Hi, I'm Rana A. Barghout,

About Me

Profile Picture

Hi, I’m Rana Barghout, a PhD student at the University of Toronto and Vector Institute, working under the supervision of Krishna Mahadevan (Laboratory for Metabolic Systems Engineering – lmse.utoronto.ca) and Benjamin Sanchez-Lengeling (Chemical Cognition Lab – www.chemicalcognition.org).

My research focuses on predicting compound-protein interaction properties and integrating them into genome-scale metabolic models. I also develop constraint-based modeling optimization frameworks for metabolic cellular systems and explore modeling these systems using graph neural networks.

Research Interests

  • Molecular Modeling
  • Cell systems
  • Personalized Medicine
  • Drug Discovery
  • Deep Learning
  • Graph Learning
  • Neural Network-based Linear Programming

Outside of Research

When I’m not in the lab, I enjoy:

  • CrossFit and trying to get faster at running
  • Gardening
  • Cooking

What I’m Currently Reading

  • Lifespan - Why We Age and Why We Don’t Have To by David Sinclair
  • Life of Pi by Yann Martel

Papers and Talks

A list of the published papers and conference talks that I have given.
For a more up to date list, check out my Google Scholar page.

Advances in generative modeling methods and datasets to design novel enzymes for renewable chemicals and fuels

Review of state-of-the-art generative models and datasets for enzyme design in renewable chemical and fuel production.
2023

RA Barghout, Z Xu, S Betala, R Mahadevan

Link to Paper

KinMod database: a tool for investigating metabolic regulation

A database resource for exploring kinetic models and metabolic regulation mechanisms.
2022

K Haddadi RA Barghout, R Mahadevan

Link to Paper

CPI-Pred: A deep learning framework for predicting functional parameters of compound-protein interactions

Deep learning approach to predict kinetic parameters from compound-protein interactions.
2025

RA Barghout, Z Xu, J Wu, D Garg, YS Song, R Mahadevan

Link to Paper

Graph Data Modeling: Molecules, Proteins, & Chemical Processes

Explores graph-based representations for modeling molecular, protein, and chemical process data.
2025

JM Barraza-Chavez RA Barghout, R Almada-Monter, A Jinich, R Mahadevan, B Sanchez-Lengeling

Link to Paper

Enhancing Compound-Protein Interaction Prediction with Confidence Assessment

Introduces confidence estimation in CPI prediction models for improved reliability.
2024

RA Barghout, Z Xu, LC Serrano, R Mahadevan

Link to Paper

Deep Learning-Based Protein Sequence-to-Function Prediction Framework

Sequence-to-function deep learning model for predicting protein functional properties.
2023

Z Xu RA Barghout, R Mahadevan

Link to Paper

Bridging Sequence and Kinetics: Utilizing Multi-scale Representations for Genome-Scale Metabolic Models

Presents a framework linking molecular level kinetics with genome-scale metabolism for GEMs.
2025

RA Barghout, LC Serrano, Z Xu, BM Sanchez, R Mahadevan

Link to Paper

Education & Experience

  • Sept 2021 – Present
    University of Toronto & Vector Institute

    PhD Candidate, Chemical Engineering & Applied Chemistry
    Supervisors: Krishna Mahadevan & Benjamin Sanchez-Lengeling
    Focus: Prediction of compound-protein interaction properties, integration into genome-scale metabolic models, and graph neural network-based optimization frameworks.

    Deep Learning Computational Biology Genome-scale Modeling Graph Learning Metabolic Modeling Constraint-Based Optimization
  • Sept 2016 – May 2021
    University of British Columbia

    BASc in Chemical and Biological Engineering
    Dean’s Honor List (3 years)
    Undergraduate thesis: Integrating Machine Learning with Kinetic Models

    Machine Learning LSTM Pyomo SciPy
  • Sept 2018 – April 2019
    Chemical Engineering Support (Co-op), BC Research Inc.

    Optimized meso-scale experimental workflows; conducted PIV fluid dynamics analysis; delivered training on measurement systems.

    Process Optimization PIV Analysis
  • Jan 2021 – Present
    Teaching Assistant, UofT & UBC

    Led tutorials and graded assignments for courses including CHE204, CHE205, CHE305, CHE222, CHE322, CHE499, CHBE221.

    MATLAB Python Process Control
  • Oct 2020 – April 2021
    Laboratory Assistant, Dr. Louise Creagh Labs, UBC

    Prepared cell culture media and maintained fed-batch systems for Pichia pastoris. Revised instructional materials.

    Lab Techniques Bioreactors

Skills

Python
C++
Java
HTML
MATLAB
Git
Bash
Slurm
PyTorch
NumPy
Pandas
Weights & Biases (wandb)
RDKit
HuggingFace
AlphaFold