2024 - ML and AI in Life Sciences: Breaking Down Barriers

Discover an informative webinar on the transformative impact of machine learning (ML) and artificial intelligence (AI) in life sciences. Attendees will gain insights into the challenges, including data quality and ethics, and how the findable, accessible, interoperable and reusable (FAIR) data principles and the Cambridge Structural Database (CSD) can advance AI applications.
Although significant barriers still exist, the outcomes of using ML and AI in life science industries, as well as healthcare, green energy and technology, could be revolutionary. Challenges span political will, sustainability, ethics, infrastructure and the talent pipeline. A basic limitation for many, however, is simply the availability, quality and trustworthiness of data, including data for testing, training and validating models.
The FAIR guiding principles for scientific data management and stewardship[1] were published in 2016 to formalize the priorities for enhancing data’s machine-actionability.
Data that are AI-ready need to be properly standardized, curated, interoperable, be of high quality and appropriately defined with standardized metadata. This ensures that the data are usable now and in the future for humans and machines.
The CSD has already been used to train ML models relevant to the pharmaceutical industry, e.g., to help predict aqueous solubility,[2] and its application for training ML models for drug discovery is already recognized[3].
The CSD contains over 1.28 million fully curated small-molecule organic and metal–organic crystal structures and is fundamentally structured and designed to be underpinned by FAIR data principles. This, along with being a CoreTrustSeal-certified data repository, mark the CSD as a trusted and high-quality resource that could break down some of the barriers preventing the advancement of ML and AI in life sciences.
Register for this webinar to learn about the challenges of using ML and AI in life sciences, the importance of quality data management and why the CSD is trusted by academic and industrial institutions around the world.
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Keywords: Drug Development, Drug Discovery, Machine Learning, Data Analytics, Medicinal Chemistry, Artificial Intelligence, AI, Data Science, Basic Research, Other Software