Applications of Artificial Intelligence in Early Drug Discovery

Drug discovery is changing, the influence and catalytic effect of Artificial Intelligence (AI) cannot be denied. History dictates this new development will likely be a synergistic addition to drug discovery rather than a revolutionary replacement of existing methods (like the history of HTS or combichem, a new tool in the toolbox). As more and more scientific data is becoming public and more and more computing power becomes available the application of AI in drug discovery offers exciting new opportunities.

Central to drug discovery in the public domain is the ChEMBL database which provides literature obtained bioactivity data for a large group of (protein) targets and chemical structures.[1, 2] Machine learning can leverage this data to obtain predictive models able to predict  the  activity probability of untestedchemical structures  contained within the large collections of chemical vendorson the basis of the chemical similarity principle. [3, 4]

In this talk I will give an overview of research going on at the computational drug discovery group in Leiden. Central in our research is the usage of machine. I will highlight some examples we have published previously and finish with an outlook of cool new possibilities just around the corner.[5, 6]

References
1. Sun, J., et al., J. Cheminf., 2017. 9, 10.1186/s13321-017-0203-5
2. Gaulton, A., et al., Nucleic Acids Res., 2012. 40, 10.1093/nar/gkr777
3. Bender, A. and R.C. Glen, Org. Biomol. Chem., 2004. 2, 10.1039/b409813g
4. Van Westen, G.J.P., et al., Med. Chem. Commun., 2011. 2, doi:10.1039/C0MD00165A
5. Liu, X., et al., J. Cheminf., 2019. 11, 10.1186/s13321-019-0355-6
6. Lenselink, E.B., et al., J. Cheminf., 2017. 9, 10.1186/s13321-017-0232-0

Meeting link

Please contact Chris Flinterman for the meeting link.

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