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de Blasio P, Elsborg J, Vegge T, Flores E, Bhowmik A. CALiSol-23: Experimental electrolyte conductivity data for various Li-salts and solvent combinations. Sci Data 2024; 11:750. [PMID: 38987528 PMCID: PMC11237020 DOI: 10.1038/s41597-024-03575-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 06/26/2024] [Indexed: 07/12/2024] Open
Abstract
Ion transport in non-aqueous electrolytes is crucial for high performance lithium-ion battery (LIB) development. The design of superior electrolytes requires extensive experimentation across the compositional space. To support data driven accelerated electrolyte discovery efforts, we curated and analyzed a large dataset covering a wide range of experimentally recorded ionic conductivities for various combinations of lithium salts, solvents, concentrations, and temperatures. The dataset is named as 'Conductivity Atlas for Lithium salts and Solvents' (CALiSol-23). Comprehensive datasets are lacking but are critical to building chemistry agnostic machine learning models for conductivity as well as data driven electrolyte optimization tasks. CALiSol-23 was derived from an exhaustive review of literature concerning experimental non-aqueous electrolyte conductivity measurement. The final dataset consists of 13,825 individual data points from 27 different experimental articles, in total covering 38 solvents, a broad temperature range, and 14 lithium salts. CALiSol-23 can help expedite machine learning model development that can help in understanding the complexities of ion transport and streamlining the optimization of non-aqueous electrolyte mixtures.
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Affiliation(s)
- Paolo de Blasio
- Technical University of Denmark, Department of Energy Conversion and Storage, Kgs. Lyngby, 2800, Denmark
| | - Jonas Elsborg
- Technical University of Denmark, Department of Energy Conversion and Storage, Kgs. Lyngby, 2800, Denmark
| | - Tejs Vegge
- Technical University of Denmark, Department of Energy Conversion and Storage, Kgs. Lyngby, 2800, Denmark
| | - Eibar Flores
- Technical University of Denmark, Department of Energy Conversion and Storage, Kgs. Lyngby, 2800, Denmark.
- SINTEF Industry, Sustainable Energy Technology, Trondheim, 7034, Norway.
| | - Arghya Bhowmik
- Technical University of Denmark, Department of Energy Conversion and Storage, Kgs. Lyngby, 2800, Denmark.
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2
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Kehrein J, Bunker A, Luxenhofer R. POxload: Machine Learning Estimates Drug Loadings of Polymeric Micelles. Mol Pharm 2024; 21:3356-3374. [PMID: 38805643 DOI: 10.1021/acs.molpharmaceut.4c00086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2024]
Abstract
Block copolymers, composed of poly(2-oxazoline)s and poly(2-oxazine)s, can serve as drug delivery systems; they form micelles that carry poorly water-soluble drugs. Many recent studies have investigated the effects of structural changes of the polymer and the hydrophobic cargo on drug loading. In this work, we combine these data to establish an extended formulation database. Different molecular properties and fingerprints are tested for their applicability to serve as formulation-specific mixture descriptors. A variety of classification and regression models are built for different descriptor subsets and thresholds of loading efficiency and loading capacity, with the best models achieving overall good statistics for both cross- and external validation (balanced accuracies of 0.8). Subsequently, important features are dissected for interpretation, and the DrugBank is screened for potential therapeutic use cases where these polymers could be used to develop novel formulations of hydrophobic drugs. The most promising models are provided as an open-source software tool for other researchers to test the applicability of these delivery systems for potential new drug candidates.
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Affiliation(s)
- Josef Kehrein
- Soft Matter Chemistry, Department of Chemistry, Faculty of Science, University of Helsinki, A. I. Virtasen aukio 1, 00014 Helsinki, Finland
- Drug Research Program, Division of Pharmaceutical Biosciences Faculty of Pharmacy, University of Helsinki, Viikinkaari 5 E, 00014 Helsinki, Finland
| | - Alex Bunker
- Drug Research Program, Division of Pharmaceutical Biosciences Faculty of Pharmacy, University of Helsinki, Viikinkaari 5 E, 00014 Helsinki, Finland
| | - Robert Luxenhofer
- Soft Matter Chemistry, Department of Chemistry, Faculty of Science, University of Helsinki, A. I. Virtasen aukio 1, 00014 Helsinki, Finland
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3
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Seo M, Choi J, Park J, Yu WJ, Kim S. Computational modeling approaches for developing a synergistic effect prediction model of estrogen agonistic activity. CHEMOSPHERE 2024; 349:140926. [PMID: 38092168 DOI: 10.1016/j.chemosphere.2023.140926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/05/2023] [Accepted: 12/07/2023] [Indexed: 12/17/2023]
Abstract
The concerns regarding the potential health threats caused by estrogenic endocrine-disrupting chemicals (EDCs) and their mixtures manufactured by the chemical industry are increasing worldwide. Conventional experimental tests for understanding the estrogenic activity of mixtures are expensive and time-consuming. Although non-testing methods using computational modeling approaches have been developed to reduce the number of traditional tests, they are unsuitable for predicting synergistic effects because current prediction models consider only a single chemical. Thus, the development of predictive models is essential for predicting the mixture toxicity, including chemical interactions. However, selecting suitable computational modeling approaches to develop a high-performance prediction model requires considerable time and effort. In this study, we provide a suitable computational approach to develop a predictive model for the synergistic effects of estrogenic activity. We collected datasets on mixture toxicity based on the synergistic effect of estrogen agonistic activity in binary mixtures. Using the model deviation ratio approach, we classified the labels of the binary mixtures as synergistic or non-synergistic effects. We assessed five molecular descriptors, four machine learning-based algorithms, and a deep learning-based algorithm to provide a suitable computational modeling approach. Compared with other modeling approaches, the prediction model using the deep learning-based algorithm and chemical-protein network descriptors exhibited the best performance in predicting the synergistic effects. In conclusion, we developed a new high-performance binary classification model using a deep neural network and chemical-protein network-based descriptors. The developed model will be helpful for the preliminary screening of the synergistic effects of binary mixtures during the development process of chemical products.
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Affiliation(s)
- Myungwon Seo
- Chemical Analysis Center, Korea Research Institute of Chemical Technology (KRICT), Daejeon, 34114, Republic of Korea.
| | - Jiwon Choi
- Chemical Analysis Center, Korea Research Institute of Chemical Technology (KRICT), Daejeon, 34114, Republic of Korea.
| | - Jongseo Park
- Chemical Analysis Center, Korea Research Institute of Chemical Technology (KRICT), Daejeon, 34114, Republic of Korea.
| | - Wook-Joon Yu
- Developmental and Reproductive Toxicology Research Group, Korea Institute of Toxicology, Daejeon, 34114, Republic of Korea.
| | - Sunmi Kim
- Chemical Analysis Center, Korea Research Institute of Chemical Technology (KRICT), Daejeon, 34114, Republic of Korea.
