About

Padideh Nouri

Computer Scientist

Email / LinkedIn / Github / Google Scholar

Education
  • M.Sc. in Computer Science – University of Montréal, Mila
    • Research focus: RL and deep learning
    • Courses: Fundamentals of Machine Learning, Data Science, Deep Learning, Modern NLP
    • Courses(Audit): Game Theory and ML, Reinforcement Learning and Optimal Control
  • B.Sc. in Bioinformatics – University of Montréal
    • Selected Courses: Calculus, Linear Algebra, Probability, Statistics, Discrete Mathematics, Data Structure, Algorithms, Biochemistry, Organic Chemistry, Genetics

Experience

  • ML Research Intern – Genentech, San Francisco Bay Area
    • Meta Learning and RL for antibody design
  • AI researcher – Mila, Montréal

Research & Competition

  • High-Throughput Simulation: Optimizing FlexAID for ultra high throughput docking by leveraging artificial intelligence techniques. Benchmarking using DUDE database. [fall 2020][Github]
    • Details: FlexAID is a virtual docking software based on surface complementarity. It uses a genetic algorithm to predict the binding affinity of a protein and a small molecule (drug). I have used machine learning methods in FlexAID to optimize the prediction of binding accuracy and efficiency. This consists of integrating classifiers within FlexAID to eliminate the molecules that have a low chance of achieving high rank. I have used NLP and DL approaches to model the molecules structure and docking-score relationship. These methods significantly accelerate FlexAID and enable this software to rank affinities of ultra-large libraries.
  • Kaggle competition: An image classification task in which our team used deep learning models to extract features and to train the network. Our suggested architecture of CNN and our data augmentation techniques led to 91% accuracy score and ranked as best model in the competition. [fall 2020][Kaggle]
  • FlexAID: Designed a wrapper for docking software which enables the automatization of the whole process. Validated by MMPBSA calculation after MD simulations using Gromacs software with CHARMM36 force fields. [summer 2020][Github]
  • Mutation Pipeline (MP): Built a protein engineering pipeline to predict the effects of Amino Acids mutations on protein stability, using ENCoM, FlexAID and Modeller softwares. Validated with ProTherm database. [summer 2019][Github]

Interest

  • AI for scientific discovery