Drug repositioning aims to identify potential new indications for existing drugs to reduce the time and financial costs associated with developing new drugs. Most existing deep learning-based drug repositioning methods predominantly utilize graph-based representations. However, graph-based drug repositioning methods struggle to perform effective inference in cold-start scenarios involving novel drugs because of the lack of association information with the diseases. Unlike traditional graph-based approaches, we propose a bidirectional behavior learning strategy for drug repositioning, known as BiBLDR. This innovative framework redefines drug repositioning as a behavior sequential learning task to capture drug-disease interaction patterns. First, we construct bidirectional behavioral sequences based on drug and disease sides. The consideration of bidirectional information ensures a more meticulous and rigorous characterization of the behavioral sequences. Subsequently, we propose a two-stage strategy for drug repositioning. In the first stage, we construct prototype spaces to characterize the representational attributes of drugs and diseases. In the second stage, these refined prototypes and bidirectional behavior sequence data are leveraged to predict potential drug-disease associations. Based on this learning approach, the model can more robustly and precisely capture the interactive relationships between drug and disease features from bidirectional behavioral sequences. Extensive experiments demonstrate that our method achieves state-of-the-art performance on benchmark datasets. Meanwhile, BiBLDR demonstrates significantly superior performance compared to previous methods in cold-start scenarios. Our code is published in https://github.com/Renyeeah/BiBLDR.

Drug repositioning is an economically efficient strategy used to discover new indications for existing drugs beyond their original approvals, expanding their applicability and usage to address challenges in disease treatment. In recent years, deep-learning techniques for drug repositioning have gained much attention. While most deep learning-based research methods focus on encoding drugs and diseases by extracting feature information from neighbors in the network, they often pay little attention to the potential relationships between the features of drugs and diseases, leading to imprecise encoding of drugs and diseases. To address this, we design a dual-feature drug repositioning neural network (DFDRNN) model to achieve precise encoding of drugs and diseases. DFDRNN uses two features to represent drugs and diseases: the similarity feature and the association feature. The model incorporates a self-attention mechanism to design two dual-feature extraction modules for achieving precisely encoding of drugs and diseases: the intra-domain dual-feature extraction (IntraDDFE) module and the inter-domain dual-feature extraction (InterDDFE) module. The IntraDDFE module extracts features from a single domain (drug or disease domain), while the InterDDFE module extracts features from the mixed domain (drug and disease domain). In particular, the feature is changed by InterDDFE, ensuring a precise encoding of drugs and diseases. Finally, a cross-dual-domain decoder is designed to predict drug-disease associations in both the drug and disease domains. Compared to six state-of-the-art methods, DFDRNN outperforms others on four benchmark datasets, with an average AUROC of 0.946 and an average AUPR of 0.597.

Algorithms for sparse matrix completion are used in recommender systems to predict user preferences to items such as news, movies, or songs [1]. The same methods can be successfully applied in other fields, for instance in systems biology to predict gene-disease associations or in computational systems pharmacology to predict adverse drug reactions [2] and to repurpose FDA approved drugs [3]. Matrix completion is the task of filling out missing entries in an observed sparse matrix. A low rank solution to matrix completion problem can be obtained via matrix factorization, a technique that approximates the input sparse matrix as a product of two lower dimensional matrices of users' and items' latent vectors [4]. Despite efforts to develop more sophisticated techniques, such as the methods based on artificial neural networks [5], matrix factorization remains the method of choice in recommender systems due to its efficiency and high accuracy [6].

In recent decades, traditional drug research and development have been facing challenges such as high cost, long timelines, and high risks. To address these issues, many computational approaches have been suggested for predicting the relationship between drugs and diseases through drug repositioning, aiming to reduce the cost, development cycle, and risks associated with developing new drugs. Researchers have explored different computational methods to predict drug-disease associations, including drug side effects-disease associations, drug-target associations, and miRNAdisease associations. In this comprehensive review, we focus on recent advances in predicting drug-disease association methods for drug repositioning. We first categorize these methods into several groups, including neural network-based algorithms, matrixbased algorithms, recommendation algorithms, link-based reasoning algorithms, and text mining and semantic reasoning. Then, we compare the prediction performance of existing drug-disease association prediction algorithms. Lastly, we delve into the present challenges and future prospects concerning drug-disease associations.

Computational drug repositioning technology is an effective tool to accelerate drug development. Although this technique has been widely used and successful in recent decades, many existing models still suffer from multiple drawbacks such as the massive number of unvalidated drug-disease associations and inner product in the matrix factorization model. The limitations of these works are mainly due to the following two reasons: first, previous works used negative sampling techniques to treat unvalidated drug-disease associations as negative samples, which is invalid in real-world settings; Second, the inner product lacks modeling on the crossover information between dimensions of the latent factor. In this paper, we propose a novel PUON framework for addressing the above deficiencies, which models the joint distribution of drug-disease associations using validated and unvalidated drug-disease associations without employing negative sampling techniques. The PUON also modeled the cross-information of the latent factor of drugs and diseases using the outer product operation. For a comprehensive comparison, we considered 7 popular baselines. Extensive experiments in two real-world datasets showed that PUON achieved the best performance based on 6 popular evaluation metrics.

Drug repositioning is designed to discover new uses of known drugs, which is an important and efficient method of drug discovery. Researchers only use one certain type of Collaborative Filtering (CF) models for drug repositioning currently, like the neighborhood based approaches which are good at mining the local information contained in few strong drug-disease associations, or the latent factor based models which are effectively capture the global information shared by a majority of drug-disease associations. Few researchers have combined these two types of CF models to derive a hybrid model with the advantages of both of them. Besides, the cold start problem has always been a major challenge in the field of computational drug repositioning, which restricts the inference ability of relevant models. Inspired by the memory network, we propose the Hybrid Attentional Memory Network (HAMN) model, a deep architecture combines two classes of CF model in a nonlinear manner. Firstly, the memory unit and the attention mechanism are combined to generate the neighborhood contribution representation to capture the local structure of few strong drug-disease associations. Then a variant version of the autoencoder is used to extract the latent factor of drugs and diseases to capture the overall information shared by a majority of drug-disease associations. In that process, ancillary information of drugs and diseases can help to alleviate the cold start problem. Finally, in the prediction stage, the neighborhood contribution representation is combined with the drug latent factor and disease latent factor to produce the predicted value. Comprehensive experimental results on two real data sets show that our proposed HAMN model is superior to other comparison models according to the AUC, AUPR and HR indicators.