AI and Predictive Analytics Driving Efficiency in Drug Discovery

AI and Predictive Analytics Driving Efficiency in Drug Discovery

By Healthcare Tech Outlook | Monday, May 20, 2019

FREMONT, CA: AI applications such as machine vision and NLP feel that their capacity for ingestion and transformation of unstructured medical data can be of great value to pharmaceutical companies. However, for software that requires more prepared and structured pharmaceutical data, a relatively equal number of usage cases remain. Leaders in the domain of clinical studies can use a predictive analytics tool to compare their plans with the previous operations and procedures. A user can also search for last drug-based trials to find what practices have led to the success of the leading drug. AI software solutions can analyze patient profiles and medical history to find out which patients are best able to respond to the test medication.   

Pharmaceutical companies also have the opportunity to use predictive analytics to identify and establish best practices for clinical trials based on past test records or external databases. The documents would contain information on the operations of the clinical trial, the protocol, and the relative trial success. Through all these data, AI software can then search for examples of similar experiments with varying success levels; further, enabling a leader to compare his plans for a new trial with those for which the drug type would be unsuccessful.

The discovery of medicine or the techniques by which companies can isolate particular molecules and test their effectiveness for the treatment of diseases are another area of pharmaceutical predictive analysis. An essential step in driving efficiency in drug discovery is AI and data analysis. After an isolated and identified molecule, a company can use predictive analytics to assess the quality of the drug, when tested. Other technologies may have a part to play in facilitating data procurement to train machine-learning algorithms to utilize predictive analytics or possibly other AI applications for molecular drug analysis. This applies to Xtalpi, an AI vendor that combines predictive test analysis with a cloud-based high-process computer engine.

It is evident that predictive analytics in medicinal products are widely available in a variety of departments and applications. Business leaders can pick processes to automate with both clinical and administrative solutions, sold to the pharmaceutical industry. The same would enable them to remain in control and integrate new technology into their operations.

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