Drug Synergy Tools

Configuration File: drug_synergy_tools.json Tool Type: Local Tools Count: 3

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

Available Tools

DrugSynergy_calculate_bliss (Type: DrugSynergyTool)

Calculate Bliss Independence synergy score for a drug combination. Model: E_expected = E_a + E_b …

DrugSynergy_calculate_bliss tool specification

Tool Information:

  • Name: DrugSynergy_calculate_bliss

  • Type: DrugSynergyTool

  • Description: Calculate Bliss Independence synergy score for a drug combination. Model: E_expected = E_a + E_b - E_a*E_b. Synergy score = (E_combo - E_expected) * 100. Positive = synergy; Negative = antagonism. Inputs are fractional inhibition values (0-1). Based on Bliss (1939). Use for: single-concentration combination screening, high-throughput synergy assessment.

Parameters:

  • operation (string) (required) Operation type

  • effect_a (number) (required) Fractional inhibition of drug A alone (0=no effect, 1=complete inhibition)

  • effect_b (number) (required) Fractional inhibition of drug B alone (0=no effect, 1=complete inhibition)

  • effect_combination (number) (required) Observed fractional inhibition of the drug combination

Example Usage:

query = {
    "name": "DrugSynergy_calculate_bliss",
    "arguments": {
        "operation": "example_value",
        "effect_a": "example_value",
        "effect_b": "example_value",
        "effect_combination": "example_value"
    }
}
result = tu.run(query)

DrugSynergy_calculate_hsa (Type: DrugSynergyTool)

Calculate Highest Single Agent (HSA) synergy score for drug combinations across dose points. HSA …

DrugSynergy_calculate_hsa tool specification

Tool Information:

  • Name: DrugSynergy_calculate_hsa

  • Type: DrugSynergyTool

  • Description: Calculate Highest Single Agent (HSA) synergy score for drug combinations across dose points. HSA expected = max(E_a, E_b) at each dose. Synergy = observed - HSA expected. Requires matching arrays of effects for drug A, drug B, and the combination. Use for: multi-dose combination matrices, identifying synergistic dose ranges.

Parameters:

  • operation (string) (required) Operation type

  • effects_a (array) (required) Array of drug A inhibition effects at each dose point (0-1 or 0-100)

  • effects_b (array) (required) Array of drug B inhibition effects at each dose point (same length as effects_a)

  • effects_combo (array) (required) Array of combination inhibition effects at each dose point (same length as effects_a)

Example Usage:

query = {
    "name": "DrugSynergy_calculate_hsa",
    "arguments": {
        "operation": "example_value",
        "effects_a": ["item1", "item2"],
        "effects_b": ["item1", "item2"],
        "effects_combo": ["item1", "item2"]
    }
}
result = tu.run(query)

DrugSynergy_calculate_zip (Type: DrugSynergyTool)

Calculate ZIP (Zero Interaction Potency) delta synergy score from a full dose-response matrix. Fi…

DrugSynergy_calculate_zip tool specification

Tool Information:

  • Name: DrugSynergy_calculate_zip

  • Type: DrugSynergyTool

  • Description: Calculate ZIP (Zero Interaction Potency) delta synergy score from a full dose-response matrix. Fits Hill curves to marginal dose-response data for each drug and calculates expected additivity. Returns delta matrix where positive values indicate synergy. Based on Yadav et al. (2015). Use for: comprehensive combination screening matrices.

Parameters:

  • operation (string) (required) Operation type

  • doses_a (array) (required) Concentration values for drug A (e.g., [0.01, 0.1, 1, 10])

  • doses_b (array) (required) Concentration values for drug B (e.g., [0.01, 0.1, 1, 10])

  • viability_matrix (array) (required) 2D matrix of cell viability percentages (0-100). Rows = doses_a, Columns = doses_b.

Example Usage:

query = {
    "name": "DrugSynergy_calculate_zip",
    "arguments": {
        "operation": "example_value",
        "doses_a": ["item1", "item2"],
        "doses_b": ["item1", "item2"],
        "viability_matrix": ["item1", "item2"]
    }
}
result = tu.run(query)