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Shaikh J, Patel S, Nagani A, Shah M, Ugharatdar S, Patel A, Shah D, Patel D. Pharmacophore mapping, 3D QSAR, molecular docking, and ADME prediction studies of novel Benzothiazinone derivatives. In Silico Pharmacol 2024; 12:79. [PMID: 39220602 PMCID: PMC11362452 DOI: 10.1007/s40203-024-00255-8] [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: 03/01/2024] [Accepted: 08/21/2024] [Indexed: 09/04/2024] Open
Abstract
In the quest to combat tuberculosis, DprE1, a challenging target for novel anti-tubercular agents due to its small size and membrane location, has been a focus of research. DprE1 catalyzes the transformation of DPR into Ketoribose DPX, with Benzothiazinone emerging as a potent pharmacophore for inhibiting DprE1. Clinical trial drugs such as BTZ043, BTZ038, PBTZ169, and TMC-207 have shown promising results as DprE1 inhibitors. This study employed pharmacophore mapping of Pyrazolopyridine, Dinitrobenzamide, and Benzothiazinone derivatives to identify crucial features for eliciting a biological response. Benzothiazinone (Ligand code: 73) emerged as a reference ligand with a fitness score of 3.000. ROC analysis validated the pharmacophore with an excellent score of 0.71. To build a 3D QSAR model, a series of Benzothiazinone congeneric derivatives were explored. The model exhibited strong performance, with a standard deviation of 0.1531, a correlation coefficient for the training set (R2) value of 0.9754, and a correlation coefficient for test set Q2 value of 0.7632, indicating robust predictive capabilities. Contour maps guided the design of novel benzothiazinone derivatives, emphasizing steric, electrostatic, hydrophobic, H-bond acceptor, and H-bond donor groups for structure-activity relationships. Docking studies against PDB ID: 4NCR demonstrated favorable scores, with interactions aligning well with the in-built ligand 26 J. Docking validation via RMSD values supported the reliability of the docking results. This comprehensive approach aids in the design of novel benzothiazinone derivatives with potential anti-tubercular properties, contributing to the development of novel anti-tubercular agents which can be pivotal in the eradication of tuberculosis.
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Affiliation(s)
- Jahaan Shaikh
- Department of Pharmaceutical Chemistry, Parul Institute of Pharmacy, Parul University, Vadodara, Gujarat India
| | - Salman Patel
- Department of Pharmaceutical Chemistry, Parul Institute of Pharmacy, Parul University, Vadodara, Gujarat India
| | - Afzal Nagani
- Department of Pharmaceutical Chemistry, Parul Institute of Pharmacy, Parul University, Vadodara, Gujarat India
- Research and Development Cell, Parul University, Vadodara, Gujarat India
| | - Moksh Shah
- Department of Pharmaceutical Chemistry, Parul Institute of Pharmacy, Parul University, Vadodara, Gujarat India
| | - Siddik Ugharatdar
- Department of Pharmaceutical Chemistry, Laxminarayandev College of Pharmacy, Bholav, Bharuch, Gujarat India
| | - Ashish Patel
- Ramanbhai Patel College of Pharmacy, Charotar University of Science and Technology, CHARUSAT Campus, Changa, Anand, Gujarat India
| | - Drashti Shah
- Ramanbhai Patel College of Pharmacy, Charotar University of Science and Technology, CHARUSAT Campus, Changa, Anand, Gujarat India
| | - Dharti Patel
- Ramanbhai Patel College of Pharmacy, Charotar University of Science and Technology, CHARUSAT Campus, Changa, Anand, Gujarat India
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Yang Z, Wang Y, Du G, Zhan Y, Zhan W. Prediction method of pharmacokinetic parameters of small molecule drugs based on GCN network model. J Mol Model 2024; 30:264. [PMID: 38995407 DOI: 10.1007/s00894-024-06051-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Accepted: 06/26/2024] [Indexed: 07/13/2024]
Abstract
CONTEXT Accurately predicting plasma protein binding rate (PPBR) and oral bioavailability (OBA) helps to better reveal the absorption and distribution of drugs in the human body and subsequent drug design. Although machine learning models have achieved good results in prediction accuracy, they often suffer from insufficient accuracy when dealing with data with irregular topological structures. METHODS In view of this, this study proposes a pharmacokinetic parameter prediction framework based on graph convolutional networks (GCN), which predicts the PPBR and OBA of small molecule drugs. In the framework, GCN is first used to extract spatial feature information on the topological structure of drug molecules, in order to better learn node features and association information between nodes. Then, based on the principle of drug similarity, this study calculates the similarity between small molecule drugs, selects different thresholds to construct datasets, and establishes a prediction model centered on the GCN algorithm. The experimental results show that compared with traditional machine learning prediction models, the prediction model constructed based on the GCN method performs best on PPBR and OBA datasets with an inter-molecular similarity threshold of 0.25, with MAE of 0.155 and 0.167, respectively. In addition, in order to further improve the accuracy of the prediction model, GCN is combined with other algorithms. Compared to using a single GCN method, the distribution of the predicted values obtained by the combined model is highly consistent with the true values. In summary, this work provides a new method for improving the rate of early drug screening in the future.
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Affiliation(s)
- Zhihua Yang
- Department of Radiation Oncology, General Hospital of Ningxia Medical University, Yinchuan, 750004, China
| | - Ying Wang
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China
| | - Getao Du
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China
| | - Yonghua Zhan
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China.
| | - Wenhua Zhan
- Department of Radiation Oncology, General Hospital of Ningxia Medical University, Yinchuan, 750004, China.
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Rodrigues Gazolla PA, Lima WP, de Aguiar AR, Gonçalves Borsodi MP, Costa AV, de Oliveira FM, de Oliveira OV, Andreazza Costa MC, Castro Ferreira MM, do Nascimento CJ, Junker J, Vaz BG, Teixeira RR. Leishmanicidal activity and 4D quantitative structure-activity relationship and molecular docking studies of vanillin-containing 1,2,3-triazole derivatives. Future Med Chem 2024; 16:139-155. [PMID: 38131191 DOI: 10.4155/fmc-2023-0246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023] Open
Abstract
Aim: The assessment of the antileishmanial potential of 22 vanillin-containing 1,2,3-triazole derivatives against Leishmania braziliensis is reported. Materials & methods: Initial screening was performed against the parasite promastigote form. The most active compound, 4b, targeted parasites within amastigotes (IC50 = 4.2 ± 1.0 μmol l-1), presenting low cytotoxicity and a selective index value of 39. 4D quantitative structure-activity relationship and molecular docking studies provided insights into structure-activity and biological effects. Conclusion: A vanillin derivative with significant antileishmanial activity was identified. Enhanced activity was linked to increased electrostatic and Van der Waals interactions near the benzyl ring of the derivatives. Molecular docking indicated the inhibition of the Leishmania amazonensis sterol 14α-demethylase, using Leishmania infantum sterol 14α-demethylase as a model, without affecting the human isoform. Inhibition was active site competition with lanosterol.
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Affiliation(s)
- Poliana Aparecida Rodrigues Gazolla
- Grupo de Pesquisa e Síntese de Compostos Bioativos (GSPCB), Departamento de Química, Universidade Federal de Viçosa, Viçosa-MG, 36570-900, Brazil
| | - Wallace Pacienza Lima
- Escola de Ciências da Saúde, Universidade do Grande Rio, Rio de Janeiro-RJ, 22775-003, Brazil
| | - Alex Ramos de Aguiar
- Grupo de Pesquisa e Síntese de Compostos Bioativos (GSPCB), Departamento de Química, Universidade Federal de Viçosa, Viçosa-MG, 36570-900, Brazil
| | - Maria Paula Gonçalves Borsodi
- Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 21941-902, Brazil
| | - Adilson Vidal Costa
- Departamento de Química e Física, Universidade Federal do Espírito Santo, Alegre-ES, 29500-000, Brazil
| | | | | | | | | | - Cláudia Jorge do Nascimento
- Departamento de Ciências Naturais, Instituto de Biociências, Universidade Federal do Estado do Rio de Janeiro (UNIRIO), Rio de Janeiro-RJ, 22290-240, Brazil
| | - Jochen Junker
- Centro de Desenvolvimento Tecnológico em Saúde, Fundação Oswaldo Cruz, Rio de Janeiro-RJ, 21040-361, Brazil
| | - Boniek Gontijo Vaz
- Instituto de Química, Universidade Federal de Goiás, Goiânia-GO, 74001-970, Brazil
| | - Róbson Ricardo Teixeira
- Grupo de Pesquisa e Síntese de Compostos Bioativos (GSPCB), Departamento de Química, Universidade Federal de Viçosa, Viçosa-MG, 36570-900, Brazil
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González JEH, Salas-Sarduy E, Alvarez LH, Valiente PA, Arni RK, Pascutti PG. Three Decades of Targeting Falcipains to Develop Antiplasmodial Agents: What have we Learned and What can be Done Next? Curr Med Chem 2024; 31:2234-2263. [PMID: 37711130 DOI: 10.2174/0929867331666230913165219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/06/2023] [Accepted: 07/25/2023] [Indexed: 09/16/2023]
Abstract
Malaria is a devastating infectious disease that affects large swathes of human populations across the planet's tropical regions. It is caused by parasites of the genus Plasmodium, with Plasmodium falciparum being responsible for the most lethal form of the disease. During the intraerythrocytic stage in the human hosts, malaria parasites multiply and degrade hemoglobin (Hb) using a battery of proteases, which include two cysteine proteases, falcipains 2 and 3 (FP-2 and FP-3). Due to their role as major hemoglobinases, FP-2 and FP-3 have been targeted in studies aiming to discover new antimalarials and numerous inhibitors with activity against these enzymes, and parasites in culture have been identified. Nonetheless, cross-inhibition of human cysteine cathepsins remains a serious hurdle to overcome for these compounds to be used clinically. In this article, we have reviewed key functional and structural properties of FP-2/3 and described different compound series reported as inhibitors of these proteases during decades of active research in the field. Special attention is also paid to the wide range of computer-aided drug design (CADD) techniques successfully applied to discover new active compounds. Finally, we provide guidelines that, in our understanding, will help advance the rational discovery of new FP-2/3 inhibitors.
