Submissions

Login or Register to make a submission.

Submission Preparation Checklist

As part of the submission process, authors are required to check off their submission's compliance with all of the following items, and submissions may be returned to authors that do not adhere to these guidelines.
  • The submission has not been previously published, nor is it before another journal for consideration (or an explanation has been provided in Comments to the Editor).
  • The submission file is in PDF which must have been generated via Microsoft Word or Latex. Latex is preferred especially for submissions under Mathematical Section
  • Where available, DOI for the references must be provided.
  • All illustrations, figures, and tables must be placed within the text at the appropriate points, rather than at the end.
  • You must attach a list of five potential reviewers. This information should include the name and title, affiliation, and email address.

Author Guidelines

The E-mail address and affiliation of all authors must be provided.
The manuscript must have been spell-checked and grammar-checked.
All references mentioned in the Reference List must be cited in the text, and vice versa.
The article must follow the JNSPS reference format. See published articles.
Permission must have been obtained for use of copyrighted material from other sources (including the internet).
A list of five potential reviewers must be provided.

Special Issue "Machine Learning Computational Methods"

This special issue aims at hybridizing and comparing the potentials of QSAR and machine learning techniques in material modeling and drug design for specific applications.  The following topics fall within the scope of this special issue

  • Investigating the anticancer potentials of probable compounds using QSAR and machine learning computational methods
  • Hybridization of machine learning techniques with population-based optimization algorithm for enhancing materials’ properties during drug design
  • Performance comparison of QSAR and machine learning computational methods for material modeling
  • Molecular docking of the lead compounds with receptors’ active sites
  • Pharmacokinetics studies of the lead compounds
  • Molecular dynamic simulations of the lead compounds

Privacy Statement

The names and email addresses entered in this journal site will be used exclusively for the stated purposes of this journal and will not be made available for any other purpose or to any other party.