Cheminformatics
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11 Segall, MEC. The Difference between Guiding and Supporting Decisions: Enhancing Decisions and Improving Success in Drug Discovery. Genetic Engineering News. 2010 September. 12 Bolton, E, Wang, Y, Thiessen, P, Bryant, S. PubChem: Integrated Platform of Small Molecules and Biological Activities. In: Annual Reports in Computational Chemistry. Vol 4. Washington DC: American Chemical Society; 2008. p. 217-241. 13 Andrews, M, Brown, A, Chiva, J, Fradet, D, Gordon, D, Lansdell, M, MacKenny, M. Design and optimisation of selective serotonin re-uptake inhibitors with high synthetic accessibility: part 2. Bioorg. Med. Chem. Lett. 2009;19:5893-5897.
uncertain predictions are combined to calculate the score, the uncertainties in the score are high, as shown by the error bars in Figure 3. Therefore, it is difficult to discriminate between compounds with confidence, particularly in the later generations. Finally, it is notable that Duloxetine itself is present in the final genera- tion, with a score that is significantly higher than the initial lead (level of significance ~0.1) and not significantly below that of the highest scoring compounds.
The structures and scores of the initial lead and Duloxetine are shown in Figure 4, along with the three highest ranking molecules generated. Although none of the top-three compounds could be identified in a search of PubChem12, the sec-
ond-ranked compound bears a strong similarity (Tanimoto similarity >0.9) to Litoxetine, also shown on the right in Figure 4, which was pro- gressed to clinical trials and is active against the
serotonin transporter with an IC50 of 6nM13. The chemical space of the data set generated is shown in Figure 5. From this it is clear that there are multiple ‘hot spots’ containing high-scoring compounds; the best scoring compounds are not concentrated in one region, indicating a number of different chemical strategies have been found that are worthy of further consideration. The top three ranked molecules are structurally diverse, within the range of diversity explored around the initial lead, and are distinct from both the initial lead and Duloxetine itself.
Rank 1
Rank 2
Litoxetine Rank 3
Duloxetine
Initial lead
Figure 4: The initial lead that ultimately gave rise to Duloxetine, the top three compounds generated from this lead, and Duloxetine, which was also generated by the algorithm. The score for each compound is show to the right along with a histogram indicating the contribution of each property to the overall score (the colour of each bar corresponds to the property key shown in Figure 2). All of these compounds are predicted to have good values for the predicted ADME
properties. However, the initial lead has a much lower score due to a significantly poorer Ki predicted for the serotonin transporter. The structure and calculated score for Litoxetine, a clinical candidate serotonin reuptake inhibitor, is shown
to the right for comparison. The predicted Ki for this compound is 10nM, in line with the reported IC50 of 6nM. Although this structure was not generated automatically in this example, it bears a strong similarity (Tanimoto similarity >0.9) with the second-ranked compound, which has a higher predicted affinity and hence a higher score
20 Drug Discovery World Fall 2011
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