51
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Kesar S, Paliwal S, Sharma S, Mishra P, Chauhan M, Arya R, Madan K, Khan S. In-Silico QSAR Modelling of Predicted Rho Kinase Inhibitors Against Cardio Vascular Diseases. Curr Comput Aided Drug Des 2020; 15:421-432. [PMID: 30848208 DOI: 10.2174/1573409915666190307163437] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2018] [Revised: 02/12/2019] [Accepted: 02/27/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND Rho-kinase is an essential downstream target of GTP-binding protein RhoA, and plays a crucial role in the calcium-sensitization pathway. Rho-kinase pathway is critically involved in phosphorylation state of myosin light chain, leading to increased contraction of smooth muscles. Inhibition of this pathway has turned out to be a promising target for several indications such as cardiovascular diseases, glaucoma and inflammatory diseases. METHODS The present work focuses on a division-based 2D quantitative structure-activity relationship (QSAR) analysis along with a docking study to predict structural features that may be essential for the enhancement of selectivity and potency of the target compounds. Furthermore, a set of indoles and azaindoles were also projected based on the regression equation as novel developments. Molecular docking was applied for exploring the binding sites of the newly predicted set of compounds with the receptor. RESULTS Results of the docked conformations suggested that introduction of non-bulky and substituted groups in the hinge region of ROCK-II ATP binding pocket would improve the activity by decreasing the bulkiness or length of the compounds. CONCLUSION ADME studies were performed to ascertain the novelty and drug-like properties of the designed molecules, respectively.
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Affiliation(s)
- Seema Kesar
- Department of Pharmacy, Banasthali Vidyapeeth, Banasthali- 304022, Rajasthan, India
| | - Sarvesh Paliwal
- Department of Pharmacy, Banasthali Vidyapeeth, Banasthali- 304022, Rajasthan, India
| | - Swapnil Sharma
- Department of Pharmacy, Banasthali Vidyapeeth, Banasthali- 304022, Rajasthan, India
| | - Pooja Mishra
- Department of Pharmacy, Banasthali Vidyapeeth, Banasthali- 304022, Rajasthan, India
| | - Monika Chauhan
- Department of Pharmacy, Banasthali Vidyapeeth, Banasthali- 304022, Rajasthan, India
| | - Richa Arya
- Department of Pharmacy, Banasthali Vidyapeeth, Banasthali- 304022, Rajasthan, India
| | - Kirtika Madan
- Department of Pharmacy, Banasthali Vidyapeeth, Banasthali- 304022, Rajasthan, India
| | - Shagufta Khan
- Department of Pharmacy, Banasthali Vidyapeeth, Banasthali- 304022, Rajasthan, India
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52
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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: 78] [Impact Index Per Article: 19.5] [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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53
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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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54
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A review on created QSPR models for predicting ionic liquids properties and their reliability from chemometric point of view. J Mol Liq 2020. [DOI: 10.1016/j.molliq.2019.112013] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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55
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Zohari N, Fareghi-Alamdari R, Sheibani N. Model development and design criteria of hypergolic imidazolium ionic liquids from ignition delay time and viscosity viewpoints. NEW J CHEM 2020. [DOI: 10.1039/d0nj00521e] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The relationships between ID time, viscosity and molecular structure of hypergolic imidazolium ILs are discussed to specify ideal structural characteristics.
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Affiliation(s)
- Narges Zohari
- Faculty of Chemistry and Chemical Engineering
- Malek-Ashtar University of Technology
- Tehran
- Iran
| | - Reza Fareghi-Alamdari
- Faculty of Chemistry and Chemical Engineering
- Malek-Ashtar University of Technology
- Tehran
- Iran
| | - Nasser Sheibani
- Faculty of Chemistry and Chemical Engineering
- Malek-Ashtar University of Technology
- Tehran
- Iran
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56
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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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57
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Wambaugh JF, Wetmore BA, Ring CL, Nicolas CI, Pearce R, Honda G, Dinallo R, Angus D, Gilbert J, Sierra T, Badrinarayanan A, Snodgrass B, Brockman A, Strock C, Setzer W, Thomas RS. Assessing Toxicokinetic Uncertainty and Variability in Risk Prioritization. Toxicol Sci 2019; 172:235-251. [PMID: 31532498 PMCID: PMC8136471 DOI: 10.1093/toxsci/kfz205] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
High(er) throughput toxicokinetics (HTTK) encompasses in vitro measures of key determinants of chemical toxicokinetics and reverse dosimetry approaches for in vitro-in vivo extrapolation (IVIVE). With HTTK, the bioactivity identified by any in vitro assay can be converted to human equivalent doses and compared with chemical intake estimates. Biological variability in HTTK has been previously considered, but the relative impact of measurement uncertainty has not. Bayesian methods were developed to provide chemical-specific uncertainty estimates for 2 in vitro toxicokinetic parameters: unbound fraction in plasma (fup) and intrinsic hepatic clearance (Clint). New experimental measurements of fup and Clint are reported for 418 and 467 chemicals, respectively. These data raise the HTTK chemical coverage of the ToxCast Phase I and II libraries to 57%. Although the standard protocol for Clint was followed, a revised protocol for fup measured unbound chemical at 10%, 30%, and 100% of physiologic plasma protein concentrations, allowing estimation of protein binding affinity. This protocol reduced the occurrence of chemicals with fup too low to measure from 44% to 9.1%. Uncertainty in fup was also reduced, with the median coefficient of variation dropping from 0.4 to 0.1. Monte Carlo simulation was used to propagate both measurement uncertainty and biological variability into IVIVE. The uncertainty propagation techniques used here also allow incorporation of other sources of uncertainty such as in silico predictors of HTTK parameters. These methods have the potential to inform risk-based prioritization based on the relationship between in vitro bioactivities and exposures.
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Affiliation(s)
- John F. Wambaugh
- National Center for Computational Toxicology, Office of Research and Development, U.S. EPA, Research Triangle Park, NC 27711
| | - Barbara A. Wetmore
- National Exposure Research Laboratory, Office of Research and Development, U.S. EPA, Research Triangle Park, NC 27711
| | - Caroline L. Ring
- National Center for Computational Toxicology, Office of Research and Development, U.S. EPA, Research Triangle Park, NC 27711
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee 37831
| | - Chantel I. Nicolas
- National Center for Computational Toxicology, Office of Research and Development, U.S. EPA, Research Triangle Park, NC 27711
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee 37831
- Office of Pollution Prevention and Toxics, U.S. EPA, Washington, D.C. 20460
| | - Robert Pearce
- National Center for Computational Toxicology, Office of Research and Development, U.S. EPA, Research Triangle Park, NC 27711
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee 37831
| | - Gregory Honda
- National Center for Computational Toxicology, Office of Research and Development, U.S. EPA, Research Triangle Park, NC 27711
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee 37831
| | | | | | | | | | | | | | | | | | - Woodrow Setzer
- National Center for Computational Toxicology, Office of Research and Development, U.S. EPA, Research Triangle Park, NC 27711
| | - Russell S. Thomas
- National Center for Computational Toxicology, Office of Research and Development, U.S. EPA, Research Triangle Park, NC 27711
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58
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Neural-based approaches to overcome feature selection and applicability domain in drug-related property prediction. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.105777] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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59
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Schneider M, Pons JL, Labesse G, Bourguet W. In Silico Predictions of Endocrine Disruptors Properties. Endocrinology 2019; 160:2709-2716. [PMID: 31265055 PMCID: PMC6804484 DOI: 10.1210/en.2019-00382] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Accepted: 06/26/2019] [Indexed: 01/12/2023]
Abstract
Endocrine-disrupting chemicals (EDCs) are a broad class of molecules present in our environment that are suspected to cause adverse effects in the endocrine system by interfering with the synthesis, transport, degradation, or action of endogenous ligands. The characterization of the harmful interaction between environmental compounds and their potential cellular targets and the development of robust in vivo, in vitro, and in silico screening methods are important for assessment of the toxic potential of large numbers of chemicals. In this context, computer-aided technologies that will allow for activity prediction of endocrine disruptors and environmental risk assessments are being developed. These technologies must be able to cope with diverse data and connect chemistry at the atomic level with the biological activity at the cellular, organ, and organism levels. Quantitative structure-activity relationship methods became popular for toxicity issues. They correlate the chemical structure of compounds with biological activity through a number of molecular descriptors (e.g., molecular weight and parameters to account for hydrophobicity, topology, or electronic properties). Chemical structure analysis is a first step; however, modeling intermolecular interactions and cellular behavior will also be essential. The increasing number of three-dimensional crystal structures of EDCs' targets has provided a wealth of structural information that can be used to predict their interactions with EDCs using docking and scoring procedures. In the present review, we have described the various computer-assisted approaches that use ligands and targets properties to predict endocrine disruptor activities.
