Virtual screening for R-groups, including predicted pIC50 contributions, within large structural databases, using topomer CoMFA

Journal of Chemical Information and Modeling

Volumn 48, Issue 11, 2008, Pages 2180-2195

  Cramer, Richard D ^a   Cruz, Phillip ^a   Stahl, Gunther ^a   Curtiss, William C ^a   Campbell, Brian ^a   Masek, Brian B ^a   Soltanshahi, Farhad ^a  

^a Tripos International   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATABASE SYSTEMS; FORECASTING; MOLECULAR GRAPHICS;

ORIGINAL STRUCTURES; PREDICTION ACCURACY; PREDICTION ERRORS; RECEIVER OPERATING CURVES; STANDARD DEVIATION; STATISTICAL QUALITY; STRUCTURAL DATABASE; VIRTUAL SCREENING;

COMPUTATIONAL CHEMISTRY;

DRUG; THROMBIN; TRYPSIN;

ARTICLE; CHEMICAL STRUCTURE; CHEMISTRY; COMPUTER INTERFACE; CONFORMATION; DRUG EFFECT; DRUG SCREENING; FACTUAL DATABASE; INFORMATION SCIENCE; QUANTITATIVE STRUCTURE ACTIVITY RELATION; STATISTICS; VALIDATION STUDY;

DATABASES, FACTUAL; DRUG EVALUATION, PRECLINICAL; INFORMATICS; MOLECULAR CONFORMATION; MOLECULAR STRUCTURE; PHARMACEUTICAL PREPARATIONS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; THROMBIN; TRYPSIN; USER-COMPUTER INTERFACE;

EID: 57649100158     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci8001556     Document Type: Article

References (40)

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3. In order to provide at least a very rough and preliminary estimate of this important baseline value, we calculated the standard deviation of pIC50s for the primary biological assay reported within 23 recent Journal of Medicinal Chemistry articles, limited to those from commercial organizations that mentioned a potential clinical candidate. The average value of these 23 pIC50 standard deviations was 0.89, with a standard deviation of 0.43. Not surprisingly, it was also apparent that in the majority of these publications the lead optimization goal in preparing and testing the most reported structures was no longer to improve potency in the primary assay but to maintain that potency while improving secondary properties. However such an activity will tend to depress the cumulative pIC50 standard deviation for the primary assay. So on balance we postulate a value somewhere between 1.0 and 1.3 for the standard deviation of any pIC50 that a project is actively seeking to improve.
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5. 2 over 61 data sets assembled from the literature is 0.46, which, as we agree, implies a pIC50 prediction error slightly under 1.0. Thus 0.46, though perhaps not what might be desired, is a long way from the 0.00 value which truly indicates nonpredictivity. Furthermore, Figure 1 in this publication summarizes the result of a further analysis, as a "typical" plot of predicted vs actual pIC50. Necessarily taking this plot at face value, and in contradiction of the original conclusion, the utility of the pIC50 predictions as shown seems very strong. If one seeks an actual pIC50 greater than 2.5 (structures to the right of the middle of the graph) and therefore accepts only candidates whose predicted pIC50 is greater than 2.5 (above the dotted line shown), the figure indicates that 7 of 8 (or 9) of the candidates would succeed, while only 2 (or 3) of 8 (or 9) of the failed candidates would have succeeded if pursued. We therefore fail to understand how it is ":lear that this model offers no predictive value".
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