The ability to estimate the probability of a drug to receive approval in clinical trials provides natural advantages to optimizing pharmaceutical research workflows. Success rates of a clinical trials have deep implications to costs, duration of development, and under pressure due to stringent regulatory approval processes. We propose a machine learning approach that can predict the outcome of trial with reliable accuracies, using biological activities, physico-chemical properties of the compounds, target related features and NLP-based compound representation.

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[1] Vidhya Murali, "Data - Predicting Clinical Trial Outcomes Using Drug Bioactivities", IEEE Dataport, 2021. [Online]. Available: http://dx.doi.org/10.21227/4wv9-q902. Accessed: May. 18, 2022.
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doi = {10.21227/4wv9-q902},
url = {http://dx.doi.org/10.21227/4wv9-q902},
author = {Vidhya Murali },
publisher = {IEEE Dataport},
title = {Data - Predicting Clinical Trial Outcomes Using Drug Bioactivities},
year = {2021} }
TY - DATA
T1 - Data - Predicting Clinical Trial Outcomes Using Drug Bioactivities
AU - Vidhya Murali
PY - 2021
PB - IEEE Dataport
UR - 10.21227/4wv9-q902
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Vidhya Murali. (2021). Data - Predicting Clinical Trial Outcomes Using Drug Bioactivities. IEEE Dataport. http://dx.doi.org/10.21227/4wv9-q902
Vidhya Murali, 2021. Data - Predicting Clinical Trial Outcomes Using Drug Bioactivities. Available at: http://dx.doi.org/10.21227/4wv9-q902.
Vidhya Murali. (2021). "Data - Predicting Clinical Trial Outcomes Using Drug Bioactivities." Web.
1. Vidhya Murali. Data - Predicting Clinical Trial Outcomes Using Drug Bioactivities [Internet]. IEEE Dataport; 2021. Available from : http://dx.doi.org/10.21227/4wv9-q902
Vidhya Murali. "Data - Predicting Clinical Trial Outcomes Using Drug Bioactivities." doi: 10.21227/4wv9-q902