Computational Drug-target Interaction Prediction based on Graph Embedding and Graph Mining

Drug-target Interactions (DTIs) prediction plays a central role in drug discovery. Computational methods in DTIs prediction have gained more attention because carrying out in vitro and in vivo experiments on a large scale is costly and time-consuming. Machine learning methods, especially deep learning, are widely applied to DTIs prediction. In this study, the main goal is to provide a comprehensive overview of deep learning-based DTIs prediction approaches. Here, we investigate the existing approaches from multiple perspectives. We explore these approaches to find out which deep network architectures are utilized to extract features from drug compound and protein sequences. Also, the advantages and limitations of each architecture are analyzed and compared. Moreover, we explore the process of how to combine descriptors for drug and protein features. Likewise, a list of datasets that are commonly used in DTIs prediction is investigated. Finally, current challenges are discussed and a short future outlook of deep learning in DTI prediction is given.

BackgroundIn silico drug-target interaction (DTI) prediction plays an integral role in drug repositioning: the discovery of new uses for existing drugs. One popular method of drug repositioning is network-based DTI prediction, which uses complex network theory to predict DTIs from a drug-target network. Currently, most network-based DTI prediction is based on machine learning – methods such as Restricted Boltzmann Machines (RBM) or Support Vector Machines (SVM). These methods require additional information about the characteristics of drugs, targets and DTIs, such as chemical structure, genome sequence, binding types, causes of interactions, etc., and do not perform satisfactorily when such information is unavailable. We propose a new, alternative method for DTI prediction that makes use of only network topology information attempting to solve this problem.ResultsWe compare our method for DTI prediction against the well-known RBM approach. We show that when applied to the MATADOR database, our approach based on node neighborhoods yield higher precision for high-ranking predictions than RBM when no information regarding DTI types is available.ConclusionThis demonstrates that approaches purely based on network topology provide a more suitable approach to DTI prediction in the many real-life situations where little or no prior knowledge is available about the characteristics of drugs, targets, or their interactions.

Computational prediction of drug-target interactions (DTIs) and drug repositioning provides a low-cost and high-efficiency approach for drug discovery and development. The traditional social network-derived methods based on the naïve DTI topology information cannot predict potential targets for new chemical entities or failed drugs in clinical trials. There are currently millions of commercially available molecules with biologically relevant representations in chemical databases. It is urgent to develop novel computational approaches to predict targets for new chemical entities and failed drugs on a large scale. In this study, we developed a useful tool, namely substructure-drug-target network-based inference (SDTNBI), to prioritize potential targets for old drugs, failed drugs and new chemical entities. SDTNBI incorporates network and chemoinformatics to bridge the gap between new chemical entities and known DTI network. High performance was yielded in 10-fold and leave-one-out cross validations using four benchmark data sets, covering G protein-coupled receptors, kinases, ion channels and nuclear receptors. Furthermore, the highest areas under the receiver operating characteristic curve were 0.797 and 0.863 for two external validation sets, respectively. Finally, we identified thousands of new potential DTIs via implementing SDTNBI on a global network. As a proof-of-principle, we showcased the use of SDTNBI to identify novel anticancer indications for nonsteroidal anti-inflammatory drugs by inhibiting AKR1C3, CA9 or CA12. In summary, SDTNBI is a powerful network-based approach that predicts potential targets for new chemical entities on a large scale and will provide a new tool for DTI prediction and drug repositioning. The program and predicted DTIs are available on request.

The identification of Drug-Target Interactions (DTIs) is an important process in drug discovery and medical research. However, the tradition experimental methods for DTIs identification are still time consuming, extremely expensive and challenging. In the past ten years, various computational methods have been developed to identify potential DTIs. In this paper, the identification methods of DTIs are summarized. What's more, several state-of-the-art computational methods are mainly introduced, containing network-based method and machine learning-based method. In particular, for machine learning-based methods, including the supervised and semisupervised models, have essential differences in the approach of negative samples. Although these effective computational models in identification of DTIs have achieved significant improvements, network-based and machine learning-based methods have their disadvantages, respectively. These computational methods are evaluated on four benchmark data sets via values of Area Under the Precision Recall curve (AUPR).

