update the descriptions

dataset_descriptions.json

@@ -1,112 +1,156 @@
 {
     "ADMET_Caco2_Wang": {
         "task_type": "regression",
+        "task_name": "Drug Permeability",
         "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",
+        "url": "https://tdcommons.ai/single_pred_tasks/adme#caco-2-cell-effective-permeability-wang-et-al",
         "num_molecules": 906
     },
     "ADMET_Bioavailability_Ma": {
         "task_type": "classification",
+        "task_name": "Drug Oral Bioavailability",
         "description": "predict oral bioavailability with binary labels, indicating the rate and extent a drug becomes available at its site of action",
+        "url": "https://tdcommons.ai/single_pred_tasks/adme#bioavailability-ma-et-al",
         "num_molecules": 640
     },
     "ADMET_Lipophilicity_AstraZeneca": {
         "task_type": "regression",
+        "task_name": "Drug Lipophilicity",
         "description": "predict lipophilicity with continuous labels, measured as a log-ratio, indicating a drug's ability to dissolve in lipid environments",
+        "url": "https://tdcommons.ai/single_pred_tasks/adme#lipophilicity-astrazeneca",
         "num_molecules": 4200
     },
     "ADMET_Solubility_AqSolDB": {
         "task_type": "regression",
+        "task_name": "Drug Aqueous Solubility",
         "description": "predict aqueous solubility with continuous labels, measured in log mol/L, indicating a drug's ability to dissolve in water",
+        "url": "https://tdcommons.ai/single_pred_tasks/adme#solubility-aqsoldb",
         "num_molecules": 9982
     },
     "ADMET_HIA_Hou": {
         "task_type": "classification",
+        "task_name": "Drug Human Intestinal Absorption",
         "description": "predict human intestinal absorption (HIA) with binary labels, indicating a drug's ability to be absorbed into the bloodstream",
+        "url": "https://tdcommons.ai/single_pred_tasks/adme#hia-human-intestinal-absorption-hou-et-al",
         "num_molecules": 578
     },
     "ADMET_Pgp_Broccatelli": {
         "task_type": "classification",
+        "task_name": "P-glycoprotein Inhibition",
         "description": "predict P-glycoprotein (Pgp) inhibition with binary labels, indicating a drug's potential to alter bioavailability and overcome multidrug resistance",
+        "url": "https://tdcommons.ai/single_pred_tasks/adme#pgp-p-glycoprotein-inhibition-broccatelli-et-al",
         "num_molecules": 1212
     },
     "ADMET_BBB_Martins": {
         "task_type": "classification",
+        "task_name": "Blood-Brain Barrier Permeability",
         "description": "predict blood-brain barrier permeability with binary labels, indicating a drug's ability to penetrate the barrier to reach the brain",
+        "url": "https://tdcommons.ai/single_pred_tasks/adme#bbb-blood-brain-barrier-martins-et-al",
         "num_molecules": 1915
     },
     "ADMET_PPBR_AZ": {
         "task_type": "regression",
+        "task_name": "Plasma Protein Binding Rate",
         "description": "predict plasma protein binding rate with continuous labels, indicating the percentage of a drug bound to plasma proteins in the blood",
+        "url": "https://tdcommons.ai/single_pred_tasks/adme#ppbr-plasma-protein-binding-rate-astrazeneca",
         "num_molecules": 1797
     },
     "ADMET_VDss_Lombardo": {
         "task_type": "regression",
+        "task_name": "Volume of Distribution at Steady State",
         "description": "predict the volume of distribution at steady state (VDss), indicating drug concentration in tissues versus blood",
+        "url": "https://tdcommons.ai/single_pred_tasks/adme#vdss-volumn-of-distribution-at-steady-state-lombardo-et-al",
         "num_molecules": 1130
     },
     "ADMET_CYP2C9_Veith": {
         "task_type": "classification",
+        "task_name": "CYP2C9 Inhibition",
         "description": "predict CYP2C9 inhibition with binary labels, indicating the drug's ability to inhibit the CYP2C9 enzyme involved in metabolism",
+        "url": "https://tdcommons.ai/single_pred_tasks/adme#cyp-p450-2c9-inhibition-veith-et-al",
         "num_molecules": 12092
     },
     "ADMET_CYP2D6_Veith": {
         "task_type": "classification",
+        "task_name": "CYP2D6 Inhibition",
         "description": "predict CYP2D6 inhibition with binary labels, indicating the drug's potential to inhibit the CYP2D6 enzyme involved in metabolism",
