BioXcepTion
KVIS Computational
Research Team
BioXcepTion Membership Recruitment 2021
If you have a passion for machine learning integrated with biology and chemistry, We invite you to be a part of our research team
We would like to invite all of KVIS batch 7 (grade 10) students to apply for the BioXcepTion research team. The students will receive an opportunity to collaborate with Scalable Data Systems Lab (SCADS), School of Information Science and Technology (IST), Vidyasirimedhi Institute of Science and Technology (VISTEC).
KVIS co-advisor: Dr. Thanasan Nilsu – KVIS Biology Teacher
VISTEC co-advisor: Bundit Boonyarit – Ph.D. student, VISTEC
Number of team(s) accepted: 1 team (only 2 students)
Research theme: Machine learning for enzyme engineering / biocatalysis
Keywords: Computational biology, Computational chemistry, Structural bioinformatics, Machine learning, Deep learning, Biocatalysis, Enzyme engineering
Deadline of Application Submission
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About BioXcepTion
BioXcepTion is a student research team that encourages to promoting advanced scientific and technological research with a particular emphasis on Computational Biology, Computational Chemistry, and Artificial Intelligence. The team is achieved by cooperating between Scalable Data Systems Lab (SCADS), School of Information Science and Technology (IST), Vidyasirimedhi Institute of Science and Technology (VISTEC), and Kamnoetvidya Science Academy (KVIS). It was founded in 2017 by students interested in Biological and Chemical Sciences as well as Computer Science. Our team is highly specialized in the domains of machine learning and deep learning, with the goal of resolving structural biology and structural chemistry problems.
Our research focus lies in Molecular Property & Interaction, Molecular Design & Discovery, and Molecular Representation & Learning with the goal of developing an leading to the understanding of molecular science knowledge that can be developed for innovation and industry applications. The main research employs knowledge relevant to Biochemistry & Molecular Biology, Chemistry, Pharmaceutical Sciences, and Computer Science, together with computational techniques such as Machine Learning, Deep Learning, Computational Biology, and Computational Chemistry.
1. Molecular Property & Interaction – focuses on the relationship of biomolecules structure, leading to predict molecular properties and interactions.
2. Molecular Design & Discovery – focuses on the structure of molecules and biomolecules, with the goal of discovering, designing, and understanding molecular phenomena.
3. Molecular Representation & Learning – focuses on designing and developing model architectures and feature engineering strategies that result in enhanced prediction in machine learning and deep learning processes.
MISSION
1. Promote computational science research involves in biology and chemistry
2. Promote scientific learning and discovery in biochemistry, biology, and molecular biology along with computer science
3. Promote research presentation and publication in international conferences and peer-reviewed journals
4. Promote the development of essential skills for living in the community and modern world
5. Counseling for further study in science, engineering, and technology
Our Identity
Research Focuses
1. Machine learning and deep learning model development for predicting drug response to accelerate drug discovery
2. Machine learning and deep learning model development for enzyme engineering and biocatalysis
3. Discovery of novel inhibitors for emerging infectious diseases (EIDs) and non-communicable diseases (NCDs)
4. Molecular dynamics of biological macromolecules to understand mechanisms and reactions
Basic Knowledge
Recommended Introductory Videos
What is a protein?
