2022
DOI: 10.26434/chemrxiv-2022-lw86k-v2
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Predicting clinical trial outcomes using drug bioactivities through graph database integration and machine learning

Abstract: 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 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 the trial with reliable accuracies, using biological activities, physicochemical properties of the compounds, target-rel… Show more

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