Discovery of a Novel Photocaged PI3K Inhibitor Capable of Real-Time Reporting of Drug Release

Tian, Hua; You, Shen; Xiong, Tao; Ji, Ming; Zhang, Kehui; Jiang, Lin; Du, Tingting; Liu, Ying; Liu, Wenqian; Lin, Songwen; Chen, Xiaoguang; Xu, Heng

doi:10.1021/acsmedchemlett.3c00240

Cited by 2 publications

(1 citation statement)

References 42 publications

(78 reference statements)

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“…Furthermore, ensemble QSAR models are expected to play a pivotal role in real-time drug discovery and personalized medicine [95]. Tailoring drug recommendations and treatment plans to individual patients' genetic and molecular profiles will rely on highly accurate and adaptable ensemble models [96].…”

Section: Future Directions In Ensemble Qsar Modelingmentioning

confidence: 99%

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

Noviandy,

Maulana,

Idroes

et al. 2023

Infolitika J. Data Sci.

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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.

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Section: Future Directions In Ensemble Qsar Modelingmentioning

confidence: 99%

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

Initiation of Fluorescence and Bioactivity Using UV Light Photocage on PI3K Inhibitor

2023

Synfacts

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Discovery of a Novel Photocaged PI3K Inhibitor Capable of Real-Time Reporting of Drug Release

Cited by 2 publications

References 42 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

Initiation of Fluorescence and Bioactivity Using UV Light Photocage on PI3K Inhibitor

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