Development of a QSAR model to predict molecular inhibition of human STAT3

Patterson, Joshua; Milligan, Kimberly; Winstead, Cherese

doi:10.1101/2021.10.29.466511

2021

DOI: 10.1101/2021.10.29.466511

|View full text |Cite

Preprint

Development of a QSAR model to predict molecular inhibition of human STAT3

Joshua Patterson

Kimberly Milligan

Cherese Winstead

Abstract: Herein is described a method to develop a QSAR model to predict the molecular inhibition of human STAT3. The model was built using machine learning using Python 3.3. A random forest machine learning algorithm was deployed. The model produced a R2 value of 0.86, indicating that our QSAR model is effective at predicting inhibitory compounds of STAT3.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Ensemble Machine Learning Approach for Quantitative Structure Activity Relationship Based Drug Discovery: A Review

Noviandy,

Maulana,

Idroes

et al. 2023

Infolitika J. Data Sci.

View full text Add to dashboard Cite

This comprehensive review explores the pivotal role of ensemble machine learning techniques in Quantitative Structure-Activity Relationship (QSAR) modeling for drug discovery. It emphasizes the significance of accurate QSAR models in streamlining candidate compound selection and highlights how ensemble methods, including AdaBoost, Gradient Boosting, Random Forest, Extra Trees, XGBoost, LightGBM, and CatBoost, effectively address challenges such as overfitting and noisy data. The review presents recent applications of ensemble learning in both classification and regression tasks within QSAR, showcasing the exceptional predictive accuracy of these techniques across diverse datasets and target properties. It also discusses the key challenges and considerations in ensemble QSAR modeling, including data quality, model selection, computational resources, and overfitting. The review outlines future directions in ensemble QSAR modeling, including the integration of multi-modal data, explainability, handling imbalanced data, automation, and personalized medicine applications while emphasizing the need for ethical and regulatory guidelines in this evolving field.

show abstract

Ensemble Machine Learning Approach for Quantitative Structure Activity Relationship Based Drug Discovery: A Review

Noviandy,

Maulana,

Idroes

et al. 2023

Infolitika J. Data Sci.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Development of a QSAR model to predict molecular inhibition of human STAT3

Cited by 1 publication

References 8 publications

Ensemble Machine Learning Approach for Quantitative Structure Activity Relationship Based Drug Discovery: A Review

Ensemble Machine Learning Approach for Quantitative Structure Activity Relationship Based Drug Discovery: A Review

Contact Info

Product

Resources

About