Calls for Papers (special): International Journal of Quantitative Structure-Property Relationships (IJQSPR)

Special Issue On: Computational Modeling Approaches in Health, Food, Environment and Materials Sciences

Submission Due Date
7/31/2021

Guest Editors
Dr. Supratik Kar
Interdisciplinary Nanotoxicity Center, Department of Chemistry, Physics and Atmospheric Sciences
Dr. Pravin Ambure
ProtoQSAR SL, European Center of Innovative Enterprises (CEEI), Technological Park of Paterna

Introduction
Computational modeling is a process to solve critical science problems with the help of computational resources employing mathematics, data science, physics, and chemistry. Many interdisciplinary fields are considered under computational approaches. The noted ones are cheminformatics and bioinformatics. A series of chemometrics algorithms as well as machine learning approaches allow the interdisciplinary exploration of knowledge on chemical compounds covering the aspects of chemistry, physics, biology, and toxicology. It provides a formalism for developing mathematical correlations between the chemical features and the behavioral manifestations of structurally homogenous/heterogenous compounds. Among chemometric approaches, quantitative structure-activity/property relationship (QSAR/QSPR) is one of the strongest mathematical algorithms, and it provides a reasonable basis for establishing a predictive correlation models. Apart from providing a mathematical correlation, QSAR technique also enables the exploration of chemical features encoded within descriptors. The QSAR technique proves to be a valuable alternative method in this perspective and is encouraged for the design and development of biologically active molecules, especially drugs, food and agrochemicals, property prediction as well as in predictive toxicology analysis of environmental pollutants. On the other hand, machine learning models can handle big data in no time and can predict a huge population of data efficiently. Again, bioinformatics deal with omics and biology data to solve the problems an understand the critical biological problems.

Objective
Although computational approaches are frequently used in modeling physical and biological properties of materials and ecotoxicity modeling of pharmaceuticals, personal care products, dyes, nanomaterials, etc., there is a lack of clearly assessing their advantages or disadvantages in building efficient and predictive models. Not only for data gap filling purpose as well as for regulatory decision making, computational models have immense roles to play in medicinal chemistry and drug design. But, the implicit knowledge, interpretation, and extracting and organizing knowledge from such models are still thought-provoking. This special issue will try to give answer to questions such as:
• How can accurate and predictive computational models be developed in health, food, material science and toxicology science?
• Which kind of chemical and biological knowledge should be required to model new drug molecules, food, advanced materials, and toxicity modeling?
• What are the next challenges for chemometric methods in health, food, material science and toxicology science?
• How and why users can employ multiple computational tools in health, food, material science and toxicology science?

Recommended Topics
Computational Modeling Approaches In Drug Design and Food Science
-Chemometric model to drug design
-Docking and molecular dynamics in drug design and development
-Chemometric model for food science
-Machine learning models in drug design
-ADMET prediction of new drugs through computational approach
-Computational modeling of natural products as drugs
-Computational modeling of small molecule development for COVID-19
Computational Modeling Approaches In Material Sciences
-Computational models in property prediction of advanced materials
-Role of QSPR/machine learning techniques in diverse property prediction of nanomaterials
-Application of QSPR in designing of solar cells
-Predictive QSPR models in ionic liquids research
-Computational approaches to model biocatalytic materials
Computational Modeling in Prediction of Drug Toxicity To Humans and Environmental Toxicity Due To Industrial Chemicals, Pharmaceuticals, Personal Care products, Dyes, Nanomaterials
-Database for modeling of drug toxicity
-Expert systems/open source software for the toxicity prediction of drug to human
-Predictive QSAR Modeling For Prenatal Developmental/Reproductive Toxicity/Mammalian Systemic toxicity/Carcinogenicity/Immunotoxicity/Neurotoxicity/Respiratory toxicity/ Nephrotoxicity/Endocrine disruption/Skin sensitization/Skin irritation and corrosion/Eye irritation and corrosion/Cardiovascular toxicity/Ocular toxicity
- Machine learning models in toxicity prediction to human and environment
-Read-across approach to predict drug toxicity to human
-In silico model to encode the complexity of drug mixture toxicity
-Ecotoxicological risk assessment in the context of different EU regulations
-Quantitative structure-activity relationships as tools in predictive ecotoxicology
-Read across for computational ecotoxicology
-Feature selection and modeling algorithms in ecotoxicological QSARs
-Validation tools for ecotoxicological QSARs
-Methods for assessment of applicability domain and reliability of predictions of ecotoxicological QSARs
-Computational toxicity modeling of mixture

Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on Computational Modeling Approaches in Health, Food, Environment and Materials Sciences on or before July 31, 2021. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

All inquiries should be directed to the attention of:
Dr. Supratik Kar & Dr. Pravin Ambure
Guest Editors
International Journal of Quantitative Structure-Property Relationships (IJQSPR)
E-mail: supratik.kar@icnanotox.org; pambure@protoqsar.com