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Webinar
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.
Bristol Myers Squibb is proud to share a proposal to standardize our data and information models. The artifacts introduced today serve as foundational knowledge of Research and Development and attempt to capture factual and tacit knowledge in this space.
In this webinar we cover the importance of subject matter 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.
Despite much ‘hype’ around how quantum computers will potentially revolutionize drug discovery, the precise pharma use cases wherein a quantum advantage will be someday demonstrable relative to classical computing have remained somewhat a mystery to the larger pharma/life science community. The goal of this event is to ‘bridge the gap’ between quantum experts and the rest of the pharma/life science industry.
This webinar explores why the FAIR data principles are so important for data that is collected during rare and paediatric clinical trials. We introduce the conect4children (c4c) project and present the network that has been established under the IMI2 grant.
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.
Drugs can cause unwanted undesirable effects called adverse reactions, or side effects. In addition to lack of drug efficacy, safety issues caused by these reactions are a major reason for clinical trials to fail. Identifying adverse reactions in preclinical stages can help to reduce the risk associated with drug development and improve patient safety.
The purpose of the data Gov Lab is to create a community to exchange ideas about data governance and share processes and methodologies in order to define best practices of Data Governance within Life Sciences.
Learn how the deployment of simple building blocks can transform your data or compute center into a modern, highly interoperable, hybrid- and multicloud-ready environment for data access and analysis.
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.
The application of Artificial Intelligence/Machine Learning (AI/ML) methods in drug discovery are maturing and their utility and impact is likely to permeate many aspects of drug discovery. Numerous methods, however, utilize structure-activity relationship (SAR) data without explicit use of 3D structural information of the ligand protein complex.
The FAIRe(nough) benchmark has been developed in AZ with a focus on integration of data products and systems against a specific context or use case. Providing both high- and low-level assessment metrics, the benchmark is an essential enabler for anyone facing a data integration and FAIRification project.
