A system framework and method for predicting adverse drug reactions (ADRs). Structures represented in three-dimensions were prepared for small drug molecules and unique human proteins and binding scores between them were generated using molecular docking. Machine learning models were developed using the molecular docking features to predict ADRs. Using the machine learning models, it can successfully predict a drug-induced ADR based on drug- target interaction features and known drug-ADR relationships. By further analyzing the binding proteins that are top ranked or closely associated with the ADRs, there may be found possible interpretation of the ADR mechanisms. The machine learning ADR models based on molecular docking features not only assist with ADR prediction for new or existing known drug molecules, but also have the advantage of providing possible explanation or hypothesis for the underlying mechanisms of ADRs.
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