+        "url": "https://tdcommons.ai/single_pred_tasks/adme#cyp-p450-2d6-inhibition-veith-et-al",
         "num_molecules": 13130
     },
     "ADMET_CYP3A4_Veith": {
         "task_type": "classification",
+        "task_name": "CPY3A4 Inhibition",
         "description": "predict CPY3A4 inhibition with binary labels, indicating the drug's ability to inhibit the CPY3A4 enzyme involved in metabolism",
+        "url": "https://tdcommons.ai/single_pred_tasks/adme#cyp-p450-3a4-inhibition-veith-et-al",
         "num_molecules": 12328
     },
     "ADMET_CYP2C9_Substrate_CarbonMangels": {
         "task_type": "classification",
+        "task_name": "CYP2C9 Substrate",
         "description": "predict whether a drug is a substrate of the CYP2C9 enzyme with binary labels, indicating its potential to be metabolized",
+        "url": "https://tdcommons.ai/single_pred_tasks/adme#cyp2c9-substrate-carbon-mangels-et-al",
         "num_molecules": 666
     },
     "ADMET_CYP2D6_Substrate_CarbonMangels": {
         "task_type": "classification",
+        "task_name": "CYP2D6 Substrate",
         "description": "predict whether a drug is a substrate of the CYP2D6 enzyme with binary labels, indicating its potential to be metabolized",
+        "url": "https://tdcommons.ai/single_pred_tasks/adme#cyp2d6-substrate-carbon-mangels-et-al",
         "num_molecules": 664
     },
     "ADMET_CYP3A4_Substrate_CarbonMangels": {
         "task_type": "classification",
+        "task_name": "CYP3A4 Substrate",
         "description": "predict whether a drug is a substrate of the CYP3A4 enzyme with binary labels, indicating its potential to be metabolized",
+        "url": "https://tdcommons.ai/single_pred_tasks/adme#cyp3a4-substrate-carbon-mangels-et-al",
         "num_molecules": 667
     },
     "ADMET_Half_Life_Obach": {
         "task_type": "regression",
+        "task_name": "Drug Half-Life Duration",
         "description": "predict the half-life duration of a drug, measured in hours, indicating the time for its concentration to reduce by half",
+        "url": "https://tdcommons.ai/single_pred_tasks/adme#half-life-obach-et-al",
         "num_molecules": 667
     },
     "ADMET_Clearance_Hepatocyte_AZ": {
         "task_type": "regression",
+        "task_name": "Drug Clearance from Hepatocyte Experiments",
         "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",
+        "url": "https://tdcommons.ai/single_pred_tasks/adme#clearance-astrazeneca",
         "num_molecules": 1020
     },
     "ADMET_Clearance_Microsome_AZ": {
         "task_type": "regression",
+        "task_name": "Drug Clearance from Microsome Experiments",
         "description": "predict drug clearance, measured in mL/min/g, from microsome experiments, indicating the rate at which the drug is removed from body",
+        "url": "https://tdcommons.ai/single_pred_tasks/adme#clearance-astrazeneca",
         "num_molecules": 1102
     },
     "ADMET_LD50_Zhu": {
         "task_type": "regression",
+        "task_name": "Drug Acute Toxicity",
         "description": "predict the acute toxicity of a drug, measured as the dose leading to lethal effects in log(kg/mol)",
+        "url": "https://tdcommons.ai/single_pred_tasks/tox#acute-toxicity-ld50",
         "num_molecules": 7385
     },
     "ADMET_hERG": {
         "task_type": "classification",
+        "task_name": "hERG Channel Blockage",
         "description": "predict whether a drug blocks the hERG channel, which is crucial for heart rhythm, potentially leading to adverse effects",
+        "url": "https://tdcommons.ai/single_pred_tasks/tox#herg-blockers",
         "num_molecules": 648
     },
     "ADMET_AMES": {
         "task_type": "classification",
+        "task_name": "Drug Mutagenicity",
         "description": "predict whether a drug is mutagenic with binary labels, indicating its ability to induce genetic alterations",
+        "url": "https://tdcommons.ai/single_pred_tasks/tox#ames-mutagenicity",
         "num_molecules": 7255
     },
     "ADMET_DILI": {
         "task_type": "classification",
+        "task_name": "Drug-Induced Liver Injury",
         "description": "predict whether a drug can cause liver injury with binary labels, indicating its potential for hepatotoxicity",
+        "url": "https://tdcommons.ai/single_pred_tasks/tox#dili-drug-induced-liver-injury",
         "num_molecules": 475
     }
 }