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Zhang R, Zhang X, Zhang Q, Li Y, Wang Y, Xu J, Cheng Z, Chen H, Yao Y, Sun H. Heterogeneous Photodegradation Behavior of Liquid Crystal Monomers in Dust: Quantitative Structure-Activity Relationship and Product Identification. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:3908-3918. [PMID: 38329000 DOI: 10.1021/acs.est.3c04753] [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: 02/09/2024]
Abstract
The heterogeneous photodegradation behavior of liquid crystal monomers (LCMs) in standard dust (standard reference material, SRM 2583) and environmental dust was investigated. The measured photodegradation ratios for 23 LCMs in SRM and environmental dust in 12 h were 11.1 ± 1.8 to 23.2 ± 1.1% and 8.7 ± 0.5 to 24.0 ± 2.8%, respectively. The degradation behavior of different LCM compounds varied depending on their structural properties. A quantitative structure-activity relationship model for predicting the degradation ratio of LCMs in SRM dust was established, which revealed that the molecular descriptors related to molecular polarizability, electronegativity, and molecular mass were closely associated with LCMs' photodegradation. The photodegradation products of the LCM compound 4'-propoxy-4-biphenylcarbonitrile (PBIPHCN) in dust, including •OH oxidation, C-O bond cleavage, and ring-opening products, were identified by nontarget analysis, and the corresponding degradation pathways were suggested. Some of the identified products, such as 4'-hydroxyethoxy-4-biphenylcarbonitrile, showed predicted toxicity (with an oral rat lethal dose of 50%) comparable to that of PBIPHCN. The half-lives of the studied LCMs in SRM dust were estimated at 32.2-82.5 h by fitting an exponential decay curve to the observed photodegradation data. The photodegradation mechanisms of LCMs in dust were revealed for the first time, enhancing the understanding of LCMs' environmental behavior and risks.
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Affiliation(s)
- Ruiqi Zhang
- School of Energy and Environmental Engineering, Hebei University of Technology, Tianjin 300401, China
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, 38 Tongyan Road, Jinnan District, Tianjin 300350, China
| | - Xiao Zhang
- School of Energy and Environmental Engineering, Hebei University of Technology, Tianjin 300401, China
| | - Qiuyue Zhang
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, 38 Tongyan Road, Jinnan District, Tianjin 300350, China
| | - Yongcheng Li
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, 38 Tongyan Road, Jinnan District, Tianjin 300350, China
| | - Yu Wang
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, 38 Tongyan Road, Jinnan District, Tianjin 300350, China
| | - Jiaping Xu
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, 38 Tongyan Road, Jinnan District, Tianjin 300350, China
| | - Zhipeng Cheng
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, 38 Tongyan Road, Jinnan District, Tianjin 300350, China
| | - Hao Chen
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, 38 Tongyan Road, Jinnan District, Tianjin 300350, China
| | - Yiming Yao
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, 38 Tongyan Road, Jinnan District, Tianjin 300350, China
| | - Hongwen Sun
- MOE Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, 38 Tongyan Road, Jinnan District, Tianjin 300350, China
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Sousa GHM, Gomes RA, de Oliveira EO, Trossini GHG. Machine learning methods applied for the prediction of biological activities of triple reuptake inhibitors. J Biomol Struct Dyn 2023; 41:10277-10286. [PMID: 36546689 DOI: 10.1080/07391102.2022.2154269] [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: 09/06/2022] [Accepted: 11/25/2022] [Indexed: 12/24/2022]
Abstract
Major depressive disorder (MDD) is characterized by a series of disabling symptoms like anhedonia, depressed mood, lack of motivation for daily tasks and self-extermination thoughts. The monoamine deficiency hypothesis states that depression is mainly caused by a deficiency of monoamine at the synaptic cleft. Thus, major efforts have been made to develop drugs that inhibit serotonin (SERT), norepinephrine (NET) and dopamine (DAT) transporters and increase the availability of these monoamines. Current gold standard treatment of MDD uses drugs that target one or more monoamine transporters. Triple reuptake inhibitors (TRIs) can target SERT, NET, and DAT simultaneously, and are believed to have the potential to be early onset antidepressants. Quantitative structure-activity relationship models were developed using machine learning algorithms in order to predict biological activities of a series of triple reuptake inhibitor compounds that showed in vitro inhibitory activity against multiple targets. The results, using mostly interpretable descriptors, showed that the internal and external predictive ability of the models are adequate, particularly of the DAT and NET by Random Forest and Support Vector Machine models. The current work shows that models developed from relatively simple, chemically interpretable descriptors can predict the activity of TRIs with similar structure in the applicability domain using ML methods.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
