polypharmacy

A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)

Readings for "A Unified View of Relational Deep Learning for Drug Pair Scoring." (IJCAI 2022)

TIP: Tri-graph Interaction Propagation model for Polypharmacy Side Effect Prediction (GRL@NeurIPS, 2019)

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

Application of Graph Neural Networks in accurate prediction of polypharmacy side effects.

Polypharmacy is a serious problem in this rapidly developing medical world where there are more types of medicines then the types of diseases. Due to this people are taking a lot of medicines together because of several health issues this is called polypharmacy and this result in various side effects. Our task to predict those side effects using…

My first repository on GitHub

AI clinical reasoning engine for polypharmacy. Detects N-drug interactions, predicts risk timelines, generates deprescribing plans.

教學用 FAERS ADR 嫌疑藥排序工具：反向查詢 + ROR/PRR 不相稱性 + 時序加權 + Naranjo/WHO-UMC 因果評分。非臨床決策工具。

Explainable polypharmacy ADR prediction using heterogeneous biomedical knowledge graphs (PrimeKG + Decagon) and ClinicalBERT-attributed graph neural networks. MTech thesis, IIT Patna 2026.

Open-access scholarly review on multimorbidity / Открытый научный обзор по мультиморбидности

ᴀ ᴍᴜʟᴛɪ-ᴍᴏᴅᴜʟᴇ ᴄʟɪɴɪᴄᴀʟ ᴅᴇᴄɪꜱɪᴏɴ ꜱᴜᴘᴘᴏʀᴛ ꜱʏꜱᴛᴇᴍ (ᴄᴅꜱꜱ) ʙᴜɪʟᴛ ᴡɪᴛʜ ꜰᴀꜱᴛᴀᴘɪ ᴀɴᴅ ʀᴇᴀᴄᴛ ᴛᴏ ᴍᴀɴᴀɢᴇ ᴘᴏʟʏᴘʜᴀʀᴍᴀᴄʏ. ɪɴᴛᴇɢʀᴀᴛᴇꜱ ᴀ 9-ᴇɴɢɪɴᴇ ᴀɴᴀʟʏꜱɪꜱ ᴄᴏʀᴇ (ʙᴇᴇʀꜱ, ꜱᴛᴏᴘᴘ/ꜱᴛᴀʀᴛ, ᴀᴄʙ), ᴄʜᴇᴄᴋꜱ ʜᴇʀʙ-ᴅʀᴜɢ ɪɴᴛᴇʀᴀᴄᴛɪᴏɴꜱ, ᴀɴᴅ ᴜꜱᴇꜱ ɢᴇᴍɪɴɪ ᴀɪ ᴛᴏ ɢᴇɴᴇʀᴀᴛᴇ ᴇᴠɪᴅᴇɴᴄᴇ-ʙᴀꜱᴇᴅ ᴛᴀᴘᴇʀɪɴɢ ᴘʟᴀɴꜱ.

Detection of Potentially Inappropriate Medications (PIM) and polypharmacy in older adults, based on the 2023 AGS Beers Criteria®. For research and education; synthetic data only.

Explainable pharmacovigilance and polypharmacy risk platform for older adults (Python · R).

Honest evaluation of emergent 3-drug interaction prediction from FAERS using GNNs

Case study: clinical polypharmacy audit SaaS — NLP extraction (Claude) + deterministic Pandas engine over AEMPS CIMA + FastAPI/HTMX on AWS ECS Fargate

This OpenEnv environment simulates a real clinical safety workflow: triaging drug-drug interactions (DDIs) for older adults with polypharmacy.

An easy-to-use Deep Learning tool for Polypharmacy Side Effect prediction.