Table 2.
Representative examples of machine learning models applied to pharmaceutically relevant end points to indicate areas used for machine learning that could be useful datasets for potential future use of deep learning. Other publically accessible datasets are available in OCHEM (69), Chembench (70), CDD (71) etc.
| Example end point modelled |
Naïve Bayesian | Support Vector Machine |
Deep Learning |
|---|---|---|---|
| Solubility | (72) | (73) | (28) |
| Drug Induced Liver Injury |
(34) | (74) | (33) |
| hERG | (75, 76) | (77, 78) | (32) |
| ADME | (36, 72) | (79) | (29, 32) |
| Blood Brain Barrier penetration |
(75) | (77) | |
| Biological Targets | (5, 80) | (81) | (32, 82) |
| Skin Permeability | (40, 41) | ||
| Transporters | (10, 83-85) | (86-88) | (32) |
| Mutagenicity | (75, 89) | (89, 90) | |
| Formulation | (91) | ||
| Adverse event prediction |
(92) | (93, 94) | |
| Counterfeit drug detection |
(95) | ||
| Docking | (96) | ||
| Small molecule pKa | (3) |