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4
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Jirasek F, Hasse H. Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures. Annu Rev Chem Biomol Eng 2023; 14:31-51. [PMID: 36944250 DOI: 10.1146/annurev-chembioeng-092220-025342] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
Thermophysical properties of fluid mixtures are important in many fields of science and engineering. However, experimental data are scarce in this field, so prediction methods are vital. Different types of physical prediction methods are available, ranging from molecular models over equations of state to models of excess properties. These well-established methods are currently being complemented by new methods from the field of machine learning (ML). This review focuses on the rapidly developing interface between these two approaches and gives a structured overview of how physical modeling and ML can be combined to yield hybrid models. We illustrate the different options with examples from recent research and give an outlook on future developments.
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Affiliation(s)
- Fabian Jirasek
- Laboratory of Engineering Thermodynamics (LTD), RPTU Kaiserslautern, Kaiserslautern, Germany; ,
| | - Hans Hasse
- Laboratory of Engineering Thermodynamics (LTD), RPTU Kaiserslautern, Kaiserslautern, Germany; ,
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5
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Metwally AA, Nayel AA, Hathout RM. In silico prediction of siRNA ionizable-lipid nanoparticles In vivo efficacy: Machine learning modeling based on formulation and molecular descriptors. Front Mol Biosci 2022; 9:1042720. [PMID: 36619167 PMCID: PMC9811823 DOI: 10.3389/fmolb.2022.1042720] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 12/07/2022] [Indexed: 12/24/2022] Open
Abstract
In silico prediction of the in vivo efficacy of siRNA ionizable-lipid nanoparticles is desirable as it can save time and resources dedicated to wet-lab experimentation. This study aims to computationally predict siRNA nanoparticles in vivo efficacy. A data set containing 120 entries was prepared by combining molecular descriptors of the ionizable lipids together with two nanoparticles formulation characteristics. Input descriptor combinations were selected by an evolutionary algorithm. Artificial neural networks, support vector machines and partial least squares regression were used for QSAR modeling. Depending on how the data set is split, two training sets and two external validation sets were prepared. Training and validation sets contained 90 and 30 entries respectively. The results showed the successful predictions of validation set log (siRNA dose) with Rval 2= 0.86-0.89 and 0.75-80 for validation sets one and two, respectively. Artificial neural networks resulted in the best Rval 2 for both validation sets. For predictions that have high bias, improvement of Rval 2 from 0.47 to 0.96 was achieved by selecting the training set lipids lying within the applicability domain. In conclusion, in vivo performance of siRNA nanoparticles was successfully predicted by combining cheminformatics with machine learning techniques.
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Affiliation(s)
- Abdelkader A. Metwally
- Department of Pharmaceutics, Faculty of Pharmacy, Health Sciences Center, Kuwait University, Kuwait City, Kuwait,Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams University, Cairo, Egypt,*Correspondence: Abdelkader A. Metwally,
| | - Amira A. Nayel
- Clinical Pharmacy Department, Alexandria Ophthalmology Hospital, Alexandria, Egypt,Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmacy, Alexandria University, Alexandria, Egypt
| | - Rania M. Hathout
- Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams University, Cairo, Egypt
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6
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Chatterjee M, Roy K. Chemical similarity and machine learning-based approaches for the prediction of aquatic toxicity of binary and multicomponent pharmaceutical and pesticide mixtures against Aliivibrio fischeri. CHEMOSPHERE 2022; 308:136463. [PMID: 36122748 DOI: 10.1016/j.chemosphere.2022.136463] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 09/10/2022] [Accepted: 09/12/2022] [Indexed: 06/15/2023]
Abstract
Different classes of chemicals are present in the environment as mixtures. Among them, pharmaceuticals and pesticides are of major concern due to their improper use and disposal, and subsequent additive and non-additive effects. To assess the environmental risk posed by the mixtures of pharmaceuticals and pesticides, a quantitative structure-activity relationship (QSAR) model has been developed in this study using the pEC50 values of 198 binary and multi-component mixtures against the marine bacterium Aliivibrio fischeri. The developed partial least squares (PLS) model has been rigorously validated and proved to be a robust and extremely predictive one. To address the chances of overestimation of validation metrics, three cross-validation tests (mixtures out, compounds out, and everything out) have been applied, and the results were satisfactory. The use of simple 2-dimensional descriptors makes the prediction much quick, and also makes the model easily interpretable. A machine learning-based chemical read-across prediction has also been performed to justify the effectiveness of selected structural features in this study. In a nutshell, this study proves QSAR and chemical read-across as effective alternative approaches for the toxicity prediction of pharmaceutical and pesticide mixtures and also approves the use of mixture descriptors for modelling mixtures successfully.
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Affiliation(s)
- Mainak Chatterjee
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India.
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7
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Chatterjee M, Roy K. Application of cross-validation strategies to avoid overestimation of performance of 2D-QSAR models for the prediction of aquatic toxicity of chemical mixtures. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2022; 33:463-484. [PMID: 35638563 DOI: 10.1080/1062936x.2022.2081255] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
The quantitative structure-activity relationship (QSAR) modelling of mixtures is not as simple as that for individual chemicals, and it needs additional care to avoid overestimation of the performance. In this research, we have developed a 2D-QSAR model using only 2D interpretable and reproducible descriptors to predict the aquatic toxicity of mixtures of polar and non-polar narcotic substances present in the environment. Partial least squares (PLS) regression has been used to model the response variable (log 1/EC50 against Photobacterium phosphoreum) and the structural features of 84 binary mixtures of polar and nonpolar narcotic toxicants complying with the Organization of Economic Co-operation and Development (OECD) protocols. The model was cross-validated by mixtures-out and compounds-out cross-validation to nullify the developmental bias. The reliability of prediction of the model has been judged by the Prediction Reliability Indicator (PRI) tool using a newly designed set. The new model is robust, reproducible, extremely predictive, easily interpretable, and can be used for reliable prediction of aquatic toxicity of any untested chemical mixtures within the applicability domain. We have additionally used a machine learning-based chemical read-across algorithm in this study to improve the quality of predictions for the toxicity of the mixtures with the modelled descriptors.
