The use of artificial intelligence (AI) has been increasing in various sectors of society, particularly the pharmaceutical industry. In this review, we highlight the use of AI in diverse sectors of the pharmaceutical industry, including drug discovery and development, drug repurposing, improving pharmaceutical productivity, and clinical trials, among others; such use reduces the human workload as well as achieving targets in a short period of time.
Understanding unmet needs
Enabling a comprehensive overview of therapeutic areas and identifying related trends can help in streamlining development processes. Discovering underserved therapeutic areas for drugs and pathways of interest can help move pharma companies in the right direction. Not only do cutting-edge AI technologies help in commercializing, they also support medical affairs, specifically in understanding unmet needs. Real-time updates in trends for various therapeutic areas can enable pharmaceutical companies to advance successfully in the drug development process.
Insights into competitor strategy
Pharma companies can generate relevant insights for competitive advantage using artificial intelligence technologies to understand patent-based dynamics. Network analysis technology can help in gaining information on desired markets, mapping out competitor activities, monitoring patent trends, staying up to date on marketed drugs, and finding top researchers for the preferred geography, Companies can use these capabilities to transform their R&D game and establish themselves as leaders in the therapeutic area.
Identifying biomarkers
Identifying connections between biological entities is like finding a needle in a haystack. It is not just challenging but also time-consuming. Research graphs developed using network analysis can facilitate the discovery of associations and possible interactions between closely and distantly related entities, such as drugs, pathways, genes, and targets. AI can provide modelling of protein-protein interactions in silico and assess binding affinities by analyzing life sciences big data.