DTIP-TC2A: An analytical framework for drug-target interactions prediction methods

BackgroundIdentification of drug target interactions (DTI) is an important part of the drug discovery process. Since prediction of DTI using laboratory tests is time consuming and laborious, automated tools using computational intelligence (CI) techniques become essential. The prediction of DTI is a challenging process due to the absence of known drug-target relationship and no experimentally verified negative samples. The datasets with limited or unbalanced data, do not perform well. The models that use heterogeneous networks, non-linear fusion techniques, and heuristic similarity selection may need a lot of computational power and experience to implement and fine-tune. The latest developments in machine learning (ML) and deep learning (DL) models can be employed for effective DTI prediction process.ResultsTo that end, this study develops a novel DTI Prediction model, namely, DTIP-WINDGRU Drug-Target Interaction Prediction with Wind-Enhanced GRU. The major aim is to determine the DTIs in both labelled and unlabelled samples accurately compared to traditional wet lab experiments. To accomplish this, the proposed DTIP-WINDGRU model primarily performs pre-processing and class labelling. In addition, drug-to-drug (D-D) and target-to-target (T-T) interactions are employed to initialize the weights of the GRU model and are employed for the, DTI prediction process. Finally, the Wind Driven Optimization (WDO) algorithm is utilized to optimally choose the hyperparameters involved in the GRU model.ConclusionsFor ensuring the effectual prediction results of the DTIP-WINDGRU model, a widespread experimentation process was carried out using four datasets. This comprehensive comparative study highlighted the better performance of the DTIP-WINDGRU model over existing techniques.

Identification of potential drug-target interactions (DTIs) plays a pivotal role in the development of drug and target discovery in the public healthcare sector. However, biological experiments for predicting interactions between drugs and targets are still expensive, complicated, and time-consuming. Thus, computational methods are widely applied for aiding drug-target interaction prediction. In this paper, we propose a novel model, named GCMCDTI, for DTIs prediction which adopts a graph convolutional network based on matrix completion. We regard the association prediction between drugs and targets as link prediction and treat the process as matrix completion, and then a graph convolutional auto-encoder framework is employed to construct the drug and target embeddings. Then, a bilinear decoder is applied to reconstruct the DTI matrix. We conduct our experiments on four benchmark datasets consisting of enzymes, G protein-coupled receptors (GPCRs), ion channels, and nuclear receptors. The five-fold cross-validation results achieve the high average AUC values of 95.78%, 95.31%, 93.90%, and 91.77%, respectively. To further evaluate our method, we compare our proposed method with other state-of-the-art approaches. The comparison results illustrate that our proposed method obtains improvement in performance on DTI prediction. The proposed method will be a good choice in the field of DTI prediction.

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Motivation:</i> The prediction of drug-target interaction (DTI) from heterogeneous biological data is critical to predict drugs and therapeutic targets for known diseases such as tumor and bowel disease. The study of DTI based on drug representation learning can strengthen or integrate our knowledge of pharmacological and chemical phenomena. Therefore, there is a strong motivation to develop effective methods that can detect these potential drug-target interactions. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> We have developed a novel Knowledge-Enhanced Multi-View framework (KEMV) to predict unknown DTIs from pharmacological data and chemical data on a large scale. The proposed method consists of two steps: (i) learning more comprehensive drug representations via the proposed multi-view attention mechanism, which bridges pharmacological and chemical information, and interactively summarizes the attention values depending on varying interactions between different pairs of drug features. (ii) predicting unknown drug-target interactions based on the drug and target representations. The method is tested on real-world dataset KEGG with three classes of important drug–target interactions involving enzymes, ion channels, and G-protein-coupled receptors. Our framework is proven to uncover potential DTIs with scientific evidences explaining the mechanism of the interactions through the processing of high-dimensional, heterogeneous, and sparse drug data. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Availability:</i> The originality of the proposed method lies in the attentive integration of pharmacological and chemical information for representation of drug candidate compounds and the prediction of drug-target interaction toward drug discovery in a unified framework. Our results are reproducible and and code is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/YuanKQ/DTI-Prediction</uri> .

