Deep Pharma Intelligence (DPI) in collaboration with the Data Science Division of Deep Knowledge Group (DKG) has developed a new AI in Drug Discovery Framework which sets the precedent as the most comprehensive and up-to-date classification system for sector and industry analysis available to date. DPI's release of an open-access version of their full proprietary framework makes it easier for industry participants and stakeholders to compare businesses internationally, and focuses on the technological aspect of each company's activity.
View AI in Drug Discovery Industry Analytical Framework Here: www.frameworks.technology/ai-in-drug-discovery
View Full Framework Documentation Here: www.frameworks.technology/ai-in-drug-discovery-full-documentation
View Deep Pharma Intelligence Big Data Analytics Dashboard Here: www.deep-innovation.tech/pharma-dashboard
Top-level classification categories featured by the framework include Focus on Applications of AI for Drug Discovery, Focus on Application of AI for Oncology Diagnostics and Treatment, and Established Drug Discovery-Oriented Entities. The last segment contains five larger subsegments, namely, Early Drug Development, Clinical Drug Development, Preclinical Development and Automation, End-to-End Drug Development, and Data Processing.
For the past decade, Deep Knowledge Group has been developing the most practical means of advancing, optimizing, predicting, and coordinating the trajectory of Artificial Intelligence in Drug Development's constant advancement and the careful, de-risked, and socially responsible delivery of its benefits for humanity, first through the activities of the Pharma Division of Deep Knowledge Analytics (, and from 2020 on with its dedicated JV with BioPharmaTrend.com, Deep Pharma Intelligence.
To this end, Deep Knowledge Group has developed the Artificial Intelligence in Drug Discovery Industry Analytical Framework as a thorough and comprehensive framework for sector and industry analysis that makes it easier to compare businesses internationally and focuses on the technological aspect of each company's business activity.
In 2022, a prototype of this Industry Analytical Framework formed the basis of the Deep Pharma Intelligence IT-Platform and Big Data Analytics Dashboard, tracking the status of 700 companies, 1,400 investors, 260 collaborations, various clinical trials, publications, news, and a large amount of other valuable information. The Dashboard delivers advanced market intelligence, interactive mindmaps, benchmarking for companies, investors, and technologies, competitive and SWOT analysis.
The Dashboard is based on more than 170 parameters, allowing for the analysis of the financial position, marketing efforts, business development, intellectual property, product development, and many other aspects of the companies included in its analysis. This, in turn, facilitates the identification of trends, benchmarking the performance of key players that form the space and relations within the industry, allowing investors and companies to stay ahead of rapid technological developments in their respective sectors and industry.
The Dashboard provides insights into emerging areas of medical research and technology, including pharmaceuticals, BioTech, medical devices, diagnostics, and HealthTech. The Dashboard is updated every day to keep the users informed about the latest changes in the industry.
The Dashboard's applications include:
- Selection of companies by various parameters
- Investment landscape profiling
- Automatic AI-based SWOT analysis of companies and technological sectors
- Identification and analysis of competitors
- Comparative analysis of each company in the Dashboard's database
- An Interactive Chart Builder allows the user to create charts based on their desired parameters
- A portfolio constructor
The main aim of Deep Pharma Intelligence's new AI in Drug Discovery Analytical Framework is to share the comprehensive metric tools that made this possible.
The Artificial Intelligence in Drug Discovery Industry Analytical Framework comprises two main components:
- An Industry Classification Framework (ICF) for assigning industry entities to sectors and subsectors without which no further analysis is possible.
- A SWOT analysis methodology and set of associated parameters, a framework for identifying and analyzing an organization's strengths, weaknesses, opportunities, and threats.
How are these frameworks and parameters devised? As the first step, DKG's flagship pharma-focused analytical subsidiary Deep Pharma Intelligence (DPI) collects data from multiple sources. Then, the sources are consulted on various scientific and financial information about companies: companies' selection, investors, financial rounds, IPO status, total funding amount, patents information, scientific publications, news, collaborations information, etc. Source data is then subject to data accuracy review, and a data aggregation pipeline process includes the search for approximately 170 different parameters, from the number and type of patents and H-index of the companies' representatives to website visiting dynamics, financial indicators, and much more. After the data aggregation, we clean and transform the data, so that they can be used for the SWOT creation.
The increasing mega complexity of this multifaceted industry comprising an increasing number of intersecting sectors, shaped by a widening range of technological, ethical, and regulatory factors, makes comparative analysis and identification of key trends, challenges, and opportunities a task that demands increasingly sophisticated analytics. The Artificial Intelligence for Drug Discovery Industry Analytical Framework and Dashboard make these analytics possible.
Key Industry Trends, Take-Aways and Findings from the Framework:
Based on the collected data from AI in Drug Development Framework and The Big Data Analytics Dashboard, we can highlight some main takeaways for AI in Drug Development Industry in 2022. Big Pharmaceutical companies are very interested in the growth and development of AI companies.
This interest can be observed not only in the high amount of collaboration between pharmaceutical companies and AI companies but also in the direct investments of big pharma in AI companies. In 2022, Roche invested $290M in Freenome, Pfizer invested $200M in Sema4, and Sanofi invested $100M in Exscientia. Besides, we can say with certainty that 80-90% of Big Pharma companies have started to implement AI technologies in their R&D.
Early drug development and Data Processing sectors have the biggest number of companies among Established Drug Discovery-Oriented Entities. It can be explained by the fact that in the early stages of drug development, there is a wealth of data available on molecules, proteins, and genes, which can be used to train AI models. This data is often more limited in later stages of drug development when trials are underway and data on patient outcomes become available. Also, clinical trials involve a lot of manual work and cannot be fully automated with AI.
End-to-end drug development companies which offer a full range of services from drug discovery to commercialization often require larger investments that can be explained by such reasons as the greater scope of services, longer development timelines, greater regulatory and compliance requirements, and greater competition. That is why companies from this sector usually have higher investments even with smaller numbers of companies compared to other sectors.
In addition to the Dashboard, Deep Pharma Intelligence is regularly providing open industry reports, covering high-growth sectors in the Life Sciences, including Artificial Intelligence, digital health and new therapies. These reports reflect hot market trends and advanced technologies, and provide a financial overview of big pharmaceutical players as well as promising start-ups.
As always, DPI remains open to discussions, partnership proposals, and other forms of collaboration and cooperation on the subject of AI and Drug Development with like-minded individuals and organizations across finance, investment, technology, and science fields. Interested parties are encouraged to contact them at [email protected] for inquiries.