Meta-Path-Based Probabilistic Soft Logic for Drug–Target Interaction Predictions

Drug–target interaction (DTI) predictions, which aim to predict whether a drug will be bounded to a target, have received wide attention recently. The goal is to automate and accelerate the costly process of drug design. Most of the recently proposed methods use single drug–drug similarity and target–target similarity information for DTI predictions; thus, they are unable to take advantage of the abundant information regarding the various types of similarities between these two types of information. Very recently, some methods have been proposed to leverage multi-similarity information; however, they still lack the ability to take into consideration the rich topological information of all sorts of knowledge bases in which the drugs and targets reside. Furthermore, the high computational cost of these approaches limits their scalability to large-scale networks. To address these challenges, we propose a novel approach named summated meta-path-based probabilistic soft logic (SMPSL). Unlike the original PSL framework, which often overlooks the quantitative path frequency, SMPSL explicitly captures crucial meta-path count information. By integrating summated meta-path counts into the PSL framework, our method not only significantly reduces the computational overhead, but also effectively models the heterogeneity of the network for robust DTI predictions. We evaluated SMPSL against five robust baselines on three public datasets. The experimental results demonstrate that our approach outperformed all of the baselines in terms of the AUPR and AUC scores.