| | - Renan Augusto Gomes
- Faculdade de Ciências Farmacêuticas, Universidade de São Paulo, São Paulo, SP, Brazil
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Caminero Gomes Soares A, Marques Sousa GH, Calil RL, Goulart Trossini GH. Absorption matters: A closer look at popular oral bioavailability rules for drug approvals. Mol Inform 2023; 42:e202300115. [PMID: 37550251 DOI: 10.1002/minf.202300115] [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: 05/16/2023] [Revised: 07/10/2023] [Accepted: 08/07/2023] [Indexed: 08/09/2023]
Abstract
This study examines how two popular drug-likeness concepts used in early development, Lipinski Rule of Five (Ro5) and Veber's Rules, possibly affected drug profiles of FDA approved drugs since 1997. Our findings suggest that when all criteria are applied, relevant compounds may be excluded, addressing the harmfulness of blindly employing these rules. Of all oral drugs in the period used for this analysis, around 66 % conform to the RO5 and 85 % to Veber's Rules. Molecular Weight and calculated LogP showed low consistent values over time, apart from being the two least followed rules, challenging their relevance. On the other hand, hydrogen bond related rules and the number of rotatable bonds are amongst the most followed criteria and show exceptional consistency over time. Furthermore, our analysis indicates that topological polar surface area and total count of hydrogen bonds cannot be used as interchangeable parameters, contrary to the original proposal. This research enhances the comprehension of drug profiles that were FDA approved in the post-Lipinski period. Medicinal chemists could utilize these heuristics as a limited guide to direct their exploration of the oral bioavailability chemical space, but they must also steer the wheel to break these rules and explore different regions when necessary.
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Affiliation(s)
- Artur Caminero Gomes Soares
- School of Pharmaceutical Sciences, University of São Paulo, Department of Pharmacy, Laboratório de Integração entre Técnicas Experimentais e Computacionais (LITEC), Av. Prof. Lineu Prestes, 580, São Paulo, SP, Brazil
| | - Gustavo Henrique Marques Sousa
- School of Pharmaceutical Sciences, University of São Paulo, Department of Pharmacy, Laboratório de Integração entre Técnicas Experimentais e Computacionais (LITEC), Av. Prof. Lineu Prestes, 580, São Paulo, SP, Brazil
| | - Raisa Ludmila Calil
- School of Pharmaceutical Sciences, University of São Paulo, Department of Pharmacy, Laboratório de Integração entre Técnicas Experimentais e Computacionais (LITEC), Av. Prof. Lineu Prestes, 580, São Paulo, SP, Brazil
| | - Gustavo Henrique Goulart Trossini
- School of Pharmaceutical Sciences, University of São Paulo, Department of Pharmacy, Laboratório de Integração entre Técnicas Experimentais e Computacionais (LITEC), Av. Prof. Lineu Prestes, 580, São Paulo, SP, Brazil
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Wang F, Yang Z, Li F, Shao JL, Xu LC. Strategic sampling with stochastic surface walking for machine learning force fields in iron's bcc-hcp phase transitions. RSC Adv 2023; 13:31728-31737. [PMID: 37908655 PMCID: PMC10614040 DOI: 10.1039/d3ra04676a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 10/24/2023] [Indexed: 11/02/2023] Open
Abstract
This study developed a machine learning-based force field for simulating the bcc-hcp phase transitions of iron. By employing traditional molecular dynamics sampling methods and stochastic surface walking sampling methods, combined with Bayesian inference, we construct an efficient machine learning potential for iron. By using SOAP descriptors to map structural data, we find that the machine learning force field exhibits good coverage in the phase transition space. Accuracy evaluation shows that the machine learning force field has small errors compared to DFT calculations in terms of energy, force, and stress evaluations, indicating excellent reproducibility. Additionally, the machine learning force field accurately predicts the stable crystal structure parameters, elastic constants, and bulk modulus of bcc and hcp phases of iron, and demonstrates good performance in predicting higher-order derivatives and phase transition processes, as evidenced by comparisons with DFT calculations and existing experimental data. Therefore, our study provides an effective tool for investigating the phase transitions of iron using machine learning methods, offering new insights and approaches for materials science and solid-state physics research.
