Research on anti-breast cancer candidate drugs analysis based on random forest model and genetic algorithm

Mei Yue

doi:10.1117/12.2692888

23 August 2023 Research on anti-breast cancer candidate drugs analysis based on random forest model and genetic algorithm

Mei Yue

Author Affiliations +

Proceedings Volume 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023); 127840F (2023) https://doi.org/10.1117/12.2692888
Event: 2023 2nd International Conference on Applied Statistics, Computational Mathematics and Software Engineering (ASCMSE 2023), 2023, Kaifeng, China

Abstract

Breast cancer is one common cancer with high fatality rate worldwide. Based on consideration of pharmacokinetics and toxicity, this study aims to build a model to investigate compounds which can be used for candidate drugs to resist breast cancer. Using SVR model, GBRT model and random forest algorithm, this study established a quantitative prediction model for the bioactivity of compounds to Erα, and SVM model, SGD model and random forest algorithm were constructed for predicting the 5 ADMET properties of the compounds. Finally, based on the above models and analysis, compounds with better biological activity against ERα and better ADMET properties were selected in this paper. This study definitively established the model for selecting compounds which can be used as anti-breast drug candidates, and further research is needed to add more pharmacokinetic properties in the model to improve its applicability.

Citation Download Citation

Mei Yue "Research on anti-breast cancer candidate drugs analysis based on random forest model and genetic algorithm", Proc. SPIE 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023), 127840F (23 August 2023); https://doi.org/10.1117/12.2692888

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available