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Lead generation of cysteine based mesenchymal epithelial transition (c-Met) kinase inhibitors: Using structure-based scaffold hopping, 3D-QSAR pharmacophore modeling, virtual screening, molecular docking, and molecular dynamics simulation. Comput Biol Med 2022; 146:105526. [PMID: 35487125 DOI: 10.1016/j.compbiomed.2022.105526] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 03/22/2022] [Accepted: 04/07/2022] [Indexed: 11/24/2022]
Abstract
Cysteine-based mesenchymal-epithelial transition (c-Met) is a receptor tyrosine kinase that plays a definitive role during cancer progression and was identified as a possible target for anti-angiogenesis drugs. In the present study, different protocols of computer-based drug design were performed. Construction of predictive pharmacophore model using HypoGen algorithm resulted in a validated model of four features of positive ionizable, hydrogen bond acceptor, hydrophobic, and ring aromatic features with a correlation coefficient of 0.87, a configuration cost of 14.95, and a cost difference of 357.92. The model revealed a promising predictive power and had >90% probability of representing true correlation with the activity data. The model was established using Fisher's validation test at the 95% confidence level and test set prediction (r = 0.96), furthermore, the model was validated by mapping of set of compounds undergoing clinical trials as class Ⅱ c-met inhibitors. The generated valid pharmacophore model was then anticipated for virtual screening of three data bases. Moreover, scaffold hopping using replace fragments protocol was implemented. Hits generated were filtered according to Lipinski's rule; 510 selected hits were anatomized and subjected to molecular docking studies into the crystal structure of c-Met kinase. The good correlation between docking scores and ligand pharmacophore mapping fit values provided a reliable foundation for designing new potentially active candidates that may target c-Met kinase. Eventually, eight hits were selected as potential leads. Subsequently, seven (Hits) have displayed a higher dock score and demonstrated key residue interactions with stable molecular dynamics simulation. Therefore, these c-Met kinase inhibitors may further serve as new chemical spaces in designing new compounds.
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Santana K, do Nascimento LD, Lima e Lima A, Damasceno V, Nahum C, Braga RC, Lameira J. Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products. Front Chem 2021; 9:662688. [PMID: 33996755 PMCID: PMC8117418 DOI: 10.3389/fchem.2021.662688] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 02/25/2021] [Indexed: 12/22/2022] Open
Abstract
Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.
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Affiliation(s)
- Kauê Santana
- Instituto de Biodiversidade, Universidade Federal do Oeste do Pará, Santarém, Brazil
| | | | - Anderson Lima e Lima
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | - Vinícius Damasceno
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | - Claudio Nahum
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | | | - Jerônimo Lameira
- Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Brazil
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Wei J, Gou Z, Wen Y, Luo Q, Huang Z. Marine compounds targeting the PI3K/Akt signaling pathway in cancer therapy. Biomed Pharmacother 2020; 129:110484. [PMID: 32768966 DOI: 10.1016/j.biopha.2020.110484] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 06/18/2020] [Accepted: 06/30/2020] [Indexed: 12/13/2022] Open
Abstract
Cancer is a disease characterized by overproliferation, including that due to transformation, apoptosis disorders, proliferation, invasion, angiogenesis and metastasis, and is one of the deadliest diseases. Currently, conservative chemotherapy is used for cancer treatment due to a lack of effective drugs. The PI3K/Akt signaling pathway plays a very essential role in the pathogenesis of many cancers, and abnormal activation of this pathway leads to abnormal expression of a series of downstream proteins, which ultimately results in the excessive proliferation of cancer cells. Therefore, the PI3K/Akt signaling pathway is a critical target in cancer treatment. Marine drugs have attracted much attention in recent years, and studies have found that many extracts from oceanic animals, plants and microorganisms or their metabolites exert antitumor effects, including antiproliferative effects or the induction of cell cycle arrest, apoptosis or autophagy. However, most anticancer targets and the mechanisms of marine compounds remain unclear. The great potential of the development of marine drugs provides a new direction for cancer treatment. This review focuses on marine compounds that target the PI3K/Akt signaling pathway for the prevention and treatment of cancer and provides comprehensive information for those interested in research on marine drugs.
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Affiliation(s)
- Jiaen Wei
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, Guangdong 523808, China; Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Guangdong Medical University, Dongguan, Guangdong 523808, China
| | - Zhanping Gou
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, Guangdong 523808, China
| | - Ying Wen
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, Guangdong 523808, China
| | - Qiaohong Luo
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, Guangdong 523808, China
| | - Zunnan Huang
- Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, Guangdong 523808, China; Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Guangdong Medical University, Dongguan, Guangdong 523808, China; Marine Biomedical Research Institute of Guangdong Zhanjiang, Zhanjiang, Guangdong 524023, China.
