DLDTI: a learning-based framework for drug-target interaction identification using neural networks and network representation

A review of machine learning-based methods for predicting drug-target interactions

The prediction of drug-target interactions (DTI) is a crucial preliminary stage in drug discovery and development, given the substantial risk of failure and the prolonged validation period associated with in vitro and in vivo experiments. In the contemporary landscape, various machine learning-based methods have emerged as indispensable tools for DTI prediction. This paper begins by placing emphasis on the data representation employed by these methods, delineating five representations for drugs and four for proteins. The methods are then categorized into traditional machine learning-based approaches and deep learning-based ones, with a discussion of representative approaches in each category and the introduction of a novel taxonomy for deep neural network models in DTI prediction. Additionally, we present a synthesis of commonly used datasets and evaluation metrics to facilitate practical implementation. In conclusion, we address current challenges and outline potential future directions in this research field.

A Combined Model Integrating Radiomics and Deep Learning Based on Contrast-Enhanced CT for Preoperative Staging of Laryngeal Carcinoma

RATIONALE AND OBJECTIVES

Accurate staging of laryngeal carcinoma can inform appropriate treatment decision-making. We developed a radiomics model, a deep learning (DL) model, and a combined model (incorporating radiomics features and DL features) based on the venous-phase CT images and explored the performance of these models in stratifying patients with laryngeal carcinoma into stage I-II and stage III-IV, and also compared these models with radiologists.

MATERIALS AND METHODS

Three hundreds and nineteen patients with pathologically confirmed laryngeal carcinoma were randomly divided into a training set (n = 223) and a test set (n = 96). In the training set, the radiomics features with inter- and intraclass correlation coefficients (ICCs)> 0.75 were screened by Spearman correlation analysis and recursive feature elimination (RFE); then support vector machine (SVM) classifier was applied to develop the radiomics model. The DL model was built using ResNet 18 by the cropped 2D regions of interest (ROIs) in the maximum tumor ROI slices and the last fully connected layer of this network served as the DL feature extractor. Finally, a combined model was developed by pooling the radiomics features and extracted DL features to predict the staging.

RESULTS

The area under the curves (AUCs) for radiomics model, DL model, and combined model in the test set were 0.704 (95% confidence interval [CI]: 0.588-0.820), 0.724 (95% CI: 0.613-0.835), and 0.849 (95% CI: 0.755-0.943), respectively. The combined model outperformed the radiomics model and the DL model in discriminating stage I-II from stage III-IV (p = 0.031 and p = 0.020, respectively). Only the combined model performed significantly better than radiologists (p < 0.050 for both).

CONCLUSION

The combined model can help tailor the therapeutic strategy for laryngeal carcinoma patients by enabling more accurate preoperative staging.

Drug-Target Interaction Prediction via Graph Auto-Encoder and Multi-Subspace Deep Neural Networks

Computational prediction of drug-target interaction (DTI) is important for the new drug discovery. Currently, the deep neural network (DNN) has been widely used in DTI prediction. However, parameters of the DNN could be insufficiently trained and features of the data could be insufficiently utilized, because the DTI data is limited and its dimension is very high. To deal with the above problems, in this paper, a graph auto-encoder and multi-subspace deep neural network (GAEMSDNN) is designed. GAEMSDNN enhances its learning ability with a graph auto-encoder, a subspace layer and an ensemble layer. The graph auto-encoder can preserve the reconstruction information. The subspace layer can obtain different strong feature subsets. The ensemble layer in the GAEMSDNN can comprehensively utilize these strong feature subsets in a unified optimization framework. As a result, more features can be extracted from the network input and the DNN network can be better trained. In experiments, the results of GAEMSDNN are significantly improved compared to the previous methods, which validates the effectiveness of our strategies.

Polydatin protects against atherosclerosis by activating autophagy and inhibiting pyroptosis mediated by the NLRP3 inflammasome

ETHNOPHARMACOLOGICAL RELEVANCE

Polydatin is a bioactive ingredient extracted from the roots of the Reynoutria japonica Houtt, and it is a natural precursor of resveratrol. Polydatin is a useful inhibitor of inflammation and acts as a regulator of lipid metabolism. However, the specific mechanisms of action of polydatin in atherosclerosis (AS) remains poorly explained.

AIM OF THE STUDY

The aim of this study was to assess the efficacy of polydatin on inflammation induced by the inflammatory cell death and autophagy in AS.

