Computational Chemist

Biotechnology Professional Hicksville, New York, United States

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Summary

I develop and apply algorithmic tools that accelerate drug discovery, using molecular modeling and machine learning to turn complex chemical data into actionable insights. My research includes MDANCE, an open-source clustering engine I developed that enables ultra-fast analysis of massive datasets, like ultra-long MD simulations and billion-compound libraries, solving scalability bottlenecks in data analysis. This work led to seven first-author publications (JCTC, JCIM). Ive also contributed to DEL hit-finding via ML (with a focus on generalization/data leakage), and worked on protein-ligand modeling during an internship at Schrödinger, contributing to the design of a new Glide algorithm.

Im now looking for full-time roles where I can apply these skills to real-world therapeutic discovery, especially in ML platforms, predictive modeling, or simulation-based pipelines. Technical skills include Python (RDKit, PyTorch, scikit-learn), simulation workflows, and reproducible research.

Expectations

I am seeking a Scientist role within an innovative biotechnology company where I can apply my expertise in computational chemistry, software development, and machine learning to tangible problems in drug discovery. I am particularly excited by opportunities to help build and scale computational platforms from the ground up in a fast-paced, goal-oriented setting. My key expectations are:

-Impact-Driven Projects: To contribute to a team that bridges computational design and experimental validation, with a clear line of sight from my work to the development of novel therapeutics.
- Technical & Scientific Growth: To solve complex problems using state-of-the-art technologies, such as large-scale docking, deep learning, and structural biology data, while further developing my skills in a collaborative R&D environment.
- Collaborative, Cross-Functional Environment: To work alongside and learn from passionate colleagues across disciplines, including medicinal chemists, structural biologists, and machine learning scientists.
- A Culture of Innovation: To join an organization that encourages creative thinking, supports the proposal of new scientific approaches, and values building robust software tools that accelerate the entire discovery pipeline.