pytdc
Category: scientific-databases · License: MIT · Version: 1.2 · Includes scripts
Tags: therapeutics, drug discovery datasets, benchmarks, machine learning, ADMET
When to use it
Uses the PyTDC package (import tdc, Therapeutics Data Commons) to discover therapeutic ML tasks from tdc.metadata, plan and load approved datasets, apply task-aware splits (random, scaffold, cold_split, combination, time), run evaluator metrics, evaluate benchmark groups such as admet_group, and run bounded molecular-oracle scoring. Use when selecting a TDC task or dataset, planning a download-free split, scoring predictions with TDC evaluators, running a benchmark group evaluation, or checking dataset licenses and cache effects before downloading. Use when scoring molecules with TDC oracles like QED. Not for generic RDKit cheminformatics or training molecule generators.Example requests
Ask your agent in plain language, for example:- “What TDC tasks exist for toxicity? Don’t download anything yet.”
- “I’m working on a Caco2_Wang permeability model and need a scaffold split with seed 42 for my paper. I’m on Python 3.12 and got
ModuleNotFoundError: No module named 'pkg_resources'when importing tdc. Can you help me get this working and set up the split? Check the dataset license and how much it will download first.” - “I have predictions from my model on the 22 ADMET benchmark endpoints from the standard leaderboard and want to get the official scores across five seeds so I can compare against published results. How should I do that?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole scientific-databases category with /plugin install scientific-databases@research-agent-skills.