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Open ADMET
ASAP Discovery is approaching a patent disclosure for its preclinical candidates for its two coronavirus Mpro drug discovery programs. This competition will mirror some of the real-world drug discovery challenges that ASAP has had to overcome in the last three years.
Since the rise of structure-informed drug discovery in the 1980s-1990s, structural biology is key to drug discovery. We challenge you to predict MERS-CoV Mpro and SARS-CoV-2 Mpro poses using knowledge from the SARS-CoV-2 Mpro crystallography data that ASAP created.
Name | Contributors | CLD | Success Rate (<2Å) | Average RMSD | References | |
---|---|---|---|---|---|---|
averkova-nika98 | a | 86.378 ± 2.466 | 1.330 ± 0.266 | |||
ernestglukhov | b | 85.363 ± 2.603 | 1.368 ± 0.265 | |||
vemikainen | b | 85.213 ± 2.601 | 1.388 ± 0.267 | |||
vemikainenalt | c | 84.673 ± 2.670 | 1.432 ± 0.266 | |||
wiwnopgm | d | 83.657 ± 2.769 | 1.549 ± 0.269 |
Likely the most popular prediction for computational modellers. Although it's an important feature, as we can see in ASAP's TCP it only tells a part of the story. You will predict potency for both Mpro targets.
Name | Contributors | CLD | Mean Absolute Error | Mean Squared Error | Pearson R | Spearman Rho | R² | Kendall τ | References | |
---|---|---|---|---|---|---|---|---|---|---|
oscar-mendez-lucio | a | 0.509 ± 0.022 | 0.514 ± 0.064 | 0.789 ± 0.028 | 0.816 ± 0.018 | 0.594 ± 0.048 | 0.626 ± 0.016 | |||
ccorbi | b | 0.516 ± 0.022 | 0.529 ± 0.068 | 0.813 ± 0.029 | 0.823 ± 0.020 | 0.603 ± 0.051 | 0.649 ± 0.018 | |||
siat353 | b | 0.517 ± 0.021 | 0.515 ± 0.062 | 0.788 ± 0.029 | 0.792 ± 0.021 | 0.598 ± 0.044 | 0.614 ± 0.018 | |||
xubf | bc | 0.518 ± 0.022 | 0.537 ± 0.067 | 0.794 ± 0.027 | 0.798 ± 0.020 | 0.594 ± 0.046 | 0.612 ± 0.018 | |||
alx-dga | c | 0.522 ± 0.022 | 0.536 ± 0.062 | 0.800 ± 0.027 | 0.811 ± 0.019 | 0.588 ± 0.042 | 0.628 ± 0.017 |
Absorption-Distribution-Metabolism-Excretion-Toxicology (ADMET) endpoints sit in the middle of the assay cascade and can make or break preclinical candidate molecules. For this blind challenge we selected several crucial endpoints for the community to predict.
Name | Contributors | CLD | Mean Absolute Error | Mean Squared Error | Pearson R | Spearman Rho | R² | Kendall τ | References | |
---|---|---|---|---|---|---|---|---|---|---|
prairiewarbler10 | a | 0.224 ± 0.009 | 0.104 ± 0.009 | 0.802 ± 0.030 | 0.722 ± 0.024 | 0.623 ± 0.053 | 0.561 ± 0.020 | |||
ebl88 | b | 0.243 ± 0.009 | 0.117 ± 0.009 | 0.783 ± 0.029 | 0.737 ± 0.022 | 0.599 ± 0.039 | 0.570 ± 0.018 | |||
vchupakhin | c | 0.269 ± 0.010 | 0.140 ± 0.010 | 0.731 ± 0.033 | 0.670 ± 0.026 | 0.523 ± 0.048 | 0.512 ± 0.021 | |||
longhung25 | d | 0.276 ± 0.010 | 0.142 ± 0.011 | 0.783 ± 0.025 | 0.688 ± 0.024 | 0.546 ± 0.049 | 0.517 ± 0.020 | |||
xiaolinpan | de | 0.277 ± 0.010 | 0.144 ± 0.010 | 0.723 ± 0.035 | 0.640 ± 0.027 | 0.486 ± 0.047 | 0.494 ± 0.022 |
If you're looking to release new data to the community and are interested in doing so through a competition, reach out! We'd love to collaborate.
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