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Affiliation(s)
- Melanie Schneider
- Centre de Biochimie Structurale, CNRS, INSERM, Université de Montpellier, Montpellier, France
| | - Jean-Luc Pons
- Centre de Biochimie Structurale, CNRS, INSERM, Université de Montpellier, Montpellier, France
| | - Gilles Labesse
- Centre de Biochimie Structurale, CNRS, INSERM, Université de Montpellier, Montpellier, France
- Correspondence: Gilles Labesse, PhD, or William Bourguet, PhD, Centre de Biochimie Structurale, 29 rue de Navacelles, 34090 Montpellier, France. E-mail: or
| | - William Bourguet
- Centre de Biochimie Structurale, CNRS, INSERM, Université de Montpellier, Montpellier, France
- Correspondence: Gilles Labesse, PhD, or William Bourguet, PhD, Centre de Biochimie Structurale, 29 rue de Navacelles, 34090 Montpellier, France. E-mail: or
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60
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Simeon S, Montanari D, Gleeson MP. Investigation of Factors Affecting the Performance of
in silico
Volume Distribution QSAR Models for Human, Rat, Mouse, Dog & Monkey. Mol Inform 2019; 38:e1900059. [DOI: 10.1002/minf.201900059] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Accepted: 07/03/2019] [Indexed: 01/09/2023]
Affiliation(s)
- Saw Simeon
- Interdisciplinary Graduate Program in Bioscience, Faculty of ScienceKasetsart University Bangkok 10900 Thailand
- Center for Advanced Studies in Nanotechnology for Chemical, Food and Agricultural Industries, KU Institute for Advanced StudiesKasetsart University Bangkok 10900 Thailand
| | - Dino Montanari
- DMPK and Bioanalysis, Aptuit Via Alessandro Fleming, 4 37135 Verona VR Italy
| | - Matthew Paul Gleeson
- Department of Chemistry, Faculty of ScienceKasetsart University Bangkok 10900 Thailand
- Department of Biomedical Engineering, Faculty of EngineeringKing Mongkut's Institute of Technology Ladkrabang Bangkok 10520 Thailand
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61
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Luque Ruiz I, Gómez-Nieto MÁ. Rivality index neighbourhood algorithm with density and distances weighted schemes for the building of robust QSAR classification models with high reliable applicability domain. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2019; 30:587-615. [PMID: 31469296 DOI: 10.1080/1062936x.2019.1644666] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2019] [Accepted: 07/14/2019] [Indexed: 06/10/2023]
Abstract
The rivality index (RI) is a normalized distance measurement between a molecule and their first nearest neighbours providing a robust prediction of the activity of a molecule based on the known activity of their nearest neighbours. Negative values of the RI describe molecules that would be correctly classified by a statistic algorithm and, vice versa, positive values of this index describe those molecules detected as outliers by the classification algorithms. In this paper, we have described a classification algorithm based on the RI and we have proposed four weighted schemes (kernels) for its calculation based on the measuring of different characteristics of the neighbourhood of molecules for each molecule of the dataset at established values of the threshold of neighbours. The results obtained have demonstrated that the proposed classification algorithm, based on the RI, generates more reliable and robust classification models than many of the more used and well-known machine learning algorithms. These results have been validated and corroborated by using 20 balanced and unbalanced benchmark datasets of different sizes and modelability. The classification models generated provide valuable information about the molecules of the dataset, the applicability domain of the models and the reliability of the predictions.
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Affiliation(s)
- I Luque Ruiz
- Department of Computing and Numerical Analysis, Campus de Rabanales, University of Córdoba , Córdoba , Spain
| | - M Á Gómez-Nieto
- Department of Computing and Numerical Analysis, Campus de Rabanales, University of Córdoba , Córdoba , Spain
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62
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Arya R, Gupta SP, Paliwal S, Kesar S, Mishra A, Prabhakar YS. QSAR and Molecular Modeling Studies on a Series of Pyrrolidine Analogs Acting as BACE-1 Inhibitors. LETT DRUG DES DISCOV 2019. [DOI: 10.2174/1570180815666180627124422] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
β-Site amyloidal precursor protein (APP) cleavage enzyme (BACE-1) is
reported as prime cause for progession of Alzheimer’s disease (AD). It is a form of dementia characterized
by degeneration of neurones in brain. Therefore, attempts have been made to find potent
inhibitors of this enzyme.
Methods:
The paper presents an division-based 2D quantitative structure-activity relationship
(QSAR) study on a series of BACE-1 inhibitors to analyse the structural features that may be important
to increase the potency of the compounds.
Results:
The study led to predict some potential leads for the development of potent inhibitors of
BACE-1. One of the molecule with pyrrolidine and pyrrolidinone substitutions exhibited drugreceptor
interactions comparable with reference drug.
Conclusion:
The hydrogen-bond interactions between the molecules and the receptor basically
control the BACE-1 inhibition activity of the compounds.
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Affiliation(s)
- Richa Arya
- Department of Pharmacy, Banasthali Vidyapeeth, Rajasthan, India
| | | | - Sarvesh Paliwal
- Department of Pharmacy, Banasthali Vidyapeeth, Rajasthan, India
| | - Seema Kesar
- Department of Pharmacy, Banasthali Vidyapeeth, Rajasthan, India
| | - Achal Mishra
- Department of Pharmacy, Banasthali Vidyapeeth, Rajasthan, India
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63
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Dmitriev AV, Lagunin AA, Karasev DА, Rudik AV, Pogodin PV, Filimonov DA, Poroikov VV. Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes. Curr Top Med Chem 2019; 19:319-336. [PMID: 30674264 DOI: 10.2174/1568026619666190123160406] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 01/02/2019] [Accepted: 01/07/2019] [Indexed: 02/07/2023]
Abstract
Drug-drug interaction (DDI) is the phenomenon of alteration of the pharmacological activity of a drug(s) when another drug(s) is co-administered in cases of so-called polypharmacy. There are three types of DDIs: pharmacokinetic (PK), pharmacodynamic, and pharmaceutical. PK is the most frequent type of DDI, which often appears as a result of the inhibition or induction of drug-metabolising enzymes (DME). In this review, we summarise in silico methods that may be applied for the prediction of the inhibition or induction of DMEs and describe appropriate computational methods for DDI prediction, showing the current situation and perspectives of these approaches in medicinal and pharmaceutical chemistry. We review sources of information on DDI, which can be used in pharmaceutical investigations and medicinal practice and/or for the creation of computational models. The problem of the inaccuracy and redundancy of these data are discussed. We provide information on the state-of-the-art physiologically- based pharmacokinetic modelling (PBPK) approaches and DME-based in silico methods. In the section on ligand-based methods, we describe pharmacophore models, molecular field analysis, quantitative structure-activity relationships (QSAR), and similarity analysis applied to the prediction of DDI related to the inhibition or induction of DME. In conclusion, we discuss the problems of DDI severity assessment, mention factors that influence severity, and highlight the issues, perspectives and practical using of in silico methods.
