Hi, I'm Rana A. Barghout,

About Me

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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) and Benjamin Sanchez-Lengeling (Chemical Cognition Lab).

My research focuses on building systems that predict the functional parameters of compound-protein interactions, deploying them within genome-scale, constraint-based metabolic models, and probing whether these models are learning real biological signal, from individual residues all the way up to whole cellular systems. I’m also interested in graph learning, and have applied graph neural networks to problems ranging from molecules and protein-protein interactions to genome-scale metabolic 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 front of a computer, I enjoy road cycling, weight lifting, and cooking, and I’m attempting to get faster at running.

What I’m Currently Reading

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Publications

Papers, preprints, and conference talks, newest first.
For a more up to date list, check out my Google Scholar page.

Summary figure for Do protein representations encode variant function? Diagnosing residue-level protein attributions via embedding-space gradients

Do protein representations encode variant function? Diagnosing residue-level protein attributions via embedding-space gradients

Introduces IGLOO, a framework that scores residue-level attributions from protein language model fitness predictors against synthetic landscapes with known per-residue importance.
Oct 2026

RA Barghout, S Wu, W Chau, JM Barraza-Chavez, P Driza, R Mahadevan, B Sanchez-Lengeling

Link to Paper Code
Protein Language Models Interpretability Protein Engineering Preprint
Summary figure for kinGEMs: A Robust and Scalable Framework for Resource-Constraint Models through Stochastic Tuning of Deep Learning-Predicted Kinetic Parameters

kinGEMs: A Robust and Scalable Framework for Resource-Constraint Models through Stochastic Tuning of Deep Learning-Predicted Kinetic Parameters

Integrates deep learning-predicted kcat values from CPI-Pred into enzyme-constrained genome-scale models for metabolic optimization.
Mar 2026

RA Barghout, L Chiñas Serrano, B Sanchez-Lengeling, R Mahadevan

Link to Paper
Genome-Scale Modeling Enzyme Kinetics Deep Learning Preprint
Summary figure for Machine Learning for Sequence-to-Function Approaches

Machine Learning for Sequence-to-Function Approaches

Book chapter reviewing machine learning models, benchmarks, and datasets for predicting protein function from sequence.
Jan 2026

RA Barghout, M Kirby, A Zheng, L Chinas, M Mohammadi, Z Xu, B Sanchez-Lengeling, R Mahadevan

Link to Paper
Protein Language Models Sequence-to-Function Book Chapter
Summary figure for Graph Data Modeling: Molecules, Proteins, & Chemical Processes

Graph Data Modeling: Molecules, Proteins, & Chemical Processes

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

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

Link to Paper
Graph Learning GNN Molecular Modeling Proteins Book
Summary figure for CPI-Pred: A deep learning framework for predicting functional parameters of compound-protein interactions

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.
Jan 2025

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

Link to Paper
CPI Prediction Deep Learning Kinetics Drug Discovery
Summary figure for Advances in generative modeling methods and datasets to design novel enzymes for renewable chemicals and fuels

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.
Dec 2023

RA Barghout, Z Xu, S Betala, R Mahadevan

Link to Paper
Generative Models Enzyme Design Deep Learning Review
Summary figure for KinMod database: a tool for investigating metabolic regulation

KinMod database: a tool for investigating metabolic regulation

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

K Haddadi RA Barghout, R Mahadevan

Link to Paper
Kinetic Modeling Metabolic Regulation Database