MATERIALS AND METHODS

Apolipoprotein E knockout (ApoE^-/-) mice were fed with a high-fat diet (HFD) for 12 weeks to induce the formation of atherosclerotic lesions. The ApoE^-/- mice were then randomly divided into the following six groups: (1) model group, (2) simvastatin group, (3) MCC950 group, (4) low dose polydatin group (Polydatin-L), (5) medium dose polydatin group (Polydatin-M), (6) and high dose polydatin group (Polydatin-H). The C57BL/6J mice were treated as controls and administered a standard chow diet. All mice were gavaged once daily for 8 weeks. The distribution of aortic plaques was observed by En Oil-red-O staining and hematoxylin and eosin staining (H&E). Oil-red-O staining was used to observe lipid content in the aortic sinus plaque; Masson trichrome staining was used to gauge collagen content in the plaque; and immunohistochemistry was used to evaluate smooth muscle actin (α-SMA) and CD68 macrophages marker expression levels in the plaque, which were used to assess the vulnerability index of the plaque. The lipid levels were measured using an enzymatic assay with an automatic biochemical analyzer. The level of inflammation was detected by enzyme-linked-immunosorbent assay (ELISA). Autophagosomes were detected by transmission electron microscopy (TEM). Pyroptosis was detected by terminal deoxynucleotidyl transferase (TdT) dUTP nick-end labeling (TUNEL)/caspase-1 and other proteins related to the expression levels of autophagy and pyroptosis were detected by Western blot analysis.

RESULTS

Nucleotide oligomerization (NOD)-like receptor (NLR) family pyrin domain-containing protein 3 (NLRP3) inflammasome activation leads to pyroptosis, including the cleavage of caspase-1, interleukin (IL)-1β and IL-18 production, and the co-expression of TUNEL/caspase-1-all of these are inhibited by polydatin, whose inhibitory effect is similar to that of MCC950, a specific inhibitor of NLRP3. Further, polydatin decreased the protein expression of NLRP3 and the phosphorylated mammalian target of rapamycin (p-mTOR), and increased the number of autophagosomes as well as the increased the cytoplasmic microtubule-associated protein light chain 3 (LC3)/autophagosome membrane-type LC3 ratio. Moreover, the protein expression levels of p62 decreased, suggesting that polydatin can increase autophagy.

CONCLUSIONS

Polydatin can inhibit the activation of the NLRP3 inflammasome and cleavage of caspase-1, thereby inhibiting pyroptosis and secretion of inflammatory cytokines, and promoting autophagy through NLRP3/mTOR pathway in AS.

Drug-target interaction prediction based on spatial consistency constraint and graph convolutional autoencoder

BACKGROUND

Drug-target interaction (DTI) prediction plays an important role in drug discovery and repositioning. However, most of the computational methods used for identifying relevant DTIs do not consider the invariance of the nearest neighbour relationships between drugs or targets. In other words, they do not take into account the invariance of the topological relationships between nodes during representation learning. It may limit the performance of the DTI prediction methods.

RESULTS

Here, we propose a novel graph convolutional autoencoder-based model, named SDGAE, to predict DTIs. As the graph convolutional network cannot handle isolated nodes in a network, a pre-processing step was applied to reduce the number of isolated nodes in the heterogeneous network and facilitate effective exploitation of the graph convolutional network. By maintaining the graph structure during representation learning, the nearest neighbour relationships between nodes in the embedding space remained as close as possible to the original space.

CONCLUSIONS

Overall, we demonstrated that SDGAE can automatically learn more informative and robust feature vectors of drugs and targets, thus exhibiting significantly improved predictive accuracy for DTIs.

A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation

The discovery and development of new drugs are extremely long and costly processes. Recent progress in artificial intelligence has made a positive impact on the drug development pipeline. Numerous challenges have been addressed with the growing exploitation of drug-related data and the advancement of deep learning technology. Several model frameworks have been proposed to enhance the performance of deep learning algorithms in molecular design. However, only a few have had an immediate impact on drug development since computational results may not be confirmed experimentally. This systematic review aims to summarize the different deep learning architectures used in the drug discovery process and are validated with further in vivo experiments. For each presented study, the proposed molecule or peptide that has been generated or identified by the deep learning model has been biologically evaluated in animal models. These state-of-the-art studies highlight that even if artificial intelligence in drug discovery is still in its infancy, it has great potential to accelerate the drug discovery cycle, reduce the required costs, and contribute to the integration of the 3R (Replacement, Reduction, Refinement) principles. Out of all the reviewed scientific articles, seven algorithms were identified: recurrent neural networks, specifically, long short-term memory (LSTM-RNNs), Autoencoders (AEs) and their Wasserstein Autoencoders (WAEs) and Variational Autoencoders (VAEs) variants; Convolutional Neural Networks (CNNs); Direct Message Passing Neural Networks (D-MPNNs); and Multitask Deep Neural Networks (MTDNNs). LSTM-RNNs were the most used architectures with molecules or peptide sequences as inputs.

