While the release of ChatGPT brought Generative AI into the public consciousness, Flagship Pioneering has been incorporating AI and Machine Learning into our companies for some time. In this introduction, we'll look at some of the recent trends in industry and government and how they factor into our strategic tenets.

Real-world data (RWD) is revolutionizing the pharmaceutical industry by offering valuable insights beyond clinical trials. It allows for the assessment of drug effectiveness, patient outcomes, and safety in real-world scenarios. However, the heterogeneity of data types, varying degrees of data completeness and incompatible legacy systems create significant challenges and opportunities to perform any scientific study. In this talk, we will discuss how Generative AI can address challenges to enhance data-driven decisions, help identify trends, correlations, and insights aiding researchers.

We have developed the AbbVie Intelligence platform—an enterprise-scale suite of capabilities enabled by generative AI and large language models. It includes Chat (a GPT-powered chat assistant), Analyze (capable of summarizing or rewriting entire documents), Translate (machine translation, customized for AbbVie), and Ask a Source (which grounds LLM answers in trusted knowledge sources). This is the first step on a broader roadmap for enabling our teams to leverage generative AI safely and effectively. This talk shares valuable lessons learned on how to scale LLM services to the enterprise, as well as useful insights on the use of retrieval-augmented generation, vectorization, database query generation, and other ways to ground LLMs in real knowledge.

In this talk, we delve into the hype, hope, and reality of Generative Artificial Intelligence (AI) in the realm of general drug discovery, Generative Chemistry and retrosynthetic analysis. Our work explores how AI and machine learning (ML) models, particularly large language models (LLMs), can be effectively utilized for complex retrosynthesis processes and drug discovery tasks. We scrutinize the practicality of AI in predicting novel, viable chemical entities, and its role in accelerating the cycle times and reducing the cost of drug discovery. The presentation will offer a balanced perspective, analyzing both the potential benefits and limitations of AI in this context, including the challenges in validating and interpreting AI-generated output. The ultimate goal of our research is to demystify the role of AI in drug discovery and establish a clear understanding of its current capabilities and future prospects in the field.

Generative AI has captured the imagination of many. ChatGPT can write articles, create images, and develop code. The question this talk asks is: Can we engineer biology using these generative AI techniques? There is evidence that this is possible. AlphaFold 2 has the capability to predict protein folds. Recently many foundation models have been proposed to represent single cell RNA sequences. Engineering a cell with desired characteristics with these models is closer than we think.

AION Labs is an alliance of global pharma companies, Amazon, and venture capital firms that have come together to co-develop and adopt groundbreaking new AI technologies that will transform drug discovery and development. AION Labs provides access to funding, data, validation, and technologies in a co-development model from day one. In this talk, I will share our model for creating or joining AI companies in the drug discovery and development space. We invest significantly in defining articulating the problem to be solved. I will share examples from antibody discovery and optimization challenges that we launched and describe the companies we launched.

This talk explores the transformative landscape where GenAI intersects drug discovery and development. Gain insights into cutting-edge applications, emphasizing responsible data practices. Learn about navigating the evolving role of GenAI, ensuring data privacy and integrity are central to innovative solutions propelling drug discovery efforts.

While the clinical trial value chain is still riddled with inefficiencies owing to the time-consuming, expensive manual way of working, Generative AI has the potential to revolutionize clinical development from optimizing trial design, automating trial document generation to regulatory filling support. In this presentation, we will share our experience in automating clinical study protocol generation and CSR narratives using Google’s PaLM2 32k, Gemini and AWS Claude 3. The exploration of GenAI use cases needs to be tightly integrated with the core business, bringing together tech and business functions. Our guiding principles for achieving value of GenAI quickly can be leveraged across pharmaceutical industry.