Predicting method for activity cliffs using machine learning

Abstract

Activity cliff (AC) is formed by a pair of structurally similar compounds with large difference in biological potency. Successful AC prediction leads to efficient exploration of lead compounds in medicinal chemistry. Using previous ligand-based approaches to the prediction of AC, it was found that similarity values between a pair of compounds consisting of the same core, but different attachment points were overestimated. Furthermore, if compounds in the training data set did not consist of the same core as in the test data set, AC prediction accuracy decreased. In the present study, we investigated whether AC can be accurately predicted in such difficult situations mentioned above. We proposed a novel AC prediction scheme for taking into account the attachment points of cores. The proposed scheme was applied to the prediction of AC in several activity classes and it was confirmed that the prediction accuracy improved compared to the previously proposed scheme.