Drug failure during experimental procedures due to low bioactivity presents a significant challenge. To mitigate this risk and enhance compound bioactivities, predicting bioactivity classes during lead optimization is essential. The existing studies on structure–activity relationships have highlighted the connection between the chemical structures of compounds and their bioactivity. However, these studies often overlook the intricate relationship between drugs and bioactivity, which encompasses multiple factors beyond the chemical structure alone. To address this issue, we propose the BioAct-Het model, employing a heterogeneous siamese neural network to model the complex relationship between drugs and bioactivity classes, bringing them into a unified latent space. In particular, we introduce a novel representation for the bioactivity classes, called Bio-Prof, and enhance the original bioactivity data sets to tackle data scarcity. These innovative approaches resulted in our model outperforming the previous ones. The evaluation of BioAct-Het is conducted through three distinct strategies: association-based, bioactivity class-based, and compound-based. The association-based strategy utilizes supervised learning classification, while the bioactivity class-based strategy adopts a retrospective study evaluation approach. On the other hand, the compound-based strategy demonstrates similarities to the concept of meta-learning. Furthermore, the model’s effectiveness in addressing real-world problems is analyzed through a case study on the application of vancomycin and oseltamivir for COVID-19 treatment as well as molnupiravir’s potential efficacy in treating COVID-19 patients. The data and code underlying this article are available on https://github.com/CBRC-lab/BioAct-Het. However, data sets were derived from sources in the public domain.

Background: The Drug repurposing is an approach that holds promise in identifying new therapeutic uses for existing drugs. Recently, knowledge graphs have emerged as significant tools for addressing the challenges of drug repurposing. However, there are still major issues in constructing and embedding knowledge graphs. Results: This study proposes a two-step method called DrugRep-HeSiaGraph to address these challenges. The method integrates the drug-diseases knowledge graph with the application of a heterogeneous siamese neural network. In the first step, a drug-diseases knowledge graph named DDKG-V1 is constructed by defining new relationship types, and then numerical vector representations for the nodes are created using distributional learning method. In the second step, a heterogeneous siamese neural network called HeSiaNet is applied to enrich the embedding of drugs and diseases by bringing them closer in a new unified latent space. Then, it predicts potential drug candidates for diseases. DrugRep-HeSiaGraph achieves impressive performance metrics, including an AUC-ROC of 91.16%, an AUC-PR of 90.32%, an accuracy of 84.52%, a BS of 0.119, and an MCC of 69.12%. Conclusion: We demonstrate the effectiveness of the proposed method in identifying potential drugs for COVID-19 as a case study. In addition, this study shows the role of dipeptidyl peptidase 4 (DPP-4) as a potential receptor for SARS-CoV-2 and the effectiveness of DPP-4 inhibitors in facing COVID-19. This highlights the practical application of the model in addressing real-world challenges in the field of drug repurposing.

Drug repurposing or repositioning (DR) refers to finding new therapeutic applications for existing drugs. Current computational DR methods face data representation and negative data sampling challenges. Although retrospective studies attempt to operate various representations, it is a crucial step for an accurate prediction to aggregate these features and bring the associations between drugs and diseases into a unified latent space. In addition, the number of unknown associations between drugs and diseases, which is considered negative data, is much higher than the number of known associations, or positive data, leading to an imbalanced dataset. In this regard, we propose the DrugRep-KG method, which applies a knowledge graph embedding approach for representing drugs and diseases, to address these challenges. Despite the typical DR methods that consider all unknown drug-disease associations as negative data, we select a subset of unknown associations, provided the disease occurs because of an adverse reaction to a drug. DrugRep-KG has been evaluated based on different settings and achieves an AUC-ROC (area under the receiver operating characteristic curve) of 90.83% and an AUC-PR (area under the precision-recall curve) of 90.10%, which are higher than in previous works. Besides, we checked the performance of our framework in finding potential drugs for coronavirus infection and skin-related diseases: contact dermatitis and atopic eczema. DrugRep-KG predicted beclomethasone for contact dermatitis, and fluorometholone, clocortolone, fluocinonide, and beclomethasone for atopic eczema, all of which have previously been proven to be effective in other studies. Fluorometholone for contact dermatitis is a novel suggestion by DrugRep-KG that should be validated experimentally. DrugRep-KG also predicted the associations between COVID-19 and potential treatments suggested by DrugBank, in addition to new drug candidates provided with experimental evidence. The data and code underlying this article are available at https://github.com/CBRC-lab/DrugRep-KG.

Drug discovery is generally difficult, expensive and the success rate is low. One of the essential steps in the early stages of drug discovery and drug repurposing is identifying drug target interactions. Although several methods developed use binary classification to predict if the interaction between a drug and its target exists or not, it is more informative and challenging to predict the strength of the binding between a drug and its target. Binding affinity indicates the strength of drug-target pair interactions. In this regard, several computational methods have been developed to predict the drug-target binding affinity. With the advent of deep learning methods, the accuracy of binding affinity prediction is improving. However, the input representation of these models is very effective in the result. The early models only use the sequence of molecules and the latter models focus on the structure of them. Although the recent models predict binding affinity more accurate than the first ones, they need more data and resources for training. In this study, we present a method that uses a pre-trained transformer to represent the protein as model input. Although pretrained transformer extracts a feature vector of the protein sequence, they can learn structural information in layers and heads. So, the extracted feature vector by transformer includes the sequence and structural properties of protein. Therefore, our method can also be run without limitations on resources (memory, CPU and GPU). The results show that our model achieves a competitive performance with the state-of-art models. Data and trained model is available at http://bioinformatics.aut.ac.ir/TranDTA/ .

As a consequence of COVID-19 crisis, CBRC has been moved to as online weekly seminar event starting in Autumn 2020. This series of seminars is run by prominent bioinformatics professors from other universities, research centers, and Ph.D. students in undergraduate degrees. These webinars are open to members of the University and to the general public. This event is free but requires Registration. The meeting will be held virtually over Adobe Connect. The meeting link can be found at http://bioinformatics.aut.ac.ir/seminars/. Wednesdays, 18:30 – 19:30