Ojochenemi A. Enejoh ^1* Chinelo Henrietta Okonkwo ² Hector Nortey ³ Ainembabazi Moses ^4,5 Olalekan A. Kemiki ⁶ Florence N. Mbaoji ⁷ Abdulrazak S. Yusuf ⁸ Olaitan I. Awe ^9*

• ¹ Genomics and Bioinformatics Department, National Biotechnology Research and Development Agency, Abuja, Nigeria
• ² Department of Pharmacy, National Hospital, Abuja, Nigeria
• ³ Department of Clinical Pathology, Noguchi Memorial Institute for Medical Research, College of Health Science, University of Ghana, Legon, Accra, Ghana
• ⁴ African Centers of Excellence in Bioinformatics, Department of Immunology and Microbiology, Makerere University, Makerere, Uganda
• ⁵ Infectious Disease Institute (IDI), Makerere University, Entebbe, Uganda
• ⁶ Molecular and Tissue Culture Laboratory, Babcock University, Ilisan-remo, Ogun State, Nigeria
• ⁷ Department of Pharmacology and Toxicology, Faculty of Pharmaceutical Sciences, University of Nigeria, Nsukka, Nigeria, Nigeria
• ⁸ Department of Biochemistry, Faculty of Basic Health Science, Bayero University, Kano, Nigeria
• ⁹ African Society for Bioinformatics and Computational Biology, Cape Town, South Africa

Treatment of type 2 diabetes (T2D) remains a significant challenge because of its multifactorial nature and complex metabolic pathways. There is growing interest in finding new therapeutic targets that could lead to safer and more effective treatment options. Takeda G protein-coupled receptor 5 (TGR5) is a promising antidiabetic target that plays a key role in metabolic regulation, especially in glucose homeostasis and energy expenditure. TGR5 agonists are attractive candidates for T2D therapy because of their ability to improve glycemic control. This study used machine learning-based models (ML), molecular docking (MD), and molecular dynamics simulations (MDS) to explore novel small molecules as potential TGR5 agonists.Bioactivity data for known TGR5 agonists were obtained from the ChEMBL database. The dataset was cleaned and molecular descriptors based on Lipinski's rule of five were selected as input features for the ML model, which was built using the Random Forest algorithm. The optimized ML model was used to screen the COCONUT database and predict potential TGR5 agonists based on their molecular features. 6,656 compounds predicted from the COCONUT database were docked within the active site of TGR5 to calculate their binding energies. The four top-scoring compounds with the lowest binding energies were selected and their activities were compared to those of the co-crystallized ligand. A 100 ns MDS was used to assess the binding stability of the compounds to TGR5.Molecular docking results showed that the lead compounds had a stronger affinity for TGR5 than the co-crystallized ligand. MDS revealed that the lead compounds were stable within the TGR5 binding pocket.The combination of ML, MD, and MDS provides a powerful approach for predicting new TGR5 agonists that can be optimised for T2D treatment.