Where AI Meets Pharma

AI companies are set to transform the Pharma industry and Patients’ care. Israel with its AI expertise, proven success in innovation, and as a global startup investment hub, is taking an active role in this global change.

At Grove Ventures, we are proud to share that our portfolio company Nucleai, which transforms Immunotherapy treatment decisions and research through the power of AI, has secured its Series A funding round, led by Swiss biopharmaceutical group Debiopharm. This supports our investment thesis that Artificial Intelligence (AI) will continue to fundamentally change the pharmaceuticals sector in the coming years.

AI and Machine Learning have fundamentally transformed the pace and scale of drug discovery and development as well as delivery of patients’ care in the recent years. All top pharma companies have collaborated with or acquired AI technologies, including Novartis, Roche, Pfizer, Merck, AstraZeneca, GSK, Sanofi, Abbvie, Bristol-Myers Squibb, and Johnson & Johnson.

Tech Giants Recognize the Potential of AI in Pharma

Tech Giants are also quick to discover the potential of using AI to transform R&D and commercialization in the pharma industry. Google, Microsoft, Amazon, Apple, Oracle, IBM Watson and Intel have formed a number of notable partnerships with pharmaceutical and biotech companies, some to discover new targets, drug candidates, and biomarkers, others to optimize clinical trials and marketing and distribution. In June 2019, Sanofi announced a partnership with Google. Through a new virtual “Innovation Lab” Sanofi and Google are set to analyze real world data to understand what treatments work for patients as well as analyze manufacturing and commercial data to forecast sales and inform marketing and supply chain activities¹. In September 2019, Novartis has partnered with Microsoft and created an AI Innovation Lab, with initial focus on personalized therapies for macular degeneration, cell and gene therapy, and drug design².

Major pharma companies are participating in joint projects and consortiums which promote the use of big data and AI, including:

• The ATOM Consortium (Accelerating Therapeutics for opportunities in Medicine), which aims to leverage AI to go from drug target to patient-ready therapy in less than a year, which is an ambitious goal;
• The MELLODDY project (Machine Learning Ledger Orchestration for drug discovery), which will train machine learning models on datasets from multiple partners, while ensuring the privacy of each partner using federated learning³;
• The Alliance for Artificial Intelligence in Healthcare (AAIH);
• The MLDPS Consortium (Machine Learning for Pharmaceutical Discovery and Synthesis), which is a collaboration with MIT to develop software for automating small molecule discovery and synthesis⁴.

Solving Major Problems, from Diagnostics to Treatment

Pharma companies are facing major challenges to its business model, including continued decline on investment in R&D and pricing pressures in its major markets, especially the US, where a move from fee-to-service to value-based models has led to an immediate need to show pharma-economic benefit of drugs. Pharma companies are partnering with AI companies to solve these challenges in a number of ways:

Pharma is using AI in a number of ways to respond to the decline in R&D IRR, including to accelerate drug discovery and development, as well as increase the rate of success. There are over 100 AI companies today that assist drug developers in identifying and evaluating novel targets that affect the disease’s progression and regression, identifying promising drug candidates, and automating the design of novel molecules and synthesized compounds with desired properties for a specified disease. A number of AI companies are also working with pharmaceutical companies to repurpose their existing compounds for new indications. In clinical trials, there are fewer companies than in drug discovery, still a large number of AI players especially Big Tech are working with pharmaceutical companies to optimize clinical trials, including using big data and AI to predict super-responders to treatment with AI-powered biomarkers. In fact, the majority of clinical trials today require pharmacogenetics biomarkers to predict patient response. AI is also used to, monitor patient adherence during trials and build synthetic controls arms, using RWE to simulate trial results.

Beyond pharmaceutical and biotech company R&D, AI, NLP, and computer vision is disrupting the diagnostics and tools market, especially in the area of oncology where AI is used in high frequency for diagnostics, monitoring and augmenting evaluation of tumors as well as for creation of digital biomarkers enabling precision medicine.

Sensors, streaming data, and Machine Learning are assisting with predictive maintenance in manufacturing as well. IoT, sensors, and AI are being employed for supply chain optimization, cold chain tracking, and supply chain compliance.

AI in Pharma: A Bright Future

The potential of AI to transform Pharma R&D and Commercialization depends on its pace and scale of adoption of AI and big data and its appetite for collaboration with AI companies and Big Tech.

AI companies which target Pharma companies are built from multi-disciplinary teams which can bring innovation into Pharma companies.

💊 If you lead an AI company in Israel, which target Pharma, click here to be included in the “AI in Pharma — Israeli Eco-System” map.

[1] Sanofi and Google to develop new healthcare Innovation Lab, 6/18/2019.

[2] Novartis and Microsoft announce collaboration to transform medicine with artificial intelligence, 10/1/2019.

[3] MELLODDY | IMI Innovative Medicines Initiative, 6/1/2019.

[4] Applying machine learning to challenges in the pharmaceutical industry, 5/17/2018.