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Affiliation(s)
- M Chatterjee
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - K Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
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8
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Makarov D, Fadeeva Y, Shmukler L, Tetko I. Beware of proper validation of models for ionic Liquids! J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.117722] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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9
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Machine learning modelling of chemical reaction characteristics: yesterday, today, tomorrow. MENDELEEV COMMUNICATIONS 2021. [DOI: 10.1016/j.mencom.2021.11.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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10
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Klimenko K, Carrera GVSM. QSPR modeling of selectivity at infinite dilution of ionic liquids. J Cheminform 2021; 13:83. [PMID: 34702358 PMCID: PMC8549394 DOI: 10.1186/s13321-021-00562-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 10/16/2021] [Indexed: 11/25/2022] Open
Abstract
The intelligent choice of extractants and entrainers can improve current mixture separation techniques allowing better efficiency and sustainability of chemical processes that are both used in industry and laboratory practice. The most promising approach is a straightforward comparison of selectivity at infinite dilution between potential candidates. However, selectivity at infinite dilution values are rarely available for most compounds so a theoretical estimation is highly desired. In this study, we suggest a Quantitative Structure–Property Relationship (QSPR) approach to the modelling of the selectivity at infinite dilution of ionic liquids. Additionally, auxiliary models were developed to overcome the potential bias from big activity coefficient at infinite dilution from the solute. Data from SelinfDB database was used as training and internal validation sets in QSPR model development. External validation was done with the data from literature. The selection of the best models was done using decision functions that aim to diminish bias in prediction of the data points associated with the underrepresented ionic liquids or extreme temperatures. The best models were used for the virtual screening for potential azeotrope breakers of aniline + n-dodecane mixture. The subject of screening was a combinatorial library of ionic liquids, created based on the previously unused combinations of cations and anions from SelinfDB and the test set extractants. Both selectivity at infinite dilution and auxiliary models show good performance in the validation. Our models’ predictions were compared to the ones of the COSMO-RS, where applicable, displaying smaller prediction error. The best ionic liquid to extract aniline from n-dodecane was suggested.
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Affiliation(s)
- Kyrylo Klimenko
- LAQV/REQUIMTE, Departamento de Química, Faculdade de Ciências E Tecnologia, Universidade Nova de Lisboa, Caparica, 2829-516, Caparica, Portugal.
| | - Gonçalo V S M Carrera
- LAQV/REQUIMTE, Departamento de Química, Faculdade de Ciências E Tecnologia, Universidade Nova de Lisboa, Caparica, 2829-516, Caparica, Portugal
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11
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Kuz’min V, Artemenko A, Ognichenko L, Hromov A, Kosinskaya A, Stelmakh S, Sessions ZL, Muratov EN. Simplex representation of molecular structure as universal QSAR/QSPR tool. Struct Chem 2021; 32:1365-1392. [PMID: 34177203 PMCID: PMC8218296 DOI: 10.1007/s11224-021-01793-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 05/07/2021] [Indexed: 10/24/2022]
Abstract
We review the development and application of the Simplex approach for the solution of various QSAR/QSPR problems. The general concept of the simplex method and its varieties are described. The advantages of utilizing this methodology, especially for the interpretation of QSAR/QSPR models, are presented in comparison to other fragmentary methods of molecular structure representation. The utility of SiRMS is demonstrated not only in the standard QSAR/QSPR applications, but also for mixtures, polymers, materials, and other complex systems. In addition to many different types of biological activity (antiviral, antimicrobial, antitumor, psychotropic, analgesic, etc.), toxicity and bioavailability, the review examines the simulation of important properties, such as water solubility, lipophilicity, as well as luminescence, and thermodynamic properties (melting and boiling temperatures, critical parameters, etc.). This review focuses on the stereochemical description of molecules within the simplex approach and details the possibilities of universal molecular stereo-analysis and stereochemical configuration description, along with stereo-isomerization mechanism and molecular fragment "topography" identification.
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Affiliation(s)
- Victor Kuz’min
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Anatoly Artemenko
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Luidmyla Ognichenko
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Alexander Hromov
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Anna Kosinskaya
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
- Department of Medical Chemistry, Odessa National Medical University, Odessa, 65082 Ukraine
| | - Sergij Stelmakh
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Zoe L. Sessions
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599 USA
| | - Eugene N. Muratov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599 USA
- Department of Pharmaceutical Sciences, Federal University of Paraiba, Joao Pessoa, PB 58059 Brazil
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12
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Rakhimbekova A, Akhmetshin TN, Minibaeva GI, Nugmanov RI, Gimadiev TR, Madzhidov TI, Baskin II, Varnek A. Cross-validation strategies in QSPR modelling of chemical reactions. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2021; 32:207-219. [PMID: 33601989 DOI: 10.1080/1062936x.2021.1883107] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 01/26/2021] [Indexed: 06/12/2023]
Abstract
In this article, we consider cross-validation of the quantitative structure-property relationship models for reactions and show that the conventional k-fold cross-validation (CV) procedure gives an 'optimistically' biased assessment of prediction performance. To address this issue, we suggest two strategies of model cross-validation, 'transformation-out' CV, and 'solvent-out' CV. Unlike the conventional k-fold cross-validation approach that does not consider the nature of objects, the proposed procedures provide an unbiased estimation of the predictive performance of the models for novel types of structural transformations in chemical reactions and reactions going under new conditions. Both the suggested strategies have been applied to predict the rate constants of bimolecular elimination and nucleophilic substitution reactions, and Diels-Alder cycloaddition. All suggested cross-validation methodologies and tutorial are implemented in the open-source software package CIMtools (https://github.com/cimm-kzn/CIMtools).
