pytdc
Build Therapeutic ML Workflows with PyTDC
Therapeutic machine learning requires reliable datasets, meaningful splits, and consistent evaluation. This skill guides PyTDC workflows from dataset selection through molecular optimization.
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Review the Skillstore skill "pytdc" from https://skillstore.io/skills/davila7-pytdc.md and its manifest at https://skillstore.io/api/skills/davila7-pytdc/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "pytdc". Recommend a dataset and split for predicting intestinal permeability.
Expected outcome:
Use Caco2_Wang from the ADME task. Apply a scaffold split and evaluate regression performance with MAE across several seeds.
Using "pytdc". How should I test generalization to unseen drugs in BindingDB_Kd?
Expected outcome:
Use a cold-drug split. Confirm that drug identifiers do not overlap between training and test partitions before fitting the model.
Using "pytdc". Outline a balanced molecular scoring workflow using QED, SA, GSK3B, and LogP.
Expected outcome:
- Normalize every oracle to a comparable direction and range.
- Set weights and acceptable property intervals before scoring candidates.
- Retain diverse candidates and validate top results experimentally.
Security Audit
SafeAll 90 static findings are false positives caused by Markdown backticks, dictionary keys, dataset partitions, and official resource links. No prompt injection, credential access, command execution, reconnaissance, or malicious network behavior was found.
Risk Factors
🌐 Network access (4)
⚙️ External commands (50)
Share & cite this report
Share the versioned assessment report, neutral badge, embed card, and citations. Skillstore reports evidence without deciding whether this Skill is safe.
Copy report link
https://skillstore.io/skills/davila7-pytdc/audits/9?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_reportMarkdown badge
[](https://skillstore.io/skills/davila7-pytdc?utm_source=security_passport_badge)HTML badge
<a href="https://skillstore.io/skills/davila7-pytdc?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/davila7-pytdc/security.svg" alt="Skillstore security assessment" loading="lazy"></a>Embed card
<iframe src="https://skillstore.io/embed/skills/davila7-pytdc.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Academic citations (APA · BibTeX · CFF)
APA citation
davila7. (2026). pytdc security audit report (audit version 9) [Author version unspecified]. Skillstore. https://skillstore.io/skills/davila7-pytdc/audits/9BibTeX citation
@techreport{davila7-davila7-pytdc-2026,
author = {davila7},
title = {pytdc security audit report (audit version 9)},
institution = {Skillstore},
year = {2026},
number = {9},
url = {https://skillstore.io/skills/davila7-pytdc/audits/9},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "pytdc security audit report (audit version 9)"
version: "unspecified"
type: report
authors:
- name: "davila7"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/davila7-pytdc/audits/9"
identifiers:
- type: other
value: "skillstore:davila7-pytdc:audit:9"
description: "Skillstore immutable audit report identifier"
Compare variants
2 installable variantsEach author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
davila7-pytdc
2026-08-21
k-dense-ai-pytdc
2026-08-21
Skillstore Score
Why this score Evidence Confidence: HighWhat You Can Build
Prepare ADMET Experiments
Choose property datasets, create scaffold splits, and select evaluation metrics for reproducible molecular prediction studies.
Benchmark Interaction Models
Evaluate drug-target or drug-drug models with cold splits, multiple seeds, and standardized PyTDC metrics.
Screen Generated Molecules
Score candidate molecules with property oracles and compare candidates against multi-objective constraints.
Try These Prompts
Recommend a PyTDC ADME dataset for [property]. Explain its columns, task type, scaffold split, and suitable evaluation metric.
Plan a PyTDC drug-target interaction experiment using [dataset]. Compare random, cold-drug, and cold-target splits, then recommend one.
Design a five-seed PyTDC benchmark for [dataset group] and [model]. Include predictions, evaluation, aggregation, and reproducibility checks.
Plan a multi-objective molecular workflow using [oracles]. Define score normalization, weights, constraints, diversity checks, and experimental validation steps.
Best Practices
- Use scaffold or cold splits when random splits would overstate real-world generalization.
- Run multiple fixed seeds and report both mean performance and variability.
- Validate dataset names, metrics, oracle ranges, and optional dependencies before large experiments.
Avoid
- Do not treat simulated predictions in the example scripts as model baselines.
- Do not compare models that use different splits, seeds, preprocessing, or metrics.
- Do not interpret oracle scores as experimental evidence or clinical recommendations.
Frequently Asked Questions
What does this skill cover?
Does the skill train a model?
Which split should I use for molecular property prediction?
How should I evaluate drug-target interaction models?
Does PyTDC require internet access?
Are molecular oracle results suitable for clinical decisions?
Developer Details
Author
davila7License
MIT
Skillstore revision
r2
Version notice
The author did not declare a version.
Ref
c43861a65bb95efcae259cd161c9d6f4dc7eec6f
Maintenance freshness
7/24/2026
Usage
8 downloads · 527 views
File structure