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Running
on
Zero
File size: 5,661 Bytes
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{
"ADMET_Caco2_Wang": {
"task_type": "regression",
"description": "predict drug permeability, measured in cm/s, using the Caco-2 cell line as an in vitro model to simulate human intestinal tissue permeability",
"num_molecules": 906
},
"ADMET_Bioavailability_Ma": {
"task_type": "classification",
"description": "predict oral bioavailability with binary labels, indicating the rate and extent a drug becomes available at its site of action",
"num_molecules": 640
},
"ADMET_Lipophilicity_AstraZeneca": {
"task_type": "regression",
"description": "predict lipophilicity with continuous labels, measured as a log-ratio, indicating a drug's ability to dissolve in lipid environments",
"num_molecules": 4200
},
"ADMET_Solubility_AqSolDB": {
"task_type": "regression",
"description": "predict aqueous solubility with continuous labels, measured in log mol/L, indicating a drug's ability to dissolve in water",
"num_molecules": 9982
},
"ADMET_HIA_Hou": {
"task_type": "classification",
"description": "predict human intestinal absorption (HIA) with binary labels, indicating a drug's ability to be absorbed into the bloodstream",
"num_molecules": 578
},
"ADMET_Pgp_Broccatelli": {
"task_type": "classification",
"description": "predict P-glycoprotein (Pgp) inhibition with binary labels, indicating a drug's potential to alter bioavailability and overcome multidrug resistance",
"num_molecules": 1212
},
"ADMET_BBB_Martins": {
"task_type": "classification",
"description": "predict blood-brain barrier permeability with binary labels, indicating a drug's ability to penetrate the barrier to reach the brain",
"num_molecules": 1915
},
"ADMET_PPBR_AZ": {
"task_type": "regression",
"description": "predict plasma protein binding rate with continuous labels, indicating the percentage of a drug bound to plasma proteins in the blood",
"num_molecules": 1797
},
"ADMET_VDss_Lombardo": {
"task_type": "regression",
"description": "predict the volume of distribution at steady state (VDss), indicating drug concentration in tissues versus blood",
"num_molecules": 1130
},
"ADMET_CYP2C9_Veith": {
"task_type": "classification",
"description": "predict CYP2C9 inhibition with binary labels, indicating the drug's ability to inhibit the CYP2C9 enzyme involved in metabolism",
"num_molecules": 12092
},
"ADMET_CYP2D6_Veith": {
"task_type": "classification",
"description": "predict CYP2D6 inhibition with binary labels, indicating the drug's potential to inhibit the CYP2D6 enzyme involved in metabolism",
"num_molecules": 13130
},
"ADMET_CYP3A4_Veith": {
"task_type": "classification",
"description": "predict CPY3A4 inhibition with binary labels, indicating the drug's ability to inhibit the CPY3A4 enzyme involved in metabolism",
"num_molecules": 12328
},
"ADMET_CYP2C9_Substrate_CarbonMangels": {
"task_type": "classification",
"description": "predict whether a drug is a substrate of the CYP2C9 enzyme with binary labels, indicating its potential to be metabolized",
"num_molecules": 666
},
"ADMET_CYP2D6_Substrate_CarbonMangels": {
"task_type": "classification",
"description": "predict whether a drug is a substrate of the CYP2D6 enzyme with binary labels, indicating its potential to be metabolized",
"num_molecules": 664
},
"ADMET_CYP3A4_Substrate_CarbonMangels": {
"task_type": "classification",
"description": "predict whether a drug is a substrate of the CYP3A4 enzyme with binary labels, indicating its potential to be metabolized",
"num_molecules": 667
},
"ADMET_Half_Life_Obach": {
"task_type": "regression",
"description": "predict the half-life duration of a drug, measured in hours, indicating the time for its concentration to reduce by half",
"num_molecules": 667
},
"ADMET_Clearance_Hepatocyte_AZ": {
"task_type": "regression",
"description": "predict drug clearance, measured in \u03bcL/min/10^6 cells, from hepatocyte experiments, indicating the rate at which the drug is removed from body",
"num_molecules": 1020
},
"ADMET_Clearance_Microsome_AZ": {
"task_type": "regression",
"description": "predict drug clearance, measured in mL/min/g, from microsome experiments, indicating the rate at which the drug is removed from body",
"num_molecules": 1102
},
"ADMET_LD50_Zhu": {
"task_type": "regression",
"description": "predict the acute toxicity of a drug, measured as the dose leading to lethal effects in log(kg/mol)",
"num_molecules": 7385
},
"ADMET_hERG": {
"task_type": "classification",
"description": "predict whether a drug blocks the hERG channel, which is crucial for heart rhythm, potentially leading to adverse effects",
"num_molecules": 648
},
"ADMET_AMES": {
"task_type": "classification",
"description": "predict whether a drug is mutagenic with binary labels, indicating its ability to induce genetic alterations",
"num_molecules": 7255
},
"ADMET_DILI": {
"task_type": "classification",
"description": "predict whether a drug can cause liver injury with binary labels, indicating its potential for hepatotoxicity",
"num_molecules": 475
}
} |