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.
Join Dr. Ola Engkvist, Head of Molecular AI in Discovery Sciences, AstraZeneca, Dr. Jonathan Betts, Senior Leadership Team, CCDC; and Dr. Clare Tovee, Senior Scientific Editor, CCDC, for the live webinar on Wednesday, June 19, 2024, at 10am EDT (3pm BST/UK).
For more information, or to register for this event, visit ML and AI in Life Sciences: Breaking Down Barriers.
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References:
[1] Wilkinson, M.D., Dumontier, M., Aalbersberg, I.J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.W., da Silva Santos, L.B., Bourne, P.E. and Bouwman, J., 2016. The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3(1), pp.1-9.
[2] Zhu, X., Polyakov, V.R., Bajjuri, K., Hu, H., Maderna, A., Tovee, C.A. and Ward, S.C., 2023. Building machine learning small molecule melting points and solubility models using CCDC melting points dataset. Journal of Chemical Information and Modeling, 63(10), pp.2948-2959.
[3] Kalash, L.N., Cole, J.C., Copley, R.C., Edge, C.M., Moldovan, A.A., Sadiq, G. and Doherty, C.L., 2021. First global analysis of the GSK database of small molecule crystal structures. CrystEngComm, 23(32), pp.5430-5442.
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