J Chem Inf Model - Introduction of the conditional correlated Bernoulli model of similarity value distributions and its application to the prospective prediction of fingerprint search performance.

Tópicos

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Resumo

A statistical approach named the conditional correlated Bernoulli model is introduced for modeling of similarity scores and predicting the potential of fingerprint search calculations to identify active compounds. Fingerprint features are rationalized as dependent Bernoulli variables and conditional distributions of Tanimoto similarity values of database compounds given a reference molecule are assessed. The conditional correlated Bernoulli model is utilized in the context of virtual screening to estimate the position of a compound obtaining a certain similarity value in a database ranking. Through the generation of receiver operating characteristic curves from cumulative distribution functions of conditional similarity values for known active and random database compounds, one can predict how successful a fingerprint search might be. The comparison of curves for different fingerprints makes it possible to identify fingerprints that are most likely to identify new active molecules in a database search given a set of known reference molecules.

Resumo Limpo

statist approach name condit correl bernoulli model introduc model similar score predict potenti fingerprint search calcul identifi activ compound fingerprint featur ration depend bernoulli variabl condit distribut tanimoto similar valu databas compound given refer molecul assess condit correl bernoulli model util context virtual screen estim posit compound obtain certain similar valu databas rank generat receiv oper characterist curv cumul distribut function condit similar valu known activ random databas compound one can predict success fingerprint search might comparison curv differ fingerprint make possibl identifi fingerprint like identifi new activ molecul databas search given set known refer molecul

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