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25 results on '"Conrad Stork"'

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1. Computational Applications in Secondary Metabolite Discovery (CAiSMD): an online workshop

2. Computational prediction of frequent hitters in target-based and cell-based assays

3. CYPstrate: A Set of Machine Learning Models for the Accurate Classification of Cytochrome P450 Enzyme Substrates and Non-Substrates

4. Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors

5. GLORY: Generator of the Structures of Likely Cytochrome P450 Metabolites Based on Predicted Sites of Metabolism

6. NP-Scout: Machine Learning Approach for the Quantification and Visualization of the Natural Product-Likeness of Small Molecules

11. CYPstrate: A Set of Machine Learning Models for the Accurate Classification of Cytochrome P450 Enzyme Substrates and Non-Substrates

12. CYPlebrity: Machine learning models for the prediction of inhibitors of cytochrome P450 enzymes

13. Predicting the Skin Sensitization Potential of Small Molecules with Machine Learning Models Trained on Biologically Meaningful Descriptors

14. BonMOLière: Small-Sized Libraries of Readily Purchasable Compounds, Optimized to Produce Genuine Hits in Biological Screens across the Protein Space

15. Computational Applications in Secondary Metabolite Discovery (CAiSMD) : an online workshop

16. Hit Dexter: A Machine-Learning Model for the Prediction of Frequent Hitters

17. Skin Doctor: Machine Learning Models for Skin Sensitization Prediction that Provide Estimates and Indicators of Prediction Reliability

18. ALADDIN: Docking Approach Augmented by Machine Learning for Protein Structure Selection Yields Superior Virtual Screening Performance

19. FAME 3: Predicting the Sites of Metabolism in Synthetic Compounds and Natural Products for Phase 1 and Phase 2 Metabolic Enzymes

20. Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters

21. PAIN(S) relievers for medicinal chemists: how computational methods can assist in hit evaluation

22. NERDD: a web portal providing access to in silico tools for drug discovery

23. FAME 2: Simple and Effective Machine Learning Model of Cytochrome P450 Regioselectivity

24. Why Are Dithienylethene-Linked Biscobaltocenes so Hard to Photoswitch?

25. Cover Picture: Why Are Dithienylethene-Linked Biscobaltocenes so Hard to Photoswitch? (ChemPhysChem 6/2017)

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