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Hierarchical Clustering and Target-Independent QSAR for Antileishmanial Oxazole and Oxadiazole Derivatives. Int J Mol Sci 2022; 23:ijms23168898. [PMID: 36012163 PMCID: PMC9408707 DOI: 10.3390/ijms23168898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/22/2022] [Accepted: 07/25/2022] [Indexed: 11/16/2022] Open
Abstract
Leishmaniasis is a neglected tropical disease that kills more than 20,000 people each year. The chemotherapy available for the treatment of the disease is limited, and novel approaches to discover novel drugs are urgently needed. Herein, 2D- and 4D-quantitative structure–activity relationship (QSAR) models were developed for a series of oxazole and oxadiazole derivatives that are active against Leishmania infantum, the causative agent of visceral leishmaniasis. A clustering strategy based on structural similarity was applied with molecular fingerprints to divide the complete set of compounds into two groups. Hierarchical clustering was followed by the development of 2D- (R2 = 0.90, R2pred = 0.82) and 4D-QSAR models (R2 = 0.80, R2pred = 0.64), which showed improved statistical robustness and predictive ability.
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Sharavyeva YO, Siutkina AI, Chashchina SV, Novikova VV, Makhmudov RR, Shipilovskikh SA. Synthesis, analgesic and antimicrobial activity of substituted 2-(3-cyano-4,5,6,7-tetrahydrobenzo[b]thiophen-2-ylamino)-4-oxo-4-phenylbut-2-enoates. Russ Chem Bull 2022. [DOI: 10.1007/s11172-022-3445-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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3
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Synthesis and anti-inflammatory activity of N′-substituted 2-[2-(diarylmethylene)hydrazinyl]-5,5-dimethyl-4-oxohex-2-enehydrazides. Russ Chem Bull 2022. [DOI: 10.1007/s11172-022-3439-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Siutkina AI, Chashchina SV, Makhmudov RR, Kizimova IA, Shipilovskikh SA, Igidov NM. Synthesis and Biological Activity of Substituted 2-[2-(Diphenylmethylene)hydrazinyl]-5,5-dimethyl-4-oxohex-2-enoates. RUSSIAN JOURNAL OF ORGANIC CHEMISTRY 2021. [DOI: 10.1134/s1070428021110105] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Two Decades of 4D-QSAR: A Dying Art or Staging a Comeback? Int J Mol Sci 2021; 22:ijms22105212. [PMID: 34069090 PMCID: PMC8156896 DOI: 10.3390/ijms22105212] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 05/11/2021] [Accepted: 05/12/2021] [Indexed: 01/01/2023] Open
Abstract
A key question confronting computational chemists concerns the preferable ligand geometry that fits complementarily into the receptor pocket. Typically, the postulated ‘bioactive’ 3D ligand conformation is constructed as a ‘sophisticated guess’ (unnecessarily geometry-optimized) mirroring the pharmacophore hypothesis—sometimes based on an erroneous prerequisite. Hence, 4D-QSAR scheme and its ‘dialects’ have been practically implemented as higher level of model abstraction that allows the examination of the multiple molecular conformation, orientation and protonation representation, respectively. Nearly a quarter of a century has passed since the eminent work of Hopfinger appeared on the stage; therefore the natural question occurs whether 4D-QSAR approach is still appealing to the scientific community? With no intention to be comprehensive, a review of the current state of art in the field of receptor-independent (RI) and receptor-dependent (RD) 4D-QSAR methodology is provided with a brief examination of the ‘mainstream’ algorithms. In fact, a myriad of 4D-QSAR methods have been implemented and applied practically for a diverse range of molecules. It seems that, 4D-QSAR approach has been experiencing a promising renaissance of interests that might be fuelled by the rising power of the graphics processing unit (GPU) clusters applied to full-atom MD-based simulations of the protein-ligand complexes.
