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Harnessing AI To Expedite R&D

Resource Tag: Harnessing AI To Expedite R&D

The IDMP Ontology

A catalyst to unleash the potential of AI and accelerate data-driven decisions with industry standards

Copyright Implications for AI Systems

The rapid growth and adoption of AI technologies have tremendous promise to drive innovation and improve operational efficiency, particularly if the risks can be managed. There are many risks, including those related to the mass consumption of copyrighted works, which is at the heart of AI systems that rely on them. In many of these systems, copyrighted content is copied, stored, and can be reproduced, analyzed, and used to create summaries, classifications, and additional works.

This event will provide insight on how copyrighted materials are retained and reused in AI systems, including large language models. Our expert speakers will discuss the copyright implications, the legal risks, and the role of licensing as an integral part of a comprehensive AI governance, risk, and compliance program.

SPEAKERS

  • Dr. Haralambos Marmanis is CCC’s Executive Vice President & CTO, where he is responsible for driving the product and technology vision as well as the implementation of all software systems at CCC. Dr.Marmanis has over 30 years of experience in computing and leading software teams. Before CCC, he was the CTO at Emptoris (IBM), a leader in supply and contract management software solutions. He is a pioneer in the adoption of machine learning techniques in enterprise software. Dr. Marmanis is the author of the book “Algorithms of the Intelligent Web,” which introduced machine learning to a wide audience of practitioners working on everyday software applications. He is also an expert in supply management, co-author of the first book on Spend Analysis, and author of several publications in peer-reviewed international scientific journals, conferences, and technical periodicals. Marmanis holds a Ph.D. in Applied Mathematics from Brown University, and an MSc from the University of Illinois at Urbana-Champaign. He was the recipient of the Sigma-Xi innovation award and an NSF graduate fellow at Brown.
  • Catherine Zaller Rowland is Vice President, General Counsel, at CCC where she oversees the Legal Department and advises on complex issues including copyright licensing, software, professional services, and the intersection of copyright and emerging technologies. Previously, Rowland held a range of positions in the private sector and federal government, focusing on intellectual property matters. Most recently, she served as Associate Register of Copyrights and Director of the Office of Public Information and Education at the U.S. Copyright Office, where she was one of four principal legal advisors to the head of the Copyright Office. She began her legal career in private practice focusing on intellectual property litigation, transactions, and counseling.

Generating Evidence-Based Hypotheses with AI-Powered Knowledge Graphs

Within the life sciences, the necessity for evidence-based decision making is clear, where wrong decisions could result in dire consequences. In the rapidly evolving landscape of artificial intelligence (AI), knowledge graphs have emerged as pivotal resources for artificial intelligence tools and agents, offering a structured and scalable way to access a wealth of explicit and implicit knowledge and thus expedite the generation of hypothesis. By synthesizing information from diverse sources and integrating ontologies, these graphs facilitate a level of interoperability and scalability that is critical for the development of intelligent systems, particularly within the domain of life sciences.

This presentation will introduce some of the approaches SciBite is taking to enhance and leverage knowledge graphs in the context of AI, including:

  • Create and enrich semantic networks by applying cutting edge AI-tools on top of our industry leading NER tools
  • Support use case specific queries and analysis with fine-tuned vocabularies and edge embeddings
  • Using generative AI to let users talk to the knowledge graphs and get sensible human-readable answers

Through this talk, we aim to showcase how cutting-edge techniques not only refine the structure and utility of knowledge graphs but also open new avenues for their application in AI-driven research and development.

Speaker

  • Thomas Woodcock, MBA, PhD, Technical Sales Manager, North America, SciBite

Tom has over 20 years of experience in biological and pharmaceutical sciences, spanning multiple continents and roles in research, consulting, and education. He is responsible for all SciBite’s technical engagements across the US business, including partners, prospects, and existing customers. By focusing on data as a valuable asset, Tom leverages both his specialist scientific domain expertise and passion for data science to help others solve data challenges and inform decision making through

Large Language Models in the Real World

We know enough about the LLM technology at this time to move it from popular hype into production. We are, however, still at the beginning of this journey. What does biopharma research need to focus on to ensure they are implementing LLMs effectively? How do we benchmark and standardise best practices for LLM usage? Join knowledge management thought leaders from AbbVie, Roche and Biorelate to take the LLM conversation forward from ‘if and why’ to ‘how, what, when and where.’

Join us for our webinar in which the panelists will discuss applications of Large Language Models in pharmaceutical R&D.

Speakers:
  • Daniel Jamieson, Biorelate 
  • Etzard Stolte, Roche 
  • Jon Stevens, Abbvie 

Data Quality for LLMs: Building a Reliable Data Foundation

Achieving value with Large Language Models (LLMs) hinges on a reliable data foundation. This is becoming increasingly relevant with the introduction of conversational AI agents that exploit RAG (retrieval augmented generation) techniques to extract information from biomedical data. What isn’t emphasized enough, is the crucial role that well-annotated data and its accessibility to the models plays.

In this webinar, we look at how data quality affects the performance of LLMs. For this, we assess how LLM-powered AI agents query across three versions of the same gene expression corpus, but with varying degrees of quality:

  • Unstructured Data from GEO (Gene expression Omnibus)
  • Structured Data from the CREEDS project
  • ML-ready data, annotated using Elucidata’s Polly
 
Speaker
  • Abhishek Jha, CEO & Co-Founder at Elucidata

Strategic Priorities Update February 2024

Join us for this inaugural update on the newly formed Strategic Priorities of the Pistoia Alliance, followed by a 30 minutes Q&A with our panelists

Agenda

Dr Becky Upton, President of the Pistoia Alliance

  • Introduction

Dr Christian Baber, Chief Portfolio Officer, Pistoia Alliance

  • Strategic Priorities Overview
  • Harnessing AI to Expedite R&D
  • Delivering Data-Driven Value

Thierry Escudier, Portfolio Lead, Pistoia Alliance  

  • Accelerating Use of Real-World Data
  • Sustainability Driven 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.