Fit 4PL or 5PL dose-response curves and compute IC50 with confidence intervals. Publication-ready plots, Hill slope, and pIC50. Data never leaves your browser.
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Load example IC50 data to see the full workflow
Two columns: Concentration and Response (% viability, % inhibition, or raw signal). Replicates at the same concentration are averaged automatically.
When to use
Do not use for
Half-log (3.16-fold) or full-log (10-fold) dilution series are standard. Linearly spaced concentrations cluster data points and leave gaps in the critical transition region, producing poorly constrained IC50 estimates with wide confidence intervals.
Constraining Top to 100% (or your vehicle control mean) and Bottom to 0% reduces the degrees of freedom and stabilizes the fit when plateau data is sparse. Only use a free fit when you have strong data at both asymptotes and want to detect incomplete inhibition or stimulation.
A Hill slope of −1 indicates standard single-site binding. Slopes steeper than −1 (e.g., −2) suggest positive cooperativity or multiple binding events. Shallow slopes (e.g., −0.5) may indicate target heterogeneity, multiple receptor subtypes, or polypharmacology.
pIC50 = −log₁₀(IC50 in M) converts potency to a linear scale where higher is more potent. A 10-fold difference in IC50 is exactly 1 unit of pIC50. This simplifies statistical analysis and makes SAR tables easier to read than raw IC50 values spanning orders of magnitude.
While 4 points can technically fit a 4PL model, the resulting confidence intervals are extremely wide. Eight or more concentrations — spanning both plateaus and the transition — provide the statistical power needed for publication-quality IC50 estimates.
Nonlinear least squares fitting of the four-parameter logistic (Hill equation) and five-parameter logistic models. Shares the Levenberg-Marquardt solver with the ELISA Curve Fitter. 95% confidence intervals computed from the Jacobian matrix.
Last validated 2026-03-22. Calculations are designed for planning and documentation support; verify procurement decisions against manufacturer specifications or institutional SOPs.
ConductScience IC50 Calculator (v1.0). ConductScience, Inc. 2026. Available at: https://conductscience.com/tools/ic50-calculator
Dose-response analysis quantifies the relationship between drug concentration and biological effect. The standard approach is to fit a sigmoidal (S-shaped) model to the data using nonlinear regression.
The 4-Parameter Logistic (4PL) model is the most widely used: Y = Bottom + (Top − Bottom) / [1 + (x / IC50)^HillSlope]. The four parameters are: Top (maximum response), Bottom (minimum response), IC50 (half-maximal inhibitory concentration), and Hill slope (curve steepness).
This tool uses the Levenberg-Marquardt algorithm, the same method used by GraphPad Prism and R's drc package. It iteratively adjusts all four parameters simultaneously to minimize the sum of squared residuals between the model and your data.
A well-designed dose-response experiment uses 8–12 concentrations spanning at least 3 log units (e.g., 0.001 µM to 100 µM), with 2–3 replicates per concentration. Half-log dilution series (3.16-fold dilutions) are standard.
Critical requirements: include concentrations that fully define both the top plateau (minimal drug effect) and bottom plateau (maximal drug effect). If the curve plateaus are missing, the fitted IC50 will be unreliable.
Normalize your data: express response as percent of untreated control (Top = 100%) or percent of maximum inhibition (Bottom = 0%). Vehicle-only controls should be included at every plate.
