A Closer Look at Elsevier's AI Strategy in Drug Discovery

by Andrii Buvailo, PhD          Interview

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My interview with Mirit Eldor, Managing Director, Life Sciences Solutions, at Elsevier, as we discuss Elsevier's recent collaboration with gen AI company Iktos and broader efforts.

In March 2024, Elsevier and Iktos announced a collaboration focused on enhancing drug discovery processes.

This partnership integrates Elsevier's Reaxys database, a leading source of chemical substance and reaction data, with Iktos's AI technology, specifically its platforms Makya™ for generative drug design and Spaya™ for retrosynthesis.

The integration aims to advance predictive retrosynthesis and synthetic accessibility within the Reaxys interface, leveraging Reaxys's extensive database, which encompasses over 280 million compounds and 74 million reactions..

The new predictive retrosynthesis and synthetic accessibility tools use a template-based disconnection prediction model to apply a proprietary filter that scores reaction feasibility and includes chemo- and regioselectivity parameters.

This collaboration introduces features such as stereochemistry support and the ability to fine-tune search parameters for identifying viable synthesis routes quickly.

These tools, available as add-on modules for Reaxys users, are designed to streamline the early stages of drug discovery, offering significant time and cost efficiencies for researchers in pharmaceuticals and related sectors.

In my interview with Mirit Eldor, Managing Director, Life Sciences Solutions, at Elsevier, we delved into the nuances of the partnership between Elsevier and Iktos.

Mirit Eldor interview

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Mirit also shared Elsevier’s broader vision for embedding AI across its platforms to not only advance science but also enhance drug discovery outcomes. This conversation underscored the transformative potential of AI in reshaping content generation, data analytics, and, more broadly, the entire scientific research landscape.

The discussion also ventured into the complexities of implementing AI within such vast data ecosystems.

Mirit emphasized the importance of responsible AI practices to mitigate biases and inaccuracies, highlighting Elsevier's commitment to maintaining the integrity and utility of its AI-powered tools, and explaining how they do it.

The interview concluded with reflections on the democratization of technology in the life sciences, a trend that significantly lowers the barrier for non-specialists to leverage sophisticated AI tools in their research.

This shift, as Mirit noted, is pivotal for the future of pharmaceuticals and underscores the critical role of adaptability and lifelong learning in the industry.

 

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