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Affiliation(s)
- A Rakhimbekova
- A.M. Butlerov Institute of Chemistry, Kazan Federal University, Kazan, Russia
| | - T N Akhmetshin
- A.M. Butlerov Institute of Chemistry, Kazan Federal University, Kazan, Russia
- Laboratory of Chemoinformatics, UMR 7140 CNRS, University of Strasbourg, Strasbourg, France
| | - G I Minibaeva
- A.M. Butlerov Institute of Chemistry, Kazan Federal University, Kazan, Russia
| | - R I Nugmanov
- A.M. Butlerov Institute of Chemistry, Kazan Federal University, Kazan, Russia
| | - T R Gimadiev
- Institute for Chemical Reaction Design and Discovery, Hokkaido University, Sapporo, Japan
| | - T I Madzhidov
- A.M. Butlerov Institute of Chemistry, Kazan Federal University, Kazan, Russia
| | - I I Baskin
- A.M. Butlerov Institute of Chemistry, Kazan Federal University, Kazan, Russia
- Department of Materials Science and Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - A Varnek
- Laboratory of Chemoinformatics, UMR 7140 CNRS, University of Strasbourg, Strasbourg, France
- Institute for Chemical Reaction Design and Discovery, Hokkaido University, Sapporo, Japan
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13
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Shibayama S, Funatsu K. Industrial Case Study: Identification of Important Substructures and Exploration of Monomers for the Rapid Design of Novel Network Polymers with Distributed Representation. BULLETIN OF THE CHEMICAL SOCIETY OF JAPAN 2021. [DOI: 10.1246/bcsj.20200220] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Shojiro Shibayama
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Kimito Funatsu
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
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14
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Hanaoka K. Deep Neural Networks for Multicomponent Molecular Systems. ACS OMEGA 2020; 5:21042-21053. [PMID: 32875241 PMCID: PMC7450624 DOI: 10.1021/acsomega.0c02599] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 07/20/2020] [Indexed: 06/11/2023]
Abstract
Deep neural networks (DNNs) represent promising approaches to molecular machine learning (ML). However, their applicability remains limited to single-component materials and a general DNN model capable of handling various multicomponent molecular systems with composition data is still elusive, while current ML approaches for multicomponent molecular systems are still molecular descriptor-based. Here, a general DNN architecture extending existing molecular DNN models to multicomponent systems called MEIA is proposed. Case studies showed that the MEIA architecture could extend two exiting molecular DNN models to multicomponent systems with the same procedure, and that the obtained models that could learn both the molecular structure and composition information with equal or better accuracies compared to a well-used molecular descriptor-based model in the best model for each case study. Furthermore, the case studies also showed that, for ML tasks where the molecular structure information plays a minor role, the performance improvements by DNN models were small; while for ML tasks where the molecular structure information plays a major role, the performance improvements by DNN models were large, and DNN models showed notable predictive accuracies for an extremely sparse dataset, which cannot be modeled without the molecular structure information. The enhanced predictive ability of DNN models for sparse datasets of multicomponent systems will extend the applicability of ML in the multicomponent material design. Furthermore, the general capability of MEIA to extend DNN models to multicomponent systems will provide new opportunities to utilize the progress of actively developed single-component DNNs for the modeling of multicomponent systems.
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15
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Baskin II, Lozano S, Durot M, Marcou G, Horvath D, Varnek A. Autoignition temperature: comprehensive data analysis and predictive models. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2020; 31:597-613. [PMID: 32646236 DOI: 10.1080/1062936x.2020.1785933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 06/18/2020] [Indexed: 06/11/2023]
Abstract
Here we report a new predictive model for autoignition temperature (AIT), an important physical parameter widely used to assess potential safety hazards of combustible materials. Available structure-AIT data extracted from different sources were critically analysed. Support vector regression (SVR) models on different data subsets were built in order to identify a reliable compound set on which a realistic model could be built. This led to a selection of the dataset containing 875 compounds annotated with AIT values. The thereupon-based SVR model performs reasonably well in cross-validation with the determination coefficient r 2 = 0.77 and mean absolute error MAE = 37.8°C. External validation on 20 industrial compounds missing in the training set confirmed its good predictive power (MAE = 28.7°C).
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Affiliation(s)
- I I Baskin
- Laboratory of Chemoinformatics, University of Strasbourg, UMR 7140 CNRS/UniStra , Strasbourg, France
| | - S Lozano
- BioLab, Centre de Recherche de Solaize, Total , Solaize, France
| | - M Durot
- BioLab, Centre de Recherche de Solaize, Total , Solaize, France
| | - G Marcou
- Laboratory of Chemoinformatics, University of Strasbourg, UMR 7140 CNRS/UniStra , Strasbourg, France
| | - D Horvath
- Laboratory of Chemoinformatics, University of Strasbourg, UMR 7140 CNRS/UniStra , Strasbourg, France
| | - A Varnek
- Laboratory of Chemoinformatics, University of Strasbourg, UMR 7140 CNRS/UniStra , Strasbourg, France
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16
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Muratov EN, Bajorath J, Sheridan RP, Tetko IV, Filimonov D, Poroikov V, Oprea TI, Baskin II, Varnek A, Roitberg A, Isayev O, Curtarolo S, Fourches D, Cohen Y, Aspuru-Guzik A, Winkler DA, Agrafiotis D, Cherkasov A, Tropsha A. QSAR without borders. Chem Soc Rev 2020; 49:3525-3564. [PMID: 32356548 PMCID: PMC8008490 DOI: 10.1039/d0cs00098a] [Citation(s) in RCA: 319] [Impact Index Per Article: 79.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.
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Affiliation(s)
- Eugene N Muratov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
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17
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Klimenko KO, Inês JM, Esperança JMSS, Rebelo LPN, Aires-de-Sousa J, Carrera GVSM. QSPR Modeling of Liquid-liquid Equilibria in Two-phase Systems of Water and Ionic Liquid. Mol Inform 2020; 39:e2000001. [PMID: 32469147 DOI: 10.1002/minf.202000001] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 05/11/2020] [Indexed: 11/06/2022]
Abstract
The increasing application of new ionic liquids (IL) creates the need of liquid-liquid equilibria data for both miscible and quasi-immiscible systems. In this study, equilibrium concentrations at different temperatures for ionic liquid+water two-phase systems were modeled using a Quantitative-Structure-Property Relationship (QSPR) method. Data on equilibrium concentrations were taken from the ILThermo Ionic Liquids database, curated and used to make models that predict the weight fraction of water in ionic liquid rich phase and ionic liquid in the aqueous phase as two separate properties. The major modeling challenge stems from the fact that each single IL is characterized by several data points, since equilibrium concentrations are temperature dependent. Thus, new approaches for the detection of potential data point outliers, testing set selection, and quality prediction have been developed. Training set comprised equilibrium concentration data for 67 and 68 ILs in case of water in IL and IL in water modeling, respectively. SiRMS, MOLMAPS, Rcdk and Chemaxon descriptors were used to build Random Forest models for both properties. Models were subjected to the Y-scrambling test for robustness assessment. The best models have also been validated using an external test set that is not part of the ILThermo database. A two-phase equilibrium diagram for one of the external test set IL is presented for better visualization of the results and potential derivation of tie lines.
