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Professional Summary

Competent computational and medicinal chemist with over 6 years of academic and industrial experience.
Proficient in de-novo molecular modelling and rational drug design; with a successful track record of applying physics-based methods in structure-based and ligand-based drug discovery projects.
Experienced Python programmer with a keen interest in machine learning/AI and data science.
Leveraging the latest cutting-edge tools to construct custom predictive models to support client endeavors.
Excellence in drug discovery driven by a passion for merging technology with chemistry, aiming to accelerate the development of life-saving treatments.

⤜ Publication Highlights ⤛


Experiences

  1. 2022 — Present

    • Impactful contributions to several client-facing projects by aiding design and chemistry efforts leading to multiple lead compounds moving into pre-clinical stage and patent filings by client.
    • Privilege of working on a variety of projects including: GPCRs, RTKs, ATPases, PPIs and Ion channels.
    • Establishing robust pipelines for virtual high throughput screening and analysis which secured additional client funding and attracted more business.
    • Development of project-specific ML models to predict various ADMET endpoints.
    • Mentorship role assisting new team members through their probationary period.

    • Virtual Screening
    • Molecular Dynamics
    • Quantum Mechanics
    • AI/ML
    • Pipelining
  2. 2018 — 2022

    Thesis Title: Synthesis, Computational Modeling and Biological Evaluation of FPR-1 Antagonists

    • Homology Modelling
    • vHTS
    • SBDD
    • AutoDock
    • MOE (CCG)
    • Schrödinger Suite
    • Cell Culture
    • Synthetic Organic Chemistry
    • Analytical Chemistry
  3. 2013 — 2018

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Proficiencies

  • Proprietary Packages

    • Molsoft (ICM)
    • Open-Eye Suite
    • Orion
    • Iktos (Makya)
    • Spartan (QM)
    • Spotfire
    • Vortex

      • My expertise lies in comprehensive understanding of
      in-silico simulation methodologies including Protein-ligand docking, Quantum Mechanics and conformational analysis, Molecular Dynamics (MD) and water map analysis just to name a few.

  • Machine Learning / AI

    • Supervised Learning
    • Neural-Nets
    • Reinforcement Learning
    • Recommenders

      • Successful implementation of several Python-based projects, ranging from building machine learning models for predicting ADME properties and multi parameter optimisation to employing APIs and web-app development.

  • General Toolkit

    • GROMACS (MD)
    • ORCA (QM)
    • Knime
    • HPC
    • Networking
    • Python
    • JavaScript
    • bash

      • • Extensive Knime pipelining experience using molecular manipulation nodes such as RDkit, CDK, Chemaxon, Vernalis etc.
      • Competent user of the open-source Gromacs dynamics engine.
      • Familiarity with the open-source Orca quantum mechanics caluclation package.
      • Python programmer with a strong focus on drug discovery and data science packages including: Pandas, NumPy, Scikit-learn, RDkit, CppTraj, MDTraj, ASE etc.