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Affiliation(s)
- Jorge Enrique Hernández González
- Multiuser Center for Biomolecular Innovation, IBILCE/UNESP, São José do Rio Preto, SP, Brazil
- Department of Pharmaceutical Sciences, UZA II, University of Vienna, Vienna, 1090, Austria
| | - Emir Salas-Sarduy
- Instituto de Investigaciones Biotecnológicas Dr. Rodolfo Ugalde, Universidad Nacional de San Martín, CONICET, San Martín, Buenos Aires, Argentina
- Escuela de Bio y Nanotecnología (EByN), Universidad de San Martín (UNSAM), San Martín, Buenos Aires, Argentina
| | | | - Pedro Alberto Valiente
- Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, Toronto, Canada
| | | | - Pedro Geraldo Pascutti
- Laboratório de Modelagem e Dinâmica Molecular, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, RJ, Brazil
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Lanka G, Begum D, Banerjee S, Adhikari N, P Y, Ghosh B. Pharmacophore-based virtual screening, 3D QSAR, Docking, ADMET, and MD simulation studies: An in silico perspective for the identification of new potential HDAC3 inhibitors. Comput Biol Med 2023; 166:107481. [PMID: 37741229 DOI: 10.1016/j.compbiomed.2023.107481] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 08/19/2023] [Accepted: 09/15/2023] [Indexed: 09/25/2023]
Abstract
Histone deacetylase 3 (HDAC3) is an epigenetic regulator that involves gene expression, apoptosis, and cell cycle progression, and the overexpression of HDAC3 is accountable for several cancers, neurodegeneracy, and many other diseases. Therefore, HDAC3 emerged as a promising drug target for the novel drug design. Here, we carried out the pharmacophore modeling using 50 benzamide-based HDAC3 selective inhibitors and utilized it for PHASE ligand screening to retrieve the hits with similar pharmacophore features. The dataset inhibitors of best hypotheses used to build the 3D QSAR model and the generated 3D QSAR model resulted in good PLS statistics with a regression coefficient (R2) of 0.89, predictive coefficient (Q2) of 0.88, and Pearson-R factor of 0.94 indicating its excellent predictive ability. The hits retrieved from pharmacophore-based virtual screening were subjected to docking against HDAC3 for the identification of potential inhibitors. A total of 10 hitsM1 to M10 were ranked using their scoring functions and further subject to lead optimization. The Prime MM/GBSA, AutoDock binding free energies, and ADMET studies were implemented for the selection of lead candidates. The four ligand molecules M1, M2, M3, and M4 were identified as potential leads against HDAC3 after lead optimization. The top two leads M1 and M2 were subjected to MD simulations for their stability evaluation with HDAC3. The newly designed leads M11 and M12 were identified as HDAC3 potential inhibitors from MD simulations studies. Therefore, the outcomes of the present study could provide insights into the discovery of new potential HDAC3 inhibitors with improved selectivity and activity against a variety of cancers and neurodegenerative diseases.
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Affiliation(s)
- Goverdhan Lanka
- Epigenetic Research Laboratory, Department of Pharmacy, Birla Institute of Technology and Science-Pilani, Hyderabad Campus, Shamirpet, Hyderabad, 500078, India
| | - Darakhshan Begum
- Epigenetic Research Laboratory, Department of Pharmacy, Birla Institute of Technology and Science-Pilani, Hyderabad Campus, Shamirpet, Hyderabad, 500078, India
| | - Suvankar Banerjee
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, P. O. Box 17020, Jadavpur University, Kolkata, 700032, West Bengal, India
| | - Nilanjan Adhikari
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, P. O. Box 17020, Jadavpur University, Kolkata, 700032, West Bengal, India
| | - Yogeeswari P
- Computer Aided Drug Design Laboratory, Department of Pharmacy, Birla Institute of Technology and Science-Pilani Hyderabad Campus, Shamirpet, Hyderabad, 500078, India
| | - Balaram Ghosh
- Epigenetic Research Laboratory, Department of Pharmacy, Birla Institute of Technology and Science-Pilani, Hyderabad Campus, Shamirpet, Hyderabad, 500078, India.
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6
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Dembitsky VM. Steroids Bearing Heteroatom as Potential Drugs for Medicine. Biomedicines 2023; 11:2698. [PMID: 37893072 PMCID: PMC10604304 DOI: 10.3390/biomedicines11102698] [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: 09/04/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/29/2023] Open
Abstract
Heteroatom steroids, a diverse class of organic compounds, have attracted significant attention in the field of medicinal chemistry and drug discovery. The biological profiles of heteroatom steroids are of considerable interest to chemists, biologists, pharmacologists, and the pharmaceutical industry. These compounds have shown promise as potential therapeutic agents in the treatment of various diseases, such as cancer, infectious diseases, cardiovascular disorders, and neurodegenerative conditions. Moreover, the incorporation of heteroatoms has led to the development of targeted drug delivery systems, prodrugs, and other innovative pharmaceutical approaches. Heteroatom steroids represent a fascinating area of research, bridging the fields of organic chemistry, medicinal chemistry, and pharmacology. The exploration of their chemical diversity and biological activities holds promise for the discovery of novel drug candidates and the development of more effective and targeted treatments.
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Affiliation(s)
- Valery M Dembitsky
- Centre for Applied Research, Innovation and Entrepreneurship, Lethbridge College, 3000 College Drive South, Lethbridge, AB T1K 1L6, Canada
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7
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Toropova AP, Toropov AA. Using the local symmetry in amino acids sequences of polypeptides to improve the predictive potential of models of their inhibitor activity. Amino Acids 2023; 55:1437-1445. [PMID: 37707646 DOI: 10.1007/s00726-023-03322-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 08/24/2023] [Indexed: 09/15/2023]
Abstract
The minimal inhibitory concentrations (pMIC) are a valuable measure of the biological activity of polypeptides. Numerical data on the pMIC are necessary to systematize knowledge on polypeptides' biochemical behaviour. The model of negative decimal logarithm of pMIC of polypeptides in the form of a mathematical function of a sequence of amino acids is suggested. The suggested model is based on the so-called correlation weights of amino acids together with the correlation weights of fragments of local symmetry (FLS). Three kinds of the FLS are considered: (i) three-symbol fragments '…xyx…', (ii) four-symbol fragments '…xyyx…', and (iii) five-symbol fragments '…xyzyx…'. The models built using the Monte Carlo technique improved by applying the index of ideality of correlation (IIC) and correlation intensity index (CII).
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Affiliation(s)
- Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milan, Italy.
| | - Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milan, Italy
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Wojtuch A, Danel T, Podlewska S, Maziarka Ł. Extended study on atomic featurization in graph neural networks for molecular property prediction. J Cheminform 2023; 15:81. [PMID: 37726841 PMCID: PMC10507875 DOI: 10.1186/s13321-023-00751-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 08/23/2023] [Indexed: 09/21/2023] Open
Abstract
Graph neural networks have recently become a standard method for analyzing chemical compounds. In the field of molecular property prediction, the emphasis is now on designing new model architectures, and the importance of atom featurization is oftentimes belittled. When contrasting two graph neural networks, the use of different representations possibly leads to incorrect attribution of the results solely to the network architecture. To better understand this issue, we compare multiple atom representations by evaluating them on the prediction of free energy, solubility, and metabolic stability using graph convolutional networks. We discover that the choice of atom representation has a significant impact on model performance and that the optimal subset of features is task-specific. Additional experiments involving more sophisticated architectures, including graph transformers, support these findings. Moreover, we demonstrate that some commonly used atom features, such as the number of neighbors or the number of hydrogens, can be easily predicted using only information about bonds and atom type, yet their explicit inclusion in the representation has a positive impact on model performance. Finally, we explain the predictions of the best-performing models to better understand how they utilize the available atomic features.
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Affiliation(s)
- Agnieszka Wojtuch
- Faculty of Mathematics and Computer Science, Jagiellonian University, Łojasiewicza 6, 30-348, Kraków, Poland.
| | - Tomasz Danel
- Faculty of Mathematics and Computer Science, Jagiellonian University, Łojasiewicza 6, 30-348, Kraków, Poland
| | - Sabina Podlewska
- Maj Institute of Pharmacology, Polish Academy of Sciences, Smętna 12, 31-343, Kraków, Poland
| | - Łukasz Maziarka
- Faculty of Mathematics and Computer Science, Jagiellonian University, Łojasiewicza 6, 30-348, Kraków, Poland
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Zhong J, Xuan W, Lu S, Cui S, Zhou Y, Tang M, Qu X, Lu W, Huo H, Zhang C, Zhang N, Niu B. Discovery of ANO1 Inhibitors based on Machine learning and molecule docking simulation approaches. Eur J Pharm Sci 2023; 184:106408. [PMID: 36842513 DOI: 10.1016/j.ejps.2023.106408] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 02/05/2023] [Accepted: 02/19/2023] [Indexed: 02/28/2023]
Abstract
Calcium-activated chloride channels (CaCCs) are chloride channels that are regulated according to intracellular calcium ion concentrations. The channel protein ANO1 is widely present in cells and is involved in physiological activities including cellular secretion, signaling, cell proliferation and vasoconstriction and diastole. In this study, the ANO1 inhibitors were investigated with machine learning and molecular simulation. Two-dimensional structure-activity relationship (2D-SAR) and three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed for the qualitative and quantitative prediction of ANO1 inhibitors. The results showed that the prediction accuracies of the model were 85.9% and 87.8% for the training and test sets, respectively, and 85.9% and 87.8% for the rotating forest (RF) in the 2D-SAR model. The CoMFA and CoMSIA methods were then used for 3D QSAR modeling of ANO1 inhibitors, respectively. The q2 coefficients for model cross-validation were all greater than 0.5, implying that we were able to obtain a stable model for drug activity prediction. Molecular docking was further used to simulate the interactions between the five most promising compounds predicted by the model and the ANO1 protein. The total score for the docking results between all five compounds and the target protein was greater than 6, indicating that they interacted strongly in the form of hydrogen bonds. Finally, simulations of amino acid mutations around the docking cavity of the target proteins showed that each molecule had two or more sites of reduced affinity following a single mutation, indicating outstanding specificity of the screened drug molecules and their protein ligands.
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Affiliation(s)
- Junjie Zhong
- School of life Science, Shanghai University, 99 Shangda Road,200444, China.
| | - Wendi Xuan
- School of life Science, Shanghai University, 99 Shangda Road,200444, China.
| | - Sheng Lu
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, PR China.
| | - Shihao Cui
- School of life Science, Shanghai University, 99 Shangda Road,200444, China.
| | - Yuhang Zhou
- School of life Science, Shanghai University, 99 Shangda Road,200444, China.
| | - Mengting Tang
- School of life Science, Shanghai University, 99 Shangda Road,200444, China.
| | - Xiaosheng Qu
- National Engineering laboratory of Southwest Endangered Medicinal Resources Development, Guangxi Botanical Garden of Medicinal Plants, China.
| | - Wencong Lu
- Chemistry Department, College of Science, Shanghai University, 99 Shangda Road,200444, China
| | - Haizhong Huo
- Department of General Surgery, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China.
| | - Chi Zhang
- Huaxia Eye Hospital of Foshan, Huaxia Eye Hospital Group, Foshan, Guangdong 528000, China.
| | - Ning Zhang
- Department of Hepatic Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China.
| | - Bing Niu
- School of life Science, Shanghai University, 99 Shangda Road,200444, China.
