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00ABOUT · ACADEMIC PROFILE
AI × BIOMOLECULAR SYSTEMS × BIOMEDICINE
RESEARCHERBundit Boonyarit//Ph.D. Candidate
Connecting molecular network.Creating possibilities.
Bundit Boonyarit is a Ph.D. candidate in Information Science and Technology at the Vidyasirimedhi Institute of Science and Technology (VISTEC), Thailand. He holds a B.Sc. in Chemistry from Prince of Songkla University and an M.S. in Biochemistry from Kasetsart University. His interdisciplinary research bridges Biochemistry, Molecular Biology, Chemistry, Pharmaceutical Sciences, and Computer Science. He focuses on leveraging advanced artificial intelligence and computational techniques to develop novel computational frameworks and machine learning/deep learning models that drive innovations and discoveries in biomolecular science and biomedicine.
00.1
Interdisciplinary Convergence
Bridging Biochemistry, Molecular Biology, Chemistry, and Machine Learning.
00.2
Translational Impact
Novel ML/DL frameworks accelerating drug discovery and precision oncology.
FOCUS DOMAINS //
Molecular RepresentationBiomolecular SystemsBiomolecular Structures & FunctionsDrug Discovery & DesignPrecision Oncology
IDPORTRAIT · AFFILIATION
VISTEC · Thailand
REF // 00.PORTRAIT
AffiliationLAB.NODE // 01
BAID Research Team
School of Information Science and Technology (IST), VISTEC
SCHOLARLY PROFILES & NETWORKS6 CHANNELS
Core Mission & Paradigm
INTEGRATING CUTTING-EDGE ARTIFICIAL INTELLIGENCE WITH BIOMOLECULAR SCIENCE TO TRANSFORMDRUG DISCOVERYANDCELLULAR UNDERSTANDING.
01 —— CORE RESEARCH PILLARS
Bridging Biomolecules and Machine Intelligence
The research focuses on discovering, designing, and developing computational frameworks and ML/DL models by integrating cutting-edge AI and computational approaches, striding forward in advancing both fundamental understanding and translational applications across biomolecular science and biomedicine.
01
REPRESENT & INTERPRET
GRAPH AI · EMBEDDINGPILLAR // 01
Multi-omics DL framework & Molecular representations for bioactivity prediction
Molecular Representation Learning & Computations
Designs and develops computational frameworks and models to represent and interpret complex molecular and biological data.
Core Pillar 01
Graph Convolutional Networks (GCN)
Graph Attention Networks (GAT)
Spatial Graph Embeddings
Transfer & Multi-task Learning
Self-Supervised Pretraining
02
DISCOVER & DESIGN
MULTI-OMICS · HIT DESIGNPILLAR // 02
Anticancer discovery & Computational therapeutic design
Molecular Discovery & Design
Discovers and designs novel therapeutic and biological molecules to specific biological activities and targets.
Core Pillar 02
Synergistic Cancer Drug Combinations
Kinase Inhibitor Discovery (EGFR/HER2)
Multi-omics & Proteomics Integration
High-Throughput Hit Compound Classification
03
UNDERSTAND & SIMULATE
ATOMISTIC MD · DOCKINGPILLAR // 03
Structural bioinformatics for molecular phenomena investigation
Molecular Modeling & Simulation
Simulates and models the structure, dynamics, and interactions of biomolecules to uncover molecular mechanisms and functions.
Core Pillar 03
Molecular Dynamics (MD) Simulations
Structure-Based Virtual Screening (SBVS)
Protein-Ligand Docking
Enzyme Kinetics & Catalysis
02 —— METHODOLOGY & APPLICATIONS
Computational Frameworks & Scientific Frontiers
Core scientific disciplines, computational modeling tools, and high-impact applications developed across our projects.
DISCOVERY DOMAINS
01
MOLECULAR GNN
Deep Learning#01
Graph Neural Networks
Molecular graph representation for deep learning overcoming extreme data scarcity in therapeutic target binding.
Domain 01
Deep Learning
02
MULTI-OMICS AI
Drug Synergy#02
Precision Oncology
Deep learning predicting synergistic anticancer drug combinations from proteomics profiles.
Domain 02
Drug Synergy
03
BIOMOLECULAR MD
Atomistic Sim#03
Molecular Docking & Simulations
Atomistic simulations and molecular docking of structural biomolecules exploring dynamics, interactions, and functions.
Domain 03
Atomistic Sim
04
KINASE SBVS
Hit Discovery#04
Kinase Inhibitor Design
High-throughput virtual screening & structure-based classification for protein kinase therapeutic targets.
Domain 04
Hit Discovery
05
BIOCATALYSIS
Kinetics & AI#05
Enzyme Engineering
Enzyme engineering with physics and data-driven based approaches for thermostability and kinetics.
Domain 05
Kinetics & AI
03 —— KEY PUBLICATIONS
Recent Landmark Research Papers
Highlighting our peer-reviewed breakthroughs across molecular graph attention, synergistic drug design, and atomistic protein dynamics.
01LANDMARK PAPERS 01–02 / 04
SLIDE 01 // 02
02GOOGLE SCHOLAR PROFILE · LIVE METRICS
OPEN SCHOLARSYNCED INDEXID // CpkGgb8AAAAJ
Cited by & Publications by Year
34 CITATIONSH-INDEX 4
CITED BYSCHOLAR METRICS
| Metric | All | Since 2021 |
|---|---|---|
| Citations | 34 | 34 |
| h-index | 4 | 4 |
| i10-index | 0 | 0 |
CITATIONS PER YEAR2021–2026
1680
202120222023202420252026
INDEXED
PUBLICATIONS BY YEAR (7)
SOURCE // GOOGLE SCHOLAR CITATIONSExplore Full Scholar Profile
04 —— COLLABORATIVE TEAMS
Transformative Research Groups
Interdisciplinary research groups and student talent incubators driving biomolecular AI discoveries.
01
RESEARCH GROUP · VISTEC
BAID Research Team
Biomolecular Artificial Intelligence & Digital Biochemistry
The Biomolecular Artificial Intelligence & Digital Biochemistry (BAID) team is an interdisciplinary research group committed to transform the dynamic landscapes of biomolecular science and biomedicine by discovering, designing, and developing advanced AI models and computational frameworks, striding toward real-world impact and accelerated scientific discovery.
Research Group
Biomolecular AI
Digital Biochemistry
VISTEC
02
STUDENT MENTORSHIP TEAM
BioXcepTion Student Team (2018 – 2023)
High School Student Research & Mentorship
BioXcepTion is a high school student research team with a particular emphasis on Computational Biology, Computational Chemistry, and Artificial Intelligence.
Mentorship Team
Biology Olympiad
Talent Incubator
STEM Outreach
Success isn't born from brilliance,
it’s built through implementation.