國立虎尾科技大學 |

QSPR/QSAR analysis using SMILES and quasi-SMILES

Record Type:	Language materials, printed : Monograph/item
Title/Author:	QSPR/QSAR analysis using SMILES and quasi-SMILES/ edited by Alla P. Toropova, Andrey A. Toropov.
other author:	Toropova, Alla P.
Published:	Cham :Springer International Publishing : : 2023.,
Description:	xviii, 467 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Computational chemistry. -
Online resource:	https://doi.org/10.1007/978-3-031-28401-4
ISBN:	9783031284014

QSPR/QSAR analysis using SMILES and quasi-SMILES
QSPR/QSAR analysis using SMILES and quasi-SMILES[electronic resource] /edited by Alla P. Toropova, Andrey A. Toropov. - Cham :Springer International Publishing :2023. - xviii, 467 p. :ill. (some col.), digital ;24 cm. - Challenges and advances in computational chemistry and physics,v. 332542-4483 ;. - Challenges and advances in computational chemistry and physics ;v.14..

Part I - Theoretical conceptions -- Fundamentals of mathematical modeling of chemicals through QSPR/QSAR -- Molecular descriptors in QSPR/QSAR modeling -- Application of SMILES to cheminformatics and generation of optimum SMILES descriptors using CORAL software -- Part II - SMILES based descriptors -- All SMILES Variational Autoencoder for Molecular Property Prediction and Optimization -- SMILES based bioactivity descriptors to model the anti-Dengue virus activity: A case study -- Part III - SMILES for QSPR/QSAR with optimal descriptors -- QSPR models for prediction of redox potentials using optimal descriptors -- Building up QSPR for polymers endpoints by using SMILES-based optimal descriptors -- Part IV - Quasi-SMILES for QSPR/QSAR -- Quasi-SMILES based QSPR/QSAR modeling -- Quasi-SMILES Based Mathematical Model for the Prediction of Percolation Threshold for Conductive Polymer Composites -- On the possibility to build up the QSAR model of different kinds of inhibitory activity for a large list of Human Intestinal Transporter using quasi-SMILES -- Quasi-SMILES as a tool for peptide QSAR modelling -- Part V - SMILES and quasi-SMILES for QSPR/QSAR -- SMILES and quasi-SMILES descriptors in QSAR/QSPR modeling of diverse materials properties in safety and environment application -- SMILES and quasi-SMILES in QSAR Modeling for Prediction of Physicochemical and Biochemical Properties -- Part VI - Possible ways of nano-QSPR/nano-QSAR evolution -- The CORAL software as a tool to develop models for nanomaterials' endpoints -- Employing Quasi-SMILES notation in development of nano-QSPR models for nanofluids -- Part VII - Possible ways of QSPR/QSAR evolution in the future -- On complementary approaches of assessing the predictive potential of QSPR/QSAR-models -- CORAL: Predictions of Quality of Rice based on Retention index using a combination of Correlation intensity index and Consensus modelling.

This contributed volume overviews recently presented approaches for carrying out QSPR/QSAR analysis by using a simplifying molecular input-line entry system (SMILES) to represent the molecular structure. In contrast to traditional SMILES, quasi-SMILES is a sequence of special symbols-codes that reflect molecular features and codes of experimental conditions. SMILES and quasi-SMILES serve as a basis to develop QSPR/QSAR as well Nano-QSPR/QSAR via the Monte Carlo calculation that provides the so-called optimal descriptors for QSPR/QSAR models. The book presents a reliable technology for developing Nano-QSPR/QSAR while it also includes the description of the algorithms of the Monte Carlo optimization. It discusses the theory and practice of the technique of variational authodecoders (VAEs) based on SMILES and analyses in detail the index of ideality of correlation (IIC) and the correlation intensity index (CII) which are new criteria for the predictive potential of the model. The mathematical apparatus used is simple so that students of relevant specializations can easily follow. This volume is a valuable contribution to the field and will be of great interest to developers of models of physicochemical properties and biological activity, chemical technologists, and toxicologists involved in the area of drug design.