Predicting possible drug-target interactions (DTIs) involves estimating the likelihood that drug molecules would bind to biological targets. By looking into the high cost and risk of in vivo and in vitro experiments, computational methods offer a cost- and time-efficient alternative for predicting DTIs in drug discovery. Many network-based methods have made significant progress in DTI prediction, but they often lack the ability to capture the topological and biological closeness between nodes, which is a crucial paradigm to identify hidden patterns in the DTI network. In this paper, we propose a novel computational-based framework─CAHNetF-DTP (Community Aware Heterogeneous Network-based Framework for Drug-Target interaction Prediction) that leverages a heterogeneous biomedical network comprising 15 distinct types of similarity-based subnetworks derived from drugs, targets, diseases, and side effects. To effectively capture the complex topological and semantic relationships among entities, we employ a community-aware framework that helps to capture local interaction patterns first before moving to another community. The resulting sequences are treated like sentences, enabling Word2Vec to generate embeddings that capture the contextual information on biomedical entities across multiple biological dimensions. To address the class imbalance issue, we incorporate a controlled negative sampling strategy and evaluate the proposed model under five experimental settings. The proposed method is further validated on benchmark data sets (KIBA and DAVIS), in real-world scenarios, and against SAR-based target prediction tools, where CAHNetF-DTP demonstrates robust and competitive performance, outperforming most of the methods. We carried out various case studies to check the model's capability to identify novel drug-target pairs. This work highlights the value of integrating heterogeneous biological data for improved DTI prediction.

BackgroundTreatment of sepsis requires prompt initiation of appropriate empiric antibiotics. Coverage for Gram-negative bacilli (GNB) is decided based on risk factors for resistant organisms, as documented in both structured and unstructured data in the electronic health record. We hypothesized that adding large language models (LLMs) that can decode clinical narratives would improve performance of deep learning models in predicting antimicrobial resistance in GNB sepsis.MethodsThis retrospective cohort analysis included all adult patients who met CDC sepsis criteria admitted to 10 BJC hospitals from 1/2018 to 12/2023. Sepsis episodes were classified as community-onset (< 48hrs from hospitalization) or hospital-onset. GNB were stratified as ceftriaxone-susceptible (SS), ceftriaxone-resistant but cefepime-susceptible (RS), and ceftriaxone- and cefepime-resistant (RR). Culture negative or non-GNB sepsis were labeled SS. Deep learning models (XGBoost, DeepAMR) were developed using all available structured data (e.g., demographics, vitals, labs). LLMs (BioGPT, Medical-LLaMa3-8B) were developed on full or partial history and physical exam notes. Models were assessed using area under the receiver operating characteristic curve (AUROC) and area under precision-recall curve (AUPRC).ResultsA total of 29,517 patients were included, for 23,949 community-onset and 25,101 hospital-onset sepsis episodes with RS+RR GNB in 9% and 17%, respectively (Table 1). Relevant features of structured data models included history of prior infections and resistant organisms. DeepAMR was the best performing deep learning model with AUROC 0.85/AUPRC 0.40 and AUROC 0.81/AUPRC 0.50 for community- and hospital-onset sepsis, respectively (Table 2). The best performing LLM was Medical-LLaMa3-8B on full notes (Figure 1), with AUROC 0.74/AUPRC 0.21 and AUROC 0.67/AUPRC 0.30 for community- and hospital-onset sepsis, respectively. Combining deep learning and LLMs did not significantly improve the prediction of resistant infections compared to deep learning alone.ConclusionStructured medical data provided the highest prediction of resistant GNB infections in sepsis. More analysis is needed to understand why the unstructured data did not improve model predictive performance.DisclosuresMaria Cristina Vazquez Guillamet, MD, Bionano: Stocks/Bonds (Public Company)|Charisma: Stocks/Bonds (Public Company)|Ocugen: Stocks/Bonds (Public Company)

The prediction of Drug-Target Interaction (DTI) is an important research direction in bioinformatics as it greatly shortens the development cycle of new drugs. State-of-the-art computational methods for DTI prediction adopt a binary classification framework. The supervision is incomplete, i.e. only a small amount of DTIs are known and treated as positive instances, while the rest are unknown and treated as negative. Two severe problems occur in such a framework: (1) the number of negative samples is overwhelming and (2) a negative label cannot rule out the possibility of a positive drug-target interaction. In this paper, we address the problem of learning from incomplete labels in DTI prediction. The key assumption here is that labels are missing not at random. For example, negative DTI labels are more likely to be missing because biomedical researchers prioritize to study DTIs that are more likely to be positive. We introduce a novel probabilistic model, factorization with non-random missing labels (FNML). It models the generative process for the DTI labels (i.e. the labels are positive or negative) and responses (i.e. the labels are observed or missing). In particular, the probability of observing or missing a label is associated with the sign of the label. In order to further reduce prediction variance and improve prediction accuracy on highly imbalanced DTI datasets, we present FNML-EN, an ensemble scheme which is designed specifically for FNML model. We conduct comprehensive experiments on the latest DTI database, demonstrating the superior and robust performance of the proposed models.

