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.
IBM scientists have been granted a patent on machine learning mode
ls to predict therapeutic indications and side effects from various drug information sources.
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.