How AI and ML can Speed Up the Process of Drug Discovery

By Pharma Tech Outlook | Monday, January 07, 2019

The current drug discovery processes need a dramatic shift in order to meet the requirements of the healthcare sector. Artificial Intelligence (AI) and Machine Learning (ML) in particular present real opportunities to do research and development activities differently to the pharmaceutical industry, also to operate more efficiently and substantially in drug development. The long-term benefit offered by ML and AI is that the vast resources and money can be deployed more effectively to develop drugs in the current process. This will help in better return on the investment and substantial increase in the delivery of new medicines for chronic diseases.

The current drug discovery process is too lengthy and expensive. It takes years to translate a drug discovery idea to a market-ready product. Identifying the right protein to manipulate in a disease, proving the idea, optimizing the molecule for the delivery to the patient, carrying out preclinical and clinical safety and efficiency tests are essential steps. But this long process contrasts with the rapidity of innovation in other industries.

 The drug discovery process can be greatly aided by the latest innovations of AI and ML. A biomedical researcher is dealing with huge amounts of information every day. This can be sorted and used effectively with the help of these innovations. AL and ML play a vital role in augmenting the work of drug development researchers. The first analysis of the mass scientific data can be carried out form essential knowledge.

These technologies are also aiding in identifying promising targets and in identifying patients for clinical trials. This enables companies to identify issues with compounds much earlier when it comes to competence and safety. So the industry has much to gain by adopting AI and machine learning approaches thereby accelerating the process of drug discovery.

In a nutshell, AI and ML is aiding drug development by

• Finding new insights into disease

• Identifying possible upshots of a drug by testing molecular compounds

• Repurposing existing drugs

• Manipulating genetic materials

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