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Affiliation(s)
| | - Alexey A Lagunin
- Institute of Biomedical Chemistry, Moscow, Russian Federation.,Pirogov Russian National Research Medical University, Moscow, RussiaN Federation
| | | | | | - Pavel V Pogodin
- Institute of Biomedical Chemistry, Moscow, Russian Federation
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64
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Fareghi‐Alamdari R, Zohari N, Sheibani N. Reliable Evaluation of Ignition Delay Time of Imidazolium Ionic Liquids as Green Hypergolic Propellants by a Novel Theoretical Approach. PROPELLANTS EXPLOSIVES PYROTECHNICS 2019. [DOI: 10.1002/prep.201800343] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Reza Fareghi‐Alamdari
- Faculty of Chemistry and Chemical Engineering Malek-ashtar University of Technology Tehran Iran
| | - Narges Zohari
- Faculty of Chemistry and Chemical Engineering Malek-ashtar University of Technology Tehran Iran
| | - Nasser Sheibani
- Faculty of Chemistry and Chemical Engineering Malek-ashtar University of Technology Tehran Iran
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65
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Luque Ruiz I, Gómez-Nieto MÁ. Building of Robust and Interpretable QSAR Classification Models by Means of the Rivality Index. J Chem Inf Model 2019; 59:2785-2804. [DOI: 10.1021/acs.jcim.9b00264] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Irene Luque Ruiz
- Department of Computing and Numerical Analysis, University of Córdoba, Albert Einstein Building, Campus de Rabanales, E-14071, Córdoba, Spain
| | - Miguel Ángel Gómez-Nieto
- Department of Computing and Numerical Analysis, University of Córdoba, Albert Einstein Building, Campus de Rabanales, E-14071, Córdoba, Spain
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66
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Sheibani N. Heat of Formation Assessment of Organic Azido Compounds Used as Green Energetic Plasticizers by QSPR Approaches. PROPELLANTS EXPLOSIVES PYROTECHNICS 2019. [DOI: 10.1002/prep.201900082] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Nasser Sheibani
- Chemometrics Research Group, Faculty of Chemistry and Chemical EngineeringMalek-Ashtar University of Technology Tehran Iran
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67
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Roubehie Fissa M, Lahiouel Y, Khaouane L, Hanini S. QSPR estimation models of normal boiling point and relative liquid density of pure hydrocarbons using MLR and MLP-ANN methods. J Mol Graph Model 2019; 87:109-120. [DOI: 10.1016/j.jmgm.2018.11.013] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Revised: 11/08/2018] [Accepted: 11/27/2018] [Indexed: 01/03/2023]
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68
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Cai C, Guo P, Zhou Y, Zhou J, Wang Q, Zhang F, Fang J, Cheng F. Deep Learning-Based Prediction of Drug-Induced Cardiotoxicity. J Chem Inf Model 2019; 59:1073-1084. [PMID: 30715873 DOI: 10.1021/acs.jcim.8b00769] [Citation(s) in RCA: 89] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Blockade of the human ether-à-go-go-related gene (hERG) channel by small molecules induces the prolongation of the QT interval which leads to fatal cardiotoxicity and accounts for the withdrawal or severe restrictions on the use of many approved drugs. In this study, we develop a deep learning approach, termed deephERG, for prediction of hERG blockers of small molecules in drug discovery and postmarketing surveillance. In total, we assemble 7,889 compounds with well-defined experimental data on the hERG and with diverse chemical structures. We find that deephERG models built by a multitask deep neural network (DNN) algorithm outperform those built by single-task DNN, naı̈ve Bayes (NB), support vector machine (SVM), random forest (RF), and graph convolutional neural network (GCNN). Specifically, the area under the receiver operating characteristic curve (AUC) value for the best model of deephERG is 0.967 on the validation set. Furthermore, based on 1,824 U.S. Food and Drug Administration (FDA) approved drugs, 29.6% drugs are computationally identified to have potential hERG inhibitory activities by deephERG, highlighting the importance of hERG risk assessment in early drug discovery. Finally, we showcase several novel predicted hERG blockers on approved antineoplastic agents, which are validated by clinical case reports, experimental evidence, and the literature. In summary, this study presents a powerful deep learning-based tool for risk assessment of hERG-mediated cardiotoxicities in drug discovery and postmarketing surveillance.
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Affiliation(s)
- Chuipu Cai
- Institute of Clinical Pharmacology , Guangzhou University of Chinese Medicine , Guangzhou 510405 , China.,School of Basic Medical Sciences , Guangzhou University of Chinese Medicine , Guangzhou 510405 , China
| | - Pengfei Guo
- Institute of Clinical Pharmacology , Guangzhou University of Chinese Medicine , Guangzhou 510405 , China
| | - Yadi Zhou
- Department of Chemistry and Biochemistry , Ohio University , Athens , Ohio 45701 , United States
| | - Jingwei Zhou
- Institute of Clinical Pharmacology , Guangzhou University of Chinese Medicine , Guangzhou 510405 , China
| | - Qi Wang
- Institute of Clinical Pharmacology , Guangzhou University of Chinese Medicine , Guangzhou 510405 , China
| | - Fengxue Zhang
- School of Basic Medical Sciences , Guangzhou University of Chinese Medicine , Guangzhou 510405 , China
| | - Jiansong Fang
- Institute of Clinical Pharmacology , Guangzhou University of Chinese Medicine , Guangzhou 510405 , China
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute , Cleveland Clinic , Cleveland , Ohio 44106 , United States.,Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine , Case Western Reserve University , 9500 Euclid Avenue , Cleveland , Ohio 44195 , United States.,Case Comprehensive Cancer Center , Case Western Reserve University School of Medicine , Cleveland , Ohio 44106 , United States
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69
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Miller TH, Gallidabino MD, MacRae JI, Owen SF, Bury NR, Barron LP. Prediction of bioconcentration factors in fish and invertebrates using machine learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 648:80-89. [PMID: 30114591 PMCID: PMC6234108 DOI: 10.1016/j.scitotenv.2018.08.122] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Revised: 08/08/2018] [Accepted: 08/09/2018] [Indexed: 04/14/2023]
Abstract
The application of machine learning has recently gained interest from ecotoxicological fields for its ability to model and predict chemical and/or biological processes, such as the prediction of bioconcentration. However, comparison of different models and the prediction of bioconcentration in invertebrates has not been previously evaluated. A comparison of 24 linear and machine learning models is presented herein for the prediction of bioconcentration in fish and important factors that influenced accumulation identified. R2 and root mean square error (RMSE) for the test data (n = 110 cases) ranged from 0.23-0.73 and 0.34-1.20, respectively. Model performance was critically assessed with neural networks and tree-based learners showing the best performance. An optimised 4-layer multi-layer perceptron (14 descriptors) was selected for further testing. The model was applied for cross-species prediction of bioconcentration in a freshwater invertebrate, Gammarus pulex. The model for G. pulex showed good performance with R2 of 0.99 and 0.93 for the verification and test data, respectively. Important molecular descriptors determined to influence bioconcentration were molecular mass (MW), octanol-water distribution coefficient (logD), topological polar surface area (TPSA) and number of nitrogen atoms (nN) among others. Modelling of hazard criteria such as PBT, showed potential to replace the need for animal testing. However, the use of machine learning models in the regulatory context has been minimal to date and is critically discussed herein. The movement away from experimental estimations of accumulation to in silico modelling would enable rapid prioritisation of contaminants that may pose a risk to environmental health and the food chain.
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Affiliation(s)
- Thomas H Miller
- Department of Analytical, Environmental & Forensic Sciences, School of Population Health & Environmental Sciences, Faculty of Life Sciences and Medicine, King's College London, 150 Stamford Street, London SE1 9NH, UK.
| | - Matteo D Gallidabino
- Department of Applied Sciences, Northumbria University, Newcastle Upon Tyne NE1 8ST, UK
| | - James I MacRae
- Metabolomics Laboratory, The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - Stewart F Owen
- AstraZeneca, Global Environment, Alderley Park, Macclesfield, Cheshire SK10 4TF, UK
| | - Nicolas R Bury
- Division of Diabetes and Nutritional Sciences, Faculty of Life Sciences and Medicine, King's College London, Franklin Wilkins Building, 150 Stamford Street, London SE1 9NH, UK; Faculty of Science, Health and Technology, University of Suffolk, James Hehir Building, University Avenue, Ipswich, Suffolk IP3 0FS, UK
| | - Leon P Barron
- Department of Analytical, Environmental & Forensic Sciences, School of Population Health & Environmental Sciences, Faculty of Life Sciences and Medicine, King's College London, 150 Stamford Street, London SE1 9NH, UK.
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70
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Liu R, Wang H, Glover KP, Feasel MG, Wallqvist A. Dissecting Machine-Learning Prediction of Molecular Activity: Is an Applicability Domain Needed for Quantitative Structure-Activity Relationship Models Based on Deep Neural Networks? J Chem Inf Model 2018; 59:117-126. [PMID: 30412667 DOI: 10.1021/acs.jcim.8b00348] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Deep neural networks (DNNs) are the major drivers of recent progress in artificial intelligence. They have emerged as the machine-learning method of choice in solving image and speech recognition problems, and their potential has raised the expectation of similar breakthroughs in other fields of study. In this work, we compared three machine-learning methods-DNN, random forest (a popular conventional method), and variable nearest neighbor (arguably the simplest method)-in their ability to predict the molecular activities of 21 in vivo and in vitro data sets. Surprisingly, the overall performance of the three methods was similar. For molecules with structurally close near neighbors in the training sets, all methods gave reliable predictions, whereas for molecules increasingly dissimilar to the training molecules, all three methods gave progressively poorer predictions. For molecules sharing little to no structural similarity with the training molecules, all three methods gave a nearly constant value-approximately the average activity of all training molecules-as their predictions. The results confirm conclusions deduced from analyzing molecular applicability domains for accurate predictions, i.e., the most important determinant of the accuracy of predicting a molecule is its similarity to the training samples. This highlights the fact that even in the age of deep learning, developing a truly high-quality model relies less on the choice of machine-learning approach and more on the availability of experimental efforts to generate sufficient training data of structurally diverse compounds. The results also indicate that the distance to training molecules offers a natural and intuitive basis for defining applicability domains to flag reliable and unreliable quantitative structure-activity relationship predictions.
