A Successful Strategy for the Prediction of Solubility in the Construction of Quantitative Structure–Activity Relationship (QSAR) and Quantitative Structure– Property Relationship (QSPR) under Synchrotron Radiations Using Genetic Function Approximation (GFA) Algorithm
Alireza Heidari*
Faculty of Chemistry, California South University, 14731 Comet St. Irvine, CA 92604, USA
- *Corresponding Author:
- Alireza Heidari
Faculty of Chemistry
California South University
14731 Comet St. Irvine
CA 92604, USA
Tel: +1-775-410-4974
E-mail: Scholar.Researcher.Scientist@gmail.com
Received date: October 14, 2016; Accepted date: October 15, 2016; Published date: October 18, 2016
Citation: Heidari A. A Successful Strategy for the Prediction of Solubility in the Construction of Quantitative Structure– Activity Relationship (QSAR) and Quantitative Structure–Property Relationship (QSPR) under Synchrotron Radiations Using Genetic Function Approximation (GFA) Algorithm. J Mol Biol Biotech. 2016, 1:1.
Abstract
In this editorial, aqueous solubility and specially water solubility under synchrotron radiations using Genetic Function Approximation (GFA) algorithm is one of the most important physiochemical and biological properties that plays a significant and important role in various chemical, physical, clinical, pharmaceutical, medical, medicinal and biological processes and has a marked impact on the design and pharmaceutical formulation development [1–20]. In addition, a successful strategy for the prediction of solubility is the construction of Quantitative Structure–Activity Relationship (QSAR) and Quantitative Structure–Property Relationship (QSPR) under synchrotron radiations using Genetic Function Approximation (GFA) algorithm [21, 22]. Moreover, the main aim of Quantitative Structure–Activity Relationship (QSAR) and Quantitative Structure–Property Relationship (QSPR) studies is to establish an empirical rule or function relating the structural descriptors of compounds under investigation to bioactivities [23–33]. A major step in constructing the Quantitative Structure–Activity Relationship (QSAR) and Quantitative Structure–Property Relationship (QSPR) models is finding one or more molecular descriptors that represent variation in the structural, topological, geometrical, quantum chemical and biospectroscopic properties of the molecules under synchrotron radiations using Genetic Function Approximation (GFA) algorithm, analytically and numerically. A wide variety of descriptors have been reported on Quantitative Structure–Activity Relationship (QSAR) and Quantitative Structure–Property Relationship (QSPR) analysis under synchrotron radiations using Genetic Function Approximation (GFA) algorithm.
In this editorial, aqueous solubility and specially water solubility under synchrotron radiations using Genetic Function Approximation (GFA) algorithm is one of the most important physiochemical and biological properties that plays a significant and important role in various chemical, physical, clinical, pharmaceutical, medical, medicinal and biological processes and has a marked impact on the design and pharmaceutical formulation development [1–20]. In addition, a successful strategy for the prediction of solubility is the construction of Quantitative Structure-Activity Relationship (QSAR) and Quantitative Structure-Property Relationship (QSPR) under synchrotron radiations using Genetic Function Approximation (GFA) algorithm [21,22]. Moreover, the main aim of Quantitative Structure- Activity Relationship (QSAR) and Quantitative Structure-Property Relationship (QSPR) studies is to establish an empirical rule or function relating the structural descriptors of compounds under investigation to bioactivities [23–33]. A major step in constructing the Quantitative Structure-Activity Relationship (QSAR) and Quantitative Structure-Property Relationship (QSPR) models is finding one or more molecular descriptors that represent variation in the structural, topological, geometrical, quantum chemical and biospectroscopic properties of the molecules under synchrotron radiations using Genetic Function Approximation (GFA) algorithm, analytically and numerically. A wide variety of descriptors have been reported on Quantitative Structure- Activity Relationship (QSAR) and Quantitative Structure-Property Relationship (QSPR) analysis under synchrotron radiations using Genetic Function Approximation (GFA) algorithm.
On the other hand, it can be concluded that quantum chemical calculations are thus an attractive source of new molecular descriptors, which can, in principle, express all of the electronic and geometric properties of molecules and their interactions under synchrotron radiations using Genetic Function Approximation (GFA) algorithm. Also, it should be noted that atomic charges, Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO) energies, molecular polarizability, dipole moments and energies of molecule are examples of quantum chemical descriptors used in Quantitative Structure-Activity Relationship (QSAR) and Quantitative Structure-Property Relationship (QSPR) studies under synchrotron radiations using Genetic Function Approximation (GFA) algorithm. Furthermore, in the current editorial, the application of quantitative chemometrics methods, particularly Partial Least Squares (PLS), to quantum chemical descriptors was described.
