Industries, including pharmaceutical and healthcare, have witnessed incremental growth because of the coordinated efforts of Artificial Intelligence and Machine Learning.
The role of AI, for example, is said very substantial, given its contribution to the wholesome improvement of diagnostics tools in facilitating interaction with patients experiencing recuperation from surgery or expediting easy transport of medical samples and drugs.
In addition, artificial intelligence has journeyed different cycles of evolutions in healthcare industries, in terms of spearheading target discovery to adaptive clinical trial design.
Just imagine the enormity of time it would have cost in absence of AI for the healthcare industry. From incurring heavy overheads to mitigating the pace of development in drug discovery, the upshot would have dimmed the sight of success for the healthcare experts. The scientists in pharmaceuticals and biotech companies are eying for quality options paving the path for a boost in the efficiency of drug discovery. And leveraging on the possibilities presented by machine learning and AI seems an efficient alternative to this context.
From diagnosis to therapeutical applications, AI companies are leaving no stone unturned in getting involved in numerous treatment processes to ensure their eventual streamlining.
The application of artificial intelligence in healthcare sectors is construed as a wise step to decipher the countless ambiguously structured clinical data. The upshot of it is said to pave the path for easy comprehension of the cryptic clinical data and their systematic categorization. It will also ensure the development of physicochemical understandings leading to an effective discovery of fresh compounds serving medical objectives.
Moreover, the AI machine can also execute evidence-based predictions pertaining to whether or not the potentiality of a compound is identical to what scientists theoretically quested. Therefore, the involvement of AI in drug discovery holds a far-reaching magnitude in terms of exigency and relevance.
The Power of Artificial Intelligence & Machine Learning in Drug Discovery
The value regarding the use of AI in drug discovery can be understood with the fact that it has brought down the cost of introducing drugs to the market, substantially. The report indicates a projected boost of global drug discovery at a CAGR (Compound Annual Growth Rate) of 12.6% by 2025 from the anticipated figure of $713.4 million in 2016.
Post application of AI in drug discovery, the value of the drug market is taking an upward swing. In fact, if this report is to be believed, AI would contribute savings by $70 billion in the drug discovery process by 2028.
With the pharmaceutical industry having gone through the technological shift in machine learning, researchers can leverage innovative computational algorithms to facilitate the highly complex process of biomedical data and designing new drugs.
In fact, the role of machine learning in various stages of the drug discovery process is many, including –
- Initial yet critical stages involving designing the chemical structure of a drug.
- Ensuring the efficacy of a drug both in basic preclinical research and clinical trials (involving the production of numerous biomedical data). Machine learning will ensure to detect of fresh patterns in those biomedical data.
Conclusively, the application of AI and ML in drug discovery is said to deliver far-reaching effects constructively in a manner such as the healthcare industry will witness a dimensional scale of drug discovery outweighing the performance of outdated mechanism applied for the same purpose.
From major reduction in expenses to expediting the potentiality of drug compounds for medical efficacy in the future, these technologies and their roles in drug discovery would be super-beneficial for pharmaceutical companies.