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Affiliation(s)
- Ruifeng Liu
- Department of Defense, Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center , U.S. Army Medical Research and Materiel Command , Fort Detrick , Maryland 21702 , United States
| | - Hao Wang
- Department of Defense, Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center , U.S. Army Medical Research and Materiel Command , Fort Detrick , Maryland 21702 , United States
| | - Kyle P Glover
- U.S. Army-Edgewood Chemical Biological Center , Aberdeen Proving Ground , Maryland 21010 , United States
| | - Michael G Feasel
- U.S. Army-Edgewood Chemical Biological Center , Aberdeen Proving Ground , Maryland 21010 , United States
| | - Anders Wallqvist
- Department of Defense, Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center , U.S. Army Medical Research and Materiel Command , Fort Detrick , Maryland 21702 , United States
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71
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Gupta P, Gutcaits A. Development and Validation of a Robust QSAR Model for Benzothiazole Hydrazone Derivatives as Bcl-XL Inhibitors. LETT DRUG DES DISCOV 2018. [DOI: 10.2174/1570180815666180502093039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Background:
B-cell Lymphoma Extra Large (Bcl-XL) belongs to B-cell Lymphoma two
(Bcl-2) family. Due to its over-expression and anti-apoptotic role in many cancers, it has been proven
to be a more biologically relevant therapeutic target in anti-cancer therapy. In this study, a Quantitative
Structure Activity Relationship (QSAR) modeling was performed to establish the link between
structural properties and inhibitory potency of benzothiazole hydrazone derivatives against Bcl-XL.
Methods:
The 53 benzothiazole hydrazone derivatives have been used for model development using
genetic algorithm and multiple linear regression methods. The data set is divided into training and
test set using Kennard-Stone based algorithm. The best QSAR model has been selected with statistically
significant r2 = 0.931, F-test =55.488 RMSE = 0.441 and Q2 0.900.
Results:
The model has been tested successfully for external validation (r2
pred = 0.752), as well as
different criteria for acceptable model predictability. Furthermore, analysis of the applicability domain
has been carried out to evaluate the prediction reliability of external set molecules. The developed
QSAR model has revealed that nThiazoles, nROH, EEig13d, WA, BEHv6, HATS6m,
RDF035u and IC4 descriptors are important physico-chemical properties for determining the inhibitory
activity of these molecules.
Conclusion:
The developed QSAR model is stable for this chemical series, indicating that test set
molecules represent the training dataset. The model is statistically reliable with good predictability.
The obtained descriptors reflect important structural features required for activity against Bcl-XL.
These properties are designated by topology, shape, size, geometry, substitution information of the
molecules (nThiazoles and nROH) and electronic properties. In a nutshell, these characteristics can
be successfully utilized for designing and screening of novel inhibitors.
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Affiliation(s)
- Pawan Gupta
- CNS Active Compound Laboratory, Latvian Institute of Organic Synthesis, Riga, LV1006, Latvia
| | - Aleksandrs Gutcaits
- CNS Active Compound Laboratory, Latvian Institute of Organic Synthesis, Riga, LV1006, Latvia
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72
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Ruiz IL, Gómez-Nieto MÁ. Study of the Applicability Domain of the QSAR Classification Models by Means of the Rivality and Modelability Indexes. Molecules 2018; 23:molecules23112756. [PMID: 30356020 PMCID: PMC6278359 DOI: 10.3390/molecules23112756] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 10/14/2018] [Accepted: 10/22/2018] [Indexed: 11/30/2022] Open
Abstract
The reliability of a QSAR classification model depends on its capacity to achieve confident predictions of new compounds not considered in the building of the model. The results of this external validation process show the applicability domain (AD) of the QSAR model and, therefore, the robustness of the model to predict the property/activity of new molecules. In this paper we propose the use of the rivality and modelability indexes for the study of the characteristics of the datasets to be correctly modeled by a QSAR algorithm and to predict the reliability of the built model to prognosticate the property/activity of new molecules. The calculation of these indexes has a very low computational cost, not requiring the building of a model, thus being good tools for the analysis of the datasets in the first stages of the building of QSAR classification models. In our study, we have selected two benchmark datasets with similar number of molecules but with very different modelability and we have corroborated the capacity of the predictability of the rivality and modelability indexes regarding the classification models built using Support Vector Machine and Random Forest algorithms with 5-fold cross-validation and leave-one-out techniques. The results have shown the excellent ability of both indexes to predict outliers and the applicability domain of the QSAR classification models. In all cases, these values accurately predicted the statistic parameters of the QSAR models generated by the algorithms.
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Affiliation(s)
- Irene Luque Ruiz
- Department of Computing and Numerical Analysis, Campus Universitario de Rabanales, Albert Einstein Building, University of Córdoba, E-14071 Córdoba, Spain.
| | - Miguel Ángel Gómez-Nieto
- Department of Computing and Numerical Analysis, Campus Universitario de Rabanales, Albert Einstein Building, University of Córdoba, E-14071 Córdoba, Spain.
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73
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Liu R, Glover KP, Feasel MG, Wallqvist A. General Approach to Estimate Error Bars for Quantitative Structure–Activity Relationship Predictions of Molecular Activity. J Chem Inf Model 2018; 58:1561-1575. [DOI: 10.1021/acs.jcim.8b00114] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Ruifeng Liu
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland 21702, United States
| | - Kyle P. Glover
- Defense Threat Reduction Agency, Aberdeen Proving Ground, Maryland 21010, United States
| | - Michael G. Feasel
- U.S. Army—Edgewood Chemical Biological Center, Operational Toxicology, Aberdeen Proving Ground, Maryland 21010, United States
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland 21702, United States
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Abstract
A number of anti-retroviral drugs are being used for treating Human Immunodeficiency Virus (HIV) infection. Due to emergence of drug resistant strains, there is a constant quest to discover more effective anti-HIV compounds. In this endeavor, computational tools have proven useful in accelerating drug discovery. Although methods were published to design a class of compounds against a specific HIV protein, but an integrated web server for the same is lacking. Therefore, we have developed support vector machine based regression models using experimentally validated data from ChEMBL repository. Quantitative structure activity relationship based features were selected for predicting inhibition activity of a compound against HIV proteins namely protease (PR), reverse transcriptase (RT) and integrase (IN). The models presented a maximum Pearson correlation coefficient of 0.78, 0.76, 0.74 and 0.76, 0.68, 0.72 during tenfold cross-validation on IC50 and percent inhibition datasets of PR, RT, IN respectively.
These models performed equally well on the independent datasets. Chemical space mapping, applicability domain analyses and other statistical tests further support robustness of the predictive models. Currently, we have identified a number of chemical descriptors that are imperative in predicting the compound inhibition potential. HIVprotI platform (http://bioinfo.imtech.res.in/manojk/hivproti) would be useful in virtual screening of inhibitors as well as designing of new molecules against the important HIV proteins for therapeutics development.![]()
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75
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Modarres HP, Janmaleki M, Novin M, Saliba J, El-Hajj F, RezayatiCharan M, Seyfoori A, Sadabadi H, Vandal M, Nguyen MD, Hasan A, Sanati-Nezhad A. In vitro models and systems for evaluating the dynamics of drug delivery to the healthy and diseased brain. J Control Release 2018; 273:108-130. [PMID: 29378233 DOI: 10.1016/j.jconrel.2018.01.024] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2017] [Revised: 01/22/2018] [Accepted: 01/23/2018] [Indexed: 12/12/2022]
Abstract
The blood-brain barrier (BBB) plays a crucial role in maintaining brain homeostasis and transport of drugs to the brain. The conventional animal and Transwell BBB models along with emerging microfluidic-based BBB-on-chip systems have provided fundamental functionalities of the BBB and facilitated the testing of drug delivery to the brain tissue. However, developing biomimetic and predictive BBB models capable of reasonably mimicking essential characteristics of the BBB functions is still a challenge. In addition, detailed analysis of the dynamics of drug delivery to the healthy or diseased brain requires not only biomimetic BBB tissue models but also new systems capable of monitoring the BBB microenvironment and dynamics of barrier function and delivery mechanisms. This review provides a comprehensive overview of recent advances in microengineering of BBB models with different functional complexity and mimicking capability of healthy and diseased states. It also discusses new technologies that can make the next generation of biomimetic human BBBs containing integrated biosensors for real-time monitoring the tissue microenvironment and barrier function and correlating it with the dynamics of drug delivery. Such integrated system addresses important brain drug delivery questions related to the treatment of brain diseases. We further discuss how the combination of in vitro BBB systems, computational models and nanotechnology supports for characterization of the dynamics of drug delivery to the brain.