Deep learning in drug discovery: an integrative review and future challenges

Recently, using artificial intelligence (AI) in drug discovery has received much attention since it significantly shortens the time and cost of developing new drugs. Deep learning (DL)-based approaches are increasingly being used in all stages of drug development as DL technology advances, and drug-related data grows. Therefore, this paper presents a systematic Literature review (SLR) that integrates the recent DL technologies and applications in drug discovery Including, drug-target interactions (DTIs), drug-drug similarity interactions (DDIs), drug sensitivity and responsiveness, and drug-side effect predictions. We present a review of more than 300 articles between 2000 and 2022. The benchmark data sets, the databases, and the evaluation measures are also presented. In addition, this paper provides an overview of how explainable AI (XAI) supports drug discovery problems. The drug dosing optimization and success stories are discussed as well. Finally, digital twining (DT) and open issues are suggested as future research challenges for drug discovery problems. Challenges to be addressed, future research directions are identified, and an extensive bibliography is also included.

multi-type neighbors enhanced global topology and pairwise attribute learning for drug-protein interaction prediction

MOTIVATION

Accurate identification of proteins interacted with drugs helps reduce the time and cost of drug development. Most of previous methods focused on integrating multisource data about drugs and proteins for predicting drug-target interactions (DTIs). There are both similarity connection and interaction connection between two drugs, and these connections reflect their relationships from different perspectives. Similarly, two proteins have various connections from multiple perspectives. However, most of previous methods failed to deeply integrate these connections. In addition, multiple drug-protein heterogeneous networks can be constructed based on multiple kinds of connections. The diverse topological structures of these networks are still not exploited completely.

RESULTS

We propose a novel model to extract and integrate multi-type neighbor topology information, diverse similarities and interactions related to drugs and proteins. Firstly, multiple drug-protein heterogeneous networks are constructed according to multiple kinds of connections among drugs and those among proteins. The multi-type neighbor node sequences of a drug node (or a protein node) are formed by random walks on each network and they reflect the hidden neighbor topological structure of the node. Secondly, a module based on graph neural network (GNN) is proposed to learn the multi-type neighbor topologies of each node. We propose attention mechanisms at neighbor node level and at neighbor type level to learn more informative neighbor nodes and neighbor types. A network-level attention is also designed to enhance the context dependency among multiple neighbor topologies of a pair of drug and protein nodes. Finally, the attribute embedding of the drug-protein pair is formulated by a proposed embedding strategy, and the embedding covers the similarities and interactions about the pair. A module based on three-dimensional convolutional neural networks (CNN) is constructed to deeply integrate pairwise attributes. Extensive experiments have been performed and the results indicate GCDTI outperforms several state-of-the-art prediction methods. The recall rate estimation over the top-ranked candidates and case studies on 5 drugs further demonstrate GCDTI's ability in discovering potential drug-protein interactions.

The role and mechanism of tetramethylpyrazine for atherosclerosis in animal models: A systematic review and meta-analysis

Background

Atherosclerosis(AS) is widely recognized as a risk factor for incident cardiovascular and cerebrovascular diseases. Tetramethylpyrazine (TMP) is the active ingredient of Ligusticum wallichii that possesses a variety of biological activities against atherosclerosis.

Objective

This systematic review and meta-analysis sought to study the impact of and mechanism of tetramethylpyrazine for atherosclerosis in animal models.

Methods

A systematic search was conducted of PubMed, Embase, Cochrane Library, Web of Science database, Chinese Biomedical (CBM) database, China National Knowledge Infrastructure (CNKI), WanFang data, and Vip Journal Integration Platform, covering the period from the respective start date of each database to December 2021. We used SYRCLE’s 10-item checklist and Rev-Man 5.3 software to analyze the data and the risk of bias.

