Download MendeleyVirtual Screening and Design with Machine Intelligence Applied to Pim-1 Kinase Inhibitors.
Schneider, P., Welin, M., Svensson, B., Walse, B., Schneider, G.(2020) Mol Inform 39: e2000109-e2000109
- PubMed: 33448694
- DOI: https://doi.org/10.1002/minf.202000109
- Primary Citation of Related Structures:
6YKD - PubMed Abstract:
Ligand-based virtual screening of large compound collections, combined with fast bioactivity determination, facilitate the discovery of bioactive molecules with desired properties. Here, chemical similarity based machine learning and label-free differential scanning fluorimetry were used to rapidly identify new ligands of the anticancer target Pim-1 kinase. The three-dimensional crystal structure complex of human Pim-1 with ligand bound revealed an ATP-competitive binding mode. Generative de novo design with a recurrent neural network additionally suggested innovative molecular scaffolds. Results corroborate the validity of the chemical similarity principle for rapid ligand prototyping, suggesting the complementarity of similarity-based and generative computational approaches.
Organizational Affiliation:
Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.
