AI in Pharmaceutical Drug Development

AI in Pharmaceutical Drug Development

By Healthcare Tech Outlook | Friday, May 03, 2019

A significantly faster, and low expensive drug discovery process with lower risk and more effective results are the prioritized targets for many pharmaceutical companies. A major revolution in digital technology augmented the natural capabilities in drug discovery with accelerated research and development. Advances in Artificial Intelligence poised to take this augmentation to the next level with greater accuracy.

With the emergence of Artificial Intelligence, the leaps of human rationality have been expanded. It enables drug researchers with a greater breadth and depth of data that is simultaneously more useful to optimize their decision-making capabilities in creating new drugs. This will be accomplished by making predictions about the reactions of a drug in a particular scenario.

Overview of AI for Pharmaceutical research and development covers the following possibilities.

Natural Language Processing (NLP)

It is a sub-set of Artificial Intelligence concerned with the interactions between computers and human (natural) languages, particularly to program computers to process and analyze large amounts of natural data. NLP-based software applications in drug research and discovery solutions can extract information from previous unstructured data sources to incorporate it into the testing of current and future drug molecules.

Predictive Analytics in drug discovery

Most of the pharmaceutical companies could use AI powered predictive analytics software to determine how likely a newly identified drug can work effectively in clinical trials. This helps researchers to identify the safest drug to test without any adverse effects.

AI for salt and polymorph testing

Determining a drug compound level of solubility in water and other liquids is one of the crucial steps in pharmaceutical research. Salt and polymorph testing or screening is the process to determine how long the drug can be usable before it expires. Pharmaceutical companies could use machine learning and AI applications to facilitate this process on crystalline structures of drug molecules to give the user an idea about the physical structure of drug under a microscope once it turns into a pill.

With all these contexts, it clearly states that a new wave of Artificial Intelligence technology dramatically improves the quality of drug discovery and testing process which may alter the revenue structure of pharmaceutical companies.

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