Admetai Tools

Configuration File: admetai_tools.json Tool Type: Local Tools Count: 9

This page contains all tools defined in the admetai_tools.json configuration file.

Available Tools

ADMETAI_predict_BBB_penetrance (Type: ADMETAITool)

Predicts blood-brain barrier (BBB) penetrance for a given list of molecules in SMILES format.

ADMETAI_predict_BBB_penetrance tool specification

Tool Information:

  • Name: ADMETAI_predict_BBB_penetrance

  • Type: ADMETAITool

  • Description: Predicts blood-brain barrier (BBB) penetrance for a given list of molecules in SMILES format.

Parameters:

  • smiles (array) (required) The list of SMILES strings.

Example Usage:

query = {
    "name": "ADMETAI_predict_BBB_penetrance",
    "arguments": {
        "smiles": ["item1", "item2"]
    }
}
result = tu.run(query)

ADMETAI_predict_CYP_interactions (Type: ADMETAITool)

Predicts CYP enzyme interactions for a given list of molecules in SMILES format.

ADMETAI_predict_CYP_interactions tool specification

Tool Information:

  • Name: ADMETAI_predict_CYP_interactions

  • Type: ADMETAITool

  • Description: Predicts CYP enzyme interactions for a given list of molecules in SMILES format.

Parameters:

  • smiles (array) (required) The list of SMILES strings.

Example Usage:

query = {
    "name": "ADMETAI_predict_CYP_interactions",
    "arguments": {
        "smiles": ["item1", "item2"]
    }
}
result = tu.run(query)

ADMETAI_predict_bioavailability (Type: ADMETAITool)

Predicts bioavailability endpoints (Bioavailability_Ma, HIA_Hou, PAMPA_NCATS, Caco2_Wang, Pgp_Bro…

ADMETAI_predict_bioavailability tool specification

Tool Information:

  • Name: ADMETAI_predict_bioavailability

  • Type: ADMETAITool

  • Description: Predicts bioavailability endpoints (Bioavailability_Ma, HIA_Hou, PAMPA_NCATS, Caco2_Wang, Pgp_Broccatelli) for a given list of molecules in SMILES format.

Parameters:

  • smiles (array) (required) The list of SMILES strings.

Example Usage:

query = {
    "name": "ADMETAI_predict_bioavailability",
    "arguments": {
        "smiles": ["item1", "item2"]
    }
}
result = tu.run(query)

ADMETAI_predict_clearance_distribution (Type: ADMETAITool)

Predicts clearance and distribution endpoints (Clearance_Hepatocyte_AZ, Clearance_Microsome_AZ, H…

ADMETAI_predict_clearance_distribution tool specification

Tool Information:

  • Name: ADMETAI_predict_clearance_distribution

  • Type: ADMETAITool

  • Description: Predicts clearance and distribution endpoints (Clearance_Hepatocyte_AZ, Clearance_Microsome_AZ, Half_Life_Obach, VDss_Lombardo, PPBR_AZ) for a given list of molecules in SMILES format.

Parameters:

  • smiles (array) (required) The list of SMILES strings.

Example Usage:

query = {
    "name": "ADMETAI_predict_clearance_distribution",
    "arguments": {
        "smiles": ["item1", "item2"]
    }
}
result = tu.run(query)

ADMETAI_predict_nuclear_receptor_activity (Type: ADMETAITool)

Predicts nuclear receptor activity endpoints (NR-AR-LBD, NR-AR, NR-AhR, NR-Aromatase, NR-ER-LBD, …

ADMETAI_predict_nuclear_receptor_activity tool specification

Tool Information:

  • Name: ADMETAI_predict_nuclear_receptor_activity

  • Type: ADMETAITool

  • Description: Predicts nuclear receptor activity endpoints (NR-AR-LBD, NR-AR, NR-AhR, NR-Aromatase, NR-ER-LBD, NR-ER, NR-PPAR-gamma) for a given list of molecules in SMILES format.

Parameters:

  • smiles (array) (required) The list of SMILES strings.

Example Usage:

query = {
    "name": "ADMETAI_predict_nuclear_receptor_activity",
    "arguments": {
        "smiles": ["item1", "item2"]
    }
}
result = tu.run(query)

ADMETAI_predict_physicochemical_properties (Type: ADMETAITool)

Predicts physicochemical properties (molecular weight, logP, hydrogen bond acceptors/donors, Lipi…

ADMETAI_predict_physicochemical_properties tool specification

Tool Information:

  • Name: ADMETAI_predict_physicochemical_properties

  • Type: ADMETAITool

  • Description: Predicts physicochemical properties (molecular weight, logP, hydrogen bond acceptors/donors, Lipinski, QED, stereo centers, TPSA) for a given list of molecules in SMILES format.

Parameters:

  • smiles (array) (required) The list of SMILES strings.

Example Usage:

query = {
    "name": "ADMETAI_predict_physicochemical_properties",
    "arguments": {
        "smiles": ["item1", "item2"]
    }
}
result = tu.run(query)

ADMETAI_predict_solubility_lipophilicity_hydration (Type: ADMETAITool)

Predicts solubility, lipophilicity, and hydration endpoints (Solubility_AqSolDB, Lipophilicity_As…

ADMETAI_predict_solubility_lipophilicity_hydration tool specification

Tool Information:

  • Name: ADMETAI_predict_solubility_lipophilicity_hydration

  • Type: ADMETAITool

  • Description: Predicts solubility, lipophilicity, and hydration endpoints (Solubility_AqSolDB, Lipophilicity_AstraZeneca, HydrationFreeEnergy_FreeSolv) for a given list of molecules in SMILES format.

Parameters:

  • smiles (array) (required) The list of SMILES strings.

Example Usage:

query = {
    "name": "ADMETAI_predict_solubility_lipophilicity_hydration",
    "arguments": {
        "smiles": ["item1", "item2"]
    }
}
result = tu.run(query)

ADMETAI_predict_stress_response (Type: ADMETAITool)

Predicts stress response endpoints (SR-ARE, SR-ATAD5, SR-HSE, SR-MMP, SR-p53) for a given list of…

ADMETAI_predict_stress_response tool specification

Tool Information:

  • Name: ADMETAI_predict_stress_response

  • Type: ADMETAITool

  • Description: Predicts stress response endpoints (SR-ARE, SR-ATAD5, SR-HSE, SR-MMP, SR-p53) for a given list of molecules in SMILES format.

Parameters:

  • smiles (array) (required) The list of SMILES strings.

Example Usage:

query = {
    "name": "ADMETAI_predict_stress_response",
    "arguments": {
        "smiles": ["item1", "item2"]
    }
}
result = tu.run(query)

ADMETAI_predict_toxicity (Type: ADMETAITool)

Predicts toxicity endpoints (AMES, Carcinogens_Lagunin, ClinTox, DILI, LD50_Zhu, Skin_Reaction, h…

ADMETAI_predict_toxicity tool specification

Tool Information:

  • Name: ADMETAI_predict_toxicity

  • Type: ADMETAITool

  • Description: Predicts toxicity endpoints (AMES, Carcinogens_Lagunin, ClinTox, DILI, LD50_Zhu, Skin_Reaction, hERG) for a given list of molecules in SMILES format.

Parameters:

  • smiles (array) (required) The list of SMILES strings.

Example Usage:

query = {
    "name": "ADMETAI_predict_toxicity",
    "arguments": {
        "smiles": ["item1", "item2"]
    }
}
result = tu.run(query)