Bundit Boonyarit

Bundit Boonyarit
Vidyasirimedhi Institute of Science and Technology · School of Information Science and Technology

Master of Science

About

4
Publications
9,880
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
4
Citations
Citations since 2016
4 Research Items
4 Citations
201620172018201920202021202201234
201620172018201920202021202201234
201620172018201920202021202201234
201620172018201920202021202201234
Introduction
I am a current Ph.D. student in Information Science and Technology at Scalable Data Systems Lab (SCADS), VISTEC, Thailand. My research interests focus lies in using computational science and machine learning techniques to understanding the molecular structure and function. I also encourage the establishment and enhancement of model architectures and algorithms for applications across life sciences and chemical sciences driven by machine learning and computational biology & chemistry techniques.
Additional affiliations
March 2014 - May 2015
National Center for Genetic Engineering and Biotechnology (BIOTEC)
Position
  • Student Research Trainee
Description
  • Mentor: Dr. Sissades Tongsima, Head of Biostatistics and Informatics Laboratory (Now, National Biobank of Thailand)
June 2011 - April 2012
Prince of Songkla University
Position
  • Undergraduate Research Trainee
Description
  • Mentor: Prof. Dr. Soottawat Benjakul
Education
August 2018 - May 2022
Vidyasirimedhi Institute of Science and Technology
Field of study
  • Information Science and Technology
June 2015 - May 2018
Kasetsart University
Field of study
  • Biochemistry
June 2011 - May 2015
Prince of Songkla University
Field of study
  • Chemistry

Publications

Publications (4)
Preprint
Full-text available
A bstract Motivation Lung cancer is a chronic non-communicable disease and is the cancer with the world’s highest incidence in the 21 st century. One of the leading mechanisms underlying the development of lung cancer in nonsmokers is an amplification of the epidermal growth factor receptor (EGFR) gene. However, laboratories employing conventional...
Presentation
Full-text available
This is Vittamin online meeting EP.1 with tutorial (Thai version) Date: March 22, 2020, 7:00 - 9.30 PM Lecture: Part 1: https://youtu.be/sOc7mqKamTQ Part 2: https://youtu.be/ypzd9TGjgJE Tutorial Github: https://github.com/manbaritone/Vittamin
Data
SARS-CoV-2 Genomic Organization in AI extension file
Article
An abnormal activation of human epidermal growth factor receptor (HER) 2 has been found to associate with several types of human cancer, and thus the protein is a prominent target for cancer therapy. Although several small chemical molecules targeting the tyrosine kinase (TK) of HER family have been identified, the development of a new class of inh...

Questions

Questions (3)
Question
I have complex (ligand + protein) from docking result and I need to run MD with amber, but I'm not sure in procedure. How can I start to use amber or Do you have some tutorial in ligand-protein MD, Please suggest to me.
Thank
Question
I would like to generate 2D protein-ligand interaction after docking procedure. I have problem in software (Ligplot) because software can't identify peptide ligand. I would like to use Schrodinger software or MOE software but I don't have commercial software and license. Do you have any ideas for solve this problem?
Question
I want to add missing atom from pdb file, among 8000 files using command line and set n-terminal = NH2, c-terminal = COOH. What's software that help me?
However, I using pdb2gmx from GROMACS, but I have a problem for set termini (-ter) because I can't set -ter = NH2 and COOH in command line when using loop iteration. Have you any ideas for solve this problem?
Thank you 

Network

Cited By

Projects

Projects (2)
Project
This project aims to develop a new method by using deep learning for the QSAR model with low data drug discovery.
Project
This project aims to develop a model for predicting bioactivity (e.g., IC50, Kd, Ki) for ligands against EGFR and Protein Tyrosine Kinase by machine learning with a large-scale and non-redundant dataset.