An extension of the BioAssay Ontology to now include pharmacokinetic/pharmacodynamic terminology for the enrichment of scientific workflows
Resource Tag: Harnessing AI To Expedite R&D
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.
How Important is Subject Matter Expertise in Life Sciences When Using Technology and AI?
With recent developments in technology, and the accessibility of artificial intelligence models, one must consider the importance of subject matter expertise in ensuring these are used in the most applicable and accurate settings. Further highlighted during a recent well-documented chatbot unveiling, even incredibly well funded efforts can provide factual errors that will only be spotted by such experts. This expert input is even more important in the highly ambiguous, synonymous and complex domain of life sciences. Here, we cover the importance of such expertise in the development, fine-tuning as well as application of, technologies, including artificial intelligence, in the life sciences – also touching on how these can impact end users
Bioassays Have An Integration Problem: Collaboration Will Be Key To Making Them FAIR
Whilst life science companies have come to recognize data as their greatest asset, it is also their greatest challenge. The answers to the biggest questions facing the industry today could already be held within the countless proprietary experiment notes, published literature, and patient records produced in previously conducted experiments. The data landscape is continuously growing in complexity and scale as organizations generate more research, but much of it is siloed in different formats and locations. This makes it difficult to discover, query, and share—rendering data essentially unusable.
Bioassay protocols are one such example where legacy data management systems are holding R&D back, and where adopting the FAIR (Findable, Accessible, Interoperable, Reusable) principles would improve the usability of the data. Bioassay protocols constitute the essential metadata for most of the experimental results collected in the process of drug discovery. While assay protocols are widely accessible—often stored in public data banks—they are universally kept in plain-text formats. This means they are not machine-readable and therefore require manual review, which takes considerable time investment by highly qualified professionals. Scientists must spend significant amounts of time sifting through vast libraries of old records; there are currently more than 1.4 million unformatted bioassays. Pistoia Alliance research found that some researchers may spend up to twelve weeks per assay selecting and planning new experiments.
Good Machine Learning Practices in the Modern Pharmaceutical Discovery Enterprise
We report results of the systematic business analysis of the personas in the modern pharmaceutical discovery enterprise in relation to their work with the AI and ML technologies. We identify 22 common business problems that individuals in these roles face when they encounter AI and ML technologies at work, and describe best practices (Good Machine Learning Practices) that address these issues.
AI / ML in R&D Automation
The Pistoia Alliance is pleased to announce a series of three webinars hosted by Accenture Boston Innovation Hub. As part of our overall theme of Improving the Efficiency and Effectiveness of R&D, each webinar will explore the power of collaboration to solve shared challenges, drive transformation and reduce barriers to innovation in R&D.
How Synthetic Data Is Unlocking a Decade’s Worth of Clinical Trial Data to Power a New Era of Drug Development
Historical clinical trial data (HCT) is emerging as an important source of evidence across clinical development. Data from past trials is often superior to real-world data from EMR records etc. as it is more structured, complete, 100% traceable and contains the typical endpoints and covariates captured in a clinical trial.
Regulators have lately been supportive of the use of HCT data with both the FDA and EMA approving hybrid trials: phase 3 trials where patients from the control arm have been replaced by synthetic patients from past trials. This talk will explore methodologies and use cases for Synthetic Patients – ‘digital twins’ of real patients that replicate their behavior to a very high degree. Synthetic Patients enable easy sharing of patient-level data without risk of subject-level or sponsor disclosure while allowing data scientists to mine deep insights on patient characteristics and behavior.
Speaker
Jacob Aptekar, MD Ph.D., is Senior Director of Product Management at Acorn AI, a Medidata Solutions company and part of Dassault Systemes. Dr. Aptekar has over 10 years of experience as a basic science researcher, business leader, and data scientist. He most recently served as an Associate at McKinsey & Company within their Digital McKinsey practice—working with public and private sector clients who use technology to deliver healthcare and develop therapies. Previously, he founded and led Qurator Inc, a data science company focused on the progression of chronic kidney disease and care planning for dialysis. Dr. Aptekar received an MD from the David Geffen School of Medicine at UCLA, his Ph.D. from UCLA in Neuroscience under the mentorship of Mark Frye, an investigator with the Howard Hughes Medical Institute, and an AB in Physics from Harvard College.