https://github.com/filipspl/tox24challenge

Dataset used in Tox24 challenge
https://github.com/filipspl/tox24challenge

dataset machine-learning molecular-descriptors qsar tox24 toxicity-prediction

Last synced: 3 months ago
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Dataset used in Tox24 challenge

• Host: GitHub
• URL: https://github.com/filipspl/tox24challenge
• Owner: filipsPL
• Created: 2024-09-04T07:34:01.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-09-11T01:41:46.000Z (almost 2 years ago)
• Last Synced: 2025-01-11T12:49:14.267Z (over 1 year ago)
• Topics: dataset, machine-learning, molecular-descriptors, qsar, tox24, toxicity-prediction
• Homepage:
• Size: 2.56 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

          
Tox24 Challenge Dataset

===========

This repository contains molecular structures and descriptors for the Tox24 challenge prepared by me (team name: **filipsPL**). The goal of the challenge was to predict the in vitro activity of compounds' activity against [Transthyretin (TTR)](https://en.wikipedia.org/wiki/Transthyretin) using chemical structure data.

- [Tox24 Challenge Dataset](#tox24-challenge-dataset)

  - [Dataset](#dataset)

  - [Descriptors](#descriptors)

  - [Importance of features](#importance-of-features)

  - [The Challenge Results](#the-challenge-results)

  - [References](#references)

## Dataset

This repository includes:

- The chemical structures in SMILES format, provided by the organizers and curated by me using my RDKit pipeline `data/smiles_org+fixed.csv`.

- Training set - a diversified set of 1000 compounds, used for training models `data/train.csv.xz`

- Validation set: a diversified set of 100 compounds, used for final validation of models `data/validation.csv.xz`

- Test set: 500 compounds used to make predictions. It contains a leaderboard set (200 compounds) and a blind set (300 compounds) `data/test.csv.xz`. 💡 This set contains compounds with known and unknown activity. Compounds with known activity are also the members of the Training/Validation set.

## Descriptors

The csv files contain 2D descriptors of molecules, including:

- DRKitDescriptors (2D)

- molecular fingerprints:

  - CDK:

    - CDKECFP4

    - CDKEState

    - CDKFCFP4

    - CDKmolprop

    - CDKpubchem

    - CDKstandard

  - Indigo fingerprints:

    - IndigoResonanceSubstructure

    - IndigoSimilarity

  - RDKit fingerprints:

    - RDkitFP-AtomPair

    - RDkitFP-Avalon

    - RDkitFP-FeatMorgan4

    - RDkitFP-Layered

    - RDkitFP-MACCS

    - RDkitFP-Morgan2

    - RDkitFP-Morgan3

    - RDkitFP-Morgan4

    - RDkitFP-Pattern

    - RDkitFP-RDKit

    - RDkitFP-Torsion

## Importance of features

Feature importances according to the final catboost model

![bar plot](feature_importance.png)

## The Challenge Results

Bar plot showing RMSE of submitted predictions (by me, based on the official results). Congratulations to the winning team Amidoff 🎉!

![rank](ranking.png)

## References

1. [OCHEM Platform for Tox24](https://ochem.eu/static/challenge.do)

2. [Chem. Res. Toxicol. 2024, 37, 6, 825–826](https://pubs.acs.org/doi/10.1021/acs.chemrestox.4c00192)