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Affiliation(s)
- Hassan Pezeshgi Modarres
- BioMEMS and Bioinspired Microfluidic Laboratory, Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, Canada; Center for BioEngineering Research and Education, University of Calgary, Calgary, Canada
| | - Mohsen Janmaleki
- BioMEMS and Bioinspired Microfluidic Laboratory, Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, Canada; Center for BioEngineering Research and Education, University of Calgary, Calgary, Canada
| | - Mana Novin
- BioMEMS and Bioinspired Microfluidic Laboratory, Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, Canada; Center for BioEngineering Research and Education, University of Calgary, Calgary, Canada
| | - John Saliba
- Biomedical Engineering, Department of Mechanical Engineering, Faculty of Engineering and Architecture, American University of Beirut, Beirut 1107 2020, Lebanon
| | - Fatima El-Hajj
- Biomedical Engineering, Department of Mechanical Engineering, Faculty of Engineering and Architecture, American University of Beirut, Beirut 1107 2020, Lebanon
| | - Mahdi RezayatiCharan
- Breast Cancer Research Center (BCRC), ACECR, Tehran, Iran; School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Amir Seyfoori
- Breast Cancer Research Center (BCRC), ACECR, Tehran, Iran; School of Metallurgy and Materials Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Hamid Sadabadi
- BioMEMS and Bioinspired Microfluidic Laboratory, Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, Canada; Center for BioEngineering Research and Education, University of Calgary, Calgary, Canada
| | - Milène Vandal
- Departments of Clinical Neurosciences, Cell Biology and Anatomy, Biochemistry and Molecular Biology, University of Calgary, Calgary, Canada
| | - Minh Dang Nguyen
- Departments of Clinical Neurosciences, Cell Biology and Anatomy, Biochemistry and Molecular Biology, University of Calgary, Calgary, Canada
| | - Anwarul Hasan
- Biomedical Engineering, Department of Mechanical Engineering, Faculty of Engineering and Architecture, American University of Beirut, Beirut 1107 2020, Lebanon; Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, Doha, 2713, Qatar
| | - Amir Sanati-Nezhad
- BioMEMS and Bioinspired Microfluidic Laboratory, Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, Canada; Center for BioEngineering Research and Education, University of Calgary, Calgary, Canada.
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76
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Gramatica P, Papa E, Sangion A. QSAR modeling of cumulative environmental end-points for the prioritization of hazardous chemicals. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2018; 20:38-47. [PMID: 29226926 DOI: 10.1039/c7em00519a] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The hazard of chemicals in the environment is inherently related to the molecular structure and derives simultaneously from various chemical properties/activities/reactivities. Models based on Quantitative Structure Activity Relationships (QSARs) are useful to screen, rank and prioritize chemicals that may have an adverse impact on humans and the environment. This paper reviews a selection of QSAR models (based on theoretical molecular descriptors) developed for cumulative multivariate endpoints, which were derived by mathematical combination of multiple effects and properties. The cumulative end-points provide an integrated holistic point of view to address environmentally relevant properties of chemicals.
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Affiliation(s)
- Paola Gramatica
- QSAR Research Unit on Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences (DiSTA), University of Insubria, Varese, Italy.
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77
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Applicability Domain: A Step Toward Confident Predictions and Decidability for QSAR Modeling. Methods Mol Biol 2018; 1800:141-169. [PMID: 29934891 DOI: 10.1007/978-1-4939-7899-1_6] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
In the context of human safety assessment through quantitative structure-activity relationship (QSAR) modeling, the concept of applicability domain (AD) has an enormous role to play. The Organization of Economic Co-operation and Development (OECD) for QSAR model validation recommended as principle 3 "A defined domain of applicability" to be present for a predictive QSAR model. The study of AD allows estimating the uncertainty in the prediction for a particular molecule based on how similar it is to the training compounds which are used in the model development. In the current scenario, AD represents an active research topic, and many methods have been designed to estimate the competence of a model and the confidence in its outcome for a given prediction task. Thus, characterization of interpolation space is significant in defining the AD. The diverse set of reported AD methods was constructed through different hypotheses and algorithms. These multiplicities of methodologies mystify the end users and make the comparison of the AD for different models a complex issue to address. We have attempted to summarize in this chapter the important concepts of AD including particulars of the available methods to compute the AD along with their thresholds and criteria for estimating AD through training set interpolation in the descriptor space. The idea about transparent domain and decision domain are also discussed. To help readers determine the AD in their projects, practical examples together with available open source software tools are provided.
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78
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Molecular dynamics and integrated pharmacophore-based identification of dual [Formula: see text] inhibitors. Mol Divers 2017; 22:95-112. [PMID: 29138965 DOI: 10.1007/s11030-017-9794-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2017] [Accepted: 10/24/2017] [Indexed: 12/14/2022]
Abstract
Despite increase in the understanding of the pathogenesis of rheumatoid arthritis (RA), it remains a tough challenge. The advent of kinases involved in key intracellular pathways in pathogenesis of RA may provide a new phase of drug discovery for RA. The present study is aimed to identify dual JAK3/[Formula: see text] inhibitors by developing an optimum pharmacophore model integrating the information revealed by ligand-based pharmacophore models and structure-based pharmacophore models (SBPMs). For JAK3 inhibitors, the addition of an aromatic ring feature and for [Formula: see text] the addition of a hydrophobic feature proposed by SBPMs lead to five-point pharmacophore (i.e., AADHR.54 (JAK3)) and six-point pharmacophore (i.e., AAAHRR.45 ([Formula: see text])). The obtained pharmacophores were validated and used for virtual screening and then for docking-based screening. Molecules were further evaluated for ADME properties, and their docked protein complexes were subjected to MM-GBSA energy calculations and molecular dynamic simulations. The top two hit compounds with novel scaffolds 2-oxo-1,2-dihydroquinoline and benzo[d]oxazole showed inhibitory activity for JAK3 and [Formula: see text].
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79
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Lei T, Sun H, Kang Y, Zhu F, Liu H, Zhou W, Wang Z, Li D, Li Y, Hou T. ADMET Evaluation in Drug Discovery. 18. Reliable Prediction of Chemical-Induced Urinary Tract Toxicity by Boosting Machine Learning Approaches. Mol Pharm 2017; 14:3935-3953. [DOI: 10.1021/acs.molpharmaceut.7b00631] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Tailong Lei
- College
of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, P. R. China
| | - Huiyong Sun
- College
of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, P. R. China
| | - Yu Kang
- College
of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, P. R. China
| | - Feng Zhu
- College
of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, P. R. China
| | - Hui Liu
- College
of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, P. R. China
| | - Wenfang Zhou
- College
of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, P. R. China
| | - Zhe Wang
- College
of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, P. R. China
| | - Dan Li
- College
of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, P. R. China
| | - Youyong Li
- Institute
of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, P. R. China
| | - Tingjun Hou
- College
of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, P. R. China
- State Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang 310058, P. R. China
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80
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Duchowicz PR, Bacelo DE, Fioressi SE, Palermo V, Ibezim NE, Romanelli GP. QSAR studies of indoyl aryl sulfides and sulfones as reverse transcriptase inhibitors. Med Chem Res 2017. [DOI: 10.1007/s00044-017-2069-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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81
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Arodola OA, Soliman MES. Quantum mechanics implementation in drug-design workflows: does it really help? Drug Des Devel Ther 2017; 11:2551-2564. [PMID: 28919707 PMCID: PMC5587087 DOI: 10.2147/dddt.s126344] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
The pharmaceutical industry is progressively operating in an era where development costs are constantly under pressure, higher percentages of drugs are demanded, and the drug-discovery process is a trial-and-error run. The profit that flows in with the discovery of new drugs has always been the motivation for the industry to keep up the pace and keep abreast with the endless demand for medicines. The process of finding a molecule that binds to the target protein using in silico tools has made computational chemistry a valuable tool in drug discovery in both academic research and pharmaceutical industry. However, the complexity of many protein-ligand interactions challenges the accuracy and efficiency of the commonly used empirical methods. The usefulness of quantum mechanics (QM) in drug-protein interaction cannot be overemphasized; however, this approach has little significance in some empirical methods. In this review, we discuss recent developments in, and application of, QM to medically relevant biomolecules. We critically discuss the different types of QM-based methods and their proposed application to incorporating them into drug-design and -discovery workflows while trying to answer a critical question: are QM-based methods of real help in drug-design and -discovery research and industry?