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Affiliation(s)
- Kyrylo Oleksandrovych Klimenko
- LAQV/REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, 2829-516 Caparica, Portugal
| | - João Miguel Inês
- LAQV/REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, 2829-516 Caparica, Portugal
| | - José Manuel Silva Simões Esperança
- LAQV/REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, 2829-516 Caparica, Portugal
| | - Luís Paulo Nieto Rebelo
- LAQV/REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, 2829-516 Caparica, Portugal
| | - João Aires-de-Sousa
- LAQV/REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, 2829-516 Caparica, Portugal
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18
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Yao J, Qi R, Pan Y, He H, Fan Y, Jiang J, Jiang J. Prediction of the flash points of binary biodiesel mixtures from molecular structures. J Loss Prev Process Ind 2020. [DOI: 10.1016/j.jlp.2020.104137] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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19
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Development of quantitative structure-property relationship (QSPR) models for predicting the thermal hazard of ionic liquids: A review of methods and models. J Mol Liq 2020. [DOI: 10.1016/j.molliq.2020.112471] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
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20
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Ambure P, Cordeiro MNDS. Importance of Data Curation in QSAR Studies Especially While Modeling Large-Size Datasets. METHODS IN PHARMACOLOGY AND TOXICOLOGY 2020. [DOI: 10.1007/978-1-0716-0150-1_5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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21
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Alves VM, Hwang D, Muratov E, Sokolsky-Papkov M, Varlamova E, Vinod N, Lim C, Andrade CH, Tropsha A, Kabanov A. Cheminformatics-driven discovery of polymeric micelle formulations for poorly soluble drugs. SCIENCE ADVANCES 2019; 5:eaav9784. [PMID: 31249867 PMCID: PMC6594770 DOI: 10.1126/sciadv.aav9784] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 05/16/2019] [Indexed: 05/29/2023]
Abstract
Many drug candidates fail therapeutic development because of poor aqueous solubility. We have conceived a computer-aided strategy to enable polymeric micelle-based delivery of poorly soluble drugs. We built models predicting both drug loading efficiency (LE) and loading capacity (LC) using novel descriptors of drug-polymer complexes. These models were employed for virtual screening of drug libraries, and eight drugs predicted to have either high LE and high LC or low LE and low LC were selected. Three putative positives, as well as three putative negative hits, were confirmed experimentally (implying 75% prediction accuracy). Fortuitously, simvastatin, a putative negative hit, was found to have the desired micelle solubility. Podophyllotoxin and simvastatin (LE of 95% and 87% and LC of 43% and 41%, respectively) were among the top five polymeric micelle-soluble compounds ever studied experimentally. The success of the strategy described herein suggests its broad utility for designing drug delivery systems.
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Affiliation(s)
- Vinicius M. Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, GO 74605-170, Brazil
| | - Duhyeong Hwang
- Center for Nanotechnology in Drug Delivery, Division of Pharmacoengineering and Molecular Pharmaceutics, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
- Department of Pharmaceutical Sciences, Federal University of Paraíba, Joao Pessoa, PB 58059, Brazil
| | - Marina Sokolsky-Papkov
- Center for Nanotechnology in Drug Delivery, Division of Pharmacoengineering and Molecular Pharmaceutics, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Ekaterina Varlamova
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, GO 74605-170, Brazil
| | - Natasha Vinod
- Center for Nanotechnology in Drug Delivery, Division of Pharmacoengineering and Molecular Pharmaceutics, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
- UNC/NC State Joint Department of Biomedical Engineering, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Chaemin Lim
- Center for Nanotechnology in Drug Delivery, Division of Pharmacoengineering and Molecular Pharmaceutics, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Carolina H. Andrade
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, GO 74605-170, Brazil
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Alexander Kabanov
- Center for Nanotechnology in Drug Delivery, Division of Pharmacoengineering and Molecular Pharmaceutics, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
- Laboratory of Chemical Design of Bionanomaterials, Faculty of Chemistry, M.V. Lomonosov Moscow State University, Moscow 119992, Russia
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22
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Shen S, Pan Y, Ji X, Ni Y, Jiang J. Prediction of the Auto-Ignition Temperatures of Binary Miscible Liquid Mixtures from Molecular Structures. Int J Mol Sci 2019; 20:ijms20092084. [PMID: 31035591 PMCID: PMC6539801 DOI: 10.3390/ijms20092084] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2019] [Revised: 04/11/2019] [Accepted: 04/23/2019] [Indexed: 11/17/2022] Open
Abstract
A quantitative structure-property relationship (QSPR) study is performed to predict the auto-ignition temperatures (AITs) of binary liquid mixtures based on their molecular structures. The Simplex Representation of Molecular Structure (SiRMS) methodology was employed to describe the structure characteristics of a series of 132 binary miscible liquid mixtures. The most rigorous “compounds out” strategy was employed to divide the dataset into the training set and test set. The genetic algorithm (GA) combined with multiple linear regression (MLR) was used to select the best subset of SiRMS descriptors, which significantly contributes to the AITs of binary liquid mixtures. The result is a multilinear model with six parameters. Various strategies were employed to validate the developed model, and the results showed that the model has satisfactory robustness and predictivity. Furthermore, the applicability domain (AD) of the model was defined. The developed model could be considered as a new way to reliably predict the AITs of existing or new binary miscible liquid mixtures, belonging to its AD.
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Affiliation(s)
- Shijing Shen
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China.
| | - Yong Pan
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China.
| | - Xianke Ji
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China.
| | - Yuqing Ni
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China.
| | - Juncheng Jiang
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China.
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23
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Ma Y, Ma K, Wang H, Geng X, Gao J, Zhu Z, Wang Y. QSPR modeling of azeotropic temperatures and compositions for binary azeotropes containing lower alcohols using a genetic function approximation. Chin J Chem Eng 2019. [DOI: 10.1016/j.cjche.2018.06.031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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24
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Pan Y, Ji X, Ding L, Jiang J. Prediction of Lower Flammability Limits for Binary Hydrocarbon Gases by Quantitative Structure-A Property Relationship Approach. Molecules 2019; 24:E748. [PMID: 30791456 PMCID: PMC6413142 DOI: 10.3390/molecules24040748] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2019] [Revised: 02/13/2019] [Accepted: 02/15/2019] [Indexed: 12/15/2022] Open
Abstract
The lower flammability limit (LFL) is one of the most important parameters for evaluating the fire and explosion hazards of flammable gases or vapors. This study proposed quantitative structure-property relationship (QSPR) models to predict the LFL of binary hydrocarbon gases from their molecular structures. Twelve different mixing rules were employed to derive mixture descriptors for describing the structures characteristics of a series of 181 binary hydrocarbon mixtures. Genetic algorithm (GA)-based multiple linear regression (MLR) was used to select the most statistically effective mixture descriptors on the LFL of binary hydrocarbon gases. A total of 12 multilinear models were obtained based on the different mathematical formulas. The best model, issued from the norm of the molar contribution formula, was achieved as a six-parameter model. The best model was then rigorously validated using multiple strategies and further extensively compared to the previously published model. The results demonstrated the robustness, validity, and satisfactory predictivity of the proposed model. The applicability domain (AD) of the model was defined as well. The proposed best model would be expected to present an alternative to predict the LFL values of existing or new binary hydrocarbon gases, and provide some guidance for prioritizing the design of safer blended gases with desired properties.
