suyash pant

I am a computational scientist passionate about accelerating the discovery of new drug-like molecules for the treatment of various diseases. I take a hands-on approach to problem-solving, with experience training and deploying automated pipelines and agentic workflows for complex chemical and biological datasets.

At Schrödinger, I currently work as a Senior Scientist II (and previously Senior Scientist I), where my core focus is evaluating and building biological foundation models/workflows—especially AlphaFold2—to reliably bridge theoretical and experimental structural data. I pair this with machine learning methods to support predictive discovery decisions.

I build end-to-end modeling workflows for drug discovery and scientific validation, including structure-based and ligand-based strategies. I have developed ML workflows for rapid screening and in-silico design of 3D-transition metal pincer catalysts (with a dataset of 503+ catalysts), and I’ve combined molecular fingerprints with QM-based descriptors to achieve strong predictive performance (including R² = 80%) for catalytic activity.

I also engineer automated computational pipelines and custom Python scripts to process Desmond and FEP+ data, streamlining large-scale simulation analysis. Beyond hands-on R&D, I mentor Ph.D. and M.S. (Pharm.) students and contribute as an active peer reviewer for Elsevier and Springer journals.