# Cloud Hosted Computational Chemistry

> Use when you have a curated dataset of ≤10,000 molecular structures with known collision cross section values for training, a target set of ≤10,000 molecules requiring CCS predictions, a compatible browser, and either lack local Python installation or prefer cloud-based execution to avoid.

- Skill: `holobiomicslab/cloud-hosted-computational-chemistry` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/cloud-hosted-computational-chemistry`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/cloud-hosted-computational-chemistry/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- License: CC-BY-4.0
- Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/holobiomicslab/cloud-hosted-computational-chemistry

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# Cloud-hosted computational chemistry

> **License: restricted** — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution. <!-- asb-license-banner -->
## Summary

Execute collision cross section (CCS) prediction workflows on user-curated molecular training sets using Google Colaboratory, eliminating the need for local Python installation while constraining computation to <10,000 molecules and ≤12 hours continuous operation. This skill leverages Google-hosted services to train scikit-learn models and export predictions without local infrastructure.

## When to use

You have a curated dataset of ≤10,000 molecular structures with known collision cross section values for training, a target set of ≤10,000 molecules requiring CCS predictions, a compatible browser, and either lack local Python installation or prefer cloud-based execution to avoid dependency management. Ideal when predictions must be completed within a single session and results exported to CSV.

## When NOT to use

- Target dataset exceeds 10,000 molecules — use locally-hosted Jupyter Lab variant instead
- Analysis requires integration into a larger workflow or chaining with downstream analyses — use Jupyter Lab variant with Python environment
- Training or target set is very large or requires persistent storage across sessions — use locally-hosted Jupyter Lab variant

## Inputs

- User-curated training dataset containing molecular structures and known collision cross section values (CSV or compatible tabular format)
- Target dataset of ≤10,000 molecules requiring CCS predictions
- Scikit-learn model configuration parameters

## Outputs

- CSV file or compatible local file format containing molecular identifiers and predicted CCS values
- Trained collision cross section prediction model (in-session)

## How to apply

Download the 'CCSP 2.0 - CCS Prediction in Google Colab' notebook (.ipynb) from the facundof2016/CCSP2.0 repository, upload it to Google Colab (colab.research.google.com), and follow embedded instructions. Import your training dataset containing molecular structures and known CCS values, configure scikit-learn V1.0.2+ model parameters, train the model on the curated training set, load your target molecules (≤10,000), apply the trained model to generate predictions, and export results (molecular identifiers and predicted CCS values) to CSV. Monitor runtime against the 12-hour continuous operation limit and ~20-minute idle disconnection threshold; save work frequently to avoid loss during auto-disconnection.

## Related tools

- **Google Colaboratory** (Cloud-hosted Jupyter notebook runtime providing 12-hour continuous operation limit and ~20-minute idle disconnection for executing CCSP 2.0 prediction pipeline without local installation) — https://colab.research.google.com/
- **Scikit-learn (Sklearn V1.0.2+)** (Machine learning library performing model training and collision cross section prediction on molecular descriptors) — https://scikit-learn.org/
- **Python** (Programming language in which CCSP 2.0 notebook is written and executed)
- **CCSP 2.0** (Open-source Jupyter notebook implementing collision cross section prediction workflow for ion-mobility scientists) — https://github.com/facundof2016/CCSP2.0

## Evaluation signals

- CSV export file is present and contains molecular identifiers and predicted CCS values with no missing values
- Predicted CCS values are numeric, within physically plausible ranges for the molecules being analyzed, and match the cardinality of the target dataset (≤10,000 rows)
- Training completes without errors in Google Colab environment and model accuracy metrics (if reported) are comparable to local Jupyter Lab variant
- Session remains active for full prediction duration without triggering idle disconnection (monitor notebook for stale kernel indicators)
- File export to local computer succeeds without truncation or format corruption

## Limitations

- Google Colab enforces a 12-hour continuous operation limit; workflows exceeding this threshold will be interrupted
- Google Colab will auto-disconnect after ~20 minutes of idle time, requiring manual kernel restart and potential loss of in-memory state
- Skill is constrained to datasets with ≤10,000 molecules in either training or target set; larger datasets require locally-hosted Jupyter Lab variant
- Google Colab does not support persistent storage across sessions; all intermediate results must be exported before session ends
- Browser compatibility limited to recent versions of Chrome, Firefox, or Safari; older browsers may not support Google Colab interface

## Evidence

- [intro] Operational constraints of Google Colab runtime for CCSP 2.0: "Google Colab will only allow continuous notebook operation for up to 12 hours and will disconnect after ~20 minutes if left idle"
- [intro] No local installation requirement for Google Colab variant: "This route does not require you to install Python or any of the packages required to run the code, as all calculations are performed through Google hosted services"
- [intro] Scale constraint for Google Colab variant: "If you plan to use CCSP 2.0 only to make small scale predictions (<10,000 molecules) and export your results to your local computer, the Google Colab option is recommended"
- [readme] Scikit-learn version requirement: "The current version of CCSP 2.0 requires Sklearn V1.0.2 or later"
- [readme] Purpose and user base of CCSP 2.0: "Collision Cross Section Predictor 2.0 is an open source Python notebook intended to help ion-mobility scientists predict collision cross sections with user-curated training sets"
- [readme] Browser requirements for Google Colab: "it is recommended that you use the most recently released version of Chrome, Firefox or Safari"