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This study proposes the first artificial intelligence framework for characterizing multiple ion channel kinetics of whole\u2010cell recordings. The framework integrates machine learning for anomaly detection and deep learning for multi\u2010class classification. The anomaly detection excludes recordings that are incompatible with ion channel behavior. The multi\u2010class classification combined a 1D convolutional neural network, bidirectional long short\u2010term memory, and an attention mechanism to capture the spatiotemporal patterns of the recordings. The framework achieves an accuracy of 97.58% in classifying 124 test datasets into six categories based on ion channel kinetics. The utility of the novel framework is demonstrated in two applications: Alzheimer's disease drug screening and nanomatrix\u2010induced neuronal differentiation. 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