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Singh R, Kumar P, Sindhu J, Devi M, Kumar A, Lal S, Singh D, Kumar H. Thiazolidinedione-triazole conjugates: design, synthesis and probing of the α-amylase inhibitory potential. Future Med Chem 2023; 15:1273-1294. [PMID: 37551699 DOI: 10.4155/fmc-2023-0144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/09/2023] Open
Abstract
Aim: The primary objective of this investigation was the synthesis, spectral interpretation and evaluation of the α-amylase inhibition of rationally designed thiazolidinedione-triazole conjugates (7a-7aa). Materials & methods: The designed compounds were synthesized by stirring a mixture of thiazolidine-2,4-dione, propargyl bromide, cinnamaldehyde and azide derivatives in polyethylene glycol-400. The α-amylase inhibitory activity of the synthesized conjugates was examined by integrating in vitro and in silico studies. Results: The investigated derivatives exhibited promising α-amylase inhibitory activity, with IC50 values ranging between 0.028 and 0.088 μmol ml-1. Various computational approaches were employed to get detailed information about the inhibition mechanism. Conclusion: The thiazolidinedione-triazole conjugate 7p, with IC50 = 0.028 μmol ml-1, was identified as the best hit for inhibiting α-amylase.
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Affiliation(s)
- Rahul Singh
- Department of Chemistry, Kurukshetra University, Kurukshetra, 136119, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, 136119, India
| | - Jayant Sindhu
- Department of Chemistry, COBS&H, CCS Haryana Agricultural University, Hisar, 125004, India
| | - Meena Devi
- Department of Chemistry, Kurukshetra University, Kurukshetra, 136119, India
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, GJUS&T, Hisar, 125001, India
| | - Sohan Lal
- Department of Chemistry, Kurukshetra University, Kurukshetra, 136119, India
| | - Devender Singh
- Department of Chemistry, Maharshi Dayanand University, Rohtak, 124001, India
| | - Harish Kumar
- Department of Chemistry, School of Basic Sciences, Central University Haryana, Mahendergarh, 123029, India
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2
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Lu J, Zhang H, Yu J, Shan D, Qi J, Chen J, Song H, Yang M. Predicting Rate Constants of Hydroxyl Radical Reactions with Alkanes Using Machine Learning. J Chem Inf Model 2021; 61:4259-4265. [PMID: 34378385 DOI: 10.1021/acs.jcim.1c00809] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The hydrogen abstraction reactions of the hydroxyl radical with alkanes play an important role in combustion chemistry and atmospheric chemistry. However, site-specific reaction constants are difficult to obtain experimentally and theoretically. Recently, machine learning has proved its ability to predict chemical properties. In this work, a machine learning approach is developed to predict the temperature-dependent site-specific rate constants of the title reactions. Multilayered neural network (NN) models are developed by training the site-specific rate constants of 11 reactions, and several schemes are designed to improve the prediction accuracy. The results show that the proposed NN models are robust in predicting the site-specific and overall rate constants.
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Affiliation(s)
- Junhui Lu
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Huimin Zhang
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China
| | - Jinhui Yu
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dezun Shan
- School of Physical Science and Technology, Wuhan University, Wuhan 430072, China
| | - Ji Qi
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China
| | - Jiawen Chen
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China
| | - Hongwei Song
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China
| | - Minghui Yang
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430071, China
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3
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Sosnina EA, Sosnin S, Nikitina AA, Nazarov I, Osolodkin DI, Fedorov MV. Recommender Systems in Antiviral Drug Discovery. ACS OMEGA 2020; 5:15039-15051. [PMID: 32632398 PMCID: PMC7315437 DOI: 10.1021/acsomega.0c00857] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 06/03/2020] [Indexed: 06/11/2023]
Abstract
Recommender systems (RSs), which underwent rapid development and had an enormous impact on e-commerce, have the potential to become useful tools for drug discovery. In this paper, we applied RS methods for the prediction of the antiviral activity class (active/inactive) for compounds extracted from ChEMBL. Two main RS approaches were applied: collaborative filtering (Surprise implementation) and content-based filtering (sparse-group inductive matrix completion (SGIMC) method). The effectiveness of RS approaches was investigated for prediction of antiviral activity classes ("interactions") for compounds and viruses, for which some of their interactions with other viruses or compounds are known, and for prediction of interaction profiles for new compounds. Both approaches achieved relatively good prediction quality for binary classification of individual interactions and compound profiles, as quantified by cross-validation and external validation receiver operating characteristic (ROC) score >0.9. Thus, even simple recommender systems may serve as an effective tool in antiviral drug discovery.
