About Me

Hi! I’m Adithya Subramanian Sahasranamam.

Call me, Adi.

I’m a computational biologist with The Daniel Higginson Lab at Memorial Sloan Kettering Cancer Center, New York.

With a background in software architecture, I build end-to-end ML pipelines—from data engineering through model deployment. My toolkit spans deep learning, reinforcement learning, and generative architectures including diffusion models and graph neural networks for molecular applications.

Outside the lab, I’m a coffee enthusiast chasing the perfect pour-over, and I find clarity on the water and in the mountains. When I need speed instead of stillness, NASCAR & F1 scratch that itch.

Research Interests

My research focuses on elucidating DNA double-strand break repair mechanisms and developing AI-driven therapeutics for cancer. I employ deep learning and reinforcement learning approaches for computational design of novel inhibitors targeting critical DNA repair proteins including ATM, Ligase IV, DNA-PKc, Rad51, and Ku70/80. My work integrates structural biology, molecular docking, and bioinformatics to identify druggable sites and optimize therapeutic candidates—including small molecules, PROTACs, and antibody-drug conjugates—aimed at selectively disrupting Non-Homologous End Joining and Alternative End Joining pathways in cancer cells. I also develop and maintain bioinformatics pipelines for high-throughput sequencing data that employ unsupervised learning models to characterize DNA repair patterns, helping to elucidate the precise roles of repair proteins in cellular mechanisms.

Publications

1. Helmuth, Richard, et al. “Integrin Activation as a Novel Therapeutic Strategy for Podocytopathies: FR-PO727.” Journal of the American Society of Nephrology 33.11S (2022): 521.
2. Subramanian Sahasranamam, Adithya. “The Role of Irak-1 Transcripts in Sepsis” (2021). Theses. 1843.

Here’s some stuff I find interesting

Recommended Reads

• AlphaFold 3 - Accurate structure prediction of biomolecular interactions with AlphaFold 3
• Evo 2 - Sequence modeling and design from molecular to genome scale with Evo
• Reinvent 4 - REINVENT4: Modern AI-driven generative molecule design
• Boltzmann Generators (BoltzGen) - Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
• Chai-1 - Multi-modal foundation model for molecular structure prediction

Books on Software Development

• Clean Code by Robert C. Martin - A handbook of agile software craftsmanship
• Design Patterns: Elements of Reusable Object-Oriented Software by Gang of Four - Essential patterns for software design
• The Pragmatic Programmer by David Thomas and Andrew Hunt - From journeyman to master
• Refactoring by Martin Fowler - Improving the design of existing code
• Test Driven Development: By Example by Kent Beck - A practical guide to TDD methodology

Essays

• How to Do Great Work by Paul Graham - On finding and doing meaningful work
• Beating the Averages by Paul Graham - Why Lisp gives a competitive advantage
• The Cathedral and the Bazaar by Eric Raymond - On open source development methodologies
• What You’ll Wish You’d Known by Paul Graham - Advice for ambitious young people
• Teach Yourself Programming in Ten Years by Peter Norvig - Why learning programming takes time