Machine Learning: The Much Needed Booster For Pharmaceutical Industry
Understanding how machine learning is impacting research in pharmaceuticals
The healthcare industry, especially the pharmaceuticals, is a goldmine of data. This data originates from companies, institutions, and hospitals, during research projects, clinic studies, patients records, and much more. However, finding relevant insights from this sea of data can be intimidating. Artificial Intelligence and Machine Learning are two disruptive technologies that are bringing revolutionary changes across industrial verticals. The ability of machine learning to mine data patterns has enabled it to infiltrate this industry and create an unparalleled impact. According to a McKinsey report, machine learning, along with big data, could generate nearly US$ 100 billion in the pharmaceutical sector alone.
The pharmaceutical industry faces two major challenges. One is the drug discovery process; the other is data sharing and regulation. Machine learning can help in making the synchronicity of data, analysis, and innovation are an everyday reality. The drug discovery process can often be exceedingly expensive and time-consuming since researchers have to study accounts of hundreds of available drugs, search for new ways to extract and synthesize a new compound, the process goes on and on. But using machine learning to focus on screening different compounds one at a time, these researchers can see if they can find a bond with the virus’ main protease or protein. This helps in accelerating the new drug development in a shorter time span. Further, machine learning offers tremendous opportunities to more efficiently access and understand vast amounts of chemical data — with great potential to improve both processes and outcomes. Therefore, this discipline of AI is quickly emerging as a vital tool for those that want to stay competitive in this dynamic industry. Let us explore how machines optimize this industry in detail.
Personalized Treatment: Also known as Behavioral modification, it is an effective treatment based on individual health data paired with predictive analytics. This allows physicians to select from more limited sets of diagnoses, for example, or estimate patient risk based on symptoms and genetic information. With the proliferation of microdevices and biosensors, this application will soon become mainstream in offering the best treatment facility with more refined health measurements and remote displaying capabilities.
Drug Discovery: As mentioned earlier, machine learning can help plan chemical synthesis pathways and help identify which chemical parts within a molecule contribute to particular properties. This will lead scientists to explore new chemical spaces, increase chemical diversity, and give them a larger opportunity to identify suitable compounds that will have specific biological functions. So whether it is designing and identifying new molecules or target-based drug validation and discoveries, machine learning can contribute to them all.
Patient Recruitment and diagnosis: By better matching patients to trials based on specific criteria using with machine learning, hospitals, and even pharmaceutical companies can draw up medical patterns of a patient– like symptoms, medications, data from wearable devices, labs, etc. The information can then be used for a timely and better diagnosis, tracking progression, and recommending personalized treatments. Because this technology possesses the ability to process and analyze massive amounts of data quickly, it helps speed up the diagnosis process, thereby helping save millions of lives.
Disease Prevention: Companies can use Machine learning to develop cures for both known diseases like Alzheimer’s and Parkinson’s and rare diseases. Generally, pharmaceutical companies do not spend their time and resources on finding treatments for such diseases since the ROI is very low compared to the time and cost it takes to develop drugs for treating rare diseases. This and AI can explore hundreds of genes that are responsible for disease sources and help to build exceptional solutions to reduce the risk of Alzheimer’s or ALS.