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Affiliation(s)
- Fang Wang
- College of Physics, Taiyuan University of Technology Jinzhong 030600 China
| | - Zhi Yang
- College of Physics, Taiyuan University of Technology Jinzhong 030600 China
| | - Fenglian Li
- College of Information and Computer, Taiyuan University of Technology Jinzhong 030600 China
| | - Jian-Li Shao
- State Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology Beijing 100081 China
| | - Li-Chun Xu
- College of Physics, Taiyuan University of Technology Jinzhong 030600 China
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Ghosh A, Panda P, Halder AK, Cordeiro MNDS. In silico characterization of aryl benzoyl hydrazide derivatives as potential inhibitors of RdRp enzyme of H5N1 influenza virus. Front Pharmacol 2022; 13:1004255. [PMID: 36225563 PMCID: PMC9548590 DOI: 10.3389/fphar.2022.1004255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 08/24/2022] [Indexed: 11/13/2022] Open
Abstract
RNA-dependent RNA polymerase (RdRp) is a potential therapeutic target for the discovery of novel antiviral agents for the treatment of life-threatening infections caused by newly emerged strains of the influenza virus. Being one of the most conserved enzymes among RNA viruses, RdRp and its inhibitors require further investigations to design novel antiviral agents. In this work, we systematically investigated the structural requirements for antiviral properties of some recently reported aryl benzoyl hydrazide derivatives through a range of in silico tools such as 2D-quantitative structure-activity relationship (2D-QSAR), 3D-QSAR, structure-based pharmacophore modeling, molecular docking and molecular dynamics simulations. The 2D-QSAR models developed in the current work achieved high statistical reliability and simultaneously afforded in-depth mechanistic interpretability towards structural requirements. The structure-based pharmacophore model developed with the docked conformation of one of the most potent compounds with the RdRp protein of H5N1 influenza strain was utilized for developing a 3D-QSAR model with satisfactory statistical quality validating both the docking and the pharmacophore modeling methodologies performed in this work. However, it is the atom-based alignment of the compounds that afforded the most statistically reliable 3D-QSAR model, the results of which provided mechanistic interpretations consistent with the 2D-QSAR results. Additionally, molecular dynamics simulations performed with the apoprotein as well as the docked complex of RdRp revealed the dynamic stability of the ligand at the proposed binding site of the receptor. At the same time, it also supported the mechanistic interpretations drawn from 2D-, 3D-QSAR and pharmacophore modeling. The present study, performed mostly with open-source tools and webservers, returns important guidelines for research aimed at the future design and development of novel anti-viral agents against various RNA viruses like influenza virus, human immunodeficiency virus-1, hepatitis C virus, corona virus, and so forth.
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Affiliation(s)
- Abhishek Ghosh
- Dr. B. C. Roy College of Pharmacy and Allied Health Sciences, Durgapur, West Bengal, India
| | - Parthasarathi Panda
- Dr. B. C. Roy College of Pharmacy and Allied Health Sciences, Durgapur, West Bengal, India
- *Correspondence: Parthasarathi Panda, ; Maria Natalia D. S. Cordeiro,
| | - Amit Kumar Halder
- Dr. B. C. Roy College of Pharmacy and Allied Health Sciences, Durgapur, West Bengal, India
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal
| | - Maria Natalia D. S. Cordeiro
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal
- *Correspondence: Parthasarathi Panda, ; Maria Natalia D. S. Cordeiro,
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Zhu T, Cao Z, Singh RP, Cheng H, Chen M. In silico prediction of polyethylene-aqueous and air partition coefficients of organic contaminants using linear and nonlinear approaches. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2021; 289:112437. [PMID: 33812149 DOI: 10.1016/j.jenvman.2021.112437] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 03/16/2021] [Accepted: 03/18/2021] [Indexed: 06/12/2023]
Abstract
Low-density polyethylene (LDPE) passive sampling is very attractive for use in determining chemicals concentrations. Crucial to the measurement is the coefficient (KPE) describing partitioning between LDPE and environmental matrices. 255, 117 and 190 compounds were collected for the development of datasets in three different matrices, i.e., water, air and seawater, respectively. Further, 3 pp-LFER models and 9 QSPR models based on classical multiple linear regression (MLR) coupled with prevalent nonlinear algorithms (artificial neural network, ANN and support vector machine, SVM) were performed to predict LDPE-water (KPE-W), LDPE-air (KPE-A) and LDPE-seawater (KPE-SW) partition coefficients. These developed models have satisfying predictability (R2adj: 0.805-0.966, 0.963-0.991 and 0.817-0.941; RMSEtra: 0.233-0.565, 0.200-0.406 and 0.260-0.459) and robustness (Q2ext: 0.840-0.943, 0.968-0.984 and 0.797-0.842; RMSEext: 0.308-0.514, 0.299-0.426 and 0.407-0.462) in three datasets (water, air and seawater), respectively. In particular, the reasonable mechanism interpretations revealed that the molecular size, hydrophobicity, polarizability, ionization potential, and molecular stability were the most relevant properties, for governing chemicals partitioning between LDPE and environmental matrices. The application domains (ADs) assessed here exhibited the satisfactory applicability. As such, the derived models can act as intelligent tools to predict unknown KPE values and fill the experimental gaps, which was further beneficial for the construction of enormous and reliable database to facilitate a distinct understanding of the distribution for organic contaminants in total environment.
