This document discusses using machine learning algorithms for chemical toxicity classification from a data science bootcamp. It notes that over 122 million chemical products exist with 11,000 new substances introduced daily. Traditional toxicity testing is limited as little toxicology information is available for many chemicals. The document examines using machine learning methods like XGBoost that can predict biological responses with around 80% accuracy as an alternative to traditional approaches. Applications are mentioned for detecting toxic substances and for use by chemists, biologists, and pharmaceutical companies.

1. Data Science Bootcamp
Bioinformatics: machine learning
algorithms for chemical toxicity
classification
Abdoul K. TRAORE

2. Is the ML algorithms relevant
for for chemical toxicity
classification ?

3. Ø CAS database : 122 M of chemical products in 2017
against 18M in 2002
Ø 11 000 new substances / day
Ø 85000 chemical are commercially available and 7 new
substances are introduced into the marketplace each day
Some figures

4. Background: potential toxicity

5. Background: potential toxicity

6. Background: how to determine the toxicity of a
chemical ?
Ø Little toxicology information is available

7. Background: traditional toxicity testing approaches

8. Is the ML algorithms relevant
for for chemical toxicity
classification ?
>80%

9. Predicting a biological response
XGBoost: ~80% Accuracy

10. Alternative biological mechanisms : ML methods
Judson et al. 2008

11. Predicting a biological response
Data:
Ø 3751 molecules (rows)
Ø 1776 features (molecular descriptors )
Ø Positif biological response (1)
Ø Or not (0)

12. Predicting a biological response
Target
variable

13. Predicting a biological response: comparison of
machine learning algorithms

14. Applications
Ø Application pour déceler les substances toxiques:
Yuka, Inci Beauty, Pharmapocket
Ø Chimistes et biologistes
Ø Pharmaceutique

15. Data Science Bootcamp
Merci ! A la prochaine :)