About Myself

Navigating the Future of Molecular Design.

My journey is driven by a single goal: to turn the impossible into possible in drug discovery. By bridging the gap between the accuracy of Quantum Chemistry and the efficiency of Machine Learning, I am developing next-generation platforms like AVENGERS and SAGE. These tools are not just for observation, but for creating new therapeutic possibilities that were previously unreachable.

The future is here,
and I am engineering its distribution.

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    • Eng.D

    Bioinformatics

    23

    Total Publications

    53

    Total Awards

    Research Expertise

    Generative Biology (Domain Specialist)

    Computer-Aided Drug and Protein Design (Domain Specialist)

    Generative AI and Machine Learning (Expert)

    Quantum Chemistry and Molecular Simulation (Expert)

    Quantum Computing and Quantum Machine Learning (Proficient)

    Publications

    Great works are performed not by strength but persistence.


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    • Drug Discovery and Protein Design


    First Author

    Development of Drug-Induced Gene Expression Ranking Analysis (DIGERA) and Its Application to Virtual Screening for Poly (ADP-Ribose) Polymerase 1 Inhibitor

    Generative Molecule Design

    Drug Discovery

    IJMS 26.1 (2024): 224

    First Author

    Extension of scoring-assisted generative exploration for ionic liquids (SAGE-IL) and its application to ionic liquid design for CO2 capture

    Generative Molecule Design

    Drug Discovery

    Materials Today Advances 24 (2024): 100529

    Non-first Author

    Development and Evaluation of Indole-Based Phospholipase D Inhibitors for Lung Cancer Immunotherapy

    Molecular Modeling

    Drug Discovery

    Journal of Medicinal Chemistry (2024)

    Non-first Author

    Inhibition of IRP2-dependent reprogramming of iron metabolism suppresses tumor growth in colorectal cancer

    Molecular Modeling

    Drug Discovery

    Cell Communication and Signaling 22.1 (2024): 412

    Non-first Author

    Discovery of Protease-activated receptor 2 antagonists derived from phenylalanine for the treatment of breast cancer

    Molecular Modeling

    Drug Discovery

    Bioorganic Chemistry (2024): 107496

    First Author

    Development of Scoring-Assisted Generative Exploration (SAGE) and Its Application to Enzyme Inhibitor Design

    Generative Molecule Design

    Drug Discovery

    Pharm. Res. Recent Adv. Trends, 5 (2024), 145-179

    First Author

    Development of scoring-assisted generative exploration (SAGE) and its application to dual inhibitor design for acetylcholinesterase and monoamine oxidase B

    Generative Molecule Design

    Drug Discovery

    Journal of Cheminformatics 16.1 (2024): 59

    First Author

    Identification of novel natural product inhibitors against matrix metalloproteinase 9 using quantum mechanical fragment molecular orbital-based virtual screening methods

    Quantum Mechanics

    Drug Discovery

    IJMS 23, no. 8 (2022): 4438

    Non-first Author

    Hot spot analysis of YAP-TEAD protein-protein interaction using the fragment molecular orbital method and its application for inhibitor discovery

    Quantum Mechanics

    Drug Discovery

    Cancers 13, no. 16 (2021): 4246

    First Author

    Hot spot profiles of SARS-CoV-2 and human ACE2 receptor protein protein interaction obtained by density functional tight binding fragment molecular orbital method

    Quantum Mechanics

    Drug Discovery

    Scientific reports 10, no. 1 (2020): 1-8

    First Author

    Investigation of protein-protein interactions and hot spot region between PD-1 and PD-L1 by fragment molecular orbital method

    Quantum Mechanics

    Drug Discovery

    Scientific reports 9, no. 1 (2019): 1-11

    First Author

    Investigation of hot spot region in XIAP inhibitor binding site by fragment molecular orbital method

    Quantum Mechanics

    Drug Discovery

    CSBJ 17 (2019): 1217-1225

    Non-first Author

    Application of the fragment molecular orbital method to discover novel natural products for prion disease

    Quantum Mechanics

    Drug Discovery

    Scientific reports 8, no. 1 (2018): 13063

    First Author

    Intracellular loop in the brain isoforms of Anoctamin 2 channels regulates calcium-dependent activation

    Quantum Mechanics

    Drug Discovery

    Exp Neurobiol 2023; 32(3): 133-146

    First Author

    Prediction of polyreactive and nonspecific single-chain fragment variables through structural biochemical features and protein language-based descriptors

    Machine Learning

    Protein Design

    BMC Bioinformatics, 23, 520 (2022)

    First Author

    Evaluation of protein descriptors in computer-aided rational protein engineering tasks and its application in property prediction in SARS-CoV-2 spike glycoprotein

    Machine Learning

    Protein Design

    CSBJ 20 (2022): 788-798

    First Author

    Quantum computational study of chloride attack on chloromethane for chemical accuracy and quantum noise effects with UCCSD and k-UpCCGSD ansatzes

    Quantum Mechanics

    Quantum Computing

    Scientific reports 12, no. 1 (2022): 1-10

    First Author

    Fragment molecular orbital-based variational quantum eigensolver for quantum chemistry in the age of quantum computing

    Quantum Mechanics

    Quantum Computing

    Scientific reports 14, no. 1 (2024): 1-13


    Quantum Chemistry and Machine Learning

    Professional Skills

    Persistence is the key to solve most mysteries.

    Accurate Vitual Engines and Effective Ranking Systems (AVENGERS)





    Virtual Screening is to reduce large virtual compound libraries to a manageable subset in order to design or develop small molecules with properties valuable for scientific, industrial, or medical applications.

    Quantum Mechanics based molecular orbital calculation methods provide an accurate description of molecular phenomena. Scoring functions with quantum mechanics can speed up drug discovery process by quantifying protein-ligand interactions and selecting new compounds to screen.


    Fast is fine,
    but accuracy is everything.


  • Drug Discovery

    • Computer-Aided Rational Protein Engineering Toolkit (CARPET)




    Protein Engineering is a progressive process to design or develop proteins with properties valuable for scientific, industrial, or medical applications.

    Machine Learning can utilize the information of unimproved sequences to differentiate protein properties. Prediction models with machine learning can speed up the evolution and optimization of protein properties by evaluating and selecting new variants to screen.


    Many will start fast,
    few will finish strong.


  • Protein Design


    • services

    I'll lay the CARPET for you to reach your goals with my AVENGERS.


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    • Virtual Screening

    Drug Design

    It provides a set of compounds by analyzing and selecting potential hit molecules from large databases or even scratch.

    Lead Optimization

    Drug Design

    It optimizes molecules from the first hits with/without structural similarity by quantitatively analyzing structure-activity relationship.

    ADME-T Prediction

    Drug Design

    It predicts ADME-T characteristics of small molecules and suggests new derivatives with better ADME-T profiles than original.

    Virtual Screening

    Protein Design

    It provides a set of variants for optimizing enzymatic activity, binding affinity, selectivity, protein expression, thermal stability, proteolytic stability, aggregation, and so on.

    Protein Optimization

    Protein Design

    It optimizes proteins for better activity and thermal stability by quantitative structure-activity relationship and machine learning.

    Property Prediction

    Protein Design

    It predicts many properties of the target proteins such as proteolytic cleavage site, immunogenic site, aggregation site, nonspecificity of single-chain fragment variables, isoelectric points, and so on.