Muammar El Khatib

Muammar El Khatib

Principal Scientist



Muammar is a principal scientist applying machine learning to solve problems in drug discovery at Bristol-Myers-Squibb. His research interests include feature extraction, deep learning, and software solutions.

He is a chemist by training from the University of Zulia in Venezuela and started his graduate studies with a European Master in Theoretical Chemistry and Computational modeling of the Erasmus Mundus Program. His Ph.D. in theoretical chemical physics was about the characterization of metallic and insulating properties of low-dimensional systems using the theory of the insulating state of Walter Kohn applied with wave function theory.

He was a postdoctoral research associate at Brown University, where he worked in the acceleration of atomistic simulations with machine learning models in the group of Prof. Andrew A. Peterson in the Catalyst Design Laboratory. He acquired experience with neural networks and kernel ridge regression models to mimic quantum mechanics simulations using interatomic machine learning potentials in this appointment.

At Lawerence Berkeley National Laboratory, he was a postdoctoral scholar working towards the development of machine learning approaches, algorithms and data sets to solve chemical science problems.

He has published more than ten papers, given presentations at international conferences, and developed the ML4Chem machine learning package, a module for the MOLPRO quantum-chemistry package, and the atomistic machine-learning package (Amp). Additionally, he has participated in the free software community and is a Debian Linux developer.

Download my CV.

  • Artificial Intelligence
  • Machine Learning
  • Software Development
  • Theoretical Chemical Physics
  • Electronic Structure
  • PhD in Theoretical Chemical Physics, 2015

    Université Paul Sabatier, France

  • Master in Theoretical Chemistry and Computational Modeling, 2012

    Université Paul Sabatier, France

  • BSc in Chemistry, 2010

    University of Zulia, Venezuela

∑ Skills = 1



Chemical Physics





Principal Scientist
Nov 2020 – Present Cambridge, MA, United States

Responsibilities include:

  • Reporting to the lead for Predictive Drug Substance Research, scenarios will involve a range of datasets and learning objectives, including for example drug discovery, structural biology, multi-modal modeling and prediction for chemical and biological datasets.
  • Formulation and implementation of predictive modeling and machine learning solutions for the optimization of chemical structures and properties.
  • Application of cutting-edge machine learning (deep learning) approaches to structural biology and molecular interaction challenges.
  • Design and generation of integrated chemical and biological data assets for predictive research in partnership with internal and external collaborators. The successful candidate will work alongside experts in familiar applications of machine learning in the biotechnology domain, including:
    1. Collaboration to develop human-in-the-loop systems to capture and operationalize machine learning datasets and algorithms used by BMS scientists.
    2. Application of supervised, self-supervised, semi-supervised deep learning methods to derive robust generalizable and reusable representations for chemical and biological assay data.
    3. Design of multi-task, multi-modal and generative neural network learning approaches to tackle real-world drug discovery optimization problems, including prediction of both assayed and abstract compound properties.
    4. Contribution to design and development of Machine Learning data repositories focused on proteins and chemical compounds.
  • Pursue leading research in applied machine learning that demonstrates the value of predictive methods to accelerate and optimize drug development.
  • Derive and apply predictive approaches in collaboration with BMS colleagues in the Informatics and Predictive Sciences, and Chemistry departments.
  • Apply rigorous internal standards for applied machine learning practice, including evaluation of methods, approaches and solutions.
  • Contribute to broader data analysis and predictive methods strategies across the business as required, including assessment of 3rd party capabilities.
  • Present strategies, approaches, results and conclusions to BMS colleagues and external audiences.
  • Contribute to enable strategic collaborations with academic and commercial collaborators to benefit therapeutic programs.
Postdoctoral Scholar
Lawrence Berkeley National Laboratory
Nov 2018 – Sep 2020 Berkeley, CA, United States

Responsibilities include:

  • Developed a python library to ease the deployment of machine learning models for chemistry and materials sciences. This package is helping us advance our research faster because we can consistently implement new methods.
  • Developed a neural network model that can learn how to predict retention times from chromatography data. These models can be used by experimentalists to get insights about the substances they study without explicitly running the experiments.
  • Applied autoencoders to extract features and systematically studied their topology to understand their effect on the predictive power of models used for material sciences.
  • Generated data sets using web-scraping and diversified their variance with active learning techniques.
  • Worked on a project for Scaling Interactive Science for Data-Intensive Discovery for the Linac Coherent Light Source (LCLS) at SLAC National Accelerator Laboratory.
  • Led and designed research projects executed by summer interns.
  • Wrote scientific publications to show our results to the community.
Postdoctoral Research Associate
Brow University
Oct 2016 – Nov 2018 Providence, RI, United States

Responsibilities include:

  • Worked on the acceleration of electronic structure calculations using machine learning models to decrease orders of magnitude the computational time needed by the simulations.
  • Was actively involved in the development of the Atomistic Machine-learning Package (Amp) created and maintained by the Catalyst Design laboratory at Brown University
  • Implemented kernel ridge regression within an atom-centered mode in their machine learning package.
  • Participated in the design of scientific projects and supervision of students during their research in our laboratory.
  • Presented scientific results in international conferences.
PhD candidate in Theoretical Chemical Physics
Université Paul Sabatier
Jul 2012 – Jul 2015 Toulouse, France
Characterization of metallic and insulating properties of low-dimensional systems.