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Ataide Martins JP, Rougeth de Oliveira MA, Oliveira de Queiroz MS. Web-4D-QSAR: A web-based application to generate 4D-QSAR descriptors. J Comput Chem 2018; 39:917-924. [DOI: 10.1002/jcc.25166] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Revised: 12/20/2017] [Accepted: 12/21/2017] [Indexed: 11/08/2022]
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Dreher J, Scheiber J, Stiefl N, Baumann K. xMaP-An Interpretable Alignment-Free Four-Dimensional Quantitative Structure-Activity Relationship Technique Based on Molecular Surface Properties and Conformer Ensembles. J Chem Inf Model 2018; 58:165-181. [PMID: 29172519 DOI: 10.1021/acs.jcim.7b00419] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
A novel alignment-free molecular descriptor called xMaP (flexible MaP descriptor) is introduced. The descriptor is the advancement of the previously published translationally and rotationally invariant three-dimensional (3D) descriptor MaP (mapping property distributions onto the molecular surface) to the fourth dimension (4D). In addition to MaP, xMaP is independent of the chosen starting conformation of the encoded molecules and is therefore entirely alignment-free. This is achieved by using ensembles of conformers, which are generated by conformational searches. This step of the procedure is similar to Hopfinger's 4D quantitative structure-activity relationship (QSAR). A five-step procedure is used to compute the xMaP descriptor. First, a conformational search for each molecule is carried out. Next, for each of the conformers an approximation to the molecular surface with equally distributed surface points is computed. Third, molecular properties are projected onto this surface. Fourth, areas of identical properties are clustered to so-called patches. Fifth, the spatial distribution of the patches is converted into an alignment-free descriptor that is based on the entire conformer ensemble. The resulting descriptor can be interpreted by superimposing the most important descriptor variables and the molecules of the data set. The most important descriptor variables are identified with chemometric regression tools. The novel descriptor was applied to several benchmark data sets and was compared to other descriptors and QSAR techniques comprising a binary fingerprint, a topological pharmacophore descriptor (Cats2D), and the field-based 3D-QSAR technique GRID/PLS which is alignment-dependent. The use of conformer ensembles renders xMaP very robust. It turns out that xMaP performs very well on (almost) all data sets and that the statistical results are comparable to GRID/PLS. In addition to that, xMaP can also be used to efficiently visualize the derived quantitative structure-activity relationships.
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Affiliation(s)
- Jan Dreher
- Institute of Medicinal and Pharmaceutical Chemistry, University of Technology Braunschweig , Beethovenstrasse 55, D 38106 Braunschweig, Germany
| | - Josef Scheiber
- Institute of Medicinal and Pharmaceutical Chemistry, University of Technology Braunschweig , Beethovenstrasse 55, D 38106 Braunschweig, Germany
| | - Nikolaus Stiefl
- Institute of Medicinal and Pharmaceutical Chemistry, University of Technology Braunschweig , Beethovenstrasse 55, D 38106 Braunschweig, Germany
| | - Knut Baumann
- Institute of Medicinal and Pharmaceutical Chemistry, University of Technology Braunschweig , Beethovenstrasse 55, D 38106 Braunschweig, Germany
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de Campos LJ, de Melo EB. A QSAR study of integrase strand transfer inhibitors based on a large set of pyrimidine, pyrimidone, and pyridopyrazine carboxamide derivatives. J Mol Struct 2017. [DOI: 10.1016/j.molstruc.2017.03.103] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Wang T, Yuan XS, Wu MB, Lin JP, Yang LR. The advancement of multidimensional QSAR for novel drug discovery - where are we headed? Expert Opin Drug Discov 2017; 12:769-784. [PMID: 28562095 DOI: 10.1080/17460441.2017.1336157] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
INTRODUCTION The Multidimensional quantitative structure-activity relationship (multidimensional-QSAR) method is one of the most popular computational methods employed to predict interesting biochemical properties of existing or hypothetical molecules. With continuous progress, the QSAR method has made remarkable success in various fields, such as medicinal chemistry, material science and predictive toxicology. Areas covered: In this review, the authors cover the basic elements of multidimensional -QSAR including model construction, validation and application. It includes and emphasizes the very recent developments of multidimensional -QSAR such as: HQSAR, G-QSAR, MIA-QSAR, multi-target QSAR. The advantages and disadvantages of each method are also discussed and typical examples of their application are detailed. Expert opinion: Although there are defects in multidimensional-QSAR modeling, it is still of enormous help to chemists, biologists and other researchers in various fields. In the authors' opinion, the latest more precise and feasible QSAR models should be further developed by integrating new descriptors, algorithms and other relevant computational techniques. Apart from being applied in traditional fields (e.g. lead optimization and predictive risk assessment), QSAR should be used more widely as a routine method in other emerging research fields including the modeling of nanoparticles(NPs), mixture toxicity and peptides.
