Machine Learning Models for Drug Discovery
At the core of stakes, we have: Lack of efficacy and adverse
side effects, drug failures in clinical trials; Computational models and
machine learning methods that can derive useful insights from large amounts of
data on drugs and diseases from various sources; reduced attrition rates and
improved drug discovery process.
I can
observe that, IBM Research has implemented a cognitive
association engine to identify significant linkages between predicted
therapeutic indications and side effects, and a visual analytics system to
support the interactive exploration of these associations.
This approach could help researchers in pharmaceutical companies to generate hypotheses
for drug discovery.
IBM was
granted U.S. Patent 9,536,194: Method and system for exploring the associations between drug
side-effects and therapeutic indications for this
invention.
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