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Affiliation(s)
- Ekaterina A. Sosnina
- Center
for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30/1, Moscow 143026, Russia
- Institute
of Physiologically Active Compounds, RAS, Severniy pr. 1, Chernogolovka 142432, Russia
| | - Sergey Sosnin
- Center
for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30/1, Moscow 143026, Russia
- Syntelly
LLC, Skolkovo Innovation Center, Bolshoy Boulevard 30, Moscow 121205, Russia
| | - Anastasia A. Nikitina
- Department
of Chemistry, Lomonosov Moscow State University, Leninskie Gory 1 bd. 3, Moscow 119991, Russia
- FSBSI
“Chumakov FSC R&D IBP RAS”, Poselok Instituta Poliomielita 8
bd. 1, Poselenie Moskovsky, Moscow 108819, Russia
| | - Ivan Nazarov
- Center
for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30/1, Moscow 143026, Russia
| | - Dmitry I. Osolodkin
- FSBSI
“Chumakov FSC R&D IBP RAS”, Poselok Instituta Poliomielita 8
bd. 1, Poselenie Moskovsky, Moscow 108819, Russia
- Institute
of Translational Medicine and Biotechnology, Sechenov First Moscow State Medical University, Trubetskaya Ulitsa 8, Moscow 119991, Russia
| | - Maxim V. Fedorov
- Center
for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30/1, Moscow 143026, Russia
- Syntelly
LLC, Skolkovo Innovation Center, Bolshoy Boulevard 30, Moscow 121205, Russia
- Physics
John Anderson Building, University of Strathclyde, 107 Rottenrow East, Glasgow G4 0NG, U.K.
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4
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Golbraikh A. Value of p-Value. Mol Inform 2019; 38:e1800152. [PMID: 31188542 DOI: 10.1002/minf.201800152] [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: 11/15/2018] [Accepted: 05/07/2019] [Indexed: 11/09/2022]
Abstract
The goal of this manuscript is to discuss important aspects of external validation of classification and category Quantitative Structure - Activity/Property/Toxicity Relationship QS/A/P/T/R models that to the best of author's knowledge are not addressed in publications. Statistical significance (in terms of p-value) and accuracy of prediction (in terms of Correct Classification Rate (CCR)) of external validation set compounds are among most important characteristics of the models. We assert that in most cases the models built for classification or category response variable should be statistically significant and predictive for each class or category. We show that three thresholds of the number of compounds in each class or category of the external validation sets should be satisfied. 1) The p-value criterion can never be satisfied, if the number of compounds is below the first threshold. 2) If the number of compounds is between the first and the second thresholds, p-value criterion should be used. 3) If it is higher than the third threshold, classification or category accuracy criterion should be used. 4) If the number of compounds is between second and third thresholds, either one or the other criterion should be used depending on the value of p-value. 5) When the number of compounds in the class approaches infinity, the maximum relative error of prediction approaches the relative expected error. The results are of interest in other areas of multidimensional data analysis.
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Affiliation(s)
- Alexander Golbraikh
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, CB #7360, Chapel Hill, NC 27599
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5
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Persuy MA, Sanz G, Tromelin A, Thomas-Danguin T, Gibrat JF, Pajot-Augy E. Mammalian olfactory receptors: molecular mechanisms of odorant detection, 3D-modeling, and structure-activity relationships. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2014; 130:1-36. [PMID: 25623335 DOI: 10.1016/bs.pmbts.2014.11.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
This chapter describes the main characteristics of olfactory receptor (OR) genes of vertebrates, including generation of this large multigenic family and pseudogenization. OR genes are compared in relation to evolution and among species. OR gene structure and selection of a given gene for expression in an olfactory sensory neuron (OSN) are tackled. The specificities of OR proteins, their expression, and their function are presented. The expression of OR proteins in locations other than the nasal cavity is regulated by different mechanisms, and ORs display various additional functions. A conventional olfactory signal transduction cascade is observed in OSNs, but individual ORs can also mediate different signaling pathways, through the involvement of other molecular partners and depending on the odorant ligand encountered. ORs are engaged in constitutive dimers. Ligand binding induces conformational changes in the ORs that regulate their level of activity depending on odorant dose. When present, odorant binding proteins induce an allosteric modulation of OR activity. Since no 3D structure of an OR has been yet resolved, modeling has to be performed using the closest G-protein-coupled receptor 3D structures available, to facilitate virtual ligand screening using the models. The study of odorant binding modes and affinities may infer best-bet OR ligands, to be subsequently checked experimentally. The relationship between spatial and steric features of odorants and their activity in terms of perceived odor quality are also fields of research that development of computing tools may enhance.