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Affiliation(s)
- Yong Pan
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China.
| | - Xianke Ji
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China.
| | - Li Ding
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China.
| | - Juncheng Jiang
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China.
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25
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Fayet G, Rotureau P. New QSPR Models to Predict the Flammability of Binary Liquid Mixtures. Mol Inform 2019; 38:e1800122. [DOI: 10.1002/minf.201800122] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Accepted: 12/12/2018] [Indexed: 12/14/2022]
Affiliation(s)
- Guillaume Fayet
- INERISAccidental Risk Division Parc Technologique Alata 60550 Verneuil-en-Halatte France
| | - Patricia Rotureau
- INERISAccidental Risk Division Parc Technologique Alata 60550 Verneuil-en-Halatte France
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26
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Low YS, Alves VM, Fourches D, Sedykh A, Andrade CH, Muratov EN, Rusyn I, Tropsha A. Chemistry-Wide Association Studies (CWAS): A Novel Framework for Identifying and Interpreting Structure-Activity Relationships. J Chem Inf Model 2018; 58:2203-2213. [PMID: 30376324 DOI: 10.1021/acs.jcim.8b00450] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Quantitative structure-activity relationships (QSAR) models are often seen as a "black box" because they are considered difficult to interpret. Meanwhile, qualitative approaches, e.g., structural alerts (SA) or read-across, provide mechanistic insight, which is preferred for regulatory purposes, but predictive accuracy of such approaches is often low. Herein, we introduce the chemistry-wide association study (CWAS) approach, a novel framework that both addresses such deficiencies and combines advantages of statistical QSAR and alert-based approaches. The CWAS framework consists of the following steps: (i) QSAR model building for an end point of interest, (ii) identification of key chemical features, (iii) determination of communities of such features disproportionately co-occurring more frequently in the active than in the inactive class, and (iv) assembling these communities to form larger (and not necessarily chemically connected) novel structural alerts with high specificity. As a proof-of-concept, we have applied CWAS to model Ames mutagenicity and Stevens-Johnson Syndrome (SJS). For the well-studied Ames mutagenicity data set, we identified 76 important individual fragments and assembled co-occurring fragments into SA both replicative of known as well as representing novel mutagenicity alerts. For the SJS data set, we identified 29 important fragments and assembled co-occurring communities into SA including both known and novel alerts. In summary, we demonstrate that CWAS provides a new framework to interpret predictive QSAR models and derive refined structural alerts for more effective design and safety assessment of drugs and drug candidates.
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Affiliation(s)
- Yen S Low
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States
| | - Vinicius M Alves
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States.,Laboratory for Molecular Modeling and Design, Department of Pharmacy , Federal University of Goias , Goiania , Goias 74605-170 , Brazil
| | - Denis Fourches
- Department of Chemistry and Bioinformatics Research Center , North Carolina State University , Raleigh , North Carolina 27695 , United States
| | - Alexander Sedykh
- Sciome LLC , Research Triangle Park , North Carolina 27709 , United States
| | - Carolina Horta Andrade
- Laboratory for Molecular Modeling and Design, Department of Pharmacy , Federal University of Goias , Goiania , Goias 74605-170 , Brazil
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States.,Department of Chemical Technology , Odessa National Polytechnic University , Odessa 65000 , Ukraine
| | - Ivan Rusyn
- Department of Veterinary Integrative Biosciences , Texas A&M University , College Station , Texas 77843 , United States
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States
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27
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From bird’s eye views to molecular communities: two-layered visualization of structure–activity relationships in large compound data sets. J Comput Aided Mol Des 2017; 31:961-977. [DOI: 10.1007/s10822-017-0070-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Accepted: 09/21/2017] [Indexed: 01/18/2023]
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28
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Structure-reactivity modeling using mixture-based representation of chemical reactions. J Comput Aided Mol Des 2017; 31:829-839. [PMID: 28752345 DOI: 10.1007/s10822-017-0044-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Accepted: 07/23/2017] [Indexed: 12/22/2022]
Abstract
We describe a novel approach of reaction representation as a combination of two mixtures: a mixture of reactants and a mixture of products. In turn, each mixture can be encoded using an earlier reported approach involving simplex descriptors (SiRMS). The feature vector representing these two mixtures results from either concatenated product and reactant descriptors or the difference between descriptors of products and reactants. This reaction representation doesn't need an explicit labeling of a reaction center. The rigorous "product-out" cross-validation (CV) strategy has been suggested. Unlike the naïve "reaction-out" CV approach based on a random selection of items, the proposed one provides with more realistic estimation of prediction accuracy for reactions resulting in novel products. The new methodology has been applied to model rate constants of E2 reactions. It has been demonstrated that the use of the fragment control domain applicability approach significantly increases prediction accuracy of the models. The models obtained with new "mixture" approach performed better than those required either explicit (Condensed Graph of Reaction) or implicit (reaction fingerprints) reaction center labeling.
