Individual skill
pytdc
Therapeutics Data Commons (PyTDC) for AI-ready therapeutic ML datasets and benchmarks; use it when you need standardized dataset loading, meaningful splits (e.g., scaffold/cold-start), and consistent evaluation for ADME/Toxicity/DTI/DDI or molecular optimization.
scientific-skills/Evidence Insight/pytdc/SKILL.md
Source commit 686e09dfdb45d9fa1a08664f06bb93769b3c9d91
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- No
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Not checked yet
- Last checked
- Nov 9, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 2,047
- Repository last updated
- Sep 17, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- bioinformatics Repository topic
- computational-biology Repository topic
- data-analysis Repository topic
- ai Repository topic
- biology Repository topic
- cancer-genomics Repository topic
- data-visualization Repository topic
- differential-expression Repository topic
- genomics Repository topic
- medical Repository topic
- engineering Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
SKILL.mdcore · 7,003 bytesSKILL.mdcore · 7,003 bytesSKILL.mdcore · 7,003 bytesSKILL.mdcore · 7,003 bytesSKILL.mdcore · 7,003 bytesSKILL.mdcore · 7,003 bytesSKILL.mdcore · 7,003 bytesSKILL.mdcore · 7,003 bytespytdc_audit_result_v1.jsonasset · 12,043 bytesreferences/datasets.mddoc · 9,093 bytesreferences/oracles.mddoc · 10,825 bytesreferences/utilities.mddoc · 17,174 bytesscripts/benchmark_evaluation.pyruns · 9,565 bytesscripts/load_and_split_data.pyruns · 6,564 bytesscripts/molecular_generation.pyruns · 11,739 bytes
Read excerpt
> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)
## When to Use
- You need **curated, AI-ready datasets** for drug discovery tasks (e.g., ADME, toxicity, bioactivity, DTI/DDI).
- You want **standardized benchmarks** with consistent evaluation protocols (including multi-seed benchmark groups).
- You require **meaningful data splits** such as **scaffold splits** (chemical diversity) or **cold-start splits** (unseen drugs/targets).
- You are building models for **single-instance prediction** (molecular/protein properties) or **multi-instance prediction** (interactions).
- You are doing **goal-directed molecular generation** and need **property Oracles** for scoring/optimization.
## Key Features
- **Dataset access by task type**
- Single-instance prediction: ADME, Toxicity (Tox), HTS, QM, and more.
- Multi-instance prediction: DTI, DDI, PPI, and more.