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Molecular de-novo design through deep reinforcement learning. J Cheminform 2017; 9:48. [PMID: 29086083 PMCID: PMC5583141 DOI: 10.1186/s13321-017-0235-x] [Citation(s) in RCA: 499] [Impact Index Per Article: 71.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2017] [Accepted: 08/23/2017] [Indexed: 01/15/2023] Open
Abstract
This work introduces a method to tune a sequence-based generative model for molecular de novo design that through augmented episodic likelihood can learn to generate structures with certain specified desirable properties. We demonstrate how this model can execute a range of tasks such as generating analogues to a query structure and generating compounds predicted to be active against a biological target. As a proof of principle, the model is first trained to generate molecules that do not contain sulphur. As a second example, the model is trained to generate analogues to the drug Celecoxib, a technique that could be used for scaffold hopping or library expansion starting from a single molecule. Finally, when tuning the model towards generating compounds predicted to be active against the dopamine receptor type 2, the model generates structures of which more than 95% are predicted to be active, including experimentally confirmed actives that have not been included in either the generative model nor the activity prediction model.. ![]()
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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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Silva DG, Rocha JR, Sartori GR, Montanari CA. Highly predictive hologram QSAR models of nitrile-containing cruzain inhibitors. J Biomol Struct Dyn 2016; 35:3232-3249. [DOI: 10.1080/07391102.2016.1252282] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Daniel Gedder Silva
- Grupo de Química Medicinal, Instituto de Química de São Carlos, Universidade de São Paulo, São Carlos – SP 13566-590, Brazil
| | - Josmar Rodrigues Rocha
- Grupo de Química Medicinal, Instituto de Química de São Carlos, Universidade de São Paulo, São Carlos – SP 13566-590, Brazil
| | - Geraldo Rodrigues Sartori
- Grupo de Química Medicinal, Instituto de Química de São Carlos, Universidade de São Paulo, São Carlos – SP 13566-590, Brazil
| | - Carlos Alberto Montanari
- Grupo de Química Medicinal, Instituto de Química de São Carlos, Universidade de São Paulo, São Carlos – SP 13566-590, Brazil
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Zhang J, Liu X, Wang SQ, Liu GY, Xu WR, Cheng XC, Wang RL. Identification of dual ligands targeting angiotensin II type 1 receptor and peroxisome proliferator-activated receptor-γ by core hopping of telmisartan. J Biomol Struct Dyn 2016; 35:2665-2680. [PMID: 27602589 DOI: 10.1080/07391102.2016.1227726] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
It has been reported previously that some angiotensin II receptor blockers not only antagonize angiotensin II type 1 receptor (AT1R), but also exert stimulation in peroxisome proliferator-activated receptor γ (PPARγ) partial activation, among which telmisartan displays the best. Telmisartan has been tested as a bifunctional ligand with antihypertensive and hypoglycemic activity. Aiming at more potent leads with selective AT1R antagonism and PPARγ partial agonism, the three parts of telmisartan including the distal benzimidazole ring, the biphenyl moiety, and the carboxylic acid group experienced modification by core hopping method in our study. The central benzimidazole ring, however, remained intact considering its great affinity toward AT1R and PPARγ. We utilized computational techniques for the sake of details on the binding interactions and conformational stability. Standard precision docking analysis and absorption, distribution, metabolism, excretion, and toxicity prediction received 10 molecules with higher Glide scores, similar interactions, and improved pharmacokinetic profiles compared to telmisartan. Comp#91 with highest scores for AT1R (-11.92 kcal/mol) and PPARγ (-13.88 kcal/mol) exhibited excellent binding modes and pharmacokinetic parameters. Molecular dynamics trajectories on best docking pose of comp#91 confirmed the docking results and verified the conformational stability with both receptors throughout the course of 20-ns simulations. Thus, comp#91 could be identified as a promising lead in the development of dual AT1R antagonist and PPARγ partial agonist against hypertension and type 2 diabetes.
