
Hi, I'm Yash.
My interest in the space between computers and biology started somewhat accidentally. A B.Sc. in Microbiology gave me a firsthand look at how complex and, frankly, messy biological systems are. During my M.Sc. in Bioinformatics it became clear that the same computational tools driving everything else could be pointed at these problems, and that the gap between what biologists need and what they have access to is still surprisingly wide. That tension between the two fields is what pulled me in and has kept me here.
I'm now a PhD scholar under Dr. Md. Imtaiyaz Hassan at Jamia Millia Islamia, working on using artificial intelligence to make drug discovery less slow and less reliant on brute-force screening. The traditional pipeline is expensive and involves a lot of trial and error that could be shortened with better predictive models. My research involves building deep learning models that learn from existing biological and chemical data to predict molecular properties and generate candidate structures. I also spend a fair amount of time building tools around these models, partly because I've learned that a good model is of limited use if the person who needs it cannot actually run it.
Beyond research, I have a habit of diving deep into topics that have nothing to do with my work, which is why Yash's Musings exists. It's a small corner of the internet where I write about whatever catches my attention, from classical Indian music to astronomy to quantum mechanics. It also serves as a running record of my PhD journey and research in general, the parts that don't make it into papers, like what actually went into building a model or why a particular approach failed before it worked.
Publications
| Year | Title | Journal | IF |
|---|---|---|---|
| 2026 | VeloBind: A structure-free protein-ligand binding affinity predictor intended for primary drug screening | Under review | NA |
| 2025 | Current advancement in AI-integrated drug discovery: Methods and applications | Biotechnology Advances | 12.5 |
| 2023 | Genome-Wide Analysis of Kidney Renal Cell Carcinoma: Exploring Differentially Expressed Genes for Diagnostic and Therapeutic Targets | OMICS | 3.3 |
| 2023 | Structure-based identification of potential inhibitors of ribosomal protein S6 kinase 1, targeting cancer therapy: a combined docking and molecular dynamics simulations approach | J. Biomol. Struct. Dyn. | 3.5 |
| 2023 | Chapter: Molecular Dynamics Simulation to Study Thermal Unfolding in Proteins | Protein Folding Dynamics and Stability (book) | NA |
| 2022 | PyPAn: An automated graphical user interface for protein sequence and structure analyses | Protein & Peptide Letters | 1.89 |
| 2021 | Genomic Variations in the Structural Proteins of SARS-CoV-2 and Their Deleterious Impact on Pathogenesis: A Comparative Genomics Approach | Frontiers Cell. Infect. Microbiol. | 5.29 |
| 2020 | InstaDock: A single-click graphical user interface for molecular docking-based virtual high-throughput screening | Briefings in Bioinformatics | 13.99 |
Projects
From the lab and beyond
Resume
Downloads and experience
Thesis: Employing Generative Deep Learning Models for Strategic Advancements in Drug Development