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Affiliation(s)
- Tao Wang
- a School of biological science , Jining Medical University , Jining , China.,b Department of Chemical and Biological Engineering , Zhejiang University , Hangzhou , China
| | - Xin-Song Yuan
- b Department of Chemical and Biological Engineering , Zhejiang University , Hangzhou , China
| | - Mian-Bin Wu
- b Department of Chemical and Biological Engineering , Zhejiang University , Hangzhou , China
| | - Jian-Ping Lin
- b Department of Chemical and Biological Engineering , Zhejiang University , Hangzhou , China
| | - Li-Rong Yang
- b Department of Chemical and Biological Engineering , Zhejiang University , Hangzhou , China
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Hamzeh-Mivehroud M, Sokouti B, Dastmalchi S. An Introduction to the Basic Concepts in QSAR-Aided Drug Design. Oncology 2017. [DOI: 10.4018/978-1-5225-0549-5.ch002] [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
The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.
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Affiliation(s)
| | | | - Siavoush Dastmalchi
- Biotechnology Research Center, Tabriz University of Medical Sciences, Iran & School of Pharmacy, Tabriz University of Medical Sciences, Iran
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de Melo EB, Martins JPA, Miranda EH, Ferreira MMC. A best comprehension about the toxicity of phenylsulfonyl carboxylates in Vibrio fischeri using quantitative structure activity/property relationship methods. JOURNAL OF HAZARDOUS MATERIALS 2016; 304:233-241. [PMID: 26551227 DOI: 10.1016/j.jhazmat.2015.10.047] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2015] [Revised: 10/17/2015] [Accepted: 10/22/2015] [Indexed: 06/05/2023]
Abstract
Aromatic sulfones comprise a class of chemicals used in agrochemical and pharmaceutical industries and as floatation and extractant agents in petrochemical and metallurgy industries. In this study, new QSA(P)R studies were carried out to predict the toxicity against Vibrio fischeri of a set of 52 aromatic sulfones. The same approach was used to evaluate the relationship between these endpoint and the water solubility, another important environmental endpoint. The study resulted in models of good statistical quality and mechanistic interpretation with a possible correlation between the two endpoints, but the toxic effect is also likely to depend on other physicochemical properties. The use of the PLS2, a method not commonly used in QSA(P)R studies, also produced models of greater reliability, and the relationship between the two endpoints was reinforced to some degree. These results are useful for better understanding the process by which these compounds exert their environmental toxicity, thus aiding in the development of industrially useful compounds with less potential environmental damage.
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Su M, Tan J, Lin CY. Development of HIV-1 integrase inhibitors: recent molecular modeling perspectives. Drug Discov Today 2015. [PMID: 26220090 DOI: 10.1016/j.drudis.2015.07.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Of the three viral enzymes essential to HIV replication, HIV-1 integrase (IN) is gaining popularity as a target for the antiviral therapy of AIDS. Substantial work focusing on IN has been done over the past three decades, which has facilitated and led to the approval of three drugs. Here, we discuss in detail the development of IN inhibitors between January 2012 and May 2014, with a particular focus on molecular simulation. We highlight controversial aspects of computational drug design and refer to alternative practices where appropriate. The analysis of these computational approaches provides some useful clues to the possible future discovery of novel IN inhibitors.
