Ingenious e-Brain Solutions forecasts a considerable trend for “AI democratization” where various machine learning/deep learning technologies become available in pre-trained, pre-conﬁgured “off-the-shelf” formats, or relatively ready-to-use formats — via cloud-based models, frameworks, and drag-and-drop AI-pipeline building platforms (for example, KNIME), companies, like Google, Tencent, Nvidia, Microsoft, and others, are expanding into healthcare space via not only offering specialized services and tools to pharma counterparties but also directly investing into biotech start-ups and larger companies, creating own centres of excellence and commercial subsidiaries with focus on Life Sciences.
AI is regarded by some top executives at big pharma (Novartis, Pfizer, GSK and others) as a tool to uncover not only new molecules but also new targets. The ability of deep neural networks to build ontologies from multimodal data (e.g., “omics” data) is believed to be among the most disruptive areas for AI in drug discovery, alongside data mining from unstructured data, like text (using natural language processing, NLP).
In this report, the use of artificial intelligence or any other computational algorithm for drug discovery, biomarker development, and advanced R&D is highlighted along with technology providers in pharma industry, computational methods used by the most advanced AI companies, an overall growth of investment and business development activity in the area of pharmaceutical AI along with some other sections which are listed in the table of content of the report.
The technology providers in Pharmaceutical Industry are profiled in the report into 6 categories: Artificial Intelligence (R&D Platforms), Quantum Computing, Supporting Services, “Big Tech” Corporations, Autonomous Labs, and Big Data Providers.
IEBS has also highlighted certain challenges which are faced by the pharma sector during drug discovery and their solutions that could be implemented.
A new drug takes an average of ten years to reach the market. According to Tufts University’s Center for the Study of Medicine Development (CSDD), the cost of producing a new prescription drug that receives FDA clearance is around $2.6 billion as of 2014. The pharmaceutical industry is reaching the end of its life cycle, and the returns on new treatments that do make it to market no longer justify the vast investments that pharma makes in R&D.
•Insilico Medicine announces the preclinical candidate for kidney ﬁbrosis discovered using end-to-end Artiﬁcial Intelligence engine. The preclinical candidate has the desirable pharmacological properties, pharmacokinetic proﬁle and demonstrated auspicious results in in-vitro and in-vivo preclinical studies. (Feb 2021)
• Cyclica launched an AI-based drug discovery platform that achieved over 60% of actionable hits for its pharma clients. Cyclica has partnered with over 100 global pharma and biotech companies and academia across many therapy areas. These partnerships have resulted in 64% of programmes resulting in actionable hits. (March 2021)
•Microsoft Cloud for healthcare brings together trusted capabilities to customers and partners that enhance patient engagement, empower health team collaboration, and improve clinical and operational data insights to improve decision-making and operational eﬃciencies.
•Tencent is entering pharma and healthcare space with a focus on AI-based platforms for analysis of research and clinical data. Also, it promotes innovations in hospitals via the digitalization of medical services and pharmaceutical supply chains. It also uniﬁes one of the largest online communities of doctors and providers for telemedicine services.
•In May 2021, Microsoft partnered with robotic surgery startup Cmr Surgical on a proof of concept to store clinical data from cmr’s next-generation surgical robot, Versius, into a glass, marking an exciting step-change for the future of health data. Rich data from hundreds of surgical robotic procedures can be stored safely and securely into one Microsoft project silica glass platter and could ultimately be harnessed to improve patient outcomes. Also, Microsoft participated (via m12) in the $21.5m series a round for lab automation startup artiﬁcial.
•In March 2021, Amgen announced that it has entered into a multi-year partnership with Mila – Quebec Artiﬁcial Intelligence Institute.
•In April 2021, NVIDIA announced a strategic partnership with Schrödinger to expand the speed and accuracy of Schrödinger’s computational drug discovery platform and enable rapid, accurate evaluation of billions of molecules for the potential development of therapeutics.
• What are the technological advancements of artificial intelligence in the pharma industry by key players?
• What are the notable technological evolutions of top players?
• What are the challenges faced by pharma industry and how they have been addressed using artificial intelligence?
• What are the applications and use cases for AI in advanced R&D?
• What are the most used computational methods for AI?
• Who are the top publicly traded companies in the domain?
• What are the recent investments, deals, and collaboration for AI in pharma?
• What is the geographical presence of AI for the pharma industry?
• Competitive benchmarking of specific companies against your company
• Cross-segment market analysis
• Consulting on any specific application
• Whitespace analysis in any specific technical domain
• Any other customization requirement of yours
Table of Content of the Report is:
1.1. The Pharma Industry’s Increasing Influence of Technology Providers
1.2. Selected Industry Developments: Overview (Pharma)
1.3. Recent advances in the drug AI industry
1.4. Companies Working Around Different Applications of AI In Pharmaceuticals
2.1. Big Pharma’s’ AI-focused partnerships
2.2. Key Business Takeaways
2.3. Key Technology Takeaways
2.4. AI in the Global Context
3.1. Technology Evolution of Few Key Players
3.2. Key challenges of Pharma industry and how are they addressed
3.3. Case Studies: Technology Providers in Pharma Industry
3.4. AI for Advanced R&D: Applications and Use Cases
3.4.1. Significant Research and Development Use Cases of AI Application in Biopharma
3.5. Computational Methods employed by the Most Advanced AI Companies
4.1. Investment, Deals & Collaborations
4.2. Leading Companies & Investors in Pharmaceutical AI
4.3. Top Publicly Traded Companies in the AI-Pharma Industry
5.1. New Business Models incorporating technological advancements
5.2. Investment and Funding Details
5.3. Geographical Presence: AI for Drug Discovery