ISBN: 9783031284014

Standard No.: 10.1007/978-3-031-28401-4doiSubjects--Topical Terms:

1190332
Computational chemistry.

LC Class. No.: QD39.3.E46

Dewey Class. No.: 542.85

QSPR/QSAR analysis using SMILES and quasi-SMILES
LDR:04371nam a2200337 a 4500 001 1105635
003 DE-He213
005 20230610082249.0
006 m d
007 cr nn 008maaau
008 231013s2023 sz s 0 eng d
020 $a 9783031284014 $q (electronic bk.)
020 $a 9783031284007 $q (paper)
024 7 $a 10.1007/978-3-031-28401-4 $2 doi
035 $a 978-3-031-28401-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QD39.3.E46
072 7 $a PNR $2 bicssc
072 7 $a SCI013000 $2 bisacsh
072 7 $a PNRA $2 thema
082 0 4 $a 542.85 $2 23
090 $a QD39.3.E46 $b Q1 2023
245 0 0 $a QSPR/QSAR analysis using SMILES and quasi-SMILES $h [electronic resource] / $c edited by Alla P. Toropova, Andrey A. Toropov.
260 $a Cham : $c 2023. $b Springer International Publishing : $b Imprint: Springer,
300 $a xviii, 467 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Challenges and advances in computational chemistry and physics, $x 2542-4483 ; $v v. 33
505 0 $a Part I - Theoretical conceptions -- Fundamentals of mathematical modeling of chemicals through QSPR/QSAR -- Molecular descriptors in QSPR/QSAR modeling -- Application of SMILES to cheminformatics and generation of optimum SMILES descriptors using CORAL software -- Part II - SMILES based descriptors -- All SMILES Variational Autoencoder for Molecular Property Prediction and Optimization -- SMILES based bioactivity descriptors to model the anti-Dengue virus activity: A case study -- Part III - SMILES for QSPR/QSAR with optimal descriptors -- QSPR models for prediction of redox potentials using optimal descriptors -- Building up QSPR for polymers endpoints by using SMILES-based optimal descriptors -- Part IV - Quasi-SMILES for QSPR/QSAR -- Quasi-SMILES based QSPR/QSAR modeling -- Quasi-SMILES Based Mathematical Model for the Prediction of Percolation Threshold for Conductive Polymer Composites -- On the possibility to build up the QSAR model of different kinds of inhibitory activity for a large list of Human Intestinal Transporter using quasi-SMILES -- Quasi-SMILES as a tool for peptide QSAR modelling -- Part V - SMILES and quasi-SMILES for QSPR/QSAR -- SMILES and quasi-SMILES descriptors in QSAR/QSPR modeling of diverse materials properties in safety and environment application -- SMILES and quasi-SMILES in QSAR Modeling for Prediction of Physicochemical and Biochemical Properties -- Part VI - Possible ways of nano-QSPR/nano-QSAR evolution -- The CORAL software as a tool to develop models for nanomaterials' endpoints -- Employing Quasi-SMILES notation in development of nano-QSPR models for nanofluids -- Part VII - Possible ways of QSPR/QSAR evolution in the future -- On complementary approaches of assessing the predictive potential of QSPR/QSAR-models -- CORAL: Predictions of Quality of Rice based on Retention index using a combination of Correlation intensity index and Consensus modelling.
520 $a This contributed volume overviews recently presented approaches for carrying out QSPR/QSAR analysis by using a simplifying molecular input-line entry system (SMILES) to represent the molecular structure. In contrast to traditional SMILES, quasi-SMILES is a sequence of special symbols-codes that reflect molecular features and codes of experimental conditions. SMILES and quasi-SMILES serve as a basis to develop QSPR/QSAR as well Nano-QSPR/QSAR via the Monte Carlo calculation that provides the so-called optimal descriptors for QSPR/QSAR models. The book presents a reliable technology for developing Nano-QSPR/QSAR while it also includes the description of the algorithms of the Monte Carlo optimization. It discusses the theory and practice of the technique of variational authodecoders (VAEs) based on SMILES and analyses in detail the index of ideality of correlation (IIC) and the correlation intensity index (CII) which are new criteria for the predictive potential of the model. The mathematical apparatus used is simple so that students of relevant specializations can easily follow. This volume is a valuable contribution to the field and will be of great interest to developers of models of physicochemical properties and biological activity, chemical technologists, and toxicologists involved in the area of drug design.
650 0 $a Computational chemistry. $3 1190332
650 0 $a QSAR (Biochemistry) $3 582212
650 1 4 $a Computational Chemistry. $3 1390200
650 2 4 $a Quantum Simulations. $3 1389653
650 2 4 $a Theoretical Chemistry. $3 1387715
650 2 4 $a Model Theory. $3 1394048
700 1 $a Toropova, Alla P. $3 1414764
700 1 $a Toropov, Andrey A. $3 1414765
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
830 0 $a Challenges and advances in computational chemistry and physics ; $v v.14. $3 1022973
856 4 0 $u https://doi.org/10.1007/978-3-031-28401-4
950 $a Chemistry and Materials Science (SpringerNature-11644)