Teach-Discover-Treat 2
Stop DHODH, Stop Plasmodium, Stop Malaria
Build off the results of last year's Teach-Discover-Treat competition and develop a high-quality pharmacophore model for inhibitors of Plasmodium dihydroorotate dehydrogenase. Inhibitors of this enzyme in the Plasmodium parasite provide an alternative mechanism for the treatment of malaria, a disease that is evolving resistance to existing treatments.

Goal

Develop a pharmacophore model and scoring function for DHODH inhibitors that is useful for virtual screening and pose prediction.

Step 1

Identify key interactions. Click on any of the six DHODH inhibitor structures below to launch a pharmacophore editor session. Although only a single structure may be used as the basis of an editing session, you will want to analyze all six structures to identify the common interactions.

Step 2

Create a pharmacophore model. Choose pharmacophore features and set their properties, such as the search radius or hydrogen bond direction, to specify a pharmacophore query.

Step 3

Evaluate the pharmacophore model. Submit your phamacophore query to search a library of the 167 compounds that were screened as a result of last year's exercise. 44 of these compounds show some inhibition of DHODH at 10μM. Analyze the results of your pharmacophore search to determine how well your model identifies active compounds.

Step 4

Iteratively refine model. Repeat steps 2 and 3 until the model demonstrates a reasonable enrichment of actives compared to inactives. When you are happy with the results, submit your results for evaluation.

Leaderboard

Name F1 TP FP
Display name for leaderboard 0.75395 167 0
Ari 0.653465 33 24
Ari 0.653465 33 24
Jimbob 0.608696 35 36
LP 0.6 33 33
Display name for leaderboard 0.592199 167 121
Charles 0.581395 25 17
Aaron 0.580645 27 22
Ari 0.568627 29 29
Divya 0.568421 27 24
Sardines 0.557692 29 31
dkoes 0.554622 33 42
lswan 0.552381 29 32
Caleb 0.548387 34 46
Jimbob 0.548387 34 46
S_Schwett 0.544218 40 63
S_Schwett 0.544218 40 63
Morgan 0.54386 31 39
/dev/null 0.543689 28 31
dnh 0.541667 39 61
Brooklyn 0.536913 40 65
lswan 0.521127 37 61
sudo rm -rf * 0.518519 21 16
Margo 0.517857 29 39
Margo 0.517857 29 39
Stephen 0.512821 40 72
Name';DROP TABLE names; 0.506024 21 18
Kulyash 0.5 39 73
S_Schwett_3 0.493976 41 81
Charoes 0.487805 20 18
Jimmy 0.479042 40 83
dnh1 0.469274 42 93
dkoes 0.461538 42 96
tob 0.453333 17 14
/ 0.44898 44 108
del 0.444444 16 12
idk 0.44 44 112
ls 0.431373 44 116
Zane 0.417062 44 123
Last year's winning query 0.417062 44 123
Zane 0.417062 44 123
S_Schwett_1 0.417062 44 123
S_Schwett_2 0.417062 44 123
Zac 0.417062 44 123
Zane 0.417062 44 123
Divya :( 0.24 15 66
RLJ 0 0 0
test1 0 0 0
RLJ 0 0 0
rr_01feat 0 0 0
RLJ 0 0 0
Huoi 0 0 0
Houir 0 0 0
0 0 0 0

This open access technology is funded through 1R01GM097082-01 from the National Institute of General Medical Sciences. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of General Medical Sciences or the National Institutes of Health.