Drug-induced QT prolongation is a major life-threatening adverse drug effect. It is crucial to predict the QT prolongation effect as early as possible in drug development, however, data on drugs that induce QT prolongation are very limited and noisy. Multi-view learning (MVL) has been applied to many challenging machine learning and data mining problems, especially when complex data from diverse domains are involved and only limited labeled examples are available. Unlike existing MVL methods that use l2-norm co-regularization to obtain a smooth objective function, in this paper we proposed an l1-norm co-regularized MVL algorithm for predicting drug-induced QT prolongation effect and reformulate the l1-norm co-regularized objective function for deriving its gradient in the analytic form, and we can optimize the mapping functions on all views simultaneously and achieve 3-4 times higher computational efficiency, while previous l2-norm co-regularized MVL methods use alternate optimization that alternately optimizes one view with the other views fixed until convergence. l1-norm co-regularization enforces sparsity in the learned mapping functions and hence the results are expected to be more interpretable. Comprehensive experimental comparisons between our proposed method and previous MVL and single-view learning methods demonstrate that our method significantly outperforms those baseline methods more efficiently.