Light-induced melatonin suppression data from 29 peer-reviewed publications was analysed by means of a machine learning approach to establish which light exposure characteristics (i.e. photopic illuminance, five α-opic equivalent daylight illuminances (EDIs), duration and timing of the light exposure, and the dichotomous variables pharmacological pupil dilation and narrowband light source) are the main determinants of melatonin suppression. Melatonin suppression in the dataset was dominated by four light exposure characteristics: 1. melanopic EDI, 2. light exposure duration, 3. pupil dilation and 4. S-cone-opic EDI. A logistic model was used to evaluate the influence of each of these parameters on the melatonin suppression response. The final logistic model was only based on the first three parameters, since melanopic EDI was the best single (photoreceptor) predictor that was only outperformed by S-cone-opic EDI for (photopic) illuminances below 21 lux. This confirms and extends findings on the importance of the metric melanopic EDI for predicting biological effects of light in integrative (human centric) lighting applications. The model provides initial and general guidance to lighting practitioners on how to combine spectrum, duration and amount of light exposure when controlling non-visual responses to light, especially melatonin suppression. The model is a starting tool for developing hypotheses on photoreceptors’ contributions to light’s non-visual responses and helps identifying areas where more data is needed, like on the S-cone contribution at low illuminances.