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Mellado M, Sariego-Kluge R, Valdés-Navarro F, González C, Sánchez-González R, Pizarro N, Villena J, Jara-Gutierrez C, Cordova C, Bravo MA, Aguilar LF. Synthesis of fluorescent chalcones, photophysical properties, quantitative structure-activity relationship and their biological application. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 291:122332. [PMID: 36652804 DOI: 10.1016/j.saa.2023.122332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 12/01/2022] [Accepted: 01/03/2023] [Indexed: 06/17/2023]
Abstract
The development of fluorescent pigments is an area of interest in several research fields due to their high sensitivity. In the current study-eight known and three new N,N-dimethylamino-chalcones (12a-k) were synthesized with good yields using the Claisen-Schmidt reaction. For each molecular system, the photophysical properties, including the maximum absorption wavelength (λAbsorption), molar absorption coefficient (ε), maximum excitation wavelength (λExcitation), maximum emission wavelength (λEmission), Stokes Shift (Δλ), fluorescence quantum yield (Φfl), fluorescence lifetime (τfl), radiative and non-radiative rate constants (kR and kNR, respectively) were evaluated. Variations in each of these properties were analyzed depending on the substituents present on each compound. To relate the chemical structures of the synthesized compounds to their photophysical properties, Hansch analysis (2D-QSPR) was applied. As a result of Hansch analysis, we found different photophysical properties related to molecular orbitals and the energy of their derivatives (Highest Occupied Molecular Orbital-HOMO, Lowest Unoccupied Molecular Orbital-LUMO, Difference between LUMO-HOMO-ΔLH, Chemical potential-µ, Hardness-η, Softness-S, and electrophilic global index-ω) as well as to the atomic charges on atoms C5, Cα, Cβ, and CO. The application of this type of analysis has made it possible to understand and subsequently design new molecules with defined photophysical properties. Finally, the compounds were use as fluorescent pigment to get living cell imaging on breast cancer cells, obtaining the compound 12a as promissory alternative.
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Affiliation(s)
- Marco Mellado
- Instituto de Investigación y Postgrado, Facultad de Ciencias de la Salud, Universidad Central de Chile, Santiago, Chile.
| | - Rafaela Sariego-Kluge
- Instituto de Química, Facultad de Ciencias, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
| | - Franco Valdés-Navarro
- Instituto de Química, Facultad de Ciencias, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
| | - César González
- Departamento de Química, Universidad Técnica Federico Santa María, Valparaíso, Chile
| | - Rodrigo Sánchez-González
- Instituto de Química, Facultad de Ciencias, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
| | - Nancy Pizarro
- Universidad Andrés Bello, Facultad de Ciencias Exactas, Departamento de Ciencias Químicas, Viña del Mar, Chile
| | - Joan Villena
- Laboratorio de Bioensayos, Facultad de Medicina, Centro de Investigaciones Biomédicas (CIB), Universidad de Valparaíso, Viña del Mar, Chile
| | - Carlos Jara-Gutierrez
- Laboratorio de Bioensayos, Escuela de Kinesiología, Facultad de Medicina, Centro de Investigaciones Biomédicas (CIB), Universidad de Valparaíso, Viña del Mar, Chile
| | - Claudio Cordova
- Laboratorio de Estructura y Función Celular, Escuela de Medicina, Facultad de Medicina, Universidad de Valparaíso, Valparaíso, Chile
| | - Manuel A Bravo
- Instituto de Química, Facultad de Ciencias, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
| | - Luis F Aguilar
- Instituto de Química, Facultad de Ciencias, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile.
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Gazolla PAR, de Aguiar AR, Costa MCA, Oliveira OV, Costa AV, da Silva CM, do Nascimento CJ, Junker J, Ferreira RS, de Oliveira FM, Vaz BG, do Carmo PHF, Santos DA, Ferreira MMC, Teixeira RR. Synthesis of vanillin derivatives with 1,2,3-triazole fragments and evaluation of their fungicide and fungistatic activities. Arch Pharm (Weinheim) 2023:e202200653. [PMID: 36922908 DOI: 10.1002/ardp.202200653] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 02/17/2023] [Accepted: 02/20/2023] [Indexed: 03/18/2023]
Abstract
Vanillin is the main component of natural vanilla extract and is responsible for its flavoring properties. Besides its well-known applications as an additive in food and cosmetics, it has also been reported that vanillin can inhibit fungi of clinical interest, such as Candida spp., Cryptococcus spp., Aspergillus spp., as well as dermatophytes. Thus, the present work approaches the synthesis of a series of vanillin derivatives with 1,2,3-triazole fragments and the evaluation of their antifungal activities against Candida albicans, Candida glabrata, Candida parapsilosis, Candida tropicalis, Cryptococcus neoformans, Cryptococcus gattii, Trichophyton rubrum, and Trichophyton interdigitale strains. Twenty-two vanillin derivatives were obtained, with yields in the range of 60%-91%, from copper(I)-catalyzed alkyne-azide cycloaddition (CuAAC) click reaction between two terminal alkynes prepared from vanillin and different benzyl azides. In general, the evaluated compounds showed moderate activity against the microorganisms tested, with minimum inhibitory concentration (MIC) values ranging from 32 to >512 µg mL-1 . Except for compound 3b against the C. gattii R265 strain, all vanillin derivatives showed fungicidal activity for the yeasts tested. The predicted physicochemical and ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties for the compounds indicated favorable profiles for drug development. In addition, a four-dimensional structure-activity relationship (4D-SAR) analysis was carried out and provided useful insights concerning the structures of the compounds and their biological profile. Finally, molecular docking calculations showed that all compounds bind favorably at the lanosterol 14α-demethylase enzyme active site with binding energies ranging from -9.1 to -12.2 kcal/mol.
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Affiliation(s)
- Poliana A R Gazolla
- Departamento de Química, Grupo de Síntese e Pesquisa de Compostos Bioativos (GSPCB), Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
| | - Alex R de Aguiar
- Departamento de Química, Grupo de Síntese e Pesquisa de Compostos Bioativos (GSPCB), Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
| | - Maria C A Costa
- Laboratório de Quimiometria Teórica e Aplicada (LQTA), Universidade Estadual de Campinas - Unicamp, São Paulo, Campinas, Brazil
| | - Osmair V Oliveira
- Instituto Federal de São Paulo - Campus Catanduva, São Paulo, Catanduva, Brazil
| | - Adilson V Costa
- Departamento de Química e Física, Universidade Federal do Espírito Santo, Alto Universitário, Alegre, Espírito Santo, Brazil
| | - Cleiton M da Silva
- Departmento de Química, ICEx, Universidade Federal de Minas Gerais, Campus Pampulha, Belo Horizonte, Minas Gerais, Brazil
| | - Claudia J do Nascimento
- Universidade Federal do Estado do Rio de Janeiro, Instituto de Biociências, Rio de Janeiro, Rio de Janeiro, Brazil
| | - Jochen Junker
- Fundação Oswaldo Cruz/CDTS, Rio de Janeiro, Rio de Janeiro, Brazil
| | - Rafaela S Ferreira
- Departamento de Bioquímica e Imunologia, Universidade Federal de Minas Gerais, Campus Pampulha, Minas Gerais, Belo Horizonte, Brazil
| | - Fabrício M de Oliveira
- Instituto Federal de Minas Gerais (IFMG), Campus Ouro Branco, Ouro Branco, Minas Gerais, Brazil
| | - Boniek G Vaz
- Instituto de Química, Universidade Federal de Goiás, Campus Samambaia, Goiânia, Goiás, Brazil
| | - Paulo H F do Carmo
- Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Campus Pampulha, Belo Horizonte, Minas Gerais, Brazil
| | - Daniel A Santos
- Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Campus Pampulha, Belo Horizonte, Minas Gerais, Brazil
| | - Márcia M C Ferreira
- Laboratório de Quimiometria Teórica e Aplicada (LQTA), Universidade Estadual de Campinas - Unicamp, São Paulo, Campinas, Brazil
| | - Róbson R Teixeira
- Departamento de Química, Grupo de Síntese e Pesquisa de Compostos Bioativos (GSPCB), Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
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12
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Xiao Z, Yang B, Feng X, Liao Z, Shi H, Jiang W, Wang C, Ren N. Density Functional Theory and Machine Learning-Based Quantitative Structure-Activity Relationship Models Enabling Prediction of Contaminant Degradation Performance with Heterogeneous Peroxymonosulfate Treatments. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:3951-3961. [PMID: 36809928 DOI: 10.1021/acs.est.2c09034] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Heterogeneous peroxymonosulfate (PMS) treatment is recognized as an effective advanced oxidation process (AOP) for the treatment of organic contaminants. Quantitative structure-activity relationship (QSAR) models have been applied to predict the oxidation reaction rates of contaminants in homogeneous PMS treatment systems but are seldom applied in heterogeneous treatment systems. Herein, we established QSAR models updated with density functional theory (DFT) and machine learning approaches to predict the degradation performance for a series of contaminants in heterogeneous PMS systems. We imported the characteristics of organic molecules calculated using constrained DFT as input descriptors and predicted the apparent degradation rate constants of contaminants as the output. The genetic algorithm and deep neural networks were used to improve the predictive accuracy. The qualitative and quantitative results from the QSAR model for the degradation of contaminants can be used to select the most appropriate treatment system. A strategy for selection of the optimum catalyst for PMS treatment of specific contaminants was also established according to the QSAR models. This work not only increases our understanding of contaminant degradation in PMS treatment systems but also highlights a novel QSAR model to predict the degradation performance in complicated heterogeneous AOPs.
