Machine learning algorithms, such as support vector machines (SVM), random forest, or neural networks, can be trained on large datasets of compounds and their corresponding IC50 values to predict the potency of new compounds. Deep learning approaches, including convolutional neural networks (CNN) and recurrent neural networks (RNN), can capture complex relationships between chemical structures and biological activity, improving the accuracy of IC50 predictions.