Jennifer HY Lin

Cytochrome P450 and small drug molecules
A preliminary look into any maximum common substructures present in CYP3A4 and 2D6 inhibitors using data from the Flockhart table of drug interactions
VS Code

Boosted trees - machine learning in drug discovery series 2.3.1
Using AdaBoost, XGBoost and Scikit-mol on ChEMBL data
VS Code

Random forest classifier - machine learning in drug discovery series 2.2.1
A random forest classifier model that used a different approach for imbalanced drug discovery data. First contribution to an open-source library, chembl_downloader, also occurred while working on this work.
VS Code

Random forest - machine learning in drug discovery series 2.2
A follow-up project on decision tree series using ChEMBL data - model building using scikit-learn with feature sections on imbalanced data, feature importances and hyperparameter tuning.
VS Code

Decision tree - machine learning in drug discovery series 2.1.1, series 2.1.2 & series 2.1.3
Application of decision tree on small molecule drug discovery data from ChEMBL database targeting acetylcholinesterase.
VS Code

Molecular visualisation (Molviz) web application - Shiny for Python web application framework , interactive data table
Using Shiny for Python web application framework with interactive data table.
VS Code

Working with scaffolds in small molecules
Manipulating SMILES strings using RDKit and Datamol.
VS Code & RStudio IDE

Shinylive app in Python - data preparation using Polars dataframe library, embedding app in Quarto document, using pyodide.http to import csv files
Embedding app in Quarto document (web page) for compounds in COVID-19 clinical trials.
RStudio IDE

Shiny app in R
A Shiny app was built based on data from ChEMBL database showing differences in physicochemical properties for small molecules in different max phases.
RStudio IDE

Pills dataset - part 1, part 2 & part 3
Polars & Pandas dataframe libraries, web scraping, text cleaning via Polars, with Rust used for visualization via Plotly in Jupyter-Evcxr on pills dataset from US National Library of Medicine (Python & Rust).
Jupyter Lab & RStudio IDE

Small molecules in ChEMBL database 1 - Parquet file in Polars dataframe library (older posts pending future updates)
Upcoming updated versions from mid-Oct onwards - Polars dataframe library (with Python binding), scikit-learn & machine learning used for ChEMBL database to predict max phases of small molecules (Python).
Jupyter Lab, RStudio IDE or VS Code

Molecular similarities in selected COVID-19 antivirals
Cheminformatics project on molecular similarities between selected COVID-19 antivirals by using RDKit (Python).
Jupyter notebook in Anaconda & RStudio IDE

Registered pharmacist
Christchurch hospital pharmacy from 2019 to 2020 and community pharmacy from 2021 to 2022 (Canterbury, New Zealand), hospital pharmacy from 2012 to 2019 (NSW, Australia), hospital and community pharmacies from 2007 to 2012 (Canterbury, New Zealand)

Doctor of Philosophy
University of Sydney - thesis title: The use of computer-aided drug design in small molecule drug discovery
New South Wales, Australia

Master of Philosophy
University of Sydney - thesis title: Design, synthesis and testing of novel anti-cancer agents targeting secretary pathway calcium ATPase
New South Wales, Australia

Bachelor of Pharmacy
University of Otago
Otago, New Zealand

Discovery of 2',6-Bis(4-hydroxybenzyl)-2-acetylcyclohexanone, a Novel FtsZ Inhibitor
By Lin H-Y J, Battaje RR, Tan J, Doddareddy M, Dhaked HPS, Srivastava S, Hawkins BA, Al-Shdifat LMH, Hibbs DE, Panda D, Groundwater PW.
Molecules. 2022; 27(20), 6993.

A novel class of thiosemicarbazones show multi-functional activity for the treatment of Alzheimer's disease
By Palanimuthu D, Poon R, Sahni S, Anjum R, Hibbs D, Lin J H-Y, Bernhardt PV, Kalinowski DS, Richardson DR.
Eur J Med Chem. 2017; 139: 612-632.

Identification of agents targeting FtsZ assembly
By Panda D, Bhattacharya D, Gao QH, Oza PM, Lin J H-Y, Hawkins B, Hibbs DE, Groundwater PW
Future Med Chem. 2016; 8(10):1111-32.

Identification of dual PPARα/γ agonists and their effects on lipid metabolism
By Gao Q, Hanh J, Váradi L, Cairns R, Sjöström H, Liao VW, Wood P, Balaban S, Ong JA, Lin J H-Y, Lai F, Hoy AJ, Grewal T, Groundwater PW, Hibbs DE
Bioorg Med Chem. 2015; 23(24):7676-84.