Drug Design 2 - Applied Bioinformatics Group
Drug Design 2 - Applied Bioinformatics Group
Drug Design 2 - Applied Bioinformatics Group
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<strong>Drug</strong>-‐Likeness<br />
• Structures were encoded using the AlogP atom types<br />
• Of the 120 atom types of Ghose & Crippen, 92 turned out to<br />
be relevant<br />
• Each molecule is encoded as a vector containing the number<br />
of atoms of each of the 92 types in the structure<br />
• This vector then serves as input for the classifica%on into<br />
drugs and non-‐drugs<br />
COOH<br />
O<br />
O<br />
CH 3<br />
<strong>Drug</strong>-‐Likeness<br />
0 4 0 2 0 0 0 1 ... 0<br />
• Predic%on based on an arDficial neural network (ANN)<br />
• Input: atom type vector<br />
• Output: drug-‐likeness<br />
(0 = non-‐drug, 1 = drug-‐like)<br />
• Topology: 92 x 5 x 1 (simple feed-‐forward ANN)<br />
WDI<br />
ACD<br />
<strong>Drug</strong>-‐Likeness<br />
ANN<br />
83% of ACD and 77% of WDI are classified correctly!<br />
Score