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Harnessing AI To Expedite R&D

Resource Tag: Harnessing AI To Expedite R&D

Emerging Regulations of AI

Most recently both the EU and the US announced new legislation aiming to regulate the development and use of Artificial Intelligence: the EU AI Act and the President Biden Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. Our panel of experts will discuss how these legal changes may affect research and development in drug discovery.

 
Agenda
  • Introductions
  • How is AI used in your organization?
  • Review of current and proposed regulations
  • Panel Discussion with our speakers
  • Closing Remarks and next steps
 
Speakers
  • Frederik van den Broek, Senior Director, Professional Services and Consulting, Elsevier
  • Koen Cobbaert, Senior Manager – Quality, Standards & Regulations, Philips
  • Sophie Ollivier, Chief Data Officer R&D, Servier
  • Gideon Rosenthal, Head of Research, Data Science Group
 

Playing FAIR with AI: Supporting Scientific Discovery

Technological advancements exhibit varying degrees of longevity. Some are tried and trusted, enduring longer than others, more often when applied strategically to address tangible business challenges. Conversely, certain technologies succumb to fleeting hype without attaining substantive fruition.

A constant, in this dynamic landscape is the data. To harness the full potential of cutting-edge technologies, it is imperative to have your house, or more specifically, your data, in order. Here, we discuss the importance of foundational data management and the role of FAIR in enabling organisations, specifically within the life sciences, are agile enough to adapt to, and make use of, state-of-the-art technologies.

We will specifically discuss how the SciBite FAIR factory can be used to enable the application of large language models (LLMs) to democratise scientific data, and expedite the extraction of insight.

Speaker: Joe Mullen, Director of Professional Services, SciBite

IDMP Ontology Community of Interest Meeting – October 2023

A well-defined ontology that bridges between regional and functional perspectives on common substance-related data objects and global and scientifically objective representations is required. The goal of our project is to build an IDMP Ontology that enables deep, semantic interoperability based on FAIR principles to enhance and augment the existing ISO IDMP standards.

DataFAIRy to drive AI adoption

The launch of the second phase of its DataFAIRy: Bioassay project, which aims to convert bioassay data into machine-readable formats that adhere to the FAIR guiding principles of Findable, Accessible, Interoperable and Reusable. 

The Application of Large Language Models in Life Science Research and Development

This webinar aims to explore the application of large language models in life science R&D from different perspectives, providing attendees with a comprehensive understanding of the topic and its potential implications for the industry.

Sessions include:

  • Large Language Models: the ZS learning journey – Helena Deus, Biomedical Semantics Lead, Manager, ZS Associates
  • Large Language Models in Life Science Research and Development – Anthony Rowe, Head of Technology – Global Scientific IT, The Janssen Pharmaceutical Companies of JNJ
  • Language Models – some perspectives from OPIG – Carlos Outeiral, EPSRC Research Associate, Oxford Protein Informatics Group and Stipendiary Lecturer in Biochemistry, St Peters College, University of Oxford

Realizing the Promise of Foundation Models in Healthcare

Large language models like ChatGPT have captured the imagination of machine learning practitioners with their potential to transform the application of AI across many fields. However, in healthcare, transitioning from impressive tech demos to deployed AI has been challenging.

In this talk, we will discuss the opportunities large language models and other medical foundation models offer in terms of providing a better paradigm of doing “AI in healthcare.” First, we will outline what foundation models are and their relevance to healthcare. Then we will highlight some key opportunities provided by the next generation of medical foundation models. Finally we will discuss the current limitations in benchmarking and evaluating foundation models for medicine and how we can do better moving forward.

Ersilia, A Hub of Open-Source AI/ML Models For Drug Discovery & Global Health

The Ersilia Open Source Initiative is a non-profit organization with the mission to equip laboratories and universities in low resource areas with AI tools for infectious disease research. Ersilia has developed a set of AI-based tools to support medicinal chemistry, parasitology and ADME experimental pipelines, offering them via a unified, open source platform the Ersilia Model Hub. With it, scientists can easily browse, select and run AI models to accelerate their drug discovery pipelines. In this talk, we will present our computational methods and infrastructure and their application to the discovery of new treatments for infectious diseases.

Mobilizing Machine Learning

Superbio.ai provides datasets, pre-trained AI models, benchmarks, visualization and inference tools, all in a no-code cloud environment, empowering scientists to advance their research with community-driven machine learning. In this webinar, company founder and CEO Berke Buyukkucak will describe his work to democratize the Artificial Intelligence.

The NLP Use Case Database Project

Pharma companies apply Natural Language Processing (NLP)
methods in hopes of automation and insight generation, and
many companies are investing in NLP initaitatives to stay ahead of
the competition.

Good Machine Learning Practices

Starting in 2021, a team of Pistoia Alliance colleagues conducted in-depth business analysis centered on the use of AI in the pharmaceutical enterprise, and identified common use cases, challenges, and best practices for application of AI, specific to particular personas. This webinar presents the interim report of the results to-date, followed by the panel discussion by the members of the Pistoia Alliance GMLP CoI.