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The role of chemoinformatics in the design of a comprehensive drug ...

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Chemist Triage Score and Naïve Bayesian Models<br />

‣ Yes/No decision from five <strong>in</strong>dividual triag<strong>in</strong>g exercises taken to build one<br />

Bayesian models<br />

‣ All five models are used <strong>in</strong> a consensus score, fraction <strong>of</strong> models vot<strong>in</strong>g<br />

aga<strong>in</strong>st <strong>the</strong> molecules<br />

Chemist Triage Score<br />

100%<br />

90%<br />

16%<br />

Histogram <strong>of</strong> NB Score<br />

Screen<strong>in</strong>g Collection<br />

Less attractive cpds.<br />

Fraction <strong>of</strong> compounds<br />

80%<br />

70%<br />

60%<br />

50%<br />

40%<br />

30%<br />

20%<br />

10%<br />

0%<br />

MCS SLIA ICLF PSEB MCS<br />

external<br />

2003<br />

MCS<br />

external<br />

2004<br />

Drugs NAPR HTS Hits<br />

2004<br />

Random<br />

Chemist<br />

Triage<br />

Score<br />

Serie<br />

1<br />

s6<br />

Serie<br />

0.8<br />

s5<br />

Serie<br />

0.6<br />

s4<br />

Serie<br />

0.4<br />

s3<br />

Serie<br />

0.2<br />

s2<br />

Serie<br />

0<br />

s1<br />

% <strong>of</strong> samples<br />

14% Primary Hit-list<br />

12%<br />

Tra<strong>in</strong><strong>in</strong>g Set<br />

10%<br />

8%<br />

6%<br />

4%<br />

2%<br />

0%<br />

-100 -80 -60 -40 -20 0 20 40 60 80 100<br />

| Obernai| 20.06.10 | Edgar Jacoby

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