Unified Python SDK for OpenFDA, PubMed, and ClinicalTrials.gov with clinical intelligence, interaction detection, and research tools.

Comprehensive bioinformatics platform for precision medicine. Explore drug-gene interactions, pharmacokinetics, clinical trials, and drug repurposing. 20+ research modules. Free & open-source (MIT).

AI-powered pharmacogenomics platform using GATv2 to predict organ health, drug interactions, and personalized longevity insights from DNA, lifestyle, and medications.

Production-ready healthcare AI agent with 9 LangChain tools, 5-layer verification, and 100-case eval dataset. Integrates with OpenEMR.

Persistent, auditable clinical memory for healthcare AI agents. MCP + A2A. Powered by mind-mem and MIND Lang scoring kernels. Built for Agents Assemble Healthcare AI Hackathon.

ML training pipelines with RunPod serverless GPU infrastructure. Includes DDI drug interaction classifier using Bio_ClinicalBERT on 176K DrugBank samples.

drug interactions with 2 drugs

Full-stack web application that analyzes drug interactions using FDA's OpenFDA API. Built with Python/Flask, featuring real-time drug label search, interaction detection, and user-friendly interface for healthcare safety information.

ML-driven platform for Glioblastoma drug recommendation using GDSC data. Features multi-model prediction (RF, XGBoost, NN, SVM, KNN), molecular similarity analysis (Tanimoto, MCS, GCN), pathway enrichment, drug interaction checking, and combination therapy optimization with interactive dashboard.

Zero-dependency Python API for herb-drug interactions: 250 plants, 53 drug classes, 592 interactions.

🧠 SafeMeds is an intelligent LangGraph-based medical assistant that checks drug interactions, recognizes medicines from images, and answers health-related questions.

Drug Interaction Cascade Analyzer — Detect dangerous multi-drug interactions using knowledge graph + CYP450 enzyme cascade analysis on real FDA data

Heterogeneous GAT with temporal attention for drug-drug interaction prediction. Multi-task learning (interaction + metabolite pathway + temporal dynamics) on Tox21 molecular data. 99.9% interaction accuracy, 0.875 temporal correlation.

A machine learning system for predicting potential drug-food interactions using ensemble methods and explainable AI techniques. This project implements multiple classification algorithms to identify interaction risks and provides interpretable predictions through SHAP and LIME analysis.

Predicting Drug-Drug Interactions (DDI) using Machine Learning and molecular features (Morgan/RDKit)

AI-powered medical guardrails API — PHI detection, drug safety, hallucination checks

Drug interaction checker & pharmaceutical compliance

Aushadhi — AI Drug Interaction Predictor. LLM-augmented multi-drug interaction prediction for polypharmacy safety.