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Affiliation(s)
- Zijie Xiao
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, P. R. China
| | - Bowen Yang
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, P. R. China
| | - Xiaochi Feng
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, P. R. China
| | - Zhenqin Liao
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, P. R. China
| | - Hongtao Shi
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, P. R. China
| | - Weiyu Jiang
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, P. R. China
| | - Caipeng Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, P. R. China
| | - Nanqi Ren
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, P. R. China
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13
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Pisani L, de Candia M, Rullo M, Altomare CD. Hansch-Type QSAR Models for the Rational Design of MAO Inhibitors: Basic Principles and Methodology. Methods Mol Biol 2023; 2558:207-220. [PMID: 36169866 DOI: 10.1007/978-1-0716-2643-6_16] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Hansch-type regression analysis enables the derivation of quantitative structure-activity relationship (QSAR) equations correlating bioactivity data with physicochemical parameters accounting for hydrophobicity, electronic properties, and steric effects of molecules or functional groups (substituents). Two datasets of MAO A and B inhibitors were enrolled in prototypical workflows employing multiparametric stepwise regression analysis, which includes linear and nonlinear (generally quadratic) terms. The optimal choice of variables (and/or combinations thereof) along with statistical validation yielded two robust equations describing MAO B potency and B/A selectivity, which included three and one parameter(s), respectively, and explained more than 80% of y-variance (r2) with low standard deviation (s) and good statistical significance (F, Fisher value).
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Affiliation(s)
- Leonardo Pisani
- Department of Pharmacy-Pharmaceutical Sciences, University of Bari "Aldo Moro", Bari, Italy
| | - Modesto de Candia
- Department of Pharmacy-Pharmaceutical Sciences, University of Bari "Aldo Moro", Bari, Italy
| | - Mariagrazia Rullo
- Department of Pharmacy-Pharmaceutical Sciences, University of Bari "Aldo Moro", Bari, Italy
| | - Cosimo D Altomare
- Department of Pharmacy-Pharmaceutical Sciences, University of Bari "Aldo Moro", Bari, Italy.
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14
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Didachos C, Kintos DP, Fousteris M, Mylonas P, Kanavos A. An Optimized Cloud Computing Method for Extracting Molecular Descriptors. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1424:247-254. [PMID: 37486501 DOI: 10.1007/978-3-031-31982-2_28] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Extracting molecular descriptors from chemical compounds is an essential preprocessing phase for developing accurate classification models. Supervised machine learning algorithms offer the capability to detect "hidden" patterns that may exist in a large dataset of compounds, which are represented by their molecular descriptors. Assuming that molecules with similar structure tend to share similar physicochemical properties, large chemical libraries can be screened by applying similarity sourcing techniques in order to detect potential bioactive compounds against a molecular target. However, the process of generating these compound features is time-consuming. Our proposed methodology not only employs cloud computing to accelerate the process of extracting molecular descriptors but also introduces an optimized approach to utilize the computational resources in the most efficient way.
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Affiliation(s)
- Christos Didachos
- Computer Engineering and Informatics Department, University of Patras, Patras, Greece
| | | | | | - Phivos Mylonas
- Department of Informatics, Ionian University, Corfu, Greece
| | - Andreas Kanavos
- Department of Informatics, Ionian University, Corfu, Greece.
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15
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5D-QSAR studies of 1H-pyrazole derivatives as EGFR inhibitors. J Mol Model 2022; 28:379. [DOI: 10.1007/s00894-022-05370-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 10/25/2022] [Indexed: 11/09/2022]
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16
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Khairullina V, Martynova Y, Safarova I, Sharipova G, Gerchikov A, Limantseva R, Savchenko R. QSPR Modeling and Experimental Determination of the Antioxidant Activity of Some Polycyclic Compounds in the Radical-Chain Oxidation Reaction of Organic Substrates. Molecules 2022; 27:molecules27196511. [PMID: 36235050 PMCID: PMC9572093 DOI: 10.3390/molecules27196511] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/28/2022] [Accepted: 09/28/2022] [Indexed: 01/18/2023] Open
Abstract
The present work addresses the quantitative structure−antioxidant activity relationship in a series of 148 sulfur-containing alkylphenols, natural phenols, chromane, betulonic and betulinic acids, and 20-hydroxyecdysone using GUSAR2019 software. Statistically significant valid models were constructed to predict the parameter logk7, where k7 is the rate constant for the oxidation chain termination by the antioxidant molecule. These results can be used to search for new potentially effective antioxidants in virtual libraries and databases and adequately predict logk7 for test samples. A combination of MNA- and QNA-descriptors with three whole molecule descriptors (topological length, topological volume, and lipophilicity) was used to develop six statistically significant valid consensus QSPR models, which have a satisfactory accuracy in predicting logk7 for training and test set structures: R2TR > 0.6; Q2TR > 0.5; R2TS > 0.5. Our theoretical prediction of logk7 for antioxidants AO1 and AO2, based on consensus models agrees well with the experimental value of the measure in this paper. Thus, the descriptor calculation algorithms implemented in the GUSAR2019 software allowed us to model the kinetic parameters of the reactions underlying the liquid-phase oxidation of organic hydrocarbons.
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Affiliation(s)
- Veronika Khairullina
- Faculty of Chemistry, Bashkir State University, 450076 Ufa, Russia
- Correspondence: ; Tel.: +7-963-906-6567
| | - Yuliya Martynova
- Faculty of Chemistry, Bashkir State University, 450076 Ufa, Russia
| | - Irina Safarova
- Faculty of Chemistry, Bashkir State University, 450076 Ufa, Russia
| | - Gulnaz Sharipova
- Faculty of Chemistry, Bashkir State University, 450076 Ufa, Russia
| | | | - Regina Limantseva
- Institute of Petrochemistry and Catalysis of the Ufa Federal Research Center of the Russian Academy of Sciences, 450075 Ufa, Russia
| | - Rimma Savchenko
- Institute of Petrochemistry and Catalysis of the Ufa Federal Research Center of the Russian Academy of Sciences, 450075 Ufa, Russia
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17
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Fundamental considerations in drug design. COMPUTER AIDED DRUG DESIGN (CADD): FROM LIGAND-BASED METHODS TO STRUCTURE-BASED APPROACHES 2022:17-55. [PMCID: PMC9212230 DOI: 10.1016/b978-0-323-90608-1.00005-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
The drug discovery paradigm has been very time-consuming, challenging, and expensive; however, the disease conditions originating from bacteria, virus, protozoa, fungus and other microorganisms are steadily shooting up. For instance, COVID-19 is the latest viral infection that affects millions of people and the world’s economy very severely. Therefore, the quest for discovery of novel and potent drug compounds against deadly pathogens is crucial at the moment. Despite a lot of drawbacks in drug discovery and development and its pertaining technology, the advancement must be taken into account so the time duration and cost would be minimized. In this chapter, basic principles in drug design and discovery have been discussed together with advances in drug development.
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18
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Toropova AP, Toropov AA, Leszczynska D, Leszczynski J. Application of quasi-SMILES to the model of gold-nanoparticles uptake in A549 cells. Comput Biol Med 2021; 136:104720. [PMID: 34364261 DOI: 10.1016/j.compbiomed.2021.104720] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 07/27/2021] [Accepted: 07/27/2021] [Indexed: 11/30/2022]
Abstract
Cell death is critical to human health and is associated with a variety of medical conditions. Therefore, new controllers of cell death are needed for the treatment of diverse diseases. In particular, nanoparticles (NP) are now regularly used in various applications, including a variety of products and medicines. Gold nanoparticles (GNPs) are widely used in the medical field against A549 lung carcinoma cells. The present study is devoted to developing computational models of the cellular uptake potentials by A549 cells of gold nanoparticles (GNPs) under various conditions. Simplified molecular input-line entry system (SMILES) is an efficient tool to represent the molecular structure by a sequence of symbols. Quasi-SMILES represents an extended version of SMILES where symbols to denote physicochemical and/or biochemical conditions are added. In other words, the quasi-SMILES represents a biochemical (medical) phenomenon related to the whole matter (not only molecular structure). We developed models for the cellular utpake potential of gold nanoparticles (GNPs) in A549 [10-11 g Au/Cell] under various conditions based on quasi-SMILES using the Monte Carlo method. The statistical quality of these models is quite good.
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Affiliation(s)
- Alla P Toropova
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy.
| | - Andrey A Toropov
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milano, Italy
| | - Danuta Leszczynska
- Interdisciplinary Nanotoxicity Center, Department of Civil and Environmental Engineering, Jackson State University, 1325 Lynch Street, Jackson, MS, 39217-0510, USA
| | - Jerzy Leszczynski
- Interdisciplinary Nanotoxicity Center, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, 1325 Lynch Street, Jackson, MS, 39217, USA
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19
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de Souza APM, Costa MCA, de Aguiar AR, Bressan GC, de Almeida Lima GD, Lima WP, Borsodi MPG, Bergmann BR, Ferreira MMC, Teixeira RR. Leishmanicidal and cytotoxic activities and 4D-QSAR of 2-arylidene indan-1,3-diones. Arch Pharm (Weinheim) 2021; 354:e2100081. [PMID: 34323311 DOI: 10.1002/ardp.202100081] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 06/23/2021] [Accepted: 06/25/2021] [Indexed: 01/07/2023]
Abstract
The indan-1,3-dione and its derivatives are important building blocks in organic synthesis and present important biological activities. Herein, the leishmanicidal and cytotoxicity evaluation of 16 2-arylidene indan-1,3-diones is described. The compounds were evaluated against the leukemia cell lines HL60 and Nalm6, and the most effective ones were 2-(4-nitrobenzylidene)-1H-indene-1,3(2H)-dione (4) and 4-[(1,3-dioxo-1H-inden-2(3H)-ylidene)methyl]benzonitrile (10), presenting IC50 values of around 30 µmol/L against Nalm6. The leishmanicidal activity was assessed on Leishmania amazonensis, with derivative 4 (IC50 = 16.6 µmol/L) being the most active. A four-dimensional quantitative structure-activity analysis (4D-QSAR) was applied to the indandione derivatives, through partial least-squares regression. The statistics presented by the regression models built with the selected field descriptors of Coulomb (C) and Lennard-Jones (L) nature, considering the activities against L. amazonensis, HL60, and Nalm6 leukemia cells, were, respectively, R2 = 0.88, 0.92, and 0.98; Q2 = 0.83, 0.88, and 0.97. The presence of positive Coulomb descriptors near the carbonyl groups indicates that these polar groups are related to the activities. Besides, the presence of positive Lennard-Jones descriptors close to substituents R3 or R1 indicates that bulky nonpolar substituents in these positions tend to increase the activities. This study provides useful insights into the mode of action of indandione derivatives for each biological activity involved.