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Affiliation(s)
- Marie-Annick Persuy
- INRA UR 1197 NeuroBiologie de l'Olfaction, Domaine de Vilvert, Jouy-en-Josas, France
| | - Guenhaël Sanz
- INRA UR 1197 NeuroBiologie de l'Olfaction, Domaine de Vilvert, Jouy-en-Josas, France
| | - Anne Tromelin
- INRA UMR 1129 Flaveur, Vision et Comportement du Consommateur, Dijon, France
| | | | - Jean-François Gibrat
- INRA UR1077 Mathématique Informatique et Génome, Domaine de Vilvert, Jouy-en-Josas, France
| | - Edith Pajot-Augy
- INRA UR 1197 NeuroBiologie de l'Olfaction, Domaine de Vilvert, Jouy-en-Josas, France.
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6
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Kim M, Sedykh A, Chakravarti SK, Saiakhov RD, Zhu H. Critical evaluation of human oral bioavailability for pharmaceutical drugs by using various cheminformatics approaches. Pharm Res 2014; 31:1002-14. [PMID: 24306326 PMCID: PMC3955412 DOI: 10.1007/s11095-013-1222-1] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2013] [Accepted: 09/30/2013] [Indexed: 01/07/2023]
Abstract
PURPOSE Oral bioavailability (%F) is a key factor that determines the fate of a new drug in clinical trials. Traditionally, %F is measured using costly and time-consuming experimental tests. Developing computational models to evaluate the %F of new drugs before they are synthesized would be beneficial in the drug discovery process. METHODS We employed Combinatorial Quantitative Structure-Activity Relationship approach to develop several computational %F models. We compiled a %F dataset of 995 drugs from public sources. After generating chemical descriptors for each compound, we used random forest, support vector machine, k nearest neighbor, and CASE Ultra to develop the relevant QSAR models. The resulting models were validated using five-fold cross-validation. RESULTS The external predictivity of %F values was poor (R(2) = 0.28, n = 995, MAE = 24), but was improved (R(2) = 0.40, n = 362, MAE = 21) by filtering unreliable predictions that had a high probability of interacting with MDR1 and MRP2 transporters. Furthermore, classifying the compounds according to the %F values (%F < 50% as "low", %F ≥ 50% as 'high") and developing category QSAR models resulted in an external accuracy of 76%. CONCLUSIONS In this study, we developed predictive %F QSAR models that could be used to evaluate new drug compounds, and integrating drug-transporter interactions data greatly benefits the resulting models.
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Affiliation(s)
- Marlene Kim
- Department of Chemistry, Rutgers University, Camden, New Jersey 08102
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey 08102
| | - Alexander Sedykh
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
| | | | | | - Hao Zhu
- Department of Chemistry, Rutgers University, Camden, New Jersey 08102
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey 08102
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7
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García GC, Palacios-Bejarano B, Ruiz IL, Gómez-Nieto MÁ. Comparison of representational spaces based on structural information in the development of QSAR models for benzylamino enaminone derivatives. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2012; 23:751-774. [PMID: 22988909 DOI: 10.1080/1062936x.2012.719543] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
In this paper we study different representational spaces of molecule data sets based on 2D representation models for the building of QSAR models for the prediction of the activity of 37 benzylamino enaminone derivatives. Approximations based on classical similarity calculated from fingerprint representation of molecules and isomorphism obtained using sub-graph matching algorithms are compared to fragmentation-based approximations using partial least squares and genetic algorithms. The influence of the anchored position of a non-common moiety and the kind of substituents in the common core structure of the data set are analysed, demonstrating the anomalous behaviour of some molecules and therefore the difficulty in building prediction models. These problems are solved by considering approximate similarity models. These models tune the prediction equations based on the size of the substituent and the anchored position, by adjusting the contribution of each substituent in similarity measurements calculated between the molecule data sets.
