DrugSimNet: Enhancing Drug Representation Learning through Drug Similarity for Accurate Drug-Drug Interaction Prediction

Abstract

The field of drug discovery has garnered significant attention, particularly in light of advancements brought about by the "Homo Hundred" generation. Among the critical processes in drug discovery is drug screening, which is an important process in identifying and eliminating candidates that may pose potential side effects in the human body before in vivo experiments. The drug screening is often conducted via prediction of drug-drug Interaction, which utilizes algorithms to assess the likelihood and potential consequences of interactions between different drugs. This approach is a multifaceted process that involves the identification of potential new therapeutic entities by employing a combination of computational, experimental, translational, and clinical models. Traditional approaches to predicting drug-drug interactions in the context of drug discovery and polypharmacy heavily rely on empirical knowledge, in vitro assays, and animal experiments. However, these methods suffer from drawbacks such as being time-consuming, resource-intensive, and limited in their ability to capture the complete complexity of drug interactions. Therefore, there is a pressing need to develop automated and efficient methods that can accurately predict drug-drug interactions. To address the aforementioned problem, we proposed a novel representation learning based framework for prediction of drug-drug interaction. The proposed framework consists of two stages: representation learning, which focuses on extracting meaningful features from drugs, and transfer learning, utilized to train the drug-drug interaction prediction network. Through extensive experimentation, we have shown that the proposed drug-drug interaction prediction framework surpasses existing methods in terms of performance.