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Affiliation(s)
- Ana P M de Souza
- Departamento de Química, Universidade Federal de Viçosa, Viçosa, Brazil
| | - Maria C A Costa
- Theoretical and Applied Chemometrics Laboratory (LQTA), Institute of Chemistry, University of Campinas, Campinas, Brazil
| | - Alex R de Aguiar
- Departamento de Química, Universidade Federal de Viçosa, Viçosa, Brazil
| | - Gustavo C Bressan
- Escola de Ciências da Saúde, Universidade do Grande Rio, Duque de Caxias, Brazil
| | | | - Wallace P Lima
- Escola de Ciências da Saúde, Universidade do Grande Rio, Duque de Caxias, Brazil.,Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Maria P G Borsodi
- Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Bartira R Bergmann
- Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Márcia M C Ferreira
- Theoretical and Applied Chemometrics Laboratory (LQTA), Institute of Chemistry, University of Campinas, Campinas, Brazil
| | - Róbson R Teixeira
- Departamento de Química, Universidade Federal de Viçosa, Viçosa, Brazil
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20
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Kobayashi Y, Yoshida K. Development of QSAR models for prediction of fish bioconcentration factors using physicochemical properties and molecular descriptors with machine learning algorithms. ECOL INFORM 2021. [DOI: 10.1016/j.ecoinf.2021.101285] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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21
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Kunde PD, Ramkumar S, Kamble SP, Ravikumar A, Kulkarni BD, Kumar VR. On the use of electronegativity and electron affinity based pseudo-molecular field descriptors in developing correlations for quantitative structure-activity relationship modeling of drug activities. Chem Biol Drug Des 2021; 98:258-269. [PMID: 34013630 DOI: 10.1111/cbdd.13895] [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: 11/28/2020] [Revised: 04/21/2021] [Accepted: 05/15/2021] [Indexed: 12/01/2022]
Abstract
For quantitative structure-activity relationship (QSAR) modeling in ligand-based drug discovery programs, pseudo-molecular field (PMF) descriptors using intrinsic atomic properties, namely, electronegativity and electron affinity are studied. In combination with partial least squares analysis and Procrustes transformation, these PMF descriptors were employed successfully to develop correlations that predict the activities of target protein inhibitors involved in various diseases (cancer, neurodegenerative disorders, HIV, and malaria). The results show that the present QSAR approach is competitive to existing QSAR models. In order to demonstrate the use of this algorithm, we present results of screening naturally occurring molecules with unknown bioactivities. The pIC50 predictions can screen molecules that have desirable activity before assessment by docking studies.
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Affiliation(s)
- Pushkar D Kunde
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory (CSIR-NCL), Pune, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Sudha Ramkumar
- Organic Chemistry Division, CSIR-National Chemical Laboratory (CSIR-NCL), Pune, India
| | - Sanjay P Kamble
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory (CSIR-NCL), Pune, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Ameeta Ravikumar
- Institute of Bioinformatics and Biotechnology (IBB), Savitribai Phule Pune University, Pune, India
| | - Bhaskar D Kulkarni
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory (CSIR-NCL), Pune, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - V Ravi Kumar
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory (CSIR-NCL), Pune, India.,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
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22
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Abdizadeh R, Heidarian E, Hadizadeh F, Abdizadeh T. QSAR Modeling, Molecular Docking and Molecular Dynamics Simulations Studies of Lysine-Specific Demethylase 1 (LSD1) Inhibitors as Anticancer Agents. Anticancer Agents Med Chem 2021; 21:987-1018. [PMID: 32698753 DOI: 10.2174/1871520620666200721134010] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 05/07/2020] [Accepted: 05/17/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Histone Lysine Demetylases1 (LSD1) is a promising medication to treat cancer, which plays a crucial role in epigenetic modulation of gene expression. Inhibition of LSD1with small molecules has emerged as a vital mechanism to treat cancer. OBJECTIVE In the present research, molecular modeling investigations, such as CoMFA, CoMFA-RF, CoMSIA and HQSAR, molecular docking and Molecular Dynamics (MD) simulations were carried out on some tranylcypromine derivatives as LSD1 inhibitors. METHODS The QSAR models were carried out on a series of Tranylcypromine derivatives as data set via the SYBYL-X2.1.1 program. Molecular docking and MD simulations were carried out by the MOE software and the SYBYL program, respectively. The internal and external predictability performances related to the generated models for these LSD1 inhibitors were justified by evaluating cross-validated correlation coefficient (q2), noncross- validated correlation coefficient (r2ncv) and predicted correlation coefficient (r2pred) of the training and test set molecules, respectively. RESULTS The CoMFA (q2, 0.670; r2ncv, 0.930; r2pred, 0.968), CoMFA-RF (q2, 0.694; r2ncr, 0.926; r2pred, 0.927), CoMSIA (q2, 0.834; r2ncv, 0.956; r2pred, 0.958) and HQSAR models (q2, 0.854; r2ncv, 0.900; r2pred, 0.728) for training as well as the test set of LSD1 inhibition resulted in significant findings. CONCLUSION These QSAR models were found to be perfect and strong with better predictability. Contour maps of all models were generated and it was proven by molecular docking studies and molecular dynamics simulation that the hydrophobic, electrostatic and hydrogen bonding fields are crucial in these models for improving the binding affinity and determining the structure-activity relationship. These theoretical results are possibly beneficial to design new strong LSD1 inhibitors with enhanced activity to treat cancer.
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Affiliation(s)
- Rahman Abdizadeh
- Department of Medical Parasitology and Mycology, Faculty of Medicine, Shahrekord University of Medical Sciences, Shahrekord, Iran
| | - Esfandiar Heidarian
- Clinical Biochemistry Research Center, Basic Health Sciences Institute, Sharekord University of Medical Sciences, Shahrekord, Iran
| | - Farzin Hadizadeh
- Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Tooba Abdizadeh
- Clinical Biochemistry Research Center, Basic Health Sciences Institute, Sharekord University of Medical Sciences, Shahrekord, Iran
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23
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Structural investigation of isatin-based benzenesulfonamides as carbonic anhydrase isoform IX inhibitors endowed with anticancer activity using molecular modeling approaches. J Mol Struct 2021. [DOI: 10.1016/j.molstruc.2020.129735] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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24
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Chen G, Shen Z, Li Y. A machine-learning-assisted study of the permeability of small drug-like molecules across lipid membranes. Phys Chem Chem Phys 2021; 22:19687-19696. [PMID: 32830206 DOI: 10.1039/d0cp03243c] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Study of the permeability of small organic molecules across lipid membranes plays a significant role in designing potential drugs in the field of drug discovery. Approaches to design promising drug molecules have gone through many stages, from experiment-based trail-and-error approaches, to the well-established avenue of the quantitative structure-activity relationship, and currently to the stage guided by machine learning (ML) and artificial intelligence techniques. In this work, we present a study of the permeability of small drug-like molecules across lipid membranes by two types of ML models, namely the least absolute shrinkage and selection operator (LASSO) and deep neural network (DNN) models. Molecular descriptors and fingerprints are used for featurization of organic molecules. Using molecular descriptors, the LASSO model uncovers that the electro-topological, electrostatic, polarizability, and hydrophobicity/hydrophilicity properties are the most important physical properties to determine the membrane permeability of small drug-like molecules. Additionally, with molecular fingerprints, the LASSO model suggests that certain chemical substructures can significantly affect the permeability of organic molecules, which closely connects to the identified main physical properties. Moreover, the DNN model using molecular fingerprints can help develop a more accurate mapping between molecular structures and their membrane permeability than LASSO models. Our results provide deep understanding of drug-membrane interactions and useful guidance for the inverse molecular design of drug-like molecules. Last but not least, while the current focus is on the permeability of drug-like molecules, the methodology of this work is general and can be applied for other complex physical chemistry problems to gain molecular insights.
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Affiliation(s)
- Guang Chen
- Department of Mechanical Engineering and Polymer Program, Institute of Materials Science, University of Connecticut, Storrs, CT 06269, USA.
| | - Zhiqiang Shen
- Department of Mechanical Engineering and Polymer Program, Institute of Materials Science, University of Connecticut, Storrs, CT 06269, USA.
| | - Ying Li
- Department of Mechanical Engineering and Polymer Program, Institute of Materials Science, University of Connecticut, Storrs, CT 06269, USA.
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25
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Li X, Paier W, Paier J. Machine Learning in Computational Surface Science and Catalysis: Case Studies on Water and Metal-Oxide Interfaces. Front Chem 2021; 8:601029. [PMID: 33425857 PMCID: PMC7793815 DOI: 10.3389/fchem.2020.601029] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 10/27/2020] [Indexed: 11/13/2022] Open
Abstract
The goal of many computational physicists and chemists is the ability to bridge the gap between atomistic length scales of about a few multiples of an Ångström (Å), i. e., 10−10 m, and meso- or macroscopic length scales by virtue of simulations. The same applies to timescales. Machine learning techniques appear to bring this goal into reach. This work applies the recently published on-the-fly machine-learned force field techniques using a variant of the Gaussian approximation potentials combined with Bayesian regression and molecular dynamics as efficiently implemented in the Vienna ab initio simulation package, VASP. The generation of these force fields follows active-learning schemes. We apply these force fields to simple oxides such as MgO and more complex reducible oxides such as iron oxide, examine their generalizability, and further increase complexity by studying water adsorption on these metal oxide surfaces. We successfully examined surface properties of pristine and reconstructed MgO and Fe3O4 surfaces. However, the accurate description of water–oxide interfaces by machine-learned force fields, especially for iron oxides, remains a field offering plenty of research opportunities.
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Affiliation(s)
- Xiaoke Li
- Institut für Chemie, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Wolfgang Paier
- Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute HHI, Berlin, Germany
| | - Joachim Paier
- Institut für Chemie, Humboldt-Universität zu Berlin, Berlin, Germany
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26
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Peach ML, Beedie SL, Chau CH, Collins MK, Markolovic S, Luo W, Tweedie D, Steinebach C, Greig NH, Gütschow M, Vargesson N, Nicklaus MC, Figg WD. Antiangiogenic Activity and in Silico Cereblon Binding Analysis of Novel Thalidomide Analogs. Molecules 2020; 25:E5683. [PMID: 33276504 PMCID: PMC7730988 DOI: 10.3390/molecules25235683] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 11/25/2020] [Accepted: 11/27/2020] [Indexed: 12/14/2022] Open
Abstract
Due to its antiangiogenic and anti-immunomodulatory activity, thalidomide continues to be of clinical interest despite its teratogenic actions, and efforts to synthesize safer, clinically active thalidomide analogs are continually underway. In this study, a cohort of 27 chemically diverse thalidomide analogs was evaluated for antiangiogenic activity in an ex vivo rat aorta ring assay. The protein cereblon has been identified as the target for thalidomide, and in silico pharmacophore analysis and molecular docking with a crystal structure of human cereblon were used to investigate the cereblon binding abilities of the thalidomide analogs. The results suggest that not all antiangiogenic thalidomide analogs can bind cereblon, and multiple targets and mechanisms of action may be involved.