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Affiliation(s)
- Jun Zhang
- a Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy , Tianjin Medical University , Tianjin 300070 , China
| | - Xin Liu
- a Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy , Tianjin Medical University , Tianjin 300070 , China
| | - Shu-Qing Wang
- a Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy , Tianjin Medical University , Tianjin 300070 , China
| | - Gui-You Liu
- a Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy , Tianjin Medical University , Tianjin 300070 , China
| | - Wei-Ren Xu
- b Tianjin Key Laboratory of Molecular Design and Drug Discovery , Tianjin Institute of Pharmaceutical Research , Tianjin 300193 , China
| | - Xian-Chao Cheng
- a Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy , Tianjin Medical University , Tianjin 300070 , China
| | - Run-Ling Wang
- a Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy , Tianjin Medical University , Tianjin 300070 , China
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O'Hagan S, Kell DB. MetMaxStruct: A Tversky-Similarity-Based Strategy for Analysing the (Sub)Structural Similarities of Drugs and Endogenous Metabolites. Front Pharmacol 2016; 7:266. [PMID: 27597830 PMCID: PMC4992690 DOI: 10.3389/fphar.2016.00266] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Accepted: 08/08/2016] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Previous studies compared the molecular similarity of marketed drugs and endogenous human metabolites (endogenites), using a series of fingerprint-type encodings, variously ranked and clustered using the Tanimoto (Jaccard) similarity coefficient (TS). Because this gives equal weight to all parts of the encoding (thence to different substructures in the molecule) it may not be optimal, since in many cases not all parts of the molecule will bind to their macromolecular targets. Unsupervised methods cannot alone uncover this. We here explore the kinds of differences that may be observed when the TS is replaced-in a manner more equivalent to semi-supervised learning-by variants of the asymmetric Tversky (TV) similarity, that includes α and β parameters. RESULTS Dramatic differences are observed in (i) the drug-endogenite similarity heatmaps, (ii) the cumulative "greatest similarity" curves, and (iii) the fraction of drugs with a Tversky similarity to a metabolite exceeding a given value when the Tversky α and β parameters are varied from their Tanimoto values. The same is true when the sum of the α and β parameters is varied. A clear trend toward increased endogenite-likeness of marketed drugs is observed when α or β adopt values nearer the extremes of their range, and when their sum is smaller. The kinds of molecules exhibiting the greatest similarity to two interrogating drug molecules (chlorpromazine and clozapine) also vary in both nature and the values of their similarity as α and β are varied. The same is true for the converse, when drugs are interrogated with an endogenite. The fraction of drugs with a Tversky similarity to a molecule in a library exceeding a given value depends on the contents of that library, and α and β may be "tuned" accordingly, in a semi-supervised manner. At some values of α and β drug discovery library candidates or natural products can "look" much more like (i.e., have a numerical similarity much closer to) drugs than do even endogenites. CONCLUSIONS Overall, the Tversky similarity metrics provide a more useful range of examples of molecular similarity than does the simpler Tanimoto similarity, and help to draw attention to molecular similarities that would not be recognized if Tanimoto alone were used. Hence, the Tversky similarity metrics are likely to be of significant value in many general problems in cheminformatics.
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Affiliation(s)
- Steve O'Hagan
- School of Chemistry, The University of ManchesterManchester, UK
- The Manchester Institute of Biotechnology, The University of ManchesterManchester, UK
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals, The University of ManchesterManchester, UK
| | - Douglas B. Kell
- School of Chemistry, The University of ManchesterManchester, UK
- The Manchester Institute of Biotechnology, The University of ManchesterManchester, UK
- Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals, The University of ManchesterManchester, UK
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Duesbury E, Holliday J, Willett P. Maximum Common Substructure-Based Data Fusion in Similarity Searching. J Chem Inf Model 2015; 55:222-30. [DOI: 10.1021/ci5005702] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Edmund Duesbury
- Information School, University of Sheffield, 211 Portobello, Sheffield S1 4DP, United Kingdom
| | - John Holliday
- Information School, University of Sheffield, 211 Portobello, Sheffield S1 4DP, United Kingdom
| | - Peter Willett
- Information School, University of Sheffield, 211 Portobello, Sheffield S1 4DP, United Kingdom
