ArticleMolecular informatics2020
Virtual Screening and Design with Machine Intelligence Applied to Pim-1 Kinase Inhibitors.
Article in Molecular informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Applying Deep-Learning-DrivenJournal of medicinal chemistry · 2026Article
- Ergodic Manipulation of Genome Chaos: Innovative Strategies against Malignant Progression.Current cancer drug targets · 2025Review
- A critical overview of computational approaches employed for COVID-19 drug discovery.Chemical Society reviews · 2021Review
- Structure-Based Virtual Screening and De Novo Design of PIM1 Inhibitors with Anticancer Activity from Natural Products.Pharmaceuticals (Basel, Switzerland) · 2021Article
- Virtual Screening and Design with Machine Intelligence Applied to Pim-1 Kinase Inhibitors.Molecular informatics · 2020Article
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Authors and funding
5 authors.
Funding
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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.
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