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Affiliation(s)
- Megan L. Peach
- Basic Science Program, Chemical Biology Laboratory, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD 21701, USA;
| | - Shaunna L. Beedie
- Molecular Pharmacology Section, Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA; (S.L.B.); (C.H.C.); (M.K.C.); (S.M.)
- School of Medicine, Medical Sciences & Nutrition, Institute of Medical Sciences, University of Aberdeen, Aberdeen AB25 2ZD, UK;
| | - Cindy H. Chau
- Molecular Pharmacology Section, Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA; (S.L.B.); (C.H.C.); (M.K.C.); (S.M.)
| | - Matthew K. Collins
- Molecular Pharmacology Section, Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA; (S.L.B.); (C.H.C.); (M.K.C.); (S.M.)
| | - Suzana Markolovic
- Molecular Pharmacology Section, Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA; (S.L.B.); (C.H.C.); (M.K.C.); (S.M.)
| | - Weiming Luo
- Drug Design & Development Section, Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, MD 21224, USA; (W.L.); (D.T.); (N.H.G.)
| | - David Tweedie
- Drug Design & Development Section, Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, MD 21224, USA; (W.L.); (D.T.); (N.H.G.)
| | - Christian Steinebach
- Pharmaceutical Institute, University of Bonn, 53121 Bonn, Germany; (C.S.); (M.G.)
| | - Nigel H. Greig
- Drug Design & Development Section, Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, MD 21224, USA; (W.L.); (D.T.); (N.H.G.)
| | - Michael Gütschow
- Pharmaceutical Institute, University of Bonn, 53121 Bonn, Germany; (C.S.); (M.G.)
| | - Neil Vargesson
- School of Medicine, Medical Sciences & Nutrition, Institute of Medical Sciences, University of Aberdeen, Aberdeen AB25 2ZD, UK;
| | - Marc C. Nicklaus
- Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, NIH, Frederick, MD 21701, USA;
| | - William D. Figg
- Molecular Pharmacology Section, Genitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA; (S.L.B.); (C.H.C.); (M.K.C.); (S.M.)
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27
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Lans I, Palacio-Rodríguez K, Cavasotto CN, Cossio P. Flexi-pharma: a molecule-ranking strategy for virtual screening using pharmacophores from ligand-free conformational ensembles. J Comput Aided Mol Des 2020; 34:1063-1077. [PMID: 32656619 PMCID: PMC7449997 DOI: 10.1007/s10822-020-00329-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 06/27/2020] [Indexed: 01/27/2023]
Abstract
Computer-aided strategies are useful for reducing the costs and increasing the success-rate in drug discovery. Among these strategies, methods based on pharmacophores (an ensemble of electronic and steric features representing the target active site) are efficient to implement over large compound libraries. However, traditional pharmacophore-based methods require knowledge of active compounds or ligand-receptor structures, and only few ones account for target flexibility. Here, we developed a pharmacophore-based virtual screening protocol, Flexi-pharma, that overcomes these limitations. The protocol uses molecular dynamics (MD) simulations to explore receptor flexibility, and performs a pharmacophore-based virtual screening over a set of MD conformations without requiring prior knowledge about known ligands or ligand-receptor structures for building the pharmacophores. The results from the different receptor conformations are combined using a "voting" approach, where a vote is given to each molecule that matches at least one pharmacophore from each MD conformation. Contrarily to other approaches that reduce the pharmacophore ensemble to some representative models and score according to the matching models or molecule conformers, the Flexi-pharma approach takes directly into account the receptor flexibility by scoring in regards to the receptor conformations. We tested the method over twenty systems, finding an enrichment of the dataset for 19 of them. Flexi-pharma is computationally efficient allowing for the screening of thousands of compounds in minutes on a single CPU core. Moreover, the ranking of molecules by vote is a general strategy that can be applied with any pharmacophore-filtering program.
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Affiliation(s)
- Isaias Lans
- Biophysics of Tropical Diseases Max Planck Tandem Group, University of Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia
| | - Karen Palacio-Rodríguez
- Biophysics of Tropical Diseases Max Planck Tandem Group, University of Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia
| | - Claudio N Cavasotto
- Computational Drug Design and Biomedical Informatics Laboratory, Translational Medicine Research Institute (IIMT), CONICET-Universidad Austral, Pilar, Buenos Aires, Argentina
- Facultad de Ciencias Biomédicas, and Facultad de Ingeniería, Universidad Austral, Pilar, Buenos Aires, Argentina
- Austral Institute for Applied Artificial Intelligence, Universidad Austral, Pilar, Buenos Aires, Argentina
| | - Pilar Cossio
- Biophysics of Tropical Diseases Max Planck Tandem Group, University of Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia.
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438, Frankfurt am Main, Germany.
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Zivkovic M, Zlatanovic M, Zlatanovic N, Golubović M, Veselinović AM. The Application of the Combination of Monte Carlo Optimization Method based QSAR Modeling and Molecular Docking in Drug Design and Development. Mini Rev Med Chem 2020; 20:1389-1402. [DOI: 10.2174/1389557520666200212111428] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 10/21/2019] [Accepted: 10/28/2019] [Indexed: 01/18/2023]
Abstract
In recent years, one of the promising approaches in the QSAR modeling Monte Carlo optimization
approach as conformation independent method, has emerged. Monte Carlo optimization has
proven to be a valuable tool in chemoinformatics, and this review presents its application in drug discovery
and design. In this review, the basic principles and important features of these methods are discussed
as well as the advantages of conformation independent optimal descriptors developed from the
molecular graph and the Simplified Molecular Input Line Entry System (SMILES) notation compared
to commonly used descriptors in QSAR modeling. This review presents the summary of obtained results
from Monte Carlo optimization-based QSAR modeling with the further addition of molecular
docking studies applied for various pharmacologically important endpoints. SMILES notation based
optimal descriptors, defined as molecular fragments, identified as main contributors to the increase/
decrease of biological activity, which are used further to design compounds with targeted activity
based on computer calculation, are presented. In this mini-review, research papers in which molecular
docking was applied as an additional method to design molecules to validate their activity further,
are summarized. These papers present a very good correlation among results obtained from Monte
Carlo optimization modeling and molecular docking studies.
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Affiliation(s)
| | | | | | - Mladjan Golubović
- Clinic for Anesthesiology and Intensive Care, Clinical Center Nis, Nis, Serbia
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Ragno R, Esposito V, Di Mario M, Masiello S, Viscovo M, Cramer RD. Teaching and Learning Computational Drug Design: Student Investigations of 3D Quantitative Structure-Activity Relationships through Web Applications. JOURNAL OF CHEMICAL EDUCATION 2020; 97:1922-1930. [PMID: 33814598 PMCID: PMC8008382 DOI: 10.1021/acs.jchemed.0c00117] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 06/06/2020] [Indexed: 05/27/2023]
Abstract
The increasing use of information technology in the discovery of new molecular entities encourages the use of modern molecular-modeling tools to help teach important concepts of drug design to chemistry and pharmacy undergraduate students. In particular, statistical models such as quantitative structure-activity relationships (QSAR)-often as its 3D QSAR variant-are commonly used in the development and optimization of a leading compound. We describe how these drug discovery methods can be taught and learned by means of free and open-source web applications, specifically the online platform www.3d-qsar.com. This new suite of web applications has been integrated into a drug design teaching course, one that provides both theoretical and practical perspectives. We include the teaching protocol by which pharmaceutical biotechnology master students at Pharmacy Faculty of Sapienza Rome University are introduced to drug design. Starting with a choice among recent articles describing the potencies of a series of molecules tested against a biological target, each student is expected to build a 3D QSAR ligand-based model from their chosen publication, proceeding as follows: creating the initial data set (Py-MolEdit); generating the global minimum conformations (Py-ConfSearch); proposing a promising mutual alignment (Py-Align); and finally, building, and optimizing a robust 3D QSAR models (Py-CoMFA). These student activities also help validate these new molecular modeling tools, especially for their usability by inexperienced hands. To more fully demonstrate the effectiveness of this protocol and its tools, we include the work performed by four of these students (four of the coauthors), detailing the satisfactory 3D QSAR models they obtained. Such scientifically complete experiences by undergraduates, made possible by the efficiency of the 3D QSAR methodology, provide exposure to computational tools in the same spirit as traditional laboratory exercises. With the obsolescence of the classic Comparative Molecular Field Analysis Sybyl host, the 3dqsar web portal offers one of the few available means of performing this well-established 3D QSAR method.
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Affiliation(s)
- Rino Ragno
- Rome
Center for Molecular Design, Department of Drug Chemistry and Technology, Sapienza Rome University, P. le A. Moro 5, 00185 Rome, Italy
| | - Valeria Esposito
- Pharmacy
and Medicine Faculty, Pharmaceutical Biotechnology Master Degree Course, Sapienza Rome University, P. le A. Moro 5, 00185 Rome, Italy
| | - Martina Di Mario
- Pharmacy
and Medicine Faculty, Pharmaceutical Biotechnology Master Degree Course, Sapienza Rome University, P. le A. Moro 5, 00185 Rome, Italy
| | - Stefano Masiello
- Pharmacy
and Medicine Faculty, Pharmaceutical Biotechnology Master Degree Course, Sapienza Rome University, P. le A. Moro 5, 00185 Rome, Italy
| | - Marco Viscovo
- Pharmacy
and Medicine Faculty, Pharmaceutical Biotechnology Master Degree Course, Sapienza Rome University, P. le A. Moro 5, 00185 Rome, Italy
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30
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Spiegel M, Kapusta K, Kołodziejczyk W, Saloni J, Żbikowska B, Hill GA, Sroka Z. Antioxidant Activity of Selected Phenolic Acids-Ferric Reducing Antioxidant Power Assay and QSAR Analysis of the Structural Features. Molecules 2020; 25:molecules25133088. [PMID: 32645868 PMCID: PMC7412039 DOI: 10.3390/molecules25133088] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 06/25/2020] [Accepted: 06/29/2020] [Indexed: 01/09/2023] Open
Abstract
Phenolic acids are naturally occurring compounds that are known for their antioxidant and antiradical activity. We present experimental and theoretical studies on the antioxidant potential of the set of 22 phenolic acids with different models of hydroxylation and methoxylation of aromatic rings. Ferric reducing antioxidant power assay was used to evaluate this property. 2,3-dihydroxybenzoic acid was found to be the strongest antioxidant, while mono hydroxylated and methoxylated structures had the lowest activities. A comprehensive structure-activity investigation with density functional theory methods elucidated the influence of compounds topology, resonance stabilization, and intramolecular hydrogen bonding on the exhibited activity. The key factor was found to be a presence of two or more hydroxyl groups being located in ortho or para position to each other. Finally, the quantitative structure-activity relationship approach was used to build a multiple linear regression model describing the dependence of antioxidant activity on structure of compounds, using features exclusively related to their topology. Coefficients of determination for training set and for the test set equaled 0.9918 and 0.9993 respectively, and Q2 value for leave-one-out was 0.9716. In addition, the presented model was used to predict activities of phenolic acids that haven't been tested here experimentally.
