Best practices for chemical data curation and QSAR model ...

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Applicability domain vs. prediction accuracy (Ames Genotoxicity dataset) 92 90 Test Set %Accuracy 88 86 84 82 80 0.5 1 2 3 5 10 kNN Z-Score Used for Prediction Cutoff
Some reasons why QSAR models may fail: Y-randomization test is not carried out QSAR Pill • Y-randomization test: – Scramble activities of the training set – Build models and get model statistics. – If statistics are comparable to those obtained for models built with real activities of the training set, the last are unreliable and should be discarded. Frequently, Y-randomization test is not carried out. Y-randomization test is of particular importance, if there is: - a small number of compounds in the training or test set - response variable is categorical
Page 1 and 2: Alexander Tropsha Laboratory for Mo
Page 3 and 4: The rumors of QSAR demise have been
Page 5 and 6: Quantitative Structure Property Rel
Page 7 and 8: QSAR Modeling appears easy… Goal:
Page 9 and 10: BEWARE OF q 2 (Kubinyi paradox)!!!
Page 11 and 12: Complexity of QSAR modeling: Choice
Page 13 and 14: Some reasons why QSAR models may fa
Page 17: Applicability domain of QSAR models
Page 23 and 24: Recently, D.Young et al. pointed ou
Page 25 and 26: Revising QSAR Modeling Process : Pr
Page 27 and 28: The importance of data curation: Wh
Page 29 and 30: Division of the Dataset into Three
Page 31 and 32: The OECD Principles of model valida
Page 33 and 34: Database Mining Reveals Unique Chem
Page 35 and 36: Recent examples of experimentally v
Page 37 and 38: Study Design (AmpC β-lactamase dat
Page 39 and 40: Descriptor Interpretation Rank Desc
Page 41 and 42: One “inactive” compound (CID 69
Page 43 and 44: Target-specific filter construction
Page 45 and 46: Docking enrichment plot for DHFR us
Page 47 and 48: Target related ligands Disease-Targ
Page 49 and 50: Comparison of the QSAR Approaches t
Page 51 and 52: Querying the cmap with Alzheimer’
Page 53 and 54: Selected Common Hits from QSAR and
Page 55 and 56: Raloxifene is predicted to bind sev
Page 57 and 58: Importance of hypothesis fusion WDI
Page 59 and 60: Chemocentric view of biological dat
Page 61 and 62: Poor global relationships between i
Page 63 and 64: Use of hybrid descriptors for struc
Page 65 and 66: A new “hierarchical QSAR” appro
Page 67 and 68: Case Study 1 Recently 1 , 51 divers
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CS1. QNTR modeling results of 44 di
Page 71 and 72:
International Virtual Collaboratory
Page 73 and 74:
Overview of the Approaches (15 meth
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Which model is best • Observation
Page 77 and 78:
Principles of “Safe” QSAR model
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Final Word Nothing that worth knowi
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