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Affiliation(s)
- Olayide A Arodola
- Department of Pharmaceutical Chemistry, University of KwaZulu-Natal, Durban, South Africa
| | - Mahmoud ES Soliman
- Department of Pharmaceutical Chemistry, University of KwaZulu-Natal, Durban, South Africa
- Department of Pharmaceutical Organic Chemistry, Faculty of Pharmacy, Zagazig University, Egypt
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82
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In silico modeling on ADME properties of natural products: Classification models for blood-brain barrier permeability, its application to traditional Chinese medicine and in vitro experimental validation. J Mol Graph Model 2017. [DOI: 10.1016/j.jmgm.2017.05.021] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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83
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Oxindole-based SYK and JAK3 dual inhibitors for rheumatoid arthritis: designing, synthesis and biological evaluation. Future Med Chem 2017; 9:1193-1211. [DOI: 10.4155/fmc-2017-0037] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Aim: Autoimmune disorders have complex pathophysiology and focus is laid on the development of multitargeted agents. Two well-established kinases: SYK and JAK3, were considered to design dual inhibitors as potential therapeutics using various molecular-modeling approaches. Mehodology: Pharmacophore models for SYK and JAK3 were generated using oxindole-based inhibitors. Furthermore, an in-house database was designed that was screened against the best selected models. The obtained hits were employed for docking analysis and subjected to MM-GBSA analysis and molecular dynamic simulation. Results: Top five oxindole derivatives were synthesized and evaluated for in vitro SYK and JAK3 activity. The most active compound 4a was evaluated for in vivo antiarthritic activity. It showed significant anti-arthritic activity. Conclusion: Thus, the designed inhibitors resulted in potential therapeutic agents for rheumatoid arthritis.
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84
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Sun J, Carlsson L, Ahlberg E, Norinder U, Engkvist O, Chen H. Applying Mondrian Cross-Conformal Prediction To Estimate Prediction Confidence on Large Imbalanced Bioactivity Data Sets. J Chem Inf Model 2017. [PMID: 28628322 DOI: 10.1021/acs.jcim.7b00159] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Conformal prediction has been proposed as a more rigorous way to define prediction confidence compared to other application domain concepts that have earlier been used for QSAR modeling. One main advantage of such a method is that it provides a prediction region potentially with multiple predicted labels, which contrasts to the single valued (regression) or single label (classification) output predictions by standard QSAR modeling algorithms. Standard conformal prediction might not be suitable for imbalanced data sets. Therefore, Mondrian cross-conformal prediction (MCCP) which combines the Mondrian inductive conformal prediction with cross-fold calibration sets has been introduced. In this study, the MCCP method was applied to 18 publicly available data sets that have various imbalance levels varying from 1:10 to 1:1000 (ratio of active/inactive compounds). Our results show that MCCP in general performed well on bioactivity data sets with various imbalance levels. More importantly, the method not only provides confidence of prediction and prediction regions compared to standard machine learning methods but also produces valid predictions for the minority class. In addition, a compound similarity based nonconformity measure was investigated. Our results demonstrate that although it gives valid predictions, its efficiency is much worse than that of model dependent metrics.
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Affiliation(s)
| | | | | | - Ulf Norinder
- Swetox, Karolinska Institutet , Unit of Toxicology Sciences, Södertälje 15136, Sweden
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85
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Canault B, Bourg S, Vayer P, Bonnet P. Comprehensive Network Map of ADME-Tox Databases. Mol Inform 2017; 36. [DOI: 10.1002/minf.201700029] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2017] [Accepted: 06/14/2017] [Indexed: 01/04/2023]
Affiliation(s)
- Baptiste Canault
- Institut de Chimie Organique et Analytique (ICOA); Université d'Orléans et CNRS; UMR7311, BP 6759 45067 Orléans France
| | - Stéphane Bourg
- Institut de Chimie Organique et Analytique (ICOA); Université d'Orléans et CNRS; UMR7311, BP 6759 45067 Orléans France
| | - Philippe Vayer
- Technologie Servier; 25-27 rue Eugène Vignat, BP 11749 45007 Orléans cedex 1 France
| | - Pascal Bonnet
- Institut de Chimie Organique et Analytique (ICOA); Université d'Orléans et CNRS; UMR7311, BP 6759 45067 Orléans France
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86
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Lei T, Chen F, Liu H, Sun H, Kang Y, Li D, Li Y, Hou T. ADMET Evaluation in Drug Discovery. Part 17: Development of Quantitative and Qualitative Prediction Models for Chemical-Induced Respiratory Toxicity. Mol Pharm 2017; 14:2407-2421. [PMID: 28595388 DOI: 10.1021/acs.molpharmaceut.7b00317] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
As a dangerous end point, respiratory toxicity can cause serious adverse health effects and even death. Meanwhile, it is a common and traditional issue in occupational and environmental protection. Pharmaceutical and chemical industries have a strong urge to develop precise and convenient computational tools to evaluate the respiratory toxicity of compounds as early as possible. Most of the reported theoretical models were developed based on the respiratory toxicity data sets with one single symptom, such as respiratory sensitization, and therefore these models may not afford reliable predictions for toxic compounds with other respiratory symptoms, such as pneumonia or rhinitis. Here, based on a diverse data set of mouse intraperitoneal respiratory toxicity characterized by multiple symptoms, a number of quantitative and qualitative predictions models with high reliability were developed by machine learning approaches. First, a four-tier dimension reduction strategy was employed to find an optimal set of 20 molecular descriptors for model building. Then, six machine learning approaches were used to develop the prediction models, including relevance vector machine (RVM), support vector machine (SVM), regularized random forest (RRF), extreme gradient boosting (XGBoost), naïve Bayes (NB), and linear discriminant analysis (LDA). Among all of the models, the SVM regression model shows the most accurate quantitative predictions for the test set (q2ext = 0.707), and the XGBoost classification model achieves the most accurate qualitative predictions for the test set (MCC of 0.644, AUC of 0.893, and global accuracy of 82.62%). The application domains were analyzed, and all of the tested compounds fall within the application domain coverage. We also examined the structural features of the compounds and important fragments with large prediction errors. In conclusion, the SVM regression model and the XGBoost classification model can be employed as accurate prediction tools for respiratory toxicity.
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Affiliation(s)
- Tailong Lei
- College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, P. R. China
| | - Fu Chen
- College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, P. R. China
| | - Hui Liu
- College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, P. R. China
| | - Huiyong Sun
- College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, P. R. China
| | - Yu Kang
- College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, P. R. China
| | - Dan Li
- College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, P. R. China
| | - Youyong Li
- Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University , Suzhou, Jiangsu 215123, P. R. China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, P. R. China.,State Key Lab of CAD&CG, Zhejiang University , Hangzhou, Zhejiang 310058, P. R. China
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87
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Sánchez-Rodríguez A, Pérez-Castillo Y, Schürer SC, Nicolotti O, Mangiatordi GF, Borges F, Cordeiro MNDS, Tejera E, Medina-Franco JL, Cruz-Monteagudo M. From flamingo dance to (desirable) drug discovery: a nature-inspired approach. Drug Discov Today 2017. [PMID: 28624633 DOI: 10.1016/j.drudis.2017.05.008] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The therapeutic effects of drugs are well known to result from their interaction with multiple intracellular targets. Accordingly, the pharma industry is currently moving from a reductionist approach based on a 'one-target fixation' to a holistic multitarget approach. However, many drug discovery practices are still procedural abstractions resulting from the attempt to understand and address the action of biologically active compounds while preventing adverse effects. Here, we discuss how drug discovery can benefit from the principles of evolutionary biology and report two real-life case studies. We do so by focusing on the desirability principle, and its many features and applications, such as machine learning-based multicriteria virtual screening.
