The world of Generative AI has taken off in recent months. The new norms of AI drug development mark a paradigm shift in pharmaceutical research, ushering in an era where the power of advanced algorithms and machine learning techniques accelerate and refine the drug discovery process and drug development. Given what's at stake, it is of paramount importance to monitor the leadership paths and benchmarks set in the segment.
Cognizant and Nvidia's BioNeMo Platform
Cognizant and Nvidia have initiated the 'BioNeMo' platform to leverage GenAI for drug discovery. By leveraging gen AI technologies, healthcare leaders can rapidly sift through extensive datasets and more accurately predict interactions. Besides the high cost, the long development cycles and high failure rate of traditional drug discovery approaches in the life sciences sector demand the analysis of extensive scientific literature and clinical data repositories to uncover relevant insights.
“More than any other technological breakthrough in recent decades, generative AI has the potential to revolutionize the way new drugs are researched, developed, and brought to market, making the creation of lifesaving discoveries faster, smarter, and more accessible to all,” says Anna Elango, EVP, Cognizant's Core Technologies & Insights.
Leaders at Cognizant are planning to give clients access to a suite of model-making services, including pre-trained models, cutting-edge frameworks, and APIs, that offer them the quickest path to train and customize enterprise models using their proprietary data. The offering is intended to enable reduced manual intervention for data analysis.
"Generative AI will drive the next wave of enterprise productivity gains across industries, enabled by the Nvidia AI Enterprise software platform. Using Nvidia BioNeMo, Cognizant will help provide its life sciences clients with advanced, secure, and reliable AI services to drive improved outcomes with custom drug discovery applications," says Alvin DaCosta, VP, Global Consulting Partner Organization, Nvidia.
Reports suggest that the US-based firm considers health sciences to be its largest segment, surpassing BFSI (banking, financial services and insurance) with revenue of $1,396 million for the quarter, despite the decline in growth of 2.1 percent year-on-year and 2.7 percent on a constant currency basis. Cognizant reported a decrease in YoY revenue of 1.7 percent and a rise of 2.4 percent in CC total revenues of $4,758 million from October to December. The company is understood to have hired. Cognizant's life sciences offerings support more than 120 global manufacturing lines and more than 18 million patients with medical device company products.
Beyond medical care, Cognizant intends to pursue additional collaborations with Nvidia in industries such as manufacturing and automobile design, where next-generation artificial intelligence can enhance efficiency, reduce expenses, and accelerate market disruption.
To further innovate with Nvidia technologies, including the Nvidia Metropolis, Nvidia Omniverse, and Nvidia AI Enterprise platforms, Cognizant intends to establish an Nvidia AI Center of Excellence this year.
Industry Leaders' Opinion on Gen AI in Pharma
Indian healthcare leaders are largely positive that Gen AI won't replace the core healthcare workforce but will enhance their effectiveness, thereby releasing more supply into the healthcare system. According to the EY report around 84 percent of healthcare and life sciences firms surveyed believe that GenAI can positively impact workforce productivity, while 60 percent believe it could amplify existing workforce potential.
Suresh Subramanian, Partner & National Life Sciences Leader, EY Parthenon India, said, “Initially, the Indian life sciences organizations were cautious when it came to AI adoption, but numerous Gen AI applications are now actively involved in drug discovery and highly targeted treatments, which is likely to propel India onto the global stage for clinical trials. Pharma and medical devices are likely to follow Gen AI in customer acquisition, delivering personalized care, patient experience and outcomes, and process optimization across the value chain to enhance overall productivity. Companies need to focus on forming co-pilots in many of the above areas and incorporating the necessary abilities into fresh ways of working.”
Kaivaan Movdawalla, Healthcare Leader, EY Parthenon India, says, “In India's healthcare sector, an air of cautious observation pervades most healthcare institutions. Decision support, medical imaging, and precision medicine are some of the areas where artificial intelligence has been adopted recently. There are significant demand and supply gaps and a lack of clinical and non-clinical talent in the healthcare sector; for example, there are only 64 doctors per 100,000 patients, compared to the global average of 150 per 100,000 patients.”
When Interacting with Emerj Artificial Intelligence Research Sina Bari, AVP, iMerit Technology, says, “The entire patient health data is not simply a matter of identification. I can't mask someone's health history, which is a totally different and potentially identifiable piece of information, even if you give them their name and medical record number. Precision health is achieved because every health problem happens within the context of a longitudinal patient history. To handle all that information, we need to adhere to the strictest data protection guidelines.”
Milind Sawant, Founder & Lead, AI/ML & DFSS Center of Excellence, Siemens Healthineers, says, “People think AI can solve all problems because of a lack of understanding. In the pharmaceutical industry, a customer asked if we could use generative AI because my boss wanted me to incorporate it into our workflow. The scenarios they encountered didn't require generative artificial intelligence; they could employ a conventional machine learning algorithm. However, they are being pressured by management, who may or may not be aware of the capabilities, strengths, and limitations of GenAI. They are forcing generative AI because they want to tell their top boss they are using it, and now are struggling to find a use case for it.”
Proper data architecture is required for Gen AI to deliver results. An intelligence layer that can understand issues such as molecular structures, clinical operations, and patient data is needed. A multipronged approach will be necessary to create a data infrastructure that can run internal and external data sets. This isn't just a technical issue: data scientists will need to work closely with experts in corporate planning, health care, legal, and risk management to establish priorities and execute plans.
Furthermore, artificial intelligence-based algorithms can enhance existing substances, altering their morphologies to enhance effectiveness or minimize adverse effects, thereby accelerating the drug optimization process. Including artificial intelligence in the drug discovery process allows pharmaceutical firms to not only uncover fresh drugs more quickly but also refine and enhance existing treatments, opening up fresh possibilities in personalized healthcare and specialized treatments.