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Harnessing AI To Expedite R&D

Resource Tag: Harnessing AI To Expedite R&D

AI for Drug Discovery in 2025

A view from the industry-academia interface

Pistoia Alliance 2025 London conference presentation by Prof Conrad Bessant, Queen Mary University of London

Leveraging AI for Enhanced Ontology and Terminology Development

In the rapidly evolving landscape of information science, the development and maintenance of ontologies and terminologies are critical for ensuring accurate data representation and interoperability across various domains.

This webinar aims to explore the transformative potential of artificial intelligence, specifically Large Language Models (LLMs), in streamlining and augmenting the creation of these essential knowledge structures. Attendees will gain insights into how AI can assist users in building comprehensive reference terminologies and ontologies more efficiently, thereby saving valuable time and resources. We will delve into early results from recent studies, demonstrating the practical applications and benefits of LLMs in this context.

Join us to discover innovative methodologies, share experiences, and discuss future directions for integrating AI into ontology and terminology development workflows.

This session is ideal for data scientists, knowledge engineers, domain experts, and anyone interested in the intersection of AI and knowledge management.

Speaker

Simon Jupp, Head of Semantic Technology, SciBite

Simon is the head of Semantic Technology at SciBite, where he leads the development of CENtree, an innovative Enterprise Ontology Management solution. Simon’s interests are focused on how semantic technologies can be utilised to address the complex challenges of large-scale data interoperability. He is an expert in developing and applying ontologies within the life sciences and is advancing these technologies at Elsevier.

Rapid Biomedical Insight Discovery with AI

Pharma R&D is increasingly costly and the industry is looking to AI for productivity and ROI improvements. A critical bottleneck in early stage R&D is insight discovery. Huge amounts of researcher time is spent interrogating the literature to find biological connections and uncover new insights, Elsevier has a history of deploying AI to transform these tasks. Guy and Ivana will take you through recent innovations in this space as well as some of the key lessons we have learned.

Digital Twins

Digital twins are increasingly being utilized across various industries (constructions, manufacturing, transportation, smart cities) and more recently in the healthcare sector by enabling more efficient, predictive, and data-driven decision-making.

The use of Digital twins in the pharma R&D sector leverages in silico models to expedite drug development and increase translational success for the benefit of the patient. Digital Twins can help to predict future behaviour’s and offer more precise and customized medicine specific to a patient, virtualize clinical trials to bring new medicines and vaccines to market faster.

Within this webinar we will explore how some Life Sciences companies are looking to use the digital twins to accelerate drug discovery and development in order to deliver safe products to patients.

Speakers
  • Julia Schueler, Charles River Laboratories
  • Damien Valour, Servier
  • Bruce Church, Aitia

Revolutionizing Scientific Literature Search

Join Mark Sheehan, VP for Data Science Life Sciences at Elsevier, to explore the power of advanced search techniques in our webinar, “Revolutionizing Scientific Literature Search: Marrying Traditional Taxo and Emerging Vector Techniques”.

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In this webinar, Mark will review the world of scientific literature search, covering the full range of technologies from traditional taxo and keyword-based search methods vs the latest innovative vector and RAG-based approaches. Mark will also give an overview of Elsevier’s ongoing research into a dynamic hybrid search approach, which aims to seamlessly merge the strengths of traditional and vector searches.

Biography – Mark Sheehan

Mark Sheehan, a seasoned professional with over two decades of experience, serves as the Vice President of Data Science for Elsevier’s Life Sciences team. His extensive contributions have been instrumental in Elsevier’s digital evolution, transforming the company from a traditional publisher to a leader in information analytics and AI-driven research tools.

Mark’s current role is to leverage the latest data science and AI to support chemists and biologists in their daily research work. By attending this webinar, you will gain insights that can help you accelerate the development of new synthetic pathways and advance drug R&D. Mark’s belief in innovation as both a technological and a collaborative endeavour underscores the practical benefits of this webinar.

Ethics in Artificial Intelligence

What is ethics in Artificial Intelligence? What exactly should an AI practitioner do to ensure alignment with ethical use of technology? What are perspectives on ethics in AI from different business stakeholders in the pharmaceutical industry, from early stage R&D to clinical trials? Our panelists will discuss these and other questions in this engaging webinar.

Speakers
  • Christopher Waller, VP, EPAM
  • Karin Schneider, Ass. Director, Janssen
  • Adarsh Srivastava, Head of DATA QA, Roche

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