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Affiliation(s)
- Aminael Sánchez-Rodríguez
- Departamento de Ciencias Naturales, Universidad Técnica Particular de Loja, Calle París S/N, EC1101608 Loja, Ecuador
| | | | - Stephan C Schürer
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine and Center for Computational Science, University of Miami, Miami, FL 33136, USA
| | - Orazio Nicolotti
- Dipartimento di Farmacia - Scienze del Farmaco, Università di Bari Aldo Moro, Bari 072006, Italy
| | | | - Fernanda Borges
- CIQUP/Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto 4169-007, Portugal
| | - M Natalia D S Cordeiro
- REQUIMTE/Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto 4169-007, Portugal
| | - Eduardo Tejera
- Facultad de Medicina, Universidad de Las Américas, 170513 Quito, Ecuador
| | - José L Medina-Franco
- Universidad Nacional Autónoma de México, Departamento de Farmacia, Facultad de Química, Avenida Universidad 3000, Mexico City 04510, Mexico
| | - Maykel Cruz-Monteagudo
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine and Center for Computational Science, University of Miami, Miami, FL 33136, USA; CIQUP/Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto 4169-007, Portugal; REQUIMTE/Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto 4169-007, Portugal; Department of General Education, West Coast University-Miami Campus, Doral, FL 33178, USA.
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88
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Wang T, Yuan XS, Wu MB, Lin JP, Yang LR. The advancement of multidimensional QSAR for novel drug discovery - where are we headed? Expert Opin Drug Discov 2017; 12:769-784. [PMID: 28562095 DOI: 10.1080/17460441.2017.1336157] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
INTRODUCTION The Multidimensional quantitative structure-activity relationship (multidimensional-QSAR) method is one of the most popular computational methods employed to predict interesting biochemical properties of existing or hypothetical molecules. With continuous progress, the QSAR method has made remarkable success in various fields, such as medicinal chemistry, material science and predictive toxicology. Areas covered: In this review, the authors cover the basic elements of multidimensional -QSAR including model construction, validation and application. It includes and emphasizes the very recent developments of multidimensional -QSAR such as: HQSAR, G-QSAR, MIA-QSAR, multi-target QSAR. The advantages and disadvantages of each method are also discussed and typical examples of their application are detailed. Expert opinion: Although there are defects in multidimensional-QSAR modeling, it is still of enormous help to chemists, biologists and other researchers in various fields. In the authors' opinion, the latest more precise and feasible QSAR models should be further developed by integrating new descriptors, algorithms and other relevant computational techniques. Apart from being applied in traditional fields (e.g. lead optimization and predictive risk assessment), QSAR should be used more widely as a routine method in other emerging research fields including the modeling of nanoparticles(NPs), mixture toxicity and peptides.
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Affiliation(s)
- Tao Wang
- a School of biological science , Jining Medical University , Jining , China.,b Department of Chemical and Biological Engineering , Zhejiang University , Hangzhou , China
| | - Xin-Song Yuan
- b Department of Chemical and Biological Engineering , Zhejiang University , Hangzhou , China
| | - Mian-Bin Wu
- b Department of Chemical and Biological Engineering , Zhejiang University , Hangzhou , China
| | - Jian-Ping Lin
- b Department of Chemical and Biological Engineering , Zhejiang University , Hangzhou , China
| | - Li-Rong Yang
- b Department of Chemical and Biological Engineering , Zhejiang University , Hangzhou , China
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89
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Dong J, Yao ZJ, Zhu MF, Wang NN, Lu B, Chen AF, Lu AP, Miao H, Zeng WB, Cao DS. ChemSAR: an online pipelining platform for molecular SAR modeling. J Cheminform 2017; 9:27. [PMID: 29086046 PMCID: PMC5418185 DOI: 10.1186/s13321-017-0215-1] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2016] [Accepted: 04/24/2017] [Indexed: 12/31/2022] Open
Abstract
Background In recent years, predictive models based on machine learning techniques have proven to be feasible and effective in drug discovery. However, to develop such a model, researchers usually have to combine multiple tools and undergo several different steps (e.g., RDKit or ChemoPy package for molecular descriptor calculation, ChemAxon Standardizer for structure preprocessing, scikit-learn package for model building, and ggplot2 package for statistical analysis and visualization, etc.). In addition, it may require strong programming skills to accomplish these jobs, which poses severe challenges for users without advanced training in computer programming. Therefore, an online pipelining platform that integrates a number of selected tools is a valuable and efficient solution that can meet the needs of related researchers. Results This work presents a web-based pipelining platform, called ChemSAR, for generating SAR classification models of small molecules. The capabilities of ChemSAR include the validation and standardization of chemical structure representation, the computation of 783 1D/2D molecular descriptors and ten types of widely-used fingerprints for small molecules, the filtering methods for feature selection, the generation of predictive models via a step-by-step job submission process, model interpretation in terms of feature importance and tree visualization, as well as a helpful report generation system. The results can be visualized as high-quality plots and downloaded as local files. Conclusion ChemSAR provides an integrated web-based platform for generating SAR classification models that will benefit cheminformatics and other biomedical users. It is freely available at: http://chemsar.scbdd.com.. ![]() Electronic supplementary material The online version of this article (doi:10.1186/s13321-017-0215-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jie Dong
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China
| | - Zhi-Jiang Yao
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China.,The Third Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Min-Feng Zhu
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China.,The Third Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Ning-Ning Wang
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China
| | - Ben Lu
- The Third Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Alex F Chen
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China.,The Third Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR, People's Republic of China
| | - Hongyu Miao
- Department of Biostatistics, School of Public Health, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Wen-Bin Zeng
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Yuelu District, Changsha, People's Republic of China. .,Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR, People's Republic of China.
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90
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Prana V, Rotureau P, André D, Fayet G, Adamo C. Development of Simple QSPR Models for the Prediction of the Heat of Decomposition of Organic Peroxides. Mol Inform 2017; 36. [PMID: 28402598 DOI: 10.1002/minf.201700024] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Accepted: 03/30/2017] [Indexed: 12/22/2022]
Abstract
Quantitative structure-property relationships represent alternative method to experiments to access the estimation of physico-chemical properties of chemicals for screening purpose at R&D level but also to gather missing data in regulatory context. In particular, such predictions were encouraged by the REACH regulation for the collection of data, provided that they are developed respecting the rigorous principles of validation proposed by OECD. In this context, a series of organic peroxides, unstable chemicals which can easily decompose and may lead to explosion, were investigated to develop simple QSPR models that can be used in a regulatory framework. Only constitutional and topological descriptors were employed to achieve QSPR models predicting the heat of decomposition, which could be used without any time consuming preliminary structure calculations at quantum chemical level. To validate the models, the original experimental dataset was divided into a training and a validation set according to two methods of partitioning, one based on the property value and the other based on the structure of the molecules by the mean of PCA. Four QSPR models were developed upon the type of descriptors and the methods of partitioning. The 2 models issuing from the PCA based method were highlighted as they presented good predictive power and they are easier to apply than our previous quantum chemical based model, since they do not need any preliminary calculations.
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Affiliation(s)
- Vinca Prana
- Institut National de l'Environnement Industriel et des Risques (INERIS), Parc Technologique Alata, BP2, 60550, Verneuil-en-Halatte, France.,Chimie ParisTech, PSL Research University, CNRS, Institut de Recherche de Chimie Paris (IRCP), F-75005, Paris, France
| | - Patricia Rotureau
- Institut National de l'Environnement Industriel et des Risques (INERIS), Parc Technologique Alata, BP2, 60550, Verneuil-en-Halatte, France
| | - David André
- ARKEMA, rue Henri Moissan, BP63, 69493, Pierre Benite, France
| | - Guillaume Fayet
- Institut National de l'Environnement Industriel et des Risques (INERIS), Parc Technologique Alata, BP2, 60550, Verneuil-en-Halatte, France
| | - Carlo Adamo
- Chimie ParisTech, PSL Research University, CNRS, Institut de Recherche de Chimie Paris (IRCP), F-75005, Paris, France.,Institut Universitaire de France, 103 Boulevard Saint Michel, F-75005, Paris, France
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91
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A comparative QSAR analysis and molecular docking studies of phenyl piperidine derivatives as potent dual NK1R antagonists/serotonin transporter (SERT) inhibitors. Comput Biol Chem 2017; 67:22-37. [DOI: 10.1016/j.compbiolchem.2016.12.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Revised: 11/01/2016] [Accepted: 12/15/2016] [Indexed: 11/24/2022]
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92
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Verras A, Waller CL, Gedeck P, Green DVS, Kogej T, Raichurkar A, Panda M, Shelat AA, Clark J, Guy RK, Papadatos G, Burrows J. Shared Consensus Machine Learning Models for Predicting Blood Stage Malaria Inhibition. J Chem Inf Model 2017; 57:445-453. [DOI: 10.1021/acs.jcim.6b00572] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Andreas Verras
- Merck & Co., Inc., Kenilworth, New Jersey 07033, United States
| | - Chris L. Waller
- Merck & Co., Inc., Boston, Massachusetts 02210, United States
| | - Peter Gedeck
- Novartis Institute for Tropical Diseases Pte. Ltd., Singapore 138670, Singapore
| | | | | | | | | | - Anang A. Shelat
- Chemical
Biology and Therapeutics Department, St. Jude Children’s Research Hospital, Memphis, Tennessee 38105, United States
| | - Julie Clark
- Chemical
Biology and Therapeutics Department, St. Jude Children’s Research Hospital, Memphis, Tennessee 38105, United States
| | - R. Kiplin Guy
- Chemical
Biology and Therapeutics Department, St. Jude Children’s Research Hospital, Memphis, Tennessee 38105, United States
| | - George Papadatos
- European
Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, United Kingdom
| | - Jeremy Burrows
- Medicines for Malaria Ventures Discovery, Geneva 1215, Switzerland
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93
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Abstract
Quantitative Structure-Activity Relationship (QSAR) models have manifold applications in drug discovery, environmental fate modeling, risk assessment, and property prediction of chemicals and pharmaceuticals. One of the principles recommended by the Organization of Economic Co-operation and Development (OECD) for model validation requires defining the Applicability Domain (AD) for QSAR models, which allows one to estimate the uncertainty in the prediction of a compound based on how similar it is to the training compounds, which are used in the model development. The AD is a significant tool to build a reliable QSAR model, which is generally limited in use to query chemicals structurally similar to the training compounds. Thus, characterization of interpolation space is significant in defining the AD. An attempt is made in this chapter to address the important concepts and methodology of the AD as well as criteria for estimating AD through training set interpolation in the descriptor space.