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Horvath D, Marcou G, Varnek A. Do not hesitate to use Tversky-and other hints for successful active analogue searches with feature count descriptors. J Chem Inf Model 2013; 53:1543-62. [PMID: 23731338 DOI: 10.1021/ci400106g] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
This study is an exhaustive analysis of the neighborhood behavior over a large coherent data set (ChEMBL target/ligand pairs of known Ki, for 165 targets with >50 associated ligands each). It focuses on similarity-based virtual screening (SVS) success defined by the ascertained optimality index. This is a weighted compromise between purity and retrieval rate of active hits in the neighborhood of an active query. One key issue addressed here is the impact of Tversky asymmetric weighing of query vs candidate features (represented as integer-value ISIDA colored fragment/pharmacophore triplet count descriptor vectors). The nearly a 3/4 million independent SVS runs showed that Tversky scores with a strong bias in favor of query-specific features are, by far, the most successful and the least failure-prone out of a set of nine other dissimilarity scores. These include classical Tanimoto, which failed to defend its privileged status in practical SVS applications. Tversky performance is not significantly conditioned by tuning of its bias parameter α. Both initial "guesses" of α = 0.9 and 0.7 were more successful than Tanimoto (at its turn, better than Euclid). Tversky was eventually tested in exhaustive similarity searching within the library of 1.6 M commercial + bioactive molecules at http://infochim.u-strasbg.fr/webserv/VSEngine.html , comparing favorably to Tanimoto in terms of "scaffold hopping" propensity. Therefore, it should be used at least as often as, perhaps in parallel to Tanimoto in SVS. Analysis with respect to query subclasses highlighted relationships of query complexity (simply expressed in terms of pharmacophore pattern counts) and/or target nature vs SVS success likelihood. SVS using more complex queries are more robust with respect to the choice of their operational premises (descriptors, metric). Yet, they are best handled by "pro-query" Tversky scores at α > 0.5. Among simpler queries, one may distinguish between "growable" (allowing for active analogs with additional features), and a few "conservative" queries not allowing any growth. These (typically bioactive amine transporter ligands) form the specific application domain of "pro-candidate" biased Tversky scores at α < 0.5.
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Affiliation(s)
- Dragos Horvath
- Laboratoire d'Infochimie, UMR 7140 CNRS-University Strasbourg, 1 Rue Blaise Pascal, Strasbourg 67000, France.
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Liu L, Ma Y, Wang RL, Xu WR, Wang SQ, Chou KC. Find novel dual-agonist drugs for treating type 2 diabetes by means of cheminformatics. Drug Des Devel Ther 2013; 7:279-88. [PMID: 23630413 PMCID: PMC3623550 DOI: 10.2147/dddt.s42113] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
The high prevalence of type 2 diabetes mellitus in the world as well as the increasing reports about the adverse side effects of the existing diabetes treatment drugs have made developing new and effective drugs against the disease a very high priority. In this study, we report ten novel compounds found by targeting peroxisome proliferator-activated receptors (PPARs) using virtual screening and core hopping approaches. PPARs have drawn increasing attention for developing novel drugs to treat diabetes due to their unique functions in regulating glucose, lipid, and cholesterol metabolism. The reported compounds are featured with dual functions, and hence belong to the category of dual agonists. Compared with the single PPAR agonists, the dual PPAR agonists, formed by combining the lipid benefit of PPARα agonists (such as fibrates) and the glycemic advantages of the PPARγ agonists (such as thiazolidinediones), are much more powerful in treating diabetes because they can enhance metabolic effects while minimizing the side effects. This was observed in the studies on molecular dynamics simulations, as well as on absorption, distribution, metabolism, and excretion, that these novel dual agonists not only possessed the same function as ragaglitazar (an investigational drug developed by Novo Nordisk for treating type 2 diabetes) did in activating PPARα and PPARγ, but they also had more favorable conformation for binding to the two receptors. Moreover, the residues involved in forming the binding pockets of PPARα and PPARγ among the top ten compounds are explicitly presented, and this will be very useful for the in-depth conduction of mutagenesis experiments. It is anticipated that the ten compounds may become potential drug candidates, or at the very least, the findings reported here may stimulate new strategies or provide useful insights for designing new and more powerful dual-agonist drugs for treating type 2 diabetes.
