Table 4.
DrugBank ID | Drug name | Targets | Prediction Confidence | References | |
---|---|---|---|---|---|
1 | DB01054 | Nitrendipine | AAK1 | 0.909 | (85) |
2 | DB12610 | Ebselen | FURIN AAK1 | 0.916 | (86, 87) |
3 | DB04954 | Tecadenoson | AAK1 | 0.912 | (88) |
4 | DB12831 | Gabexate | ACE2 CTSL |
0.912 | (89) |
5 | DB12945 | Dihydralazine | TMPRSS2 CTSL ACE2 |
0.946 | (90) |
6 | DB13014 | Hypericin | AAK1 | 0.901 | (91) |
7 | DB13025 | Tiapride | FURIN AAK1 |
0.917 | (92) |
8 | DB13132 | Artemisinin | CTSL ACE2 |
0.964 | (93, 94) |
9 | DB13141 | Ambroxol acefyllinate | TMPRSS2 CTSL ACE2 |
0.94 | (95, 96) |
10 | DB13620 | Potassium gluconate | AAK1 | 0.911 | (97, 98) |
11 | DB13875 | Harmaline | GAK FURIN ACE2 |
0.943 | (99, 100) |
12 | DB13876 | Brofaromine | TMPRSS2 CTSL ACE2 |
0.948 | (101) |
The mentioned drugs are the results of the drugs from the Random Forest model and model combination (Random Forest + Tree Ensembl). The first two drugs are the result of the combination models. Moreover, the table contains the drugs and their DrugBank IDs which are validated from the literature for their use in COVID-19. It supports and adds to the credibility of our developed ML models. Additionally, the COVID-19 targets in the form of Uniprot IDs along with prediction confidence and literature-based evidence.