QSPR/QSAR analysis using SMILES and quasi-SMILES / Alla P. Toropova, Andrey A. Toropov, editors.
Toropova, Alla P., editor.; Toropov, Andrey A. editor.
2023
QD39.3.E46
Available Online

Linked e-resources

Linked Resource Online Access
Concurrent users Unlimited
Authorized users Authorized users
Document Delivery Supplied Can lend chapters, not whole ebooks

Details

Title QSPR/QSAR analysis using SMILES and quasi-SMILES / Alla P. Toropova, Andrey A. Toropov, editors.
ISBN 9783031284014 (electronic bk.)
3031284011 (electronic bk.)
3031284003
9783031284007
DOI https://doi.org/10.1007/978-3-031-28401-4
Published Cham : Springer, [2023]
Copyright ©2023
Language English
Description 1 online resource (xviii, 467 pages) : illustrations (some color).
Item Number 10.1007/978-3-031-28401-4 doi
Call Number QD39.3.E46
Dewey Decimal Classification 542/.85
Summary 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.
Note Includes index.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Added Author Toropova, Alla P., editor.
Toropov, Andrey A. editor.
Series Challenges and advances in computational chemistry and physics ; 33.
Available in Other Form QSPR/QSAR ANALYSIS USING SMILES AND QUASI-SMILES.
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
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.

Browse Subjects

Show more subjects...

Statistics

from
to
Export