Results

Twelve studies, including 258 animals, met the inclusion criteria. Compared with the control group, TMP significantly reduced aortic atherosclerotic lesion area, and induced significant decreases in levels of TC (SMD = ‐2.67, 95% CI -3.68 to -1.67, P < 0.00001), TG (SMD = ‐2.43, 95% CI -3.39 to -1.47, P < 0.00001), and LDL-C (SMD = ‐2.87, 95% CI -4.16 to -1.58, P < 0.00001), as well as increasing HDL-C (SMD = 2.04, 95% CI 1.05 to 3.03, P = 0.001). TMP also significantly modulated plasma inflammatory responses and biological signals associated with atherosclerosis. In subgroup analysis, the groups of high-dose TMP (≥50 mg/kg) showed better results than those of the control group. No difference between various durations of treatment groups or various assessing location groups.

Conclusion

TMP exerts anti-atherosclerosis functions in an animal model of AS mediated by anti-inflammatory action, antioxidant action, ameliorating lipid metabolism disorder, protection of endothelial function, antiplatelet activity, reducing the proliferation and migration of smooth muscle cells, inhibition of angiogenesis, antiplatelet aggregation. Due to the limitations of the quantity and quality of current studies, the above conclusions need to be verified by more high-quality studies.

Trial registration number

PROSPERO registration no.CRD42021288874.

Antiplatelet Activity of Tetramethylpyrazine via Regulation of the P2Y12 Receptor Downstream Signaling Pathway

Background

Tetramethylpyrazine (TMP) is an alkaloid in Chinese herbal medicine, which possesses antiplatelet activity. TMP inhibits platelet activation in many ways. The platelet P2Y₁₂ receptor for adenosine 5′ diphosphate (ADP) plays a central role in platelet function, hemostasis, and thrombosis. Here, we investigated the inhibitory effect of TMP on P2Y₁₂ receptor-related platelet function.

Methods

The inhibitory potential of TMP was assessed using agonist-induced platelet aggregation, flow cytometric analysis of CD62p expression, PAC-1 activation, and fibrin clot retraction. After the P2Y12 receptor-related signaling pathway was inhibited using the blocker, platelet activation was studied by platelet aggregation, CD62p expression, and PAC-1 activation. The secretion of cyclic adenosine monophosphate (cAMP) was measured using enzyme-linked immunosorbent assay (ELISA), and the expression of signaling pathway protein, phosphorylation of vasodilator-stimulated phosphoprotein, and phosphorylation of Akt were investigated using western blotting. The release of platelet inflammatory mediators was measured using ELISA.

Results

TMP had an antiplatelet effect by inhibiting ADP-induced aggregation, P-selectin secretion, and glycoprotein (GP) IIb/IIIa expression and reducing the release of atherosclerotic-related inflammatory mediators (sCD40L and IL-1β). TMP decreased the area of clot retraction, reflecting inhibition of GPIIb/IIIa activation. TMP inhibited adenosine diphosphate-induced platelet activation via increased cAMP production, VASP^ser157 phosphorylation, and Akt dephosphorylation.

Conclusion

TMP selectively inhibits ADP-induced platelet activation via P2Y₁₂ receptor-related signaling pathways.

ALDPI: adaptively learning importance of multi-scale topologies and multi-modality similarities for drug-protein interaction prediction

MOTIVATION

Effective computational methods to predict drug-protein interactions (DPIs) are vital for drug discovery in reducing the time and cost of drug development. Recent DPI prediction methods mainly exploit graph data composed of multiple kinds of connections among drugs and proteins. Each node in the graph usually has topological structures with multiple scales formed by its first-order neighbors and multi-order neighbors. However, most of the previous methods do not consider the topological structures of multi-order neighbors. In addition, deep integration of the multi-modality similarities of drugs and proteins is also a challenging task.

RESULTS

We propose a model called ALDPI to adaptively learn the multi-scale topologies and multi-modality similarities with various significance levels. We first construct a drug-protein heterogeneous graph, which is composed of the interactions and the similarities with multiple modalities among drugs and proteins. An adaptive graph learning module is then designed to learn important kinds of connections in heterogeneous graph and generate new topology graphs. A module based on graph convolutional autoencoders is established to learn multiple representations, which imply the node attributes and multiple-scale topologies composed of one-order and multi-order neighbors, respectively. We also design an attention mechanism at neighbor topology level to distinguish the importance of these representations. Finally, since each similarity modality has its specific features, we construct a multi-layer convolutional neural network-based module to learn and fuse multi-modality features to obtain the attribute representation of each drug-protein node pair. Comprehensive experimental results show ALDPI's superior performance over six state-of-the-art methods. The results of recall rates of top-ranked candidates and case studies on five drugs further demonstrate the ability of ALDPI to discover potential drug-related protein candidates.

