IBM receives patent that could accelerate discovery of more effective and safer drugs

4/7/2017

ARMONK, N.Y. — IBM on Friday announced its scientists have been granted a patent ( U.S. Patent 9,536,194, “Method and system for exploring the associations between drug side-effects and therapeutic indications”) on machine learning models to predict therapeutic indications and side effects from various drug information sources, which could accelerate discovery of more effective and safer drugs.


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.


According to IBM, this approach could help researchers in pharmaceutical companies to generate hypotheses for drug discovery. For example, strongly correlated disease-side-effect pairs identified by the patented invention could be beneficial for drug discovery in many ways. One could use the side-effect information to repurpose existing treatments (e.g. drugs causing postural hypotension could be potential candidates for treating hypertension). If a new drug is being designed for a disease that is strongly correlated with severe side effects, then special attention could be paid to controlling the formulation and dosing of the drug in the clinical trials to prevent serious safety issues.


"As inventors at IBM, we have the opportunity to help solve real-world problems," said Jianying Hu, senior manager and program director, Center for Computational Health, IBM Research. "Our team is dedicated to this research and we continue to search for new ways to improve people's health around the world through innovation and invention."


Lack of efficacy and adverse side effects are two of the primary reasons a drug fails clinical trials, each accounting for around 30% of failures, IBM stated. Computational models and machine learning methods that can derive useful insights from large amounts of data on drugs and diseases from various sources hold great promise for reducing these attrition rates and improving the drug discovery process.


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