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Affiliation(s)
- Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China.
| | - Zaizhi Cao
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | | | - Haomiao Cheng
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | - Ming Chen
- School of Civil Engineering, Southeast University, Nanjing, 210096, China
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Beglari M, Goudarzi N, Shahsavani D, Arab Chamjangali M, Mozafari Z. Combination of radial distribution functions as structural descriptors with ligand-receptor interaction information in the QSAR study of some 4-anilinoquinazoline derivatives as potent EGFR inhibitors. Struct Chem 2020. [DOI: 10.1007/s11224-020-01505-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
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Performance of radial distribution function-based descriptors in the chemoinformatic studies of HIV-1 protease. Future Med Chem 2020; 12:299-309. [DOI: 10.4155/fmc-2019-0241] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Aim: This letter investigates the role of radial distribution function-based descriptors for in silico design of new drugs. Methodology: The multiple linear regression models for HIV-1 protease and its complexes with a series of inhibitors were constructed. A detailed analysis of major atomic contributions to the radial distribution function descriptor weighted by the number of valence shell electrons identified residues Arg8, Asp29 and residues of the catalytic triad as crucial for the correlation with the inhibition constant, together with residues Asp30 and Ile50, whose mutations are known to cause an emergence of drug resistant variants. Conclusion: This study demonstrates an easy and fast assessment of the activity of potential drugs and the derivation of structural information of their complexes with the receptor or enzyme.
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Irandoost A, Tahmasbpour E, Beigi Harchegani A, Borna H, Iman M. A Quantitative Structure-Activity Relationship Study of Calpeptin (Calpain Inhibitor) as an Anticancer Agent. J CHIN CHEM SOC-TAIP 2018. [DOI: 10.1002/jccs.201700322] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Ali Irandoost
- Chemical Injuries Research Center; System Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences; P.O. Box 19945-581, Tehran Iran
| | - Eisa Tahmasbpour
- Laboratory of Regenerative Medicine & Biomedical Innovations; Pasteur Institute of Iran; Tehran Iran
| | - Asghar Beigi Harchegani
- Chemical Injuries Research Center; System Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences; P.O. Box 19945-581, Tehran Iran
| | - Hojat Borna
- Chemical Injuries Research Center; System Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences; P.O. Box 19945-581, Tehran Iran
| | - Maryam Iman
- Chemical Injuries Research Center; System Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences; P.O. Box 19945-581, Tehran Iran
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Amin SA, Adhikari N, Baidya SK, Gayen S, Jha T. Structural refinement and prediction of potential CCR2 antagonists through validated multi-QSAR modeling studies. J Biomol Struct Dyn 2018; 37:75-94. [PMID: 29251559 DOI: 10.1080/07391102.2017.1418679] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Chemokines trigger numerous inflammatory responses and modulate the immune system. The interaction between monocyte chemoattractant protein-1 and chemokine receptor 2 (CCR2) may be the cause of atherosclerosis, obesity, and insulin resistance. However, CCR2 is also implicated in other inflammatory diseases such as rheumatoid arthritis, multiple sclerosis, asthma, and neuropathic pain. Therefore, there is a paramount importance of designing potent and selective CCR2 antagonists despite a number of drug candidates failed in clinical trials. In this article, 83 CCR2 antagonists by Jhonson and Jhonson Pharmaceuticals have been considered for robust validated multi-QSAR modeling studies to get an idea about the structural and pharmacophoric requirements for designing more potent CCR2 antagonists. All these QSAR models were validated and statistically reliable. Observations resulted from different modeling studies correlated and validated results of other ones. Finally, depending on these QSAR observations, some new molecules were proposed that may exhibit higher activity against CCR2.