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Affiliation(s)
- G Cerruela García
- Department of Computing and Numerical Analysis, University of Córdoba, Spain.
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8
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Zhang S. Application of Machine Leaning in Drug Discovery and Development. Mach Learn 2012. [DOI: 10.4018/978-1-60960-818-7.ch517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Machine learning techniques have been widely used in drug discovery and development, particularly in the areas of cheminformatics, bioinformatics and other types of pharmaceutical research. It has been demonstrated they are suitable for large high dimensional data, and the models built with these methods can be used for robust external predictions. However, various problems and challenges still exist, and new approaches are in great need. In this Chapter, the authors will review the current development of machine learning techniques, and especially focus on several machine learning techniques they developed as well as their application to model building, lead discovery via virtual screening, integration with molecular docking, and prediction of off-target properties. The authors will suggest some potential different avenues to unify different disciplines, such as cheminformatics, bioinformatics and systems biology, for the purpose of developing integrated in silico drug discovery and development approaches.
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Affiliation(s)
- Shuxing Zhang
- The University of Texas at M.D. Anderson Cancer Center, USA
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9
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West C, Guenegou G, Zhang Y, Morin-Allory L. Insights into chiral recognition mechanisms in supercritical fluid chromatography. II. Factors contributing to enantiomer separation on tris-(3,5-dimethylphenylcarbamate) of amylose and cellulose stationary phases. J Chromatogr A 2011; 1218:2033-57. [DOI: 10.1016/j.chroma.2010.11.085] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2010] [Revised: 11/06/2010] [Accepted: 11/29/2010] [Indexed: 10/18/2022]
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10
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Del Rio A. Exploring enantioselective molecular recognition mechanisms with chemoinformatic techniques. J Sep Sci 2009; 32:1566-84. [DOI: 10.1002/jssc.200800693] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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11
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Hohman M, Gregory K, Chibale K, Smith PJ, Ekins S, Bunin B. Novel web-based tools combining chemistry informatics, biology and social networks for drug discovery. Drug Discov Today 2009; 14:261-70. [PMID: 19231313 DOI: 10.1016/j.drudis.2008.11.015] [Citation(s) in RCA: 115] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2008] [Revised: 10/25/2008] [Accepted: 11/05/2008] [Indexed: 11/28/2022]
Abstract
A convergence of different commercial and publicly accessible chemical informatics, databases and social networking tools is positioned to change the way that research collaborations are initiated, maintained and expanded, particularly in the realm of neglected diseases. A community-based platform that combines traditional drug discovery informatics with Web2.0 features in secure groups is believed to be the key to facilitating richer, instantaneous collaborations involving sensitive drug discovery data and intellectual property. Heterogeneous chemical and biological data from low-throughput or high-throughput experiments are archived, mined and then selectively shared either just securely between specifically designated colleagues or openly on the Internet in standardized formats. We will illustrate several case studies for anti-malarial research enabled by this platform, which we suggest could be easily expanded more broadly for pharmaceutical research in general.
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Affiliation(s)
- Moses Hohman
- Collaborative Drug Discovery, Inc, Burlingame, CA 94403, USA
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12
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Zhang L, Zhu H, Oprea TI, Golbraikh A, Tropsha A. QSAR Modeling of the Blood–Brain Barrier Permeability for Diverse Organic Compounds. Pharm Res 2008; 25:1902-14. [DOI: 10.1007/s11095-008-9609-0] [Citation(s) in RCA: 111] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2007] [Accepted: 04/23/2008] [Indexed: 01/16/2023]
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13
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Zhang QY, Aires-de-Sousa J. Physicochemical stereodescriptors of atomic chiral centers. J Chem Inf Model 2007; 46:2278-87. [PMID: 17125170 DOI: 10.1021/ci600235w] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Physicochemical atomic stereodescriptors (PAS) were implemented that represent the chirality of an atomic chiral center on the basis of empirical physicochemical properties of the ligands. The ligands are ranked according to a specific property, and the chiral center takes an S/R-like descriptor relative to that property. The procedure is performed for a series of properties, yielding a chirality profile. Application of the PAS descriptors to the prediction of enantioselectivity in chemical reactions, from the molecular structures, is illustrated here. The relationship between the molecular structures, represented by the PAS descriptors, and the enantioselectivity was learned by neural networks, decision trees, or random forests. In a first application, a data set was employed with chiral amino alcohols that enantioselectively catalyze the addition of diethylzinc to benzaldehyde. Prediction of the major enantiomer obtained in the reaction, from the molecular structure of the catalyst, was achieved with accuracy up to 90%. The second application investigated the enantiopreference of Pseudomonas cepacia lipase (PCL) toward primary alcohols. The learned models could make correct predictions about the preferred enantiomer, from the molecular structure of the substrate, in up to 93% of the cases. These included substrates with and without O-atoms bonded to the chiral center. The properties automatically selected to build the models can give indications on the relevant factors guiding the observed chemical behavior.
