potent and Selective Hetero-Bivalent AChE Inhibitor via Bioisosteric Replacement. Mol Inform 2011; 30:133-6. [DOI: 10.1002/minf.201000126] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2010] [Accepted: 12/18/2010] [Indexed: 11/08/2022]
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Glick M, Jacoby E. The role of computational methods in the identification of bioactive compounds. Curr Opin Chem Biol 2011; 15:540-6. [PMID: 21411361 DOI: 10.1016/j.cbpa.2011.02.021] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2011] [Revised: 02/01/2011] [Accepted: 02/21/2011] [Indexed: 10/18/2022]
Abstract
Computational methods play an ever increasing role in lead finding. A vast repertoire of molecular design and virtual screening methods emerged in the past two decades and are today routinely used. There is increasing awareness that there is no single best computational protocol and correspondingly there is a shift recommending the combination of complementary methods. A promising trend for the application of computational methods in lead finding is to take advantage of the vast amounts of HTS (High Throughput Screening) data to allow lead assessment by detailed systems-based data analysis, especially for phenotypic screens where the identification of compound-target pairs is the primary goal. Herein, we review trends and provide examples of successful applications of computational methods in lead finding.
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Affiliation(s)
- Meir Glick
- Novartis Institutes for BioMedical Research, Inc., 250 Massachusetts Avenue, Cambridge, MA 02139, USA
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16
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Kutchukian PS, Shakhnovich EI. De novo design: balancing novelty and confined chemical space. Expert Opin Drug Discov 2010; 5:789-812. [PMID: 22827800 DOI: 10.1517/17460441.2010.497534] [Citation(s) in RCA: 62] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
IMPORTANCE OF THE FIELD De novo drug design serves as a tool for the discovery of new ligands for macromolecular targets as well as optimization of known ligands. Recently developed tools aim to address the multi-objective nature of drug design in an unprecedented manner. AREAS COVERED IN THIS REVIEW This article discusses recent advances in de novo drug design programs and accessory programs used to evaluate compounds post-generation. WHAT THE READER WILL GAIN The reader is introduced to the challenges inherent in de novo drug design and will become familiar with current trends in de novo design. Furthermore, the reader will be better prepared to assess the value of a tool, and be equipped to design more elegant tools in the future. TAKE HOME MESSAGE De novo drug design can assist in the efficient discovery of new compounds with a high affinity for a given target. The inclusion of existing chemoinformatic methods with current structure-based de novo design tools provides a means of enhancing the therapeutic value of these generated compounds.
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Affiliation(s)
- Peter S Kutchukian
- Harvard University, Chemistry and Chemical Biology Department, 12 Oxford Street, Cambridge, MA 02138, USA
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17
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Langdon SR, Ertl P, Brown N. Bioisosteric Replacement and Scaffold Hopping in Lead Generation and Optimization. Mol Inform 2010; 29:366-85. [PMID: 27463193 DOI: 10.1002/minf.201000019] [Citation(s) in RCA: 145] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2010] [Accepted: 04/01/2010] [Indexed: 11/09/2022]
Abstract
Bioisosteric replacement and scaffold hopping are twin methods used in drug design to improve the synthetic accessibility, potency and drug like properties of a compound and to move into novel chemical space. Bioisosteric replacement involves swapping functional groups of a molecule with other functional groups that have similar biological properties. Scaffold hopping is the replacement of the core framework of a molecule with another scaffold that will improve the properties of the molecule or to find similar potent compounds that exist in novel chemical space. This review outlines the key concepts, importance and challenges of both methods using examples and comparisons of techniques available for finding bioisosteric replacements and scaffold hops. There are many methods available for bioisosteric replacement and scaffold hopping, all with their own advantages and disadvantages. Drug design projects would benefit from a combination of these methods to retrieve diverse and complimentary results. Continuing progress in these fields will allow further validation of both methods as well as the accumulation of knowledge on bioisosteres and possible scaffold replacements.
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Affiliation(s)
- Sarah R Langdon
- In Silico Medicinal Chemistry, Cancer Research UK Centre for Cancer Therapeutics, The Institute of Cancer Research, 15 Cotswold Road, Sutton, SM2 5NG, UK phone/fax: +44 (0) 20 8722 4033/+44 (0) 20 8722 4205
| | - Peter Ertl
- Novartis Institutes for BioMedical Research, Novartis Campus, CH-4056 Basel, Switzerland
| | - Nathan Brown
- In Silico Medicinal Chemistry, Cancer Research UK Centre for Cancer Therapeutics, The Institute of Cancer Research, 15 Cotswold Road, Sutton, SM2 5NG, UK phone/fax: +44 (0) 20 8722 4033/+44 (0) 20 8722 4205.
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18
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Baringhaus KH, Hessler G, Klabunde T. Current aspects and future trends of computer-aided rescaffolding. J Cheminform 2010. [PMCID: PMC2867127 DOI: 10.1186/1758-2946-2-s1-o19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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19
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Compound Collection Enhancement and Paradigms for High-Throughput Screening — an Update. ANNUAL REPORTS IN MEDICINAL CHEMISTRY 2010. [DOI: 10.1016/s0065-7743(10)45025-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
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