Data science to accelerate drug discovery with artificial intelligence and machine learning: report
Pharmaceutical companies and hospitals are adopting data science rapidly, and its application is going to be established in all branches of healthcare
SANTA CLARA, Calif. — Frost & Sullivan’s recent analysis, Data Science Impacting the Pharmaceutical Industry, finds that data science tools are promising technologies transforming drug discovery costs, speed, and efficiency. When combined with other emerging tech areas, artificial intelligence (AI) technologies move to the next phase of advancements. Hence, they are expected to witness adoption by pharma and biotech companies in the next four to five years.
Further, with the COVID-19 pandemic, AI and machine learning (ML) can be used for drug research and clinical trials against the coronavirus to screen large databases and perform docking studies to identify existing potential drugs or design new drugs using advanced learning algorithms.
“Applying data science tools in healthcare, especially for drug discovery, has a huge potential to systematically change the entire existing practices and methods,” said Aarthi Janakiraman, Technical Insights Research Manager at Frost & Sullivan, in a prepared statement. “Additionally, pharmaceutical companies and hospitals are adopting this system rapidly, and its application is going to be established in all branches of healthcare.”
Janakiraman added: “Integrating AI and ML methods into drug discovery pipelines would cut down cost and time, and increase the efficiency of the entire research and development (R&D) process. Going forward, big pharma and mid-sized biotech companies can benefit by partnering with core AI startups and reducing the costs involved in setting up their own capabilities.”
Integrating data science in drug discovery and clinical trials presents immense growth prospects for market participants, including:
- Partnering with emerging AI companies will boost research momentum and help speed up clinical trials.
- Using AI for therapeutics and screening, including for COVID-19, as it provides access to a large library of compounds and can present a lower risk of unexpected toxicity or side effects in human trials.
- Developing AI-assisted drug safety and toxicity science that are at a nascent stage, yet evolving. This requires further research and collaborations to evaluate the potential clinical impact. Large amounts of newly available data provide an opportunity to leverage AI and ML to improve drug safety.
- Modernizing clinical trials as the FDA has created Master Clinical Trial Protocols (MAPs) to increase trial efficiency and lower costs. MAPs use common clinical trial infrastructure to streamline trial processes and enhance data collection.