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Affiliation(s)
- Maciej Spiegel
- Department of Pharmacognosy, Wroclaw Medical University, Borowska 211, 50-556 Wroclaw, Poland; (M.S.); (B.Ż.); (Z.S.)
| | - Karina Kapusta
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, 1400 J. R. Lynch str., Jackson, MS 39217, USA; (W.K.); (J.S.); (G.A.H.)
- Correspondence:
| | - Wojciech Kołodziejczyk
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, 1400 J. R. Lynch str., Jackson, MS 39217, USA; (W.K.); (J.S.); (G.A.H.)
| | - Julia Saloni
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, 1400 J. R. Lynch str., Jackson, MS 39217, USA; (W.K.); (J.S.); (G.A.H.)
| | - Beata Żbikowska
- Department of Pharmacognosy, Wroclaw Medical University, Borowska 211, 50-556 Wroclaw, Poland; (M.S.); (B.Ż.); (Z.S.)
| | - Glake A. Hill
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, 1400 J. R. Lynch str., Jackson, MS 39217, USA; (W.K.); (J.S.); (G.A.H.)
| | - Zbigniew Sroka
- Department of Pharmacognosy, Wroclaw Medical University, Borowska 211, 50-556 Wroclaw, Poland; (M.S.); (B.Ż.); (Z.S.)
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31
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Abdulfatai U, Uzairu A, Shallangwa GA, Uba S. Designing and estimating antioxidant properties of some lubricant additives via QSPR and MD methodologies. SCIENTIFIC AFRICAN 2020. [DOI: 10.1016/j.sciaf.2020.e00451] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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32
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Muratov EN, Bajorath J, Sheridan RP, Tetko IV, Filimonov D, Poroikov V, Oprea TI, Baskin II, Varnek A, Roitberg A, Isayev O, Curtarolo S, Fourches D, Cohen Y, Aspuru-Guzik A, Winkler DA, Agrafiotis D, Cherkasov A, Tropsha A. QSAR without borders. Chem Soc Rev 2020; 49:3525-3564. [PMID: 32356548 PMCID: PMC8008490 DOI: 10.1039/d0cs00098a] [Citation(s) in RCA: 327] [Impact Index Per Article: 81.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.
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Affiliation(s)
- Eugene N Muratov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
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Keretsu S, Bhujbal SP, Cho SJ. Docking and 3D-QSAR Studies of Hydrazone and Triazole Derivatives for Selective Inhibition of GRK2 over ROCK2. LETT DRUG DES DISCOV 2020. [DOI: 10.2174/1570180816666190618105320] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Introduction:
G protein-coupled receptor kinase 2 (GRK2) is known to be implicated in
heart failure, and therefore serves as an important drug target. GRK2 belongs to the protein kinase A,
G, and C family and shares high sequence similarity with its closely related protein, the Rhoassociated
coiled-coil protein kinase 2 (ROCK2). Therefore, selective inhibition of GRK2 over
ROCK2 is considered crucial for heart failure therapy.
Objective:
To understand the structural factors for enhancing the inhibitory activity for GRK2 and
selectivity over ROCK2, we analyzed and compared molecular interactions using the same set of
ligands against both receptors.
Methods:
We have performed molecular docking and three-dimensional quantitative structure
activity relationship (3D-QSAR) studies on a series of hydrazone and triazole derivatives.
Results:
The presence of hydrophobic substituents at the triazole ring, electronegative substituents
between the pyridine and triazole ring and hydrophobic substituents near the benzene ring increases
the activity of both kinases. Whereas, having non-bulky substituents near the triazole ring, bulky and
hydrophobic substations at the benzene ring and electronegative and H-bond acceptor substituents at
the triazole ring showed a higher inhibitory preference for GRK2 over ROCK2.
Conclusion:
The outcome of this study may be used in the future development of potent GRK2
inhibitors having ROCK2 selectivity.
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Affiliation(s)
- Seketoulie Keretsu
- Department of Biomedical Sciences, College of Medicine, Chosun University, Gwangju 501-759, Korea
| | | | - Seung Joo Cho
- Department of Biomedical Sciences, College of Medicine, Chosun University, Gwangju 501-759, Korea
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Muhire J, Li BQ, Zhai HL, Li SS, Mi JY. A Simple Approach to the Toxicity Prediction of Anilines and Phenols Towards Aquatic Organisms. ARCHIVES OF ENVIRONMENTAL CONTAMINATION AND TOXICOLOGY 2020; 78:545-554. [PMID: 31915850 DOI: 10.1007/s00244-019-00703-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2019] [Accepted: 12/27/2019] [Indexed: 06/10/2023]
Abstract
Chemicals pollution in the environment has attracted attention all over the world, and the toxicity prediction of chemical pollutants has become quite important. In this paper, we introduce a simple approach to predict the toxicity of some chemical components, in which the Tchebichef image moment (TM) method was employed to extract useful chemical information from the images of molecular structures to establish quantitative structure-activity relationship (QSAR) prediction models. The proposed approach was applied to predict the toxicity of anilines and phenols for the aquatic organisms of P. subcapitata and V. fischeri, in which the obtained TMs were defined as the independent variables, while the biological toxicity (pEC50) was regarded to be the dependent variable. Then, the predictive models were established by stepwise regression, respectively. The obtained squared correlation coefficients of leave-one-out cross-validation (Q2) for training sets and the predictive squared correlation coefficients (Rp2) for test sets of the two groups of data were higher than 0.79 and 0.75, respectively, which indicated that the obtained models possessed satisfactory accuracy and reliability. Compared with several reported methods, the proposed approach was more convenient and has a higher predictive capability. Our study provides another perspective in QSAR research.
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Affiliation(s)
- Jules Muhire
- College of Chemistry & Chemical Engineering, Lanzhou University, Lanzhou, 730000, People's Republic of China
| | - Bao Qiong Li
- School of Biotechnology & Health Sciences, Wuyi University, Jiangmen, 529020, People's Republic of China
| | - Hong Lin Zhai
- College of Chemistry & Chemical Engineering, Lanzhou University, Lanzhou, 730000, People's Republic of China.
| | - Sha Sha Li
- College of Chemistry & Chemical Engineering, Lanzhou University, Lanzhou, 730000, People's Republic of China
| | - Jia Ying Mi
- College of Chemistry & Chemical Engineering, Lanzhou University, Lanzhou, 730000, People's Republic of China
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Schaduangrat N, Lampa S, Simeon S, Gleeson MP, Spjuth O, Nantasenamat C. Towards reproducible computational drug discovery. J Cheminform 2020; 12:9. [PMID: 33430992 PMCID: PMC6988305 DOI: 10.1186/s13321-020-0408-x] [Citation(s) in RCA: 85] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 01/02/2020] [Indexed: 12/11/2022] Open
Abstract
The reproducibility of experiments has been a long standing impediment for further scientific progress. Computational methods have been instrumental in drug discovery efforts owing to its multifaceted utilization for data collection, pre-processing, analysis and inference. This article provides an in-depth coverage on the reproducibility of computational drug discovery. This review explores the following topics: (1) the current state-of-the-art on reproducible research, (2) research documentation (e.g. electronic laboratory notebook, Jupyter notebook, etc.), (3) science of reproducible research (i.e. comparison and contrast with related concepts as replicability, reusability and reliability), (4) model development in computational drug discovery, (5) computational issues on model development and deployment, (6) use case scenarios for streamlining the computational drug discovery protocol. In computational disciplines, it has become common practice to share data and programming codes used for numerical calculations as to not only facilitate reproducibility, but also to foster collaborations (i.e. to drive the project further by introducing new ideas, growing the data, augmenting the code, etc.). It is therefore inevitable that the field of computational drug design would adopt an open approach towards the collection, curation and sharing of data/code.
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Affiliation(s)
- Nalini Schaduangrat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, 10700, Bangkok, Thailand
| | - Samuel Lampa
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24, Uppsala, Sweden
| | - Saw Simeon
- Interdisciplinary Graduate Program in Bioscience, Faculty of Science, Kasetsart University, 10900, Bangkok, Thailand
| | - Matthew Paul Gleeson
- Department of Biomedical Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, 10520, Bangkok, Thailand.
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24, Uppsala, Sweden.
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, 10700, Bangkok, Thailand.
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In silico studies of novel scaffold of thiazolidin-4-one derivatives as anti-Toxoplasma gondii agents by 2D/3D-QSAR, molecular docking, and molecular dynamics simulations. Struct Chem 2020. [DOI: 10.1007/s11224-019-01458-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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37
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Siedhoff NE, Schwaneberg U, Davari MD. Machine learning-assisted enzyme engineering. Methods Enzymol 2020; 643:281-315. [DOI: 10.1016/bs.mie.2020.05.005] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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38
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QSAR analysis of coumarin-based benzamides as histone deacetylase inhibitors using CoMFA, CoMSIA and HQSAR methods. J Mol Struct 2020. [DOI: 10.1016/j.molstruc.2019.126961] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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39
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Application of in silico approaches for the generation of milk protein-derived bioactive peptides. J Funct Foods 2020. [DOI: 10.1016/j.jff.2019.103636] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
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40
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Tüzün B, Saripinar E. Molecular docking and 4D-QSAR model of methanone derivatives by electron conformational-genetic algorithm method. JOURNAL OF THE IRANIAN CHEMICAL SOCIETY 2019. [DOI: 10.1007/s13738-019-01835-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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41
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Iman M, Davood A, Khamesipour A. Design of antimalarial agents based on pyrimidine derivatives as methionine aminopeptidase 1b inhibitor: Molecular docking, quantitative structure activity relationships, and molecular dynamics simulation studies. J CHIN CHEM SOC-TAIP 2019. [DOI: 10.1002/jccs.201900165] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Maryam Iman
- Chemical Injuries Research Center, Systems Biology and Poisonings InstituteBaqiyatallah University of Medical Sciences Tehran Iran
| | - Asghar Davood
- Department of Medicinal Chemistry, Pharmaceutical Sciences BranchIslamic Azad University Tehran Iran
| | - Ali Khamesipour
- Center for Research and Training in Skin Diseases and LeprosyTehran University of Medical Sciences Tehran Iran
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Shirmohammadi M, Mohammadinasab E, Bayat Z. Prediction of Lipophilicity of some Quinolone Derivatives by using Quantitative Structure-Activity Relationship. Curr Drug Discov Technol 2019; 18:83-94. [PMID: 31701848 DOI: 10.2174/1570163816666191108145026] [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: 05/28/2019] [Revised: 07/24/2019] [Accepted: 09/27/2019] [Indexed: 11/22/2022]
Abstract
OBJECTIVES Quantitative structure activity relationship (QSAR) was used to study the partition coefficient of some quinolones and their derivatives. METHODS These molecules are broad-spectrum antibiotic pharmaceutics. First, data were divided into two categories of train and test (validation) sets using a random selection method. Second, three approaches, including stepwise selection (STS) (forward), genetic algorithm (GA), and simulated annealing (SA) were used to select the descriptors, to examine the effect feature selection methods. To find the relation between descriptors and partition coefficient, multiple linear regression (MLR), principal component regression (PCR) and partial least squares (PLS) were used. RESULTS QSAR study showed that both regression and descriptor selection methods have a vital role in the results. Different statistical metrics showed that the MLR-SA approach with (r2=0.96, q2=0.91, pred_r2=0.95) gives the best outcome. CONCLUSION The proposed expression by the MLR-SA approach can be used in the better design of novel quinolones and their derivatives.