Prediction of drug-target interactions is an essential step in drug discovery. Given drug-target interactions network, the objective of this task is to predict probable missing edges from known interactions. Computationally predicting drug-target interactions is an appropriate alternative for the time-consuming and costly experimental process of drug-target interaction prediction. A large number of computational methods for solving this problem have been proposed in recent years. In recent years, several review articles have been published in the field of drug-target interactions prediction. Compared to other review articles, this paper includes a qualitative analysis in the form of a framework, a drug-target interactions prediction (DTIP) framework. The framework consists of three sections. Initially, a classification has been presented for drug-target interactions prediction methods based on the link prediction approaches used in these methods. Secondly, general evaluation criteria have been introduced for analyzing approaches. Finally, a qualitative comparison is made between each approach in terms of their advantages and disadvantages. By providing a new classification of the drug-target interactions prediction approaches and comparing them with the proposed evaluation criteria, this framework provides a convenient and efficient way to select and compare the methods. Moreover, using the framework, we can improve these techniques further. This paper provides a study to select, compare, and improve chemogenomic drugtarget interactions prediction methods. To this aim, an analytical framework is presented.

Drug-target interactions (DTIs) prediction plays a vital role in drug discovery and design. Current studies typically use only standard drug similarity and target similarity, but the influence of known interactions has not been taken into account. In this paper, we propose an ensembled computational approach called multi-similarity fusion and sparse dual-graph regularized matrix factorization (MSDGRMF) for DTIs prediction. Specifically, different similarities are integrated to mine more useful information from the known interactions. The dual-graph regularized matrix factorization is used to predict the DTIs, in which the manifold learning is used for the low-dimensional representation of the drugs and targets data. In addition, not all the information of drug pairs and target pairs is useful. Thus, the useless information is discarded by sparse process. The proposed MSDGRMF is evaluated and compared on some benchmark datasets. Comparison results show that the MSDGRMF is better than some state-of-the-art approaches. More importantly, the proposed method can contribute to predicting potential DTIs.

Exploring drug-target interaction prediction on cold-start scenarios via meta-learning-based graph transformer

BackgroundIdentifying drug–target interactions (DTIs) plays a key role in drug development. Traditional wet experiments to identify DTIs are expensive and time consuming. Effective computational methods to predict DTIs are useful to narrow the searching scope of potential drugs and speed up the process of drug discovery. There are a variety of non-negativity matrix factorization based methods to predict DTIs, but the convergence of the algorithms used in the matrix factorization are often overlooked and the results can be further improved.ResultsIn order to predict DTIs more accurately and quickly, we propose an alternating direction algorithm to solve graph regularized non-negative matrix factorization with prior knowledge consistency constraint (ADA-GRMFC). Based on known DTIs, drug chemical structures and target sequences, ADA-GRMFC at first constructs a DTI matrix, a drug similarity matrix and a target similarity matrix. Then DTI prediction is modeled as the non-negative factorization of the DTI matrix with graph dual regularization terms and a prior knowledge consistency constraint. The graph dual regularization terms are used to integrate the information from the drug similarity matrix and the target similarity matrix, and the prior knowledge consistency constraint is used to ensure the matrix decomposition result should be consistent with the prior knowledge of known DTIs. Finally, an alternating direction algorithm is used to solve the matrix factorization. Furthermore, we prove that the algorithm can converge to a stationary point. Extensive experimental results of 10-fold cross-validation show that ADA-GRMFC has better performance than other state-of-the-art methods. In the case study, ADA-GRMFC is also used to predict the targets interacting with the drug olanzapine, and all of the 10 highest-scoring targets have been accurately predicted. In predicting drug interactions with target estrogen receptors alpha, 17 of the 20 highest-scoring drugs have been validated.