References
- Xu X, Li L, Yan F, Jia Q, Wang Q, et al. (2016) Predicting Solubility of Fullerene C60 in Diverse Organic Solvents Using Norm Indexes. Journal of Molecular Liquids 223: 603-610.
- Rybacka A, Andersson PL (2016) Considering Ionic State in Modeling Sorption of Pharmaceuticals to Sewage Sludge. Chemosphere 165: 284-293.
- Gonfa G, Bustam A, Shariff AM (2016) Quantum-Chemical-Based Quantitative Structure-Activity Relationships for Estimation of CO2 Absorption/Desorption Capacities of Amine-Based Absorbents. International Journal of Greenhouse Gas Control 49: 372-378.
- Pizzo F, Lombardo A, Manganaro A, Cappelli C, Petoumenou MI, et al. (2016) Integrated in Silico Strategy for Pbt Assessment and Prioritization Under Reach. Environmental Research 151:478-492.
- Kar S, Sizochenko N, Ahmed L, Batista VS, Leszczynski J (2016) Quantitative Structure-Property Relationship Model Leading to Virtual Screening of Fullerene Derivatives: Exploring Structural Attributes Critical for Photoconversion Efficiency of Polymer Solar Cell Acceptors. Nano Energy 26:677-691.
- Zou JW, Huang M, Huang JX, Hu GX, Jiang yz (2016) Quantitative Structure– Hydrophobicity Relationships of Molecular Fragments and Beyond. Journal Of Molecular Graphics And Modelling 64:110-120.
- Ermondi G, Caron G (2016) Do Surface-Based Match Solution-Based Techniques? The Case of Drug-Liposome Interaction. International Journal of Pharmaceutics 508: 123-127.
- Freitas MR, Barigye SJ, Daré JK, Freitas MP (2016) Quantitative Modeling of Bioconcentration Factors of Carbonyl Herbicides Using Multivariate Image Analysis. Chemosphere 152:190-195.
- Winkler DA (2016) Recent Advances, and Unresolved Issues, In The Application Of Computational Modelling to The Prediction of The Biological Effects of Nanomaterials. Toxicology and Applied Pharmacology 299: 96-100.
- Das RN, Roy K (2016) Computation of Chromatographic Lipophilicity Parameter Logk0 of Ionic Liquid Cations from “ETA” Descriptors: Application in Modeling of Toxicity of Ionic Liquids to Pathogenic Bacteria. Journal of Molecular Liquids 216: 754-763.
- Golubovic J, Birkemeyer C, Protic A, Otaševic B, Zecevic M (2016) Structure–Response Relationship in Electrospray Ionization-Mass Spectrometry of Sartans by Artificial Neural Networks. Journal of Chromatography A 1438:123-132.
- Fernandez M, Breedon M,Cole IS, Barnard AS(2016) Modeling Corrosion Inhibition Efficacy of Small Organic Molecules as Non-Toxic Chromate Alternatives Using Comparative Molecular Surface Analysis (Comsa). Chemosphere 160:80-88.
- Grzonkowska M, Sosnowska A, Barycki M, Rybinska A, Puzyn T (2016) How The Structure of Ionic Liquid Affects its Toxicity to Vibrio Fischeri?. Chemosphere 159:199-207.
- Veselinovic JB, Veselinovic AM, Toropova AP, Toropov AA (2016) The Monte Carlo Technique as a Tool to Predict LOAEL. European Journal of Medicinal Chemistry 116:71-75.
- Adl A, Zein M, Hassanien AE (2016) PQSAR: The Membrane Quantitative Structure-Activity Relationships in Cheminformatics. Expert Systems with Applications 54:219-227.
- Hamadache M, Benkortbi O, Hanini S, Amrane A, Khaouane L, et al. (2016) A Quantitative Structure Activity Relationship for Acute Oral Toxicity of Pesticides on Rats: Validation, Domain of Application and Prediction. Journal of Hazardous Materials 303:28-40.
- Toropova AP, Toropov AA, Manganelli S, Leone C, Baderna D, et al. (2016) Quasi-SMILES as a Tool to Utilize Eclectic Data for Predicting The Behavior of Nanomaterials. NanoImpact 260-64.
