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

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Why QSAR models may fail: insensitive descriptors. [Example: Optimal (left panel) and traditional (right panel) orientations of androgen (DHT shown in gold) and estrogen (estradiol shown in green) within human SHBG steroid-binding site]. Identical q 2 (CoMFA*) of 0.53 *CoMFA – Completely Misleading Famous Aberration A. Cherkasov, JMC, 2008
Recently, D.Young et al. pointed out the importance of cleaning data, especially, in the context of QSAR modeling. They investigated several public and commercial databases to calculate their error rates: the latter were ranging from 0.1 to 3.4% depending on the database. Their main conclusions were that small structural errors within dataset could lead to significant loss of predictive abilities for the QSAR models which have been built using those erroneous input data.
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 and 18: Applicability domain of QSAR models
Page 21: Some reasons why QSAR models may fa
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
Page 69 and 70: CS1. QNTR modeling results of 44 di
Page 71 and 72: International Virtual Collaboratory
Page 73 and 74:
Overview of the Approaches (15 meth
Page 75 and 76:
Which model is best • Observation
Page 77 and 78:
Principles of “Safe” QSAR model
Page 79:
Final Word Nothing that worth knowi
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Best practices for chemical data curation and QSAR model ...

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