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29
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30
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Zhou L, Jiang J, Ni L, Pan Y, Yao J, Wang Z. Predicting the superheat limit temperature of binary mixtures based on the quantitative structure property relationship. J Loss Prev Process Ind 2016. [DOI: 10.1016/j.jlp.2016.06.022] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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31
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Horvath D, Marcou G, Varnek A, Kayastha S, de la Vega de León A, Bajorath J. Prediction of Activity Cliffs Using Condensed Graphs of Reaction Representations, Descriptor Recombination, Support Vector Machine Classification, and Support Vector Regression. J Chem Inf Model 2016; 56:1631-40. [DOI: 10.1021/acs.jcim.6b00359] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Dragos Horvath
- Laboratoire
de Chemoinformatique, UMR 7140, Université de Strasbourg, 1 rue
Blaise Pascal, Strasbourg 67000, France
| | - Gilles Marcou
- Laboratoire
de Chemoinformatique, UMR 7140, Université de Strasbourg, 1 rue
Blaise Pascal, Strasbourg 67000, France
| | - Alexandre Varnek
- Laboratoire
de Chemoinformatique, UMR 7140, Université de Strasbourg, 1 rue
Blaise Pascal, Strasbourg 67000, France
| | - Shilva Kayastha
- Laboratoire
de Chemoinformatique, UMR 7140, Université de Strasbourg, 1 rue
Blaise Pascal, Strasbourg 67000, France
- Department
of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology
and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany
| | - Antonio de la Vega de León
- Department
of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology
and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany
| | - Jürgen Bajorath
- Department
of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology
and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstrasse 2, D-53113 Bonn, Germany
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32
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Mokshyna EG, Polishchuk PG, Nedostup VI, Kuz’min VE. QSPR modeling of critical properties of organic binary mixtures. RUSSIAN JOURNAL OF ORGANIC CHEMISTRY 2016. [DOI: 10.1134/s1070428016010024] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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33
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Zakharov AV, Varlamova EV, Lagunin AA, Dmitriev AV, Muratov EN, Fourches D, Kuz'min VE, Poroikov VV, Tropsha A, Nicklaus MC. QSAR Modeling and Prediction of Drug-Drug Interactions. Mol Pharm 2016; 13:545-56. [PMID: 26669717 DOI: 10.1021/acs.molpharmaceut.5b00762] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100,000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug-drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27,966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72-79% for the external test sets with a coverage of 81.36-100% when a conservative threshold for the model's applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database.
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Affiliation(s)
- Alexey V Zakharov
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, DHHS, NCI-Frederick , 376 Boyles Street, Frederick, Maryland 21702, United States
| | - Ekaterina V Varlamova
- Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine , Lustdorfskaya Doroga 86, Odessa 65080, Ukraine.,Chemical-Technological Department, Odessa National Polytechnic University , 1 Shevchenko Ave, Odessa 65000, Ukraine
| | - Alexey A Lagunin
- Institute of Biochemical Chemistry , 10/8, Pogodinskaya street, 119121 Moscow, Russia.,Medico-Biological Department, Pirogov Russian National Research Medical University , Ostrovitianov str. 1, Moscow 117997, Russia
| | - Alexander V Dmitriev
- Institute of Biochemical Chemistry , 10/8, Pogodinskaya street, 119121 Moscow, Russia
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina , Beard Hall 301, CB#7568, Chapel Hill, North Carolina 27599, United States
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University , Raleigh, North Carolina 27695, United States
| | - Victor E Kuz'min
- Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine , Lustdorfskaya Doroga 86, Odessa 65080, Ukraine
| | - Vladimir V Poroikov
- Institute of Biochemical Chemistry , 10/8, Pogodinskaya street, 119121 Moscow, Russia
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina , Beard Hall 301, CB#7568, Chapel Hill, North Carolina 27599, United States
| | - Marc C Nicklaus
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, DHHS, NCI-Frederick , 376 Boyles Street, Frederick, Maryland 21702, United States
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34
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Modelling of compound combination effects and applications to efficacy and toxicity: state-of-the-art, challenges and perspectives. Drug Discov Today 2015; 21:225-38. [PMID: 26360051 DOI: 10.1016/j.drudis.2015.09.003] [Citation(s) in RCA: 105] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2015] [Revised: 07/30/2015] [Accepted: 09/01/2015] [Indexed: 01/18/2023]
Abstract
The development of treatments involving combinations of drugs is a promising approach towards combating complex or multifactorial disorders. However, the large number of compound combinations that can be generated, even from small compound collections, means that exhaustive experimental testing is infeasible. The ability to predict the behaviour of compound combinations in biological systems, whittling down the number of combinations to be tested, is therefore crucial. Here, we review the current state-of-the-art in the field of compound combination modelling, with the aim to support the development of approaches that, as we hope, will finally lead to an integration of chemical with systems-level biological information for predicting the effect of chemical mixtures.
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Kaneko H, Funatsu K. Strategy of Structure Generation within Applicability Domains with One-Class Support Vector Machine. BULLETIN OF THE CHEMICAL SOCIETY OF JAPAN 2015. [DOI: 10.1246/bcsj.20150054] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Hiromasa Kaneko
- Department of Chemical System Engineering, School of Engineering, The University of Tokyo
| | - Kimito Funatsu
- Department of Chemical System Engineering, School of Engineering, The University of Tokyo
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Gaudin T, Rotureau P, Fayet G. Mixture Descriptors toward the Development of Quantitative Structure–Property Relationship Models for the Flash Points of Organic Mixtures. Ind Eng Chem Res 2015. [DOI: 10.1021/acs.iecr.5b01457] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Théophile Gaudin
- INERIS, Parc Technologique Alata, BP2, 60550 Verneuil-en-Halatte, France
| | - Patricia Rotureau
- INERIS, Parc Technologique Alata, BP2, 60550 Verneuil-en-Halatte, France
| | - Guillaume Fayet
- INERIS, Parc Technologique Alata, BP2, 60550 Verneuil-en-Halatte, France
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Mokshyna E, Polishchuk PG, Nedostup VI, Kuzmin VE. Predictive QSPR Modelling for the Second Virial Coefficient of the Pure Organic Compounds. Mol Inform 2015; 34:53-9. [PMID: 27490862 DOI: 10.1002/minf.201400081] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2014] [Accepted: 10/14/2014] [Indexed: 11/07/2022]
Abstract
In this article we developed a system of the predictive models for the second virial coefficients of the pure compounds. Second virial coefficient is the property derived from the virial equation of state, and is of particular interest as it describes pair intermolecular interactions. The two-layer QSPR models were developed, which exploited the well-known physical equations and allowed us to include this information into traditional QSPR methodology. This shows some new perspectives for work with temperature-dependent properties. It was shown that 2D descriptors can be successfully used for modeling of complex thermodynamic properties like virial coefficients.