- Kar S, Gajewicz A, Roy K, Leszczynski J, Puzyn T (2016) Extrapolating Between Toxicity Endpoints of Metal Oxide Nanoparticles: Predicting Toxicity to Escherichia Coli and Human Keratinocyte Cell Line (Hacat) With Nano-QTTR. Ecotoxicology and Environmental Safety 126:238-244.
- Markelj J, Pompe M (2016) Modeling of Atmospheric OH Reaction Rates Using Newly Developed Variable Distance Weighted Zero Order Connectivity Index. Atmospheric Environment 131:418-423.
- Nesmerák K, Toropov AA, Toropova AP (2016) Model for Electrochemical Parameters for 4-(Benzylsulfanyl)Pyridines Calculated From The Molecular Structure. Journal of Electroanalytical Chemistry 766:24-29.
- Nadeem MF, Zafar S, Zahid Z (2016) On Topological Properties of The Line Graphs of Subdivision Graphs of Certain Nanostructures. Applied Mathematics And Computation 273:125-130.
- Mercader AG, Duchowicz PR (2016) Encoding Alternatives for The Prediction of Polyacrylates Glass Transition Temperature by Quantitative Structure–Property Relationships. Materials Chemistry and Physics 172:158-164.
- Xu QS, Xu J, Cao DS, Liang YZ (2016) Boosting in Block Variable Subspaces: An Approach of Additive Modeling for Structure–Activity Relationship. Chemometrics And Intelligent Laboratory Systems 152:134-139.
- Cho CW, Park YS, Stolte S, Yun YS (2016) Modelling for Antimicrobial Activities of Ionic Liquids Towards Escherichia Coli, Staphylococcus Aureus And Candida Albicans Using Linear Free Energy Relationship Descriptors. Journal of Hazardous Materials 311:168-175.
- Melo EBD, Martins JPE, Miranda EH, Ferreira EEC (2016) A Best Comprehension About The Toxicity of Phenylsulfonyl Carboxylates in Vibrio Fischeri Using Quantitative Structure Activity/Property Relationship Methods. Journal of Hazardous Materials 304:233-241.
- Hongmao S (2016) Chapter 7 - In Silico ADMET Profiling: Predictive Models for CYP450, P-Gp, PAMPA, and Herg, in a Practical Guide to Rational Drug Design. Woodhead Publishing 225-268.
- Nanavati C, Mager DE(2016) Calculated Log D is Inversely Correlated With Select Camptothecin Clearance and Efficacy in Colon Cancer Xenografts. Journal of Pharmaceutical Sciences 105:1561-1566.
- Fernandez M, Shi H, Barnard AS (2016) Geometrical Features Can Predict Electronic Properties of Graphene Nanoflakes. Carbon 103:142-150.
- Puri M, Solanki A, Padawer T, Tipparaju SM, Moreno WA , et al.(2016) Chapter 1 - Introduction to Artificial Neural Network (ANN) as a Predictive Tool for Drug Design, Discovery, Delivery, and Disposition: Basic Concepts and Modeling, in Artificial Neural Network for Drug Design, Delivery and Disposition. Academic Press, Boston 3-13.
- Kustrin AC and Morton D (2016) Chapter 9 - Data Mining in Drug Discovery and Design, in Artificial Neural Network for Drug Design, Delivery and Disposition, Edited By Munish Puri, Yashwant Pathak, Vijay Kumar Sutariya, Srinivas Tipparaju and Wilfrido Moreno. Academic Press, Boston 181-193.
- Shoombuatong W, Nabu S, Simeon S, Prachayasittikul V, Lapins M, et al. (2016) Extending Proteochemometric Modeling for Unraveling The Sorption Behavior of Compound–Soil Interaction. Chemometrics and Intelligent Laboratory Systems 151: 219-227.
- Chauhan H, Bernick J, Prasad D, Masand V (2016) Chapter 2 - The Role of Artificial Neural Networks on Target Validation in Drug Discovery and Development, in Artificial Neural Network for Drug Design, Delivery and Disposition, Edited by Munish Puri, Yashwant Pathak, Vijay Kumar Sutariya, Srinivas Tipparaju And Wilfrido Moreno Academic Press, Boston. 15-27.
- Lang R, Li T, Mo D, Shi Y(2016) A Novel Method for Analyzing Inverse Problem of Topological Indices of Graphs Using Competitive Agglomeration. Applied Mathematics and Computation 291: 115-121.