The pharmaceutical sector is always under pressure to provide safe and effective healthcare solutions with no delays or issues. Taking into account the time crunch, businesses must use cutting-edge technologies to streamline traditionally lengthy processes. This is how artificial intelligence has appeared in the field and is increasingly being used in pharmaceutical research and development.
According to a recent McKinsey study, pharmaceutical businesses were already employing AI in many cases before the general public became aware of generative AI. Even yet, multimodal AI in healthcare has enormous potential – $60 billion to $110 billion in new economic value for the sector each year. Expect AI-powered technologies to have a significant impact on medicine discovery and development, time to market, and other elements of R&D. Keep reading to know how corporations use AI to reduce the cost of pharmaceutical R&D and what's on the horizon.
Key Functions of AI in Pharmaceutical Research and Development
The pharmaceutical sector has generally been hesitant about embracing new technology. There are many reasons explaining this hesitance. The potentially life-changing nature of pharmaceutical products, stringent restrictions, and significant financial risks associated with drug development were always the main constraints. Nevertheless, industry executives realize the enormous potential of AI, particularly generative AI, in pharmaceutical R&D. These technologies are changing the way new pharmaceuticals are found, produced, tested, and introduced to the market. That’s why more and more companies are starting to use AI.
Applications of AI in Pharma
Modern AI solutions can help firms access information quicker, enhance processes, and unleash creativity. The potential applications of AI in the pharmaceutical and life sciences sectors are only limited by creativity and technological boundaries. Here are some of the most common applications of AI in pharmaceuticals, which will continue to expand as new capabilities emerge.
#1 — Target Identification
Target identification is critical in drug development, especially for complicated diseases such as colorectal and pancreatic malignancies. Companies such as Phenomic AI and Boehringer Ingelheim are using AI to better identify cancer targets. This AI-driven technique enables researchers to leverage digital screening and experimental validation to enhance assessments of these particular tumors, perhaps revealing better therapeutic targets.
Virtual AI screening is an effective method for comparing chemical structures to targets, estimating binding probabilities, and finding potential targets for future investigation. All of this can be done on a larger scale and faster than earlier computational or manual efforts.
#2 — Drug Design
AI technology is speeding up conventional drug design methods. For example, Cradle, a biotech firm, is employing generative AI to accelerate protein design and optimization. The business has a large number of industrial partners and is working on more than ten R&D projects that concentrate on generative AI capabilities for protein modalities.
Separately, university academics are using AI techniques to accelerate their study of Parkinson's illness. An AI engine swiftly analyzed a library of chemical compounds and selected a small number of intriguing compounds for future investigation.
#3 — Knowledge Management
Roche has used artificial intelligence to ensure fast and easy access to essential information throughout its worldwide network for further verification and insights. This artificial intelligence-driven strategy has changed Roche's communication and innovation. The changes have impacted not only operations; Roche is fostering an integrated culture of information sharing that benefits both corporate success and patient outcomes.
Roche has witnessed significant growth in platform engagement after implementing Starmind in 2020, with user numbers expected to increase from 1,000 to over 9,000 by 2024. The platform's AI capabilities have saved over 91,000 hours previously spent looking for information, demonstrating a considerable improvement in operational efficiency.
#4 — Clinical Trial Documentation
Clinical trial documentation is an essential yet time-consuming procedure that may cause delays in medication development. Generative AI techniques are being utilized to generate complicated texts including clinical trial reports, patient narratives, and summary clinical safety records. Employees spend less time creating, evaluating, and approving these critical components.
For example, Yseo employs pre-trained big language models designed exclusively for biopharma applications. These artificial intelligence systems compile clinical documentation automatically, producing over 10,000 reports by 2023 and saving thousands of hours of human work. The startup plans to automate other areas of document processing, such as FDA clearances.
#5 — Manufacturing
AI can help pharmaceutical companies like Amgen increase operational efficiency and speed up the manufacturing of critical drugs. The company collaborated with Amazon Web Services to improve the throughput and reliability of pharmaceutical manufacturing. The new production facility of Amgen will use Amazon SageMaker and other machine learning technology to evaluate manufacturing data in real-time.
It Is Just the Beginning
AI in pharmaceutical research and development is revolutionizing the whole process, from drug discovery and design to clinical trials and production. Companies are already using AI to reduce the cost of pharmaceutical R&D, optimize processes, and reduce time to market. Taking into account how AI impacts the development of custom-branded apps, more is to come in the pharma industry in the future.
