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Related Experiment Video

Updated: Sep 22, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning.

Vidhya Murali1, Y Pradyumna Muralidhar2, Cassandra Königs3

  • 1Department of Computer Science and Engineering, Amrita School of Engineering, Bengaluru, India.

Chemical Biology & Drug Design
|May 19, 2022
PubMed
Summary

Predicting clinical trial outcomes using machine learning improves drug development. Our model, incorporating biological activity, achieved 93% accuracy, highlighting bioactivity

Keywords:
bioactivityclinical trialdata integrationensemble algorithmsgraph databasemachine learning

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Area of Science:

  • Pharmacology and Cheminformatics
  • Computational Biology and Bioinformatics

Background:

  • Clinical trial success rates significantly impact pharmaceutical research costs and timelines.
  • Stringent regulatory processes increase pressure on drug development.
  • Accurate prediction of trial outcomes is crucial for optimizing research workflows.

Purpose of the Study:

  • To develop a machine learning (ML) approach for predicting clinical trial outcomes.
  • To investigate the utility of biological activities as predictors for trial success.
  • To create an integrated dataset linking clinical trial information with protein targets.

Main Methods:

  • Utilized a Random Forest classifier, an ensemble learning method.
  • Incorporated features such as biological activities, physicochemical properties, target-related features, and NLP-based compound representations.
  • Extracted drug-disease pairs and mapped corresponding protein targets from multiple data sources.

Main Results:

  • Achieved an average accuracy of 93% and an F1 score of 0.96 for predicting trial success ('Pass' class).
  • Demonstrated that biological activity is a statistically significant predictor of clinical trial outcomes.
  • Ensemble learning models outperformed smaller, independently trained ML models.

Conclusions:

  • The developed ML model reliably predicts clinical trial outcomes, particularly success.
  • Biological activity is a key factor in determining clinical trial success.
  • The open-source dataset and code facilitate further research in predicting drug development success.