Responsibilities include:

• Drive strategic implementation of chemical informatics and AI/ML techniques to expedite drug discovery guided by the needs of teams to advance Discovery assets to the clinic.
• Champion the integration of chemical informatics with proteomic data, utilizing three-dimensional molecular modeling to elucidate precise mechanisms of protein-ligand interactions, critical for target validation and lead optimization.
• Orchestrate the synthesis and analysis of multidimensional datasets, with a focus on integrating 3D structural insights with chemical properties to pioneer predictive models for molecular behavior and drug efficacy.
• Develop and maintain cutting-edge frameworks for the semantic integration and intelligent retrieval of multidimensional chemical and biological data.
• Facilitate and expand strategic partnerships with leading data vendors, academic pioneers, and technology innovators to advance Odyssey’s capabilities in cheminformatics and computational drug discovery.
• Act as a pivotal thought leader, demystifying the complexities of cheminformatics and machine learning for cross-functional teams, fostering a culture of innovation and knowledge sharing.

Responsibilities include:

• Report to the lead for Predictive Sciences of the IPS organization; scenarios involve a range of datasets and learning objectives, including drug discovery, multi-modal modeling, uncertainty quantification, and predictive models for chemical and biological datasets.
  • Formulation and implementation of predictive modeling and machine learning solutions for optimizing chemical structures, drug-target interactions, and physicochemical properties.
  • Application of cutting-edge machine learning (deep learning) approaches to physicochemical property predictions and molecular interaction challenges.
  • Work alongside experts in similar 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 and 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.
  • Pursue leading research in applied scientific 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 Predictive Sciences and Chemistry departments.
  • Apply rigorous internal standards for applied machine learning practice, including evaluation of methods, approaches, and solutions.
  • Present strategies, techniques, results, and conclusions to BMS colleagues and external audiences.
  • Enable strategic collaborations with academic and commercial collaborators to benefit therapeutic programs. I am the BMS representative before the “Machine Learning for Pharmaceutical Discovery and Synthesis Consortium” collaboration with the Massachusetts Institute of Technology. For more information, see https://mlpds.mit.edu/.
  • Implement and apply uncertainty quantification of predictive models to steer scientific discovery.
  • Supervise and mentor interns to develop their careers and benefit therapeutic programs at BMS.

Responsibilities include:

• Developed a Python library to ease the deployment of machine learning models for chemistry and materials sciences. https://github.com/muammar/ml4chem. 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.

Responsibilities include:

• Worked on the acceleration of electronic structure calculations using machine learning models to decrease orders of magnitude of 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 https://bitbucket.org/andrewpeterson/amp.
• 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.