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Affiliation(s)
- Min Su
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China
| | - Jianjun Tan
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China.
| | - Chun-Yuan Lin
- Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan 33302, Taiwan.
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Kanhed AM, Dash RC, Parmar N, Das TK, Giridhar R, Yadav MR. Benzo[e]pyrimido[5,4-b][1,4]diazepin-6(11H)-one derivatives as Aurora A kinase inhibitors: LQTA-QSAR analysis and detailed systematic validation of the developed model. Mol Divers 2015; 19:965-74. [PMID: 26183841 DOI: 10.1007/s11030-015-9618-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2015] [Accepted: 07/02/2015] [Indexed: 12/22/2022]
Abstract
Aurora kinases are sub-divided into Aurora A, Aurora B, and Aurora C kinases that are considered as prospective targets for a new class of anticancer drugs. In this work, a 4-D-QSAR model using an LQTA-QSAR approach with previously reported 31 derivatives of benzo[e]pyrimido[5,4 -b][1,4]diazepin -6(11H)-one as potent Aurora kinase A inhibitors has been created. Instead of single conformation, the conformational ensemble profile generated for each ligand by using trajectories and topology information retrieved from molecular dynamics simulations from GROMACS package were aligned and used for the calculation of intermolecular interaction energies at each grid point. The descriptors generated on the basis of these Coulomb and Lennard-Jones potentials as independent variables were used to perform a PLS analysis using biological activity as dependent variable. A good predictive model was generated with nine field descriptors and five latent variables. The model showed [Formula: see text]; [Formula: see text] and [Formula: see text]. This model was further validated systematically by using different validation parameters. This 4D-QSAR model gave valuable information to recognize features essential to adapt and develop novel potential Aurora kinase inhibitors.
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Affiliation(s)
- Ashish M Kanhed
- Pharmacy Department, Faculty of Technology & Engineering, The Maharaja Sayajirao University of Baroda, Kalabhavan, Vadodara, Gujarat, 390001, India
| | - Radha Charan Dash
- Visiting Research Associate to Pharmacy Department, The Maharaja Sayajirao University of Baroda, Vadodara, Gujarat, 390001, India
| | - Nishant Parmar
- Department of Mathematics, Faculty of Science, The Maharaja Sayajirao University of Baroda, Vadodara, Gujarat, 390001, India
| | - Tarun Kumar Das
- Department of Mathematics, Faculty of Science, The Maharaja Sayajirao University of Baroda, Vadodara, Gujarat, 390001, India
| | - Rajani Giridhar
- Pharmacy Department, Faculty of Technology & Engineering, The Maharaja Sayajirao University of Baroda, Kalabhavan, Vadodara, Gujarat, 390001, India
| | - Mange Ram Yadav
- Pharmacy Department, Faculty of Technology & Engineering, The Maharaja Sayajirao University of Baroda, Kalabhavan, Vadodara, Gujarat, 390001, India.
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Hamzeh-Mivehroud M, Sokouti B, Dastmalchi S. An Introduction to the Basic Concepts in QSAR-Aided Drug Design. QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS IN DRUG DESIGN, PREDICTIVE TOXICOLOGY, AND RISK ASSESSMENT 2015. [DOI: 10.4018/978-1-4666-8136-1.ch001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.
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Affiliation(s)
- Maryam Hamzeh-Mivehroud
- Biotechnology Research Center & School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Babak Sokouti
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Siavoush Dastmalchi
- Biotechnology Research Center & School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
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Lang KL, Silva IT, Machado VR, Zimmermann LA, Caro MS, Simões CM, Schenkel EP, Durán FJ, Bernardes LS, de Melo EB. Multivariate SAR and QSAR of cucurbitacin derivatives as cytotoxic compounds in a human lung adenocarcinoma cell line. J Mol Graph Model 2014; 48:70-9. [DOI: 10.1016/j.jmgm.2013.12.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2013] [Revised: 11/18/2013] [Accepted: 12/03/2013] [Indexed: 01/11/2023]
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