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Affiliation(s)
- Lei Liu
- PET/CT Center, General Hospital of Tianjin Medical University, Tianjin, People’s Republic of China
| | - Ying Ma
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, People’s Republic of China
| | - Run-Ling Wang
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, People’s Republic of China
| | - Wei-Ren Xu
- Tianjin Institute of Pharmaceutical Research (TIPR), Tianjin, People’s Republic of China
| | - Shu-Qing Wang
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin, People’s Republic of China
- Gordon Life Science Institute, Belmont, MA, USA
| | - Kuo-Chen Chou
- Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia
- Gordon Life Science Institute, Belmont, MA, USA
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Li R, Stumpfe D, Vogt M, Geppert H, Bajorath J. Development of a Method To Consistently Quantify the Structural Distance between Scaffolds and To Assess Scaffold Hopping Potential. J Chem Inf Model 2011; 51:2507-14. [DOI: 10.1021/ci2003945] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Ruifang Li
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany
| | - Dagmar Stumpfe
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany
| | - Martin Vogt
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany
| | - Hanna Geppert
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany
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13
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Lin FY, Tseng YJ. Structure-Based Fragment Hopping for Lead Optimization Using Predocked Fragment Database. J Chem Inf Model 2011; 51:1703-15. [DOI: 10.1021/ci200136j] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Fang-Yu Lin
- Graduate Institute of Biomedical Electronics and Bioinformatics and ‡Department of Computer Science and Information Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, Taiwan 106
| | - Yufeng J. Tseng
- Graduate Institute of Biomedical Electronics and Bioinformatics and ‡Department of Computer Science and Information Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, Taiwan 106
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Yoo J, Medina-Franco JL. Chemoinformatic Approaches for Inhibitors of DNA Methyltransferases: Comprehensive Characterization of Screening Libraries. ACTA ACUST UNITED AC 2011. [DOI: 10.4236/cmb.2011.11002] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Computational medicinal chemistry in fragment-based drug discovery: what, how and when. Future Med Chem 2011; 3:95-134. [DOI: 10.4155/fmc.10.277] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
The use of fragment-based drug discovery (FBDD) has increased in the last decade due to the encouraging results obtained to date. In this scenario, computational approaches, together with experimental information, play an important role to guide and speed up the process. By default, FBDD is generally considered as a constructive approach. However, such additive behavior is not always present, therefore, simple fragment maturation will not always deliver the expected results. In this review, computational approaches utilized in FBDD are reported together with real case studies, where applicability domains are exemplified, in order to analyze them, and then, maximize their performance and reliability. Thus, a proper use of these computational tools can minimize misleading conclusions, keeping the credit on FBDD strategy, as well as achieve higher impact in the drug-discovery process. FBDD goes one step beyond a simple constructive approach. A broad set of computational tools: docking, R group quantitative structure–activity relationship, fragmentation tools, fragments management tools, patents analysis and fragment-hopping, for example, can be utilized in FBDD, providing a clear positive impact if they are utilized in the proper scenario – what, how and when. An initial assessment of additive/non-additive behavior is a critical point to define the most convenient approach for fragments elaboration.
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Vogt M, Stumpfe D, Geppert H, Bajorath J. Scaffold Hopping Using Two-Dimensional Fingerprints: True Potential, Black Magic, or a Hopeless Endeavor? Guidelines for Virtual Screening. J Med Chem 2010; 53:5707-15. [DOI: 10.1021/jm100492z] [Citation(s) in RCA: 78] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Martin Vogt
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany
| | - Dagmar Stumpfe
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany
| | - Hanna Geppert
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany
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17
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Geppert H, Vogt M, Bajorath J. Current trends in ligand-based virtual screening: molecular representations, data mining methods, new application areas, and performance evaluation. J Chem Inf Model 2010; 50:205-16. [PMID: 20088575 DOI: 10.1021/ci900419k] [Citation(s) in RCA: 231] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Hanna Geppert
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universitat, Dahlmannstrasse 2, D-53113 Bonn, Germany
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18
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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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19
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Hu Y, Bajorath J. Global assessment of scaffold hopping potential for current pharmaceutical targets. MEDCHEMCOMM 2010. [DOI: 10.1039/c0md00156b] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A scaffold-based target network is shown together with scaffold pairs having different scaffold hopping potential for a given target.
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Affiliation(s)
- Ye Hu
- Department of Life Science Informatics
- B-IT
- LIMES Program Unit Chemical Biology and Medicinal Chemistry
- Rheinische Friedrich-Wilhelms-Universität
- Germany
| | - Jürgen Bajorath
- Department of Life Science Informatics
- B-IT
- LIMES Program Unit Chemical Biology and Medicinal Chemistry
- Rheinische Friedrich-Wilhelms-Universität
- Germany
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20
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Abstract
This chapter reviews the use of molecular fingerprints for chemical similarity searching. The fingerprints encode the presence of 2D substructural fragments in a molecule, and the similarity between a pair of molecules is a function of the number of fragments that they have in common. Although this provides a very simple way of estimating the degree of structural similarity between two molecules, it has been found to provide an effective and an efficient tool for searching large chemical databases. The review describes the historical development of similarity searching since it was first described in the mid-1980s, reviews the many different coefficients, representations, and weightings that can be combined to form a similarity measure, describes quantitative measures of the effectiveness of similarity searching, and concludes by looking at current developments based on the use of data fusion and machine learning techniques.
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Affiliation(s)
- Peter Willett
- Department of Information Studies, The University of Sheffield, Sheffield, UK
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