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94
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Kleandrova VV, Luan F, Speck-Planche A, Cordeiro MNDS. QSAR-Based Studies of Nanomaterials in the Environment. PHARMACEUTICAL SCIENCES 2017. [DOI: 10.4018/978-1-5225-1762-7.ch051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Nanotechnology is a newly emerging field, posing substantial impacts on society, economy, and the environment. In recent years, the development of nanotechnology has led to the design and large-scale production of many new materials and devices with a vast range of applications. However, along with the benefits, the use of nanomaterials raises many questions and generates concerns due to the possible health-risks and environmental impacts. This chapter provides an overview of the Quantitative Structure-Activity Relationships (QSAR) studies performed so far towards predicting nanoparticles' environmental toxicity. Recent progresses on the application of these modeling studies are additionally pointed out. Special emphasis is given to the setup of a QSAR perturbation-based model for the assessment of ecotoxic effects of nanoparticles in diverse conditions. Finally, ongoing challenges that may lead to new and exciting directions for QSAR modeling are discussed.
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Affiliation(s)
| | - Feng Luan
- Yantai University, China & University of Porto, Portugal
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95
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Cui Y, Chen Q, Li Y, Tang L. A new model of flavonoids affinity towards P-glycoprotein: genetic algorithm-support vector machine with features selected by a modified particle swarm optimization algorithm. Arch Pharm Res 2016; 40:214-230. [DOI: 10.1007/s12272-016-0876-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Accepted: 12/16/2016] [Indexed: 01/04/2023]
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96
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Kaur M, Silakari O. Ligand-based and e-pharmacophore modeling, 3D-QSAR and hierarchical virtual screening to identify dual inhibitors of spleen tyrosine kinase (Syk) and janus kinase 3 (JAK3). J Biomol Struct Dyn 2016; 35:3043-3060. [PMID: 27678281 DOI: 10.1080/07391102.2016.1240108] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
The clinical efficacy of multiple kinase inhibitors has caught the interest of Pharmaceutical and Biotech researchers to develop potential drugs with multi-kinase inhibitory activity for complex diseases. In the present work, we attempted to identify dual inhibitors of spleen tyrosine kinase (Syk) and janus kinase 3 (JAK3), keys players in immune signaling, by developing ideal pharmacophores integrating Ligand-based pharmacophore models (LBPMs) and Structure-based pharmacophore models (SBPMs), thereby projecting the optimum pharmacophoric required for inhibition of both the kinases. The four point LBPM; ADPR.14 suggested the presence of one hydrogen bond acceptor, one hydrogen bond donor, one positive ionizable, and one ring aromatic feature for Syk inhibitory activity and AADH.54 proposed the necessity of two hydrogen bond acceptor, one hydrogen bond donor, and one hydrophobic feature for JAK3 inhibitory activity. To our interest, SBPMs identified additional ring aromatic features required for inhibition of both the kinases. For Syk inhibitory activity, the hydrogen bond acceptor feature indicated by LBPM was devoid of forming hydrogen bonding interaction with the hinge region amino acid residue (Ala451). Thus merging the information revealed by both LBPMs and SBPMs, ideal pharmacophore models i.e. ADPRR.14 (Syk) and AADHR.54 (JAK3) were generated. These models after rigorous statistical validation were used for screening of Asinex database. The systematic virtual screening protocol, including pharmacophore and docking-based screening, ADME property, and MM-GBSA energy calculations, retrieved final 10 hits as dual inhibitors of Syk and JAK3. Final 10 hits thus obtained can aid in the development of potential therapeutic agents for autoimmune disorders. Also the top two hits were evaluated against both the enzymes.
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Affiliation(s)
- Maninder Kaur
- a Molecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research , Punjabi University , Patiala , Punjab 147002 , India
| | - Om Silakari
- a Molecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research , Punjabi University , Patiala , Punjab 147002 , India
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97
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He SB, Ben Hu, Kuang ZK, Wang D, Kong DX. Predicting Subtype Selectivity for Adenosine Receptor Ligands with Three-Dimensional Biologically Relevant Spectrum (BRS-3D). Sci Rep 2016; 6:36595. [PMID: 27812030 PMCID: PMC5095671 DOI: 10.1038/srep36595] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2016] [Accepted: 10/18/2016] [Indexed: 02/02/2023] Open
Abstract
Adenosine receptors (ARs) are potential therapeutic targets for Parkinson’s disease, diabetes, pain, stroke and cancers. Prediction of subtype selectivity is therefore important from both therapeutic and mechanistic perspectives. In this paper, we introduced a shape similarity profile as molecular descriptor, namely three-dimensional biologically relevant spectrum (BRS-3D), for AR selectivity prediction. Pairwise regression and discrimination models were built with the support vector machine methods. The average determination coefficient (r2) of the regression models was 0.664 (for test sets). The 2B-3 (A2Bvs A3) model performed best with q2 = 0.769 for training sets (10-fold cross-validation), and r2 = 0.766, RMSE = 0.828 for test sets. The models’ robustness and stability were validated with 100 times resampling and 500 times Y-randomization. We compared the performance of BRS-3D with 3D descriptors calculated by MOE. BRS-3D performed as good as, or better than, MOE 3D descriptors. The performances of the discrimination models were also encouraging, with average accuracy (ACC) 0.912 and MCC 0.792 (test set). The 2A-3 (A2Avs A3) selectivity discrimination model (ACC = 0.882 and MCC = 0.715 for test set) outperformed an earlier reported one (ACC = 0.784). These results demonstrated that, through multiple conformation encoding, BRS-3D can be used as an effective molecular descriptor for AR subtype selectivity prediction.
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Affiliation(s)
- Song-Bing He
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, China.,College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Ben Hu
- Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Zheng-Kun Kuang
- Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Dong Wang
- College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - De-Xin Kong
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, China.,Agricultural Bioinformatics Key Laboratory of Hubei Province, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
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98
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Porta N, ra Roncaglioni A, Marzo M, Benfenati E. QSAR Methods to Screen Endocrine Disruptors. NUCLEAR RECEPTOR RESEARCH 2016. [DOI: 10.11131/2016/101203] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Affiliation(s)
- Nicola Porta
- Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche “Mario Negri”, Via La Masa 19, 20159 Milan, Italy
| | - Aless ra Roncaglioni
- Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche “Mario Negri”, Via La Masa 19, 20159 Milan, Italy
| | - Marco Marzo
- Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche “Mario Negri”, Via La Masa 19, 20159 Milan, Italy
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche “Mario Negri”, Via La Masa 19, 20159 Milan, Italy
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99
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Žuvela P, Macur K, Jay Liu J, Bączek T. Exploiting non-linear relationships between retention time and molecular structure of peptides originating from proteomes and comparing three multivariate approaches. J Pharm Biomed Anal 2016; 127:94-100. [DOI: 10.1016/j.jpba.2016.01.055] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2015] [Revised: 01/11/2016] [Accepted: 01/23/2016] [Indexed: 12/21/2022]
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100
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The prediction of topologically partitioned intra-atomic and inter-atomic energies by the machine learning method kriging. Theor Chem Acc 2016. [DOI: 10.1007/s00214-016-1951-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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