Results-oriented Computational Drug Discovery Scientist with 10+ years of experience in leveraging computational approaches to accelerate the drug discovery process. Proven track record in applying computational chemistry, cheminformatics, and machine learning, Bioinformatics techniques to identify promising drug candidates and optimize lead compounds. Passionate about utilizing cutting-edge technologies to tackle complex challenges in drug discovery and development

Computational Chemistry: Extensive experience in molecular modeling, dockings, simulations, ligand and strcuture-based drug discovery approaches. Proficient in utilizing various software packages such as Schrödinger, MOE, Discovery Studio, etc.

Cheminformatics: Expertise in QSAR modeling, virtual screening, chemical space analysis, and molecular descriptor calculation. Strong understanding of chemical databases and data mining techniques.

Machine Learning: Skilled in applying machine learning algorithms (e.g., random forests, support vector machines, neural networks) to predict molecular properties, build predictive models, and guide drug design decisions.

Data Analysis and Visualization: Adept at processing and analyzing large-scale biological and chemical datasets. Proficient in data visualization tools like matplotlib, plotly, seaborn to communicate findings effectively.

Scientific Programming: Fluent in Python,and Bash scripting for automating tasks, developing workflows, and analyzing data. Experience with cloud computing environments and high-performance computing clusters.

You can find my publications on Google Scholar https://scholar.google.co.za/citations?user=JOdUTHMAAAAJ&hl=en

I'm eager to collaborate on exciting drug discovery projects. Feel free to connect with me on LinkedIn https://www.linkedin.com/in/elumalai-pavadai/