By: RCSB
A basic introduction to drugs, drug targets, and molecular interactions
By: Schrödinger
Machine learning roadmap
By: Daniel Bourke
The 7 steps of machine learning
By: Google Cloud Tech
Introduction to Structural Bioinformatics
Machine Learning for Molecular Chemistry
Recommended Online Courses & Resources
Dept. Computer Engineering, Chulalongkorn University
Meet our team
( Senior Advisors & Co-founders )
Jiramet Kinchakawat
KVIS#1
Dept. Bio and Brain Engineering, KAIST, South Korea
Thanakrit Wongsatit
KVIS#2
Dept. Bio and Brain Engineering, KAIST, South Korea
Anupap Chauyjaroensuk
KVIS#2
Dept. Biological Sciences, Kyoto University, Japan
( Student Researchers : 2018-2019 )
Chawapon Tritipchatsakun
KVIS#3
Dept. Bio and Brain Engineering, KAIST, South Korea
Thanis Prucksikanont
KVIS#3
Dept. Medicine Ramathibodi Hospital, Mahidol University, Thailand
( Student Researchers : 2019-2021 )
Natthakan Saeng-nil
KVIS#5
Kamnoetvidya Science Academy
Puri Virakarin
KVIS#5
Kamnoetvidya Science Academy
( Student Researchers : 2020-2021 )
Parajaree Ungudonpakdee
KVIS#5
Kamnoetvidya Science Academy
Thanasan Kumdee
KVIS#5
Kamnoetvidya Science Academy
( Student Researchers : 2020- )
Theeradon Sakpetch
KVIS#6
Kamnoetvidya Science Academy
Phandej Soisamuth
KVIS#6
Kamnoetvidya Science Academy
( Student Researchers : 2021- )
Tisorn Naphattalung
KVIS#7
Kamnoetvidya Science Academy
Surapa Panjaphakdee
KVIS#7
Kamnoetvidya Science Academy
Matin Kositchutima
KVIS#7
Kamnoetvidya Science Academy
( Research Advisors )
Pimsiri Danphitsanuparn
KVIS Teacher
Kamnoetvidya Science Academy
Thanasan Nilsu
KVIS Teacher
Kamnoetvidya Science Academy
Tanawan Leeboonngam
KVIS Teacher
Kamnoetvidya Science Academy
Sarana Nutanong
Principal Investigator
VISTEC
Our Achievements
2021–2022
Semi-Supervised Learning with Graph Neural Networks for Drug Response Prediction Leads Discovery of HIV Inhibitors
Fair: Young Scientist Competition 2022 (YSC2022), Thailand
Date: March 7, 2022
By: 1. Theeradon Sakpetch and 2. Phandej Soisamuth
Advisors: 1. Thanasan Nilsu and 2. Bundit Boonyarit
Machine Learning Enables Prediction for Enzyme Kinetics of Glycosidases Based on Molecular Descriptors and Physicochemical Properties
Fair: Young Scientist Competition 2022 (YSC2022), Thailand
Date: January 31, 2022
By: 1. Tisorn Naphattalung, 2. Surapa Panjaphakdee, and 3. Matin Kositchutima
Advisors: 1. Thanasan Nilsu and 2. Bundit Boonyarit
Deep Transfer Learning-Based Heart Failure Drug Classification on Small Datasets
Fair: 5th KVIS Invitational Science Fair (5th KVIS-ISF), Thailand
Date: January 28, 2022
By: 1. Parajaree Ungudonpakdee and 2. Thanasan Kumdee
Advisors: 1. Thanasan Nilsu and 2. Bundit Boonyarit
Application of Machine Learning Technique Assisted Prediction of Bioactivities of Ligands in Targeted Drug Discovery Process of Lung Cancer for EGFR Target
Fair: 5th KVIS Invitational Science Fair (5th KVIS-ISF), Thailand
Date: January 28, 2022
By: 1. Natthakan Saeng-nil and 2. Puri Virakarin
Advisors: 1. Thanasan Nilsu and 2. Bundit Boonyarit
EGFRNet: Transfer and Multi-Task Learning Based on Graph Convolutional Network Toward Multi-Target Drug Discovery Against Cancers for EGFR-Family Proteins
Fair: Graduation ceremony for Vidyasirimedhi Institute of Science and Technology & Kamnoetvidya Science Academy, Thailand
Date: November 16, 2021
By: 1. Natthakan Saeng-nil and 2. Puri Virakarin
Advisors: 1. Thanasan Nilsu and 2. Bundit Boonyarit
Deep Transfer Learning-Based Heart Failure Drug Classification on Small Datasets
Fair: Asia Pacific Conference of Young Scientists 2021 (APCYS 2021), Mexico
Date: September 27 – October 1, 2021
