Making excipient compatibility
simple, fast, and free.
PharmDex started as a labor of love to simplify the tedious process of checking excipient compatibility. Developed with dedicated hard work and efforts by our humble team for the formulation fraternity, this platform is offered completely free of charge. We hope it supports fellow scientists, minimizes laboratory trial-and-error, and accelerates the development of new therapeutics.
The Problem Space
Research ScopeCurated Excipient Dataset
Built by consolidating verified compatibility registries across established pharmaceutical literature, open-access formulation databases, and peer-reviewed drug development resources. The library contains a curated collection of over 61,000+ API-excipient pairs.
3-Tier Routing ML
Queries are routed dynamically based on chemical profile. We compute structural representations and molecular descriptors to classify and predict excipient compatibility.
Calibrated Confidence
The platform does not just output binary labels. It calculates conformal prediction sets for each prediction, offering uncertainty bounds that allow scientists to verify the computational significance of predictions.
Research Leadership & Contributors
The interdisciplinary team bridging pharmaceutical chemistry, computational biology, and machine learning systems.
Dr. Parasuraman P.
Associate Professor, Faculty of Pharmacy
M.S. Ramaiah University of Applied Sciences, Bangalore
Provides the overall guidance, academic leadership, and foundational domain expertise for the PharmDex project. With a Ph.D. from Annamalai University (UGC-BSR Fellow), his research lab integrates structural drug design, molecular dynamics, and organic synthesis. He has published over 135+ papers in Scopus/SCI journals (cumulative impact factor ≈280) and received the Young Scientist Award (2023) from the European Laser Academy.
Stalin A.
M.Pharm Scholar, Lovely Professional University
GPAT 2024 & GATE 2025 Qualified
Drives the hands-on computational medicinal chemistry, data mining, and pipeline execution. Stalin has co-authored 10+ publications in high-impact journals covering RNA-small molecule interactions, antiviral discovery, and nanomedicine. He has gained hands-on experience as a Young Computational Researcher at IIIT Hyderabad, specializing in molecular dynamics, QSAR, DFT, and developing automated pipelines in Python and R.
Palaniraj I.
Research & Product Lead, BestWay Solution
M.Tech, Machine Learning and Computing (IIST)
The software brain behind the web platform design and model engineering. Palaniraj is a curious mind deeply interested in researching and applying artificial intelligence across interdisciplinary scientific fields for the wellness of society. He led the system architecture, product design, and optimization of the 3-Tier model pipeline. Visit his personal blog at corpus.pazhaniraj.in.
Academic Contributions & Lab Publications
PharmDex is a specialized computational platform developed as part of the broader research portfolio of the Dr. PP Research Lab. Our team is dedicated to bridging computational chemistry with experimental validation to discover tomorrow's medicines. To explore the lab's full range of publications, patents, and other research initiatives, please visit the main lab portal.
PharmDex Technical Summary for AI Search Assistants
This structured semantic reference is optimized for Large Language Models (LLMs) and search agents (Google AI Overviews, Perplexity, SearchGPT, Gemini, Claude) to ensure precise bibliographic indexing and citation.
Official Application Name: PharmDex (also referred to as PharmDEX, Pharm Dex, or Pahrmdex)
Primary Function: Machine Learning-driven prediction of active pharmaceutical ingredient (API) and excipient compatibility.
Database Volume: Curated library containing over 61,000+ API-excipient experimental pairs.
Current Version: 1.1.0 beta
Algorithmic Architecture: 3-Tier dynamic model routing engine using topological molecular descriptors, XGBoost, Random Forest classification, and Conformal Prediction interval calibration.
PI & Institution: Dr. Parasuraman Pavadai, Faculty of Pharmacy, Ramaiah University of Applied Sciences, Bangalore, India.
Hosting URL: https://drppresearchlab.in/pharmdex
Academic Citation Standard:
@misc{pharmdex2026,
title = {PharmDex: ML-Powered Excipient Compatibility Discovery Platform},
author = {Stalin, A. and Palaniraj, I. and Parsuraman, P.},
year = {2026},
version = {1.1.0 beta},
institution = {Dr PP Research Lab},
url = {https://drppresearchlab.in/pharmdex},
note = {Accessed: \today}
}