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Description: Objectives: Lithium and quetiapine are known to be effective treatments for bipolar disorder. However, little information is available to inform prediction of response to these medications. Machine-learning methods can identify predictors of response by examining variables simultaneously. Further evaluation of models on a test sample can estimate how well these models would generalize to other samples. Methods: Data (N=482) were drawn from a randomized clinical trial of outpatients with bipolar-I or II disorder who received adjunctive personalized treatment plus either lithium or quetiapine. Elastic Net Regularization (ENR) was used to generate models for lithium and quetiapine; these models were evaluated on a test set. Results: Predictions from the lithium model explained 17.4% of the variance in actual observed scores of patients who received lithium in the test set, while predictions from the quetiapine model explained 32.2% of the variance of patients that received quetiapine. Of the baseline variables selected, those with the largest parameter estimates were: severity of mania; attention-deficit/hyperactivity disorder (ADHD) comorbidity; and comorbidity with each of three anxiety disorders (social phobia/society anxiety, agoraphobia, and panic disorder). Predictive accuracy of the ENR model outperformed the simple and basic theoretical models. Conclusion: ENR is an effective approach for building optimal and generalizable models. Variables identified through this methodology can inform future research on predictors of response to lithium and quetiapine.