By: 1. Parajaree Ungudonpakdee and 2. Thanasan Kumdee
Advisors: 1. Thanasan Nilsu and 2. Bundit Boonyarit
An Integrated Multi-Task and Transfer Learning With Graph Convolutional Network Assisted Drug Response Prediction Leads Discovery of HIV/HCV Co-Inhibitors
Fair: International Students’ Science Fair 2021 & The 3rd Beihang International Science Fair (ISSF-BHISF 2021), China
Date: July 16 – 19, 2021
By: 1. Theeradon Sakpetch and 2. Phandej Soisamuth
Advisors: 1. Thanasan Nilsu and 2. Bundit Boonyarit
Deep Transfer Learning-Based Hit Compound Classification for Therapeutic Targets in Heart Failure Drug Discovery on Small Datasets
Fair: The Virtual Korea Science Academy of KAIST Science Fair (KSASF) 2021, South Korea
Date: June 29 – July 1, 2021
By: 1. Parajaree Ungudonpakdee and 2. Thanasan Kumdee
Advisors: 1. Thanasan Nilsu and 2. Bundit Boonyarit
An Integrated Multi-Task and Transfer Learning With Graph Convolutional Network Assisted Drug Response Prediction Leads Discovery of HIV/HCV Co-Inhibitors
Fair: The Virtual Korea Science Academy of KAIST Science Fair (KSASF) 2021, South Korea
Date: June 29 – July 1, 2021
By: 1. Theeradon Sakpetch and 2. Phandej Soisamuth
Advisors: 1. Thanasan Nilsu and 2. Bundit Boonyarit
2020–2021
EGFRNet: Transfer and Multi-Task Learning Based on Graph Convolutional Network Toward Multi-Target Drug Discovery Against Cancers for EGFR-Family Proteins
Link: https://projectboard.world/isef/project/56057
Fair: Regeneron International Science and Engineering Fair 2021 (Regeneron ISEF 2021), USA
Date: May 4 & 16 – 21, 2021
By: 1. Natthakan Saeng-nil and 2. Puri Virakarin
Advisors: 1. Thanasan Nilsu and 2. Bundit Boonyarit
Application of Machine Learning Technique Assisted Prediction of Bioactivities of Ligands in Targeted Drug Discovery Process of Lung Cancer for EGFR Target
Fair: Genius Olympiad 2021, USA (Postponed from 2020 due to COVID-19)
Date: May 15 & June 12, 2021
By: 1. Puri Virakarin and 2. Natthakan Saeng-nil
Advisors: 1. Thanasan Nilsu and 2. Bundit Boonyarit
Application of Machine Learning Technique Assisted Prediction of Bioactivities of Ligands in Targeted Drug Discovery Process of Lung Cancer for EGFR Target
Link: http://www.school.ioffe.ru/readings/2021/results2.html
Fair: The XXXI International Scientific Student Conference Sakharov Readings, Russia
Date: May 15, 2021
By: 1. Natthakan Saeng-nil and 2. Puri Virakarin
Advisors: 1. Thanasan Nilsu and 2. Bundit Boonyarit
Deep Transfer Learning-Based Hit Compound Classification for Therapeutic Targets in Heart Failure Drug Discovery on Small Datasets
Fair: The 21st Kolmogorov Readings, Russia
Date: May 3 – 6, 2021
By: 1. Parajaree Ungudonpakdee and 2. Thanasan Kumdee
Advisors: 1. Thanasan Nilsu and 2. Bundit Boonyarit
Application of Machine Learning Technique Assisted Prediction of Bioactivities of Ligands in Targeted Drug Discovery Process of Lung Cancer for EGFR Target
Fair: Young Scientist Competition 2021 (YSC2021), Thailand
Date: March 4 & 19, 2021
By: 1. Natthakan Saeng-nil and 2. Puri Virakarin
Advisors: 1. Thanasan Nilsu and 2. Bundit Boonyarit
An Integrated Multi-Task Learning and Transfer Learning-Based Approach for Structure-Property Landscapes Leads Drug Discovery for Protease Inhibitors of HIV and HCV
Fair: Young Scientist Competition 2021 (YSC2021), Thailand
Date: January 25 & 31, 2021
By: 1. Theeradon Sakpetch and 2. Phandej Soisamuth
Advisors: 1. Thanasan Nilsu and 2. Bundit Boonyarit
LigEGFR: Deep Learning-Assisted Drug Discovery for Lung Cancer
Fair: International Music, Science, Engineering Energy Fair 2020 (BUCA IMSEF 2020), Turkey
Date: December 14 – 18, 2020
By: 1. Natthakan Saeng-nil and 2. Puri Virakarin