CONTACT

zhang@hlju.edu.cn.

Proteome-Scale Drug-Target Interaction Predictions: Approaches and Applications

Drug-Target interaction predictions are an important cornerstone of computer-aided drug discovery. While predictive methods around individual targets have a long history, the application of proteome-scale models is relatively recent. In this overview, we will provide the context required to understand advances in this emerging field within computational drug discovery, evaluate emerging technologies for suitability to given tasks, and provide guidelines for the design and implementation of new drug-target interaction prediction models. We will discuss the validation approaches used, and propose a set of key criteria that should be applied to evaluate their validity. We note that we find widespread deficiencies in the existing literature, making it difficult to judge the practical effectiveness of some of the techniques proposed from their publications alone. We hope that this review may help remedy this situation and increase awareness of several sources of bias that may enter into commonly used cross-validation methods. © 2021 Cyclica Inc. Current Protocols published by Wiley Periodicals LLC.

Learning multi-scale heterogeneous representations and global topology for drug-target interaction prediction

Identification of drug-target interactions (DTIs) plays a critical role in drug discovery and repositioning. Deep integration of inter-connections and intra-similarities between heterogeneous multi-source data related to drugs and targets, however, is a challenging issue. We propose a DTI prediction model by learning from drug and protein related multi-scale attributes and global topology formed by heterogeneous connections. A drug-protein-disease heterogeneous network (RPD-Net) is firstly constructed to associate diverse similarities, interactions and associations across nodes. Secondly, we propose a multi-scale pairwise deep representation learning module consisting of a new embedding strategy to integrate diverse inter-relations and intra-relations, and dilation convolutions for multi-scale deep representation extraction. A global topology learning module is proposed which is composed of strategy based on non-negative matrix factorization (NMF) to extract topology from RPD-Net, and a new relational-level attention mechanism for discriminative topology embedding. Experimental results using public dataset demonstrate improved performance over state-of-the-art methods and contributions of our major innovations. Evaluation results by top k recall rates and case studies on five drugs further show the effectiveness in retrieving potential target candidates for drugs.

Comprehensive Survey of Recent Drug Discovery Using Deep Learning

Drug discovery based on artificial intelligence has been in the spotlight recently as it significantly reduces the time and cost required for developing novel drugs. With the advancement of deep learning (DL) technology and the growth of drug-related data, numerous deep-learning-based methodologies are emerging at all steps of drug development processes. In particular, pharmaceutical chemists have faced significant issues with regard to selecting and designing potential drugs for a target of interest to enter preclinical testing. The two major challenges are prediction of interactions between drugs and druggable targets and generation of novel molecular structures suitable for a target of interest. Therefore, we reviewed recent deep-learning applications in drug-target interaction (DTI) prediction and de novo drug design. In addition, we introduce a comprehensive summary of a variety of drug and protein representations, DL models, and commonly used benchmark datasets or tools for model training and testing. Finally, we present the remaining challenges for the promising future of DL-based DTI prediction and de novo drug design.

Artificial Intelligence in Chemistry: Current Trends and Future Directions

The application of artificial intelligence (AI) to chemistry has grown tremendously in recent years. In this Review, we studied the growth and distribution of AI-related chemistry publications in the last two decades using the CAS Content Collection. The volume of both journal and patent publications have increased dramatically, especially since 2015. Study of the distribution of publications over various chemistry research areas revealed that analytical chemistry and biochemistry are integrating AI to the greatest extent and with the highest growth rates. We also investigated trends in interdisciplinary research and identified frequently occurring combinations of research areas in publications. Furthermore, topic analyses were conducted for journal and patent publications to illustrate emerging associations of AI with certain chemistry research topics. Notable publications in various chemistry disciplines were then evaluated and presented to highlight emerging use cases. Finally, the occurrence of different classes of substances and their roles in AI-related chemistry research were quantified, further detailing the popularity of AI adoption in the life sciences and analytical chemistry. In summary, this Review offers a broad overview of how AI has progressed in various fields of chemistry and aims to provide an understanding of its future directions.