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


Special Issue On: Applications of in silico Molecular Modeling Tools in the Field of Drug Discovery

Submission Due Date
9/1/2018

Guest Editors
Prof. Swastika Ganguly
Department of Pharmaceutical Sciences & Technology, Birla Institute of Technology, Mesra-835215. India.
E.mail: swastikaganguly@bitmesra.ac.in
Dr. S. Murugesan
Department of Pharmacy, Birla Institute of Technology and Science, Pilani-333031. Rajasthan. India
E.mail: murugesan@pilani.bits-pilani.ac.in

Introduction
The term ‘in silico / computational’ is usually used to mean experimentation performed by computer and is considered as complement to in vivo and in vitro experimentation work. It is a cost effective, rapidly growing area that covers the development of techniques for using software to capture, analyze and integrate biological and medical data from many diverse sources. More specifically, it defines the use of this information in the creation of computational models or simulations that can be used to make predictions, suggest hypotheses, and ultimately provide discoveries or advances in medicine and therapeutics. Thisn i silico methods are helping us to make decisions and simulate virtually every facet of drug discovery and development, moving the pharmaceutical industry closer to engineering-based disciplines. These in silico tools help us in the progress by increasingly demonstrating their ability to deliver enrichment in identifying active molecules for the target of interest when compared with random selection or other traditional methods. In-silico drug design skills are used in nanotechnology, molecular biology, biochemistry etc. It can take part considerably in all stages of drug development from the preclinical discovery stage to late stage clinical development. In silico tools play an important role in target identification, design innovative proteins or novel drugs, in biotechnology or the pharmaceutical field.
Drug discovery is a hugely complex information handling, time consuming and inter-disciplinary interpretation exercise and there are many factors responsible for the failure of different drugs such as lack of effectiveness, side effects, poor pharmacokinetics and marketable reasons. It helps in finding the shortcuts or the rules that will point us as quickly as possible to the targets and molecules that are likely to proceed to the clinic then onto the market. This discovery results in better medicines that are iterative improvements on current medications and are valuable as they may offer benefits over existing medications in terms of potency, safety, tolerability, or convenience, but they usually do not involve the manipulation of biological targets different from those directly affected by existing medications. The drug development process is set up, particularly at the stage of clinical development, to “fail fast, fail early” in a strategy to eliminate key risks before making a expensive late-stage investment.

Objective
This special issue of International Journal of Quantitative Structure-Property Relationships (IJQSPR) focuses on “Applications of in silico molecular modeling tools in the field of Drug Discovery.” This will focus on both the development of novel approaches / methods or application of techniques, such as in silico, in-vitro, in-vivo, ex vivo screening, assay development, safety evaluation, virtual screening, lead identification, optimization and visualization methods that have a potential in aiding early phases of drug discovery, as well as therapeutics development using structure / ligand, de-novo, QSAR, HQSAR, QSPR, Homology modeling, Protein folding-based drug designing processes. We also encourage submissions that involve design, development of in silico tools, online servers for evaluation and prediction of any NME's for it's drug potential.

Recommended Topics
Topics to be discussed in this special issue include (but are not limited to) the following:
• Biological and Chemical applications
• Chemometric modeling
• Development of new descriptors and/or validation metrics
• Drug design applications
• Molecular Modeling including HTS, Homology modeling, Virtual screening and affinity profiling, Docking, pharmacophore modeling, de-novo design, ab-initio method, QSAR (2D, 3D), QSPR, Ligand based approaches, Similarity search, Combinatorial library design, Cheminformatics, Bioinformatics
• New software / program development of QSAR / QSPR, HQSAR, CoMFA, CoMSIA applications • Physicochemical properties prediction including Ro5, Ro3
• Predictive ADME and toxicology, PBPK/PD modeling
• Molecular Mechanics, Quantum Mechanics and Molecular Dynamics
• Drug Repurposing using in silico tools
• Machine learning, data mining, network analysis tools and data analysis tools

Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on "Applications of in silico molecular modeling tools in the field of Drug Discovery" on or before September 01, 2018. 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 submissions and inquiries should be directed to the attention of:
Prof. Swastika Ganguly
Department of Pharmaceutical Sciences & Technology, Birla Institute of Technology, Mesra-835215. India.
E.mail: swastikaganguly@bitmesra.ac.in
Dr. S. Murugesan
Department of Pharmacy, Birla Institute of Technology and Science, Pilani-333031. Rajasthan. India.
E.mail:murugesan@pilani.bits-pilani.ac.inn International Journal of Quantitative Structure-Property Relationships (IJQSPR)

Special Issue On: Application of Machine Learning Theories in QSAR/QSPR

Submission Due Date
10/1/2018

Guest Editors
Giuseppina Gini, DEIB, Politecnico di Milano, Italy

Introduction
Many forces drive modern QSAR/QSPR models:
- first of all the requirements of creating new chemicals with wanted properties in fields as large as drugs, cosmetics, biocides, and industrial products;
- second, the growing concern about the risks of chemicals that has produces advanced norms to regulate their use;
- third, the availability of more data in the public domain that opened the door to more advanced models;
- finally, the tremendous improving of hardware and software that allows more complex modeling, as represented in particular by the development of Artificial Intelligence methods.
About 20 years ago the Artificial Intelligence (AI) and the Toxicology Communities joined together in the so-called “Predictive Challenge”, where a few tens of molecules were given to researchers to produce a QSAR model of cancerogenicity. The results then showed that it is possible to build a QSAR model only using the chemical structure. In the following years QSAR methods started to embrace new learning methods, to incorporate more kinds of descriptors, and to move from linear to non-linear models.
More recently, a new challenge promoted by the pharm industry was launched in 2012 with thousands of chemical structures; among the many models developed a deep neural net was found to be at the core of the winning model.

Objective
Even though Artificial Intelligence methods are commonly used in modeling physical and biological properties of chemicals, there is a lack of clearly assessing their advantages or disadvantages in building QSAR/QSPR. Moreover, AI models may incorporate both symbolic and implicit knowledge representations, and extracting and organizing knowledge from such models is still challenging. This special issue will try to give answer to questions such as:

• How AI based methods work in making accurate and predictive QSAR/QSPR models?
• What kind of knowledge is represented or hidden in AI-based models?
• Which kind of chemical and biological knowledge should and could be given to AI based models?
• How and why users can accept AI-based models?
• What are the next challenges for QSAR/QSPR methods?

Recommended Topics
• Application of AI tools, including Support Vector Machines and Neural Networks, to QSAR/QSPR
• Use of ensemble methods, as Random Forests, to build QSAR/QSPR
• Learning methods for modeling: pros and cons
• Deep Neural Nets and deep learning for building QSAR/QSPR
• Learning from chemical structures
• AI methods to define and choose descriptors
• AI methods to automatically extract relevant functional subgroups to build SAR models
• AI methods to integrate SAR and QSAR
• Extracting knowledge from AI models of chemical/biological properties
• Interpreting AI models in terms of chemical and biological knowledge
• AI methods to integrate statistical results and expert knowledge
• AI tools to integrate in vivo and in vitro data
• Acceptance of AI methods in various user contexts
• Hardware and software to develop AI-based QSAR/QSPR models
• Theoretical developments of new QSARs using machine learning principles

Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on Application of machine learning theories in QSAR/QSPR on or before October 1 2018. 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 submissions and inquiries should be directed to the attention of:
Giuseppina Gini
Guest Editor
International Journal of Quantitative Structure-Property Relationships (IJQSPR)
E-mail: giuseppina.gini@polimi.it