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Affiliation(s)
- Qing-You Zhang
- CQFB and REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
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14
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Kaiser KLE. Evolution of the international workshops on quantitative structure-activity relationships (QSARs) in environmental toxicology. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2007; 18:3-20. [PMID: 17365955 DOI: 10.1080/10629360601053927] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
This presentation will review the evolution of the workshops from a scientific and personal perspective. From their modest beginning in 1983, the workshops have developed into larger international meetings, regularly held every two years. Their initial focus on the aquatic sphere soon expanded to include properties and effects on atmospheric and terrestrial species, including man. Concurrent with this broadening of their scientific scope, the workshops have become an important forum for the early dissemination of all aspects of qualitative and quantitative structure-activity research in ecotoxicology and human health effects. Over the last few decades, the field of quantitative structure/activity relationships (QSARs) has quickly emerged as a major scientific method in understanding the properties and effects of chemicals on the environment and human health. From substances that only affect cell membranes to those that bind strongly to a specific enzyme, QSARs provides insight into the biological effects and chemical and physical properties of substances. QSARs are useful for delineating the quantitative changes in biological effects resulting from minor but systematic variations of the structure of a compound with a specific mode of action. In addition, more holistic approaches are being devised that result in our ability to predict the effects of structurally unrelated compounds with (potentially) different modes of action. Research in QSAR environmental toxicology has led to many improvements in the manufacturing, use, and disposal of chemicals. Furthermore, it has led to national policies and international agreements, from use restrictions or outright bans of compounds, such as polychlorinated biphenyls (PCBs), mirex, and highly chlorinated pesticides (e.g. DDT, dieldrin) for the protection of avian predators, to alternatives for ozone-depleting compounds, to better waste treatment systems, to more powerful and specific acting drugs. Most of the recent advances in drug development could not have been achieved without the use of QSARs in one form or another. The pace of such developments is rapid and QSARs are the keystone to that progress. These workshops have contributed to this progress and will continue to do so in the future.
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Affiliation(s)
- K L E Kaiser
- TerraBase Inc., 1063 King St. West, Hamilton, Ontario. Canada.
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15
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Dervarics M, Otvös F, Martinek TA. Development of a Chirality-Sensitive Flexibility Descriptor for 3+3D-QSAR. J Chem Inf Model 2006; 46:1431-8. [PMID: 16711763 DOI: 10.1021/ci0505574] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Multidimensional QSAR methodologies can be used to predict the active conformation encoded in the conformational preferences of molecules in an active series by utilizing conformational sampling. In the 3+3D-QSAR approach, the conformational free energy loss is modeled with internal coordinate-based flexibility descriptors. While the pharmacophore point pair distance descriptors introduced earlier proved useful in the construction of QSAR models and in the prediction of important features of the active conformation, they are inherently incapable of describing the chiral arrangement of the pharmacophores. As an improvement, a chirality-sensitive flexibility (CSF) descriptor is now introduced, which is based on the distance between a pharmacophore point and a plane defined by three pharmacophore points. The performance of the CSF descriptor was tested on two active series: 37 endomorphin analogues with opiate activity and 38 PGF2alpha analogues with antinidatory activity. The newly devised descriptor resulted in improved QSAR models in terms of both prediction accuracy and precision of the chiral geometric features of the predicted active conformations.
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Affiliation(s)
- Maté Dervarics
- Institute of Pharmaceutical Chemistry, University of Szeged, H-6701 Szeged, POB 121, Hungary
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