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Affiliation(s)
- Sk Abdul Amin
- a Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology , Jadavpur University , P. O. Box 17020, Kolkata 700032 , West Bengal , India
| | - Nilanjan Adhikari
- a Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology , Jadavpur University , P. O. Box 17020, Kolkata 700032 , West Bengal , India
| | - Sandip Kumar Baidya
- a Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology , Jadavpur University , P. O. Box 17020, Kolkata 700032 , West Bengal , India
| | - Shovanlal Gayen
- b Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences , Dr. Harisingh Gour University , Sagar 470003 , Madhya Pradesh , India
| | - Tarun Jha
- a Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology , Jadavpur University , P. O. Box 17020, Kolkata 700032 , West Bengal , India
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Filipic S, Ruzic D, Vucicevic J, Nikolic K, Agbaba D. Quantitative structure-retention relationship of selected imidazoline derivatives on α1-acid glycoprotein column. J Pharm Biomed Anal 2016; 127:101-11. [DOI: 10.1016/j.jpba.2016.02.053] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2015] [Revised: 02/18/2016] [Accepted: 02/28/2016] [Indexed: 10/22/2022]
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Benchmarking ligand-based virtual High-Throughput Screening with the PubChem database. Molecules 2013; 18:735-56. [PMID: 23299552 PMCID: PMC3759399 DOI: 10.3390/molecules18010735] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2012] [Revised: 10/11/2012] [Accepted: 12/17/2012] [Indexed: 01/04/2023] Open
Abstract
With the rapidly increasing availability of High-Throughput Screening (HTS) data in the public domain, such as the PubChem database, methods for ligand-based computer-aided drug discovery (LB-CADD) have the potential to accelerate and reduce the cost of probe development and drug discovery efforts in academia. We assemble nine data sets from realistic HTS campaigns representing major families of drug target proteins for benchmarking LB-CADD methods. Each data set is public domain through PubChem and carefully collated through confirmation screens validating active compounds. These data sets provide the foundation for benchmarking a new cheminformatics framework BCL::ChemInfo, which is freely available for non-commercial use. Quantitative structure activity relationship (QSAR) models are built using Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Decision Trees (DTs), and Kohonen networks (KNs). Problem-specific descriptor optimization protocols are assessed including Sequential Feature Forward Selection (SFFS) and various information content measures. Measures of predictive power and confidence are evaluated through cross-validation, and a consensus prediction scheme is tested that combines orthogonal machine learning algorithms into a single predictor. Enrichments ranging from 15 to 101 for a TPR cutoff of 25% are observed.
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A quantitative structure-retention relationship for the prediction of retention indices of the essential oils of Ammoides atlantica. JOURNAL OF THE SERBIAN CHEMICAL SOCIETY 2011. [DOI: 10.2298/jsc100219076a] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
A simple, descriptive and interpretable model, based on a quantitative
structure-retention relationship (QSRR), was developed using the genetic
algorithm-multiple linear regression (GA-MLR) approach for the prediction of
the retention indices (RI) of essential oil components. By molecular
modeling, three significant descriptors related to the RI values of the
essential oils were identified. A data set was selected consisting of the
retention indices for 32 essential oil molecules with a range of more than
931 compounds. Then, a suitable set of the molecular descriptors was
calculated and the important descriptors were selected with the aid of the
genetic algorithm and multiple regression method. A model with a low
prediction error and a good correlation coefficient was obtained. This model
was used for the prediction of the RI values of some essential oil
components which were not used in the modeling procedure.
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