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Affiliation(s)
- E Mokshyna
- O.V. Bogatsky Physico-Chemical Institute NAS of Ukraine, 86 Lustdorfs'ka doroga, 65081 Odesa, Ukraine.
| | - P G Polishchuk
- O.V. Bogatsky Physico-Chemical Institute NAS of Ukraine, 86 Lustdorfs'ka doroga, 65081 Odesa, Ukraine
| | - V I Nedostup
- O.V. Bogatsky Physico-Chemical Institute NAS of Ukraine, 86 Lustdorfs'ka doroga, 65081 Odesa, Ukraine
| | - V E Kuzmin
- O.V. Bogatsky Physico-Chemical Institute NAS of Ukraine, 86 Lustdorfs'ka doroga, 65081 Odesa, Ukraine
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Mokshyna E, Nedostup VI, Polishchuk PG, Kuzmin VE. 'Quasi-Mixture' Descriptors for QSPR Analysis of Molecular Macroscopic Properties. The Critical Properties of Organic Compounds. Mol Inform 2014; 33:647-54. [PMID: 27485300 DOI: 10.1002/minf.201400036] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2014] [Accepted: 06/11/2014] [Indexed: 11/09/2022]
Abstract
Rational approach towards the QSAR/QSPR modeling requires the descriptors to be computationally efficient, yet physically and chemically meaningful. On the basis of existing simplex representation of molecular structure (SiRMS) the novel 'quasi-mixture' descriptors were developed in order to accomplish the goal of characterization molecules on 2D level (i.e. without explicit generation of 3D structure and exhaustive conformational search) with account for potential intermolecular interactions. The critical properties of organic compounds were chosen as target properties for the estimation of descriptors' efficacy because of their well-known physical nature, rigorously estimated experimental errors and large quantity of experimental data. Among described properties are critical temperature, pressure and volume. Obtained models have high statistical characteristics, therefore showing the efficacy of suggested 'quasi-mixture' approach. Moreover, 'quasi-mixture' approach, as a branch of the SiRMS, allows to interpret results in terms of simple basic molecular properties. The obtained picture of influences corresponds to the accepted theoretical views.
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Affiliation(s)
- E Mokshyna
- Physico-Chemical Institute NAS of Ukraine, 86 Lustdorfs'ka doroga, 65081 Odesa, Ukraine.
| | - V I Nedostup
- Physico-Chemical Institute NAS of Ukraine, 86 Lustdorfs'ka doroga, 65081 Odesa, Ukraine
| | - P G Polishchuk
- Physico-Chemical Institute NAS of Ukraine, 86 Lustdorfs'ka doroga, 65081 Odesa, Ukraine
| | - V E Kuzmin
- Physico-Chemical Institute NAS of Ukraine, 86 Lustdorfs'ka doroga, 65081 Odesa, Ukraine
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Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V, Kuz'min VE, Cramer R, Benigni R, Yang C, Rathman J, Terfloth L, Gasteiger J, Richard A, Tropsha A. QSAR modeling: where have you been? Where are you going to? J Med Chem 2014; 57:4977-5010. [PMID: 24351051 PMCID: PMC4074254 DOI: 10.1021/jm4004285] [Citation(s) in RCA: 1040] [Impact Index Per Article: 104.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.
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Affiliation(s)
- Artem Cherkasov
- Vancouver Prostate Centre, University of British Columbia, Vancouver, BC, V6H3Z6, Canada
| | - Eugene N. Muratov
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
- Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Odessa, 65080, Ukraine
| | - Denis Fourches
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Alexandre Varnek
- Department of Chemistry, L. Pasteur University of Strasbourg, Strasbourg, 67000, France
| | - Igor I. Baskin
- Department of Physics, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Mark Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool L33AF, UK
| | - John Dearden
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool L33AF, UK
| | - Paola Gramatica
- Department of Structural and Functional Biology, University of Insubria, Varese, 21100, Italy
| | | | - Roberto Todeschini
- Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, 20126, Italy
| | - Viviana Consonni
- Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, 20126, Italy
| | - Victor E. Kuz'min
- Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Odessa, 65080, Ukraine
| | | | - Romualdo Benigni
- Environment and Health Department, Istituto Superiore di Sanita’, Rome, 00161, Italy
| | | | - James Rathman
- Altamira LLC, Columbus OH 43235, USA
- Department of Chemical and Biomolecular Engineering, the Ohio State University, Columbus, OH 43215, USA
| | | | | | - Ann Richard
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27519, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
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Vassiliev PM, Spasov AA, Kosolapov VA, Kucheryavenko AF, Gurova NA, Anisimova VA. Consensus Drug Design Using IT Microcosm. CHALLENGES AND ADVANCES IN COMPUTATIONAL CHEMISTRY AND PHYSICS 2014. [DOI: 10.1007/978-94-017-9257-8_12] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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Zare-Shahabadi V, Lotfizadeh M, Gandomani ARA, Papari MM. Determination of boiling points of azeotropic mixtures using quantitative structure–property relationship (QSPR) strategy. J Mol Liq 2013. [DOI: 10.1016/j.molliq.2013.09.037] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Modeling of non-additive mixture properties using the Online CHEmical database and Modeling environment (OCHEM). J Cheminform 2013; 5:4. [PMID: 23321019 PMCID: PMC3568005 DOI: 10.1186/1758-2946-5-4] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2012] [Accepted: 01/08/2013] [Indexed: 11/10/2022] Open
Abstract
The Online Chemical Modeling Environment (OCHEM, http://ochem.eu) is a web-based platform that provides tools for automation of typical steps necessary to create a predictive QSAR/QSPR model. The platform consists of two major subsystems: a database of experimental measurements and a modeling framework. So far, OCHEM has been limited to the processing of individual compounds. In this work, we extended OCHEM with a new ability to store and model properties of binary non-additive mixtures. The developed system is publicly accessible, meaning that any user on the Web can store new data for binary mixtures and develop models to predict their non-additive properties.The database already contains almost 10,000 data points for the density, bubble point, and azeotropic behavior of binary mixtures. For these data, we developed models for both qualitative (azeotrope/zeotrope) and quantitative endpoints (density and bubble points) using different learning methods and specially developed descriptors for mixtures. The prediction performance of the models was similar to or more accurate than results reported in previous studies. Thus, we have developed and made publicly available a powerful system for modeling mixtures of chemical compounds on the Web.
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Varnek A, Baskin I. Machine learning methods for property prediction in chemoinformatics: Quo Vadis? J Chem Inf Model 2012; 52:1413-37. [PMID: 22582859 DOI: 10.1021/ci200409x] [Citation(s) in RCA: 148] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
This paper is focused on modern approaches to machine learning, most of which are as yet used infrequently or not at all in chemoinformatics. Machine learning methods are characterized in terms of the "modes of statistical inference" and "modeling levels" nomenclature and by considering different facets of the modeling with respect to input/ouput matching, data types, models duality, and models inference. Particular attention is paid to new approaches and concepts that may provide efficient solutions of common problems in chemoinformatics: improvement of predictive performance of structure-property (activity) models, generation of structures possessing desirable properties, model applicability domain, modeling of properties with functional endpoints (e.g., phase diagrams and dose-response curves), and accounting for multiple molecular species (e.g., conformers or tautomers).
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Affiliation(s)
- Alexandre Varnek
- Laboratoire d'Infochimie, UMR 7177 CNRS, Université de Strasbourg, 4, rue B. Pascal, Strasbourg 67000, France.
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