About
Hi, I'm Naisha.
I'm a B.Pharm student at NMIMS exploring the intersection of pharmaceutical sciences, computational biology, and artificial intelligence. Through research analysis, programming, and computational projects, I'm building the skills needed to understand how computation can accelerate biological discovery and drug development.
Why pharmacy & computation
I didn't grow up wanting to be a traditional pharmacist. I grew up asking a lot of "but why" questions that most people stopped answering after the second one. Pharmacy turned out to be the field where that habit is actually useful — every medication has a story about how it interacts with a specific receptor, enzyme, or pathway.
I became fascinated by how modern biology increasingly relies on computation. Questions about protein structure, drug-target interactions, and molecular systems are now explored using programming, machine learning, and reproducible data analysis alongside experimental science.
Where my curiosity keeps landing
Right now, my curiosity is pulling me toward AI-driven drug discovery, computational biology, and reproducible research. I'm documenting that journey by learning Python, studying the computational methods behind published research, and gradually building the skills needed to work with tools such as Biopython, RDKit, and machine learning frameworks.
Where I'm Going
My long-term goal is to contribute to computational biology and AI-driven drug discovery by combining a foundation in pharmaceutical sciences with quantitative and computational methods. This website documents that journey—from learning Python and analyzing research papers to building increasingly sophisticated computational projects.
What I try to hold onto
Evidence first
I'd rather change my mind because of a good study or quantitative benchmark than defend a position because I said it first.
Simple, not simplified
Making complex biological mechanisms or machine learning architectures accessible should never mean making them less accurate. If I can't explain something correctly in plain language, I don't understand it well enough yet.
Curiosity, on purpose
Some of this is genuine love of the subject. Some of it is just discipline — showing up to learn, write code, and keep asking questions even on the days I don't feel like it.
Build in public
I believe learning becomes more meaningful when it's documented. This website is my public notebook—a record of what I've learned, built, questioned, and occasionally misunderstood before figuring it out.
"This isn't a portfolio of everything I already know. It's a record of how I'm learning to think like a computational biologist."