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Affiliation(s)
| | | | - Zakiyeh Bayat
- Department of Chemistry, Quchan Branch, Islamic Azad University, Quchan, Iran
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43
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Ragno R. www.3d-qsar.com: a web portal that brings 3-D QSAR to all electronic devices—the Py-CoMFA web application as tool to build models from pre-aligned datasets. J Comput Aided Mol Des 2019; 33:855-864. [DOI: 10.1007/s10822-019-00231-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Accepted: 09/28/2019] [Indexed: 11/28/2022]
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44
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Wu Y, Meng D, Zhao F, Wang Y, Gao J. Quantitative structure property relationship for relative volatility of isopropanol and water mixture. SEP SCI TECHNOL 2019. [DOI: 10.1080/01496395.2019.1675697] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Yumin Wu
- College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao, China
| | - Dapeng Meng
- College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao, China
| | - Fei Zhao
- College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao, China
| | - Yinglong Wang
- College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao, China
| | - Jun Gao
- College of Chemical and Environmental Engineering, Shandong University of Science and Technology, Qingdao, China
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45
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Insights into the EGFR SAR of N-phenylquinazolin-4-amine-derivatives using quantum mechanical pairwise-interaction energies. J Comput Aided Mol Des 2019; 33:745-757. [PMID: 31494804 DOI: 10.1007/s10822-019-00221-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2019] [Accepted: 08/21/2019] [Indexed: 10/26/2022]
Abstract
Protein kinases are an important class of enzymes that play an essential role in virtually all major disease areas. In addition, they account for approximately 50% of the current targets pursued in drug discovery research. In this work, we explore the generation of structure-based quantum mechanical (QM) quantitative structure-activity relationship models (QSAR) as a means to facilitate structure-guided optimization of protein kinase inhibitors. We explore whether more accurate, interpretable QSAR models can be generated for a series of 76 N-phenylquinazolin-4-amine inhibitors of epidermal growth factor receptor (EGFR) kinase by comparing and contrasting them to other standard QSAR methodologies. The QM-based method involved molecular docking of inhibitors followed by their QM optimization within a ~ 300 atom cluster model of the EGFR active site at the M062X/6-31G(d,p) level. Pairwise computations of the interaction energies with each active site residue were performed. QSAR models were generated by splitting the datasets 75:25 into a training and test set followed by modelling using partial least squares (PLS). Additional QSAR models were generated using alignment dependent CoMFA and CoMSIA methods as well as alignment independent physicochemical, e-state indices and fingerprint descriptors. The structure-based QM-QSAR model displayed good performance on the training and test sets (r2 ~ 0.7) and was demonstrably more predictive than the QSAR models built using other methods. The descriptor coefficients from the QM-QSAR models allowed for a detailed rationalization of the active site SAR, which has implications for subsequent design iterations.
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Ma Y, Cui P, Wang Y, Zhu Z, Wang Y, Gao J. A review of extractive distillation from an azeotropic phenomenon for dynamic control. Chin J Chem Eng 2019. [DOI: 10.1016/j.cjche.2018.08.015] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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47
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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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48
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Bellera CL, Talevi A. Quantitative structure-activity relationship models for compounds with anticonvulsant activity. Expert Opin Drug Discov 2019; 14:653-665. [PMID: 31072145 DOI: 10.1080/17460441.2019.1613368] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Introduction: Third-generation antiepileptic drugs have seemingly failed to improve the global figures of seizure control and can still be regarded as symptomatic treatments. Quantitative structure-activity relationships (QSAR) can be used to guide hit-to-lead and lead optimization projects and applied to the large-scale virtual screening of chemical libraries. Areas covered: In this review, the authors cover reports on QSAR models related to antiepileptic drugs and drug targets in epilepsy, analyzing whether they refer to classic or non-classic QSAR and if they apply QSAR as a descriptive or predictive approach, among other considerations. The article finally focuses on a more detailed discussion of those predictive studies which include some sort of experimental validation, i.e. papers in which the reported models have been used to identify novel active compounds which have been tested in vitro and/or in vivo. Expert opinion: There are significant opportunities to apply the QSAR methodology to assist the discovery of more efficacious antiepileptic drugs. Considering the intrinsic complexity of the disorder, such applications should focus on state-of-the-art approximations (e.g. systemic, multi-target and multi-scale QSAR as well as ensemble and deep learning) and modeling the effects on novel drug targets and modern screening tools.
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Affiliation(s)
- Carolina L Bellera
- a Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences , University of La Plata (UNLP) , La Plata, Buenos Aires , Argentina.,b CCT La Plata , Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) , Buenos Aires , Argentina
| | - Alan Talevi
- a Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences , University of La Plata (UNLP) , La Plata, Buenos Aires , Argentina.,b CCT La Plata , Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) , Buenos Aires , Argentina
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Spaczyńska E, Mrozek-Wilczkiewicz A, Malarz K, Kos J, Gonec T, Oravec M, Gawecki R, Bak A, Dohanosova J, Kapustikova I, Liptaj T, Jampilek J, Musiol R. Design and synthesis of anticancer 1-hydroxynaphthalene-2-carboxanilides with a p53 independent mechanism of action. Sci Rep 2019; 9:6387. [PMID: 31011161 PMCID: PMC6476888 DOI: 10.1038/s41598-019-42595-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Accepted: 04/03/2019] [Indexed: 12/19/2022] Open
Abstract
A series of 116 small-molecule 1-hydroxynaphthalene-2-carboxanilides was designed based on the fragment-based approach and was synthesized according to the microwave-assisted protocol. The biological activity of all of the compounds was tested on human colon carcinoma cell lines including a deleted TP53 tumor suppressor gene. The mechanism of activity was studied according to the p53 status in the cell. Several compounds revealed a good to excellent activity that was similar to or better than the standard anticancer drugs. Some of these appeared to be more active against the p53 null cells than their wild-type counterparts. Intercalating the properties of these compounds could be responsible for their mechanism of action.
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Affiliation(s)
- Ewelina Spaczyńska
- Institute of Chemistry, University of Silesia, 75 Pułku Piechoty 1a, 41-500, Chorzów, Poland
| | - Anna Mrozek-Wilczkiewicz
- A. Chełkowski Institute of Physics and Silesian Center for Education and Interdisciplinary Research, University of Silesia, 75 Pułku Piechoty 1a, 41-500, Chorzów, Poland
| | - Katarzyna Malarz
- A. Chełkowski Institute of Physics and Silesian Center for Education and Interdisciplinary Research, University of Silesia, 75 Pułku Piechoty 1a, 41-500, Chorzów, Poland
| | - Jiri Kos
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Comenius University, Odbojarov 10, 832 32, Bratislava, Slovakia
| | - Tomas Gonec
- Department of Chemical Drugs, Faculty of Pharmacy, University of Veterinary and Pharmaceutical Sciences, Palackeho 1, Brno, 612 42, Czech Republic
| | - Michal Oravec
- Global Change Research Institute CAS, Belidla 986/4a, Brno, 603 00, Czech Republic
| | - Robert Gawecki
- A. Chełkowski Institute of Physics and Silesian Center for Education and Interdisciplinary Research, University of Silesia, 75 Pułku Piechoty 1a, 41-500, Chorzów, Poland
| | - Andrzej Bak
- Institute of Chemistry, University of Silesia, 75 Pułku Piechoty 1a, 41-500, Chorzów, Poland
| | - Jana Dohanosova
- Central Laboratories, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinskeho 9, Bratislava, 81237, Slovakia
| | - Iva Kapustikova
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Comenius University, Odbojarov 10, 832 32, Bratislava, Slovakia
| | - Tibor Liptaj
- Central Laboratories, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinskeho 9, Bratislava, 81237, Slovakia
| | - Josef Jampilek
- Department of Analytical Chemistry, Faculty of Natural Sciences, Comenius University, Ilkovicova 6, 842 15, Bratislava, Slovakia. .,Regional Centre of Advanced Technologies and Materials, Faculty of Science, Palacky University, Slechtitelu 27, 783 71, Olomouc, Czech Republic.
| | - Robert Musiol
- Institute of Chemistry, University of Silesia, 75 Pułku Piechoty 1a, 41-500, Chorzów, Poland.
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Desai NC, Bhatt NB, Joshi SB. Synthetic modifications in ethyl 2-amino-4-methylthiazole-5-carboxylate: 3D QSAR analysis and antimicrobial study. SYNTHETIC COMMUN 2019. [DOI: 10.1080/00397911.2019.1587777] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Nisheeth C. Desai
- Division of Medicinal Chemistry, Department of Chemistry (DST-FIST Sponsored & UGC NON-SAP), Maharaja Krishnakumarsinhji Bhavnagar University, Bhavnagar, Gujarat, India
| | - Nayan B. Bhatt
- Division of Medicinal Chemistry, Department of Chemistry (DST-FIST Sponsored & UGC NON-SAP), Maharaja Krishnakumarsinhji Bhavnagar University, Bhavnagar, Gujarat, India
| | - Surbhi B. Joshi
- Division of Medicinal Chemistry, Department of Chemistry (DST-FIST Sponsored & UGC NON-SAP), Maharaja Krishnakumarsinhji Bhavnagar University, Bhavnagar, Gujarat, India
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