MRNDR: Multihead Attention-Based Recommendation Network for Drug Repurposing

Abstract

As is well-known, the process of developing new drugs is extremely expensive, whereas drug repurposing represents a promising approach to augment the efficiency of new drug development. While this method can indeed spare us from expensive drug toxicity and safety experiments, it still demands a substantial amount of time to carry out precise efficacy experiments for specific diseases, thereby consuming a significant quantity of resources. Therefore, if we can prescreen potential other indications for selected drugs, it could result in substantial cost savings. In light of this, this paper introduces a drug repurposing recommendation model called MRNDR, which stands for Multi-head attention-based Recommendation Network for Drug Repurposing. This model serves as a prediction tool for drug-disease relationships, leveraging the multihead self-attention mechanism that demonstrates robust generalization capabilities. These capabilities stem not only from our extensive million-level training data set, BioRE (Biology Recommended Entity data), but also from the utilization of the WRDS (Weighted Representation Distance Score) algorithm proposed by us. The MRNDR model has achieved new state-of-the-art results on the GP-KG public data set, with an MRR (Mean Reciprocal Rank) score of 0.308 and a Hits@10 score of 0.628. This represents significant improvements of 4.7% (MRR) and 18.1% (Hits@10) over the current best-performing models. Additionally, to further validate the practical utility of the model, we examined results recommended by MRNDR that were not present in the training data set. Some of these recommendations have undergone clinical trials, as evidenced by their presence on ClinicalTrials.gov and the China Clinical Trials Center, indirectly confirming the applicability of MRNDR. The MRNDR model can predict the reusability of candidate drugs, reducing the need for manual expert assessments and enabling efficient drug repurposing.

Editorial: Machine Learning in Bio-cheminformatics

“…Some methods, algorithms, and functional tools were constructed to facilitate the application or improve the performance of the classic virtual screening strategy. − Machine learning methods were also adopted in this collection to identify new hit compounds, discover promising leads for cholestasis, interpret QSAR models, and learn molecular representations. − DiStefano et al and Mao et al conducted research on toxicity prediction and antiviral drug design, respectively. Moreover, ML was also applied to explore the pharmaceutical properties of diverse drug candidates. − A novel knowledge base for nonalcoholic fatty liver disease was developed . Heyndrickx et al adopted cross-pharma federated learning to unleash the benefit of QSAR.…”