Artificial intelligence-driven prediction and validation of blood-brain barrier permeability and absorption, distribution, metabolism, excretion profiles in natural product research laboratory compounds.

Journal: BioMedicine
Published Date: Dec 1, 2024

Abstract

INTRODUCTION: Our previous research demonstrated that a large language model (LLM) based on the transformer architecture, specifically the MegaMolBART encoder with an XGBoost classifier, effectively predicts the blood-brain barrier (BBB) permeability of compounds. However, the permeability coefficients of compounds that can traverse this barrier remain unclear. Additionally, the absorption, distribution, metabolism, and excretion (ADME) characteristics of substances obtained from the Natural Product Research Laboratory (NPRL) at China Medical University Hospital (CMUH) have not yet been determined.

Authors

  • Jai-Sing Yang
    Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan.
  • Eddie Tc Huang
    NVIDIA AI Technology Center, NVIDIA Corporation, USA.
  • Ken Yk Liao
    NVIDIA AI Technology Center, NVIDIA Corporation, USA.
  • Da-Tian Bau
    Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan.
  • Shih-Chang Tsai
    Department of Biological Science and Technology, China Medical University, Taichung, Taiwan.
  • Chao-Jung Chen
    Proteomics Core Laboratory, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan.
  • Kuan-Wen Chen
    Genetics Generation Advancement Corporation, Molecular Science and Digital Innovation Center, Taipei, Taiwan.
  • Ting-Yuan Liu
    Million-Person Precision Medicine Initiative, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan.
  • Yu-Jen Chiu
    Division of Plastic and Reconstructive Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, Taiwan.
  • Fuu-Jen Tsai
    Million-Person Precision Medicine Initiative, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan.

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