Advisors: 1. Thanasan Nilsu and 2. Bundit Boonyarit
Deep Transfer Learning-Based Hit Compound Classification for Therapeutic Targets in Heart Failure Drug Discovery on Small Datasets
Fair: Young Scientist Competition 2021 (YSC2021), Thailand
Date: December 10 – 11, 2020
By: 1. Parajaree Ungudonpakdee and 2. Thanasan Kumdee
Advisors: 1. Thanasan Nilsu and 2. Bundit Boonyarit
LigEGFR: Deep Learning-Assisted Drug Discovery Process of Lung Cancer for EGFR Target
Fair: Japan Super Science Fair 2020 (18th JSSF), Japan
Date: November 7 – 8, 2020
By: 1. Natthakan Saeng-nil and 2. Puri Virakarin
Advisors: 1. Thanasan Nilsu and 2. Bundit Boonyarit
LigEGFR: Deep Learning-Assisted Drug Discovery for Lung Cancer
Fair: AI-JAM US Online Competition 2020, Silicon Valley, USA
Date: September 19, 2020
By: 1. Puri Virakarin and 2. Natthakan Saeng-nil
Advisors: 1. Thanasan Nilsu and 2. Bundit Boonyarit
2019–2020
Application of Machine Learning Technique Assisted Prediction of Bioactivities of Ligands in Targeted Drug Discovery Process of Lung Cancer for EGFR Target
Fair: Young Scientist Competition 2020 (YSC2020), Thailand
Date: July 27, 2020
By: 1. Natthakan Saeng-nil and 2. Puri Virakarin
Advisors: 1. Thanasan Nilsu and 2. Bundit Boonyarit
Computational Analysis of Glucagon Receptor (GCGR) and Antagonists Structure for Further Screening Anti-diabetics compounds
Fair: Canada–Wide Science Fair 2020, Canada (Cancelled due to COVID-19)
Date: May 9 – 15, 2020
By: 1. Chawapon Tritipchatsakun and 2. Thanis Prucksikanont
Advisors: 1. Pimsiri Danphitsanuparn and 2. Bundit Boonyarit
Application of Machine Learning Technique Assisted Prediction of Bioactivities of Ligands in Targeted Drug Discovery Process of Lung Cancer for EGFR Target
Fair: Young Scientist Competition 2020 (YSC2020), Thailand
Date: January 31, 2020
By: 1. Puri Virakarin and 2. Natthakan Saeng-nil
Advisors: 1. Thanasan Nilsu and 2. Bundit Boonyarit
Computational Analysis of Glucagon Receptor (GCGR) and Antagonists Structure for Further Screening Anti-diabetics compounds
Fair: 16th International Students’ Science Fair (ISSF 2020) and 3rd KVIS Invitation Science Fair, 2020, Thailand
Date: January 15 – 20, 2020
By: 1. Chawapon Tritipchatsakun and 2. Thanis Prucksikanont
Advisors: 1. Pimsiri Danphitsanuparn and 2. Bundit Boonyarit
Computational Analysis of Glucagon Receptor (GCGR) and Antagonists Structure for Further Screening Anti-diabetics compounds
Fair: Graduation ceremony for Vidyasirimedhi Institute of Science and Technology & Kamnoetvidya Science Academy, Thailand
Date: November 11, 2019
By: 1. Chawapon Tritipchatsakun and 2. Thanis Prucksikanont
Advisors: 1. Pimsiri Danphitsanuparn and 2. Bundit Boonyarit
2018–2019
Studies on structure of glucagon receptor (GCGR) related to type 2 diabetes pathogenesis by computational biophysics methods
Fair: Singapore International Science Challenge – International Students’ Science Fair 2019 (SISC-ISSF 2019), Singapore
Date: March 17 – 22, 2019
By: 1. Chawapon Tritipchatsakun and 2. Thanis Prucksikanont
Advisors: 1. Pimsiri Danphitsanuparn and 2. Bundit Boonyarit
Our Publication
(1) Puri Virakarin, Natthakan Saengnil, Bundit Boonyarit, Jiramet Kinchagawat, Rattasat Laotaew, Treephop Saeteng, Thanasan Nilsu, Naravut Suvannang, Thanyada Rungrotmongkol, and Sarana Nutanong. “LigEGFR: Spatial graph embedding and molecular descriptors assisted bioactivity prediction of ligand molecules for epidermal growth factor receptor on a cell line-based dataset.” bioRxiv (2020).
DOI: https://doi.org/10.1101/2020.12.24.423424
Website: http://ligegfr.vistec.ist/
Contact
Bundit Boonyarit
Email: [email protected]
Email: [email protected]