1
|
Abdelrady YA, Thabet HS, Sayed AM. The future of metronomic chemotherapy: experimental and computational approaches of drug repurposing. Pharmacol Rep 2024:10.1007/s43440-024-00662-w. [PMID: 39432183 DOI: 10.1007/s43440-024-00662-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2024] [Revised: 09/30/2024] [Accepted: 10/01/2024] [Indexed: 10/22/2024]
Abstract
Metronomic chemotherapy (MC), long-term continuous administration of anticancer drugs, is gaining attention as an alternative to the traditional maximum tolerated dose (MTD) chemotherapy. By combining MC with other treatments, the therapeutic efficacy is enhanced while minimizing toxicity. MC employs multiple mechanisms, making it a versatile approach against various cancers. However, drug resistance limits the long-term effectiveness of MC, necessitating ongoing development of anticancer drugs. Traditional drug discovery is lengthy and costly due to processes like target protein identification, virtual screening, lead optimization, and safety and efficacy evaluations. Drug repurposing (DR), which screens FDA-approved drugs for new uses, is emerging as a cost-effective alternative. Both experimental and computational methods, such as protein binding assays, in vitro cytotoxicity tests, structure-based screening, and several types of association analyses (Similarity-Based, Network-Based, and Target Gene), along with retrospective clinical analyses, are employed for virtual screening. This review covers the mechanisms of MC, its application in various cancers, DR strategies, examples of repurposed drugs, and the associated challenges and future directions.
Collapse
Affiliation(s)
- Yousef A Abdelrady
- Institute of Pharmaceutical Sciences, University of Freiburg, 79104, Freiburg, Germany
| | - Hayam S Thabet
- Microbiology Department, Faculty of Veterinary Medicine, Assiut University, Asyut, 71526, Egypt
| | - Ahmed M Sayed
- Biochemistry Laboratory, Chemistry Department, Faculty of Science, Assiut University, Asyut, 71516, Egypt
- Bioscience Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Kingdom of Saudi Arabia
| |
Collapse
|
2
|
Funari A, Fiscon G, Paci P. Network medicine and systems pharmacology approaches to predicting adverse drug effects. Br J Pharmacol 2024. [PMID: 39262113 DOI: 10.1111/bph.17330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 07/30/2024] [Accepted: 08/02/2024] [Indexed: 09/13/2024] Open
Abstract
Identifying and understanding the relationships between drug intake and adverse effects that can occur due to inadvertent molecular interactions between drugs and targets is a difficult task, especially considering the numerous variables that can influence the onset of such events. The ability to predict these side effects in advance would help physicians develop strategies to avoid or counteract them. In this article, we review the main computational methods for predicting side effects caused by drug molecules, highlighting their performance, limitations and application cases. Furthermore, we provide an overall view of resources, such as databases and tools, useful for building side effect prediction analyses.
Collapse
Affiliation(s)
- Alessio Funari
- Department of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
| | - Giulia Fiscon
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
- Department of Computer, Control and Management Engineering "Antonio Ruberti", Sapienza University of Rome, Rome, Italy
| | - Paola Paci
- Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy
- Department of Computer, Control and Management Engineering "Antonio Ruberti", Sapienza University of Rome, Rome, Italy
| |
Collapse
|
3
|
Firoozbakht F, Elkjaer ML, Handy D, Wang R, Chervontseva Z, Rarey M, Loscalzo J, Baumbach J, Tsoy O. DREAMER: Exploring Common Mechanisms of Adverse Drug Reactions and Disease Phenotypes through Network-Based Analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.20.602911. [PMID: 39091742 PMCID: PMC11291051 DOI: 10.1101/2024.07.20.602911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Adverse drug reactions (ADRs) are a major concern in clinical healthcare, significantly affecting patient safety and drug development. This study introduces DREAMER, a novel network-based method for exploring the mechanisms underlying ADRs and disease phenotypes at a molecular level by leveraging a comprehensive knowledge graph obtained from various datasets. By considering drugs and diseases that cause similar phenotypes, and investigating their commonalities regarding their impact on specific modules of the protein-protein interaction network, DREAMER can robustly identify protein sets associated with the biological mechanisms underlying ADRs and unravel the causal relationships that contribute to the observed clinical outcomes. Applying DREAMER to 649 ADRs, we identified proteins associated with the mechanism of action for 67 ADRs across multiple organ systems. In particular, DREAMER highlights the importance of GABAergic signaling and proteins of the coagulation pathways for personality disorders and intracranial hemorrhage, respectively. We further demonstrate the application of DREAMER in drug repurposing and propose sotalol, ranolazine, and diltiazem as candidate drugs to be repurposed for cardiac arrest. In summary, DREAMER effectively detects molecular mechanisms underlying phenotypes.
Collapse
|
4
|
Inoue N, Shibata T, Tanaka Y, Taguchi H, Sawada R, Goto K, Momokita S, Aoyagi M, Hirao T, Yamanishi Y. Revealing Comprehensive Food Functionalities and Mechanisms of Action through Machine Learning. J Chem Inf Model 2024; 64:5712-5724. [PMID: 38950938 DOI: 10.1021/acs.jcim.4c00061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/03/2024]
Abstract
Foods possess a range of unexplored functionalities; however, fully identifying these functions through empirical means presents significant challenges. In this study, we have proposed an in silico approach to comprehensively predict the functionalities of foods, encompassing even processed foods. This prediction is accomplished through the utilization of machine learning on biomedical big data. Our focus revolves around disease-related protein pathways, wherein we statistically evaluate how the constituent compounds collaboratively regulate these pathways. The proposed method has been employed across 876 foods and 83 diseases, leading to an extensive revelation of both food functionalities and their underlying operational mechanisms. Additionally, this approach identifies food combinations that potentially affect molecular pathways based on interrelationships between food functions within disease-related pathways. Our proposed method holds potential for advancing preventive healthcare.
Collapse
Affiliation(s)
- Nanako Inoue
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Tomokazu Shibata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Yusuke Tanaka
- Research & Development Headquarters, House Foods Group Inc., 1-4 Takanodai, Yotsukaido, Chiba 284-0033, Japan
| | - Hiromu Taguchi
- Research & Development Headquarters, House Foods Group Inc., 1-4 Takanodai, Yotsukaido, Chiba 284-0033, Japan
| | - Ryusuke Sawada
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
- Graduate School of Medicine, Dentistry and Pharmaceutical Science, Okayama University, Shikata-cho, Kita-ku, Okayama 700-8558, Japan
| | - Kenshin Goto
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Shogo Momokita
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Morihiro Aoyagi
- Research & Development Headquarters, House Foods Group Inc., 1-4 Takanodai, Yotsukaido, Chiba 284-0033, Japan
| | - Takashi Hirao
- Research & Development Headquarters, House Foods Group Inc., 1-4 Takanodai, Yotsukaido, Chiba 284-0033, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
- Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Aichi 464-8601, Japan
| |
Collapse
|
5
|
Sawada R, Sakajiri Y, Shibata T, Yamanishi Y. Predicting therapeutic and side effects from drug binding affinities to human proteome structures. iScience 2024; 27:110032. [PMID: 38868195 PMCID: PMC11167438 DOI: 10.1016/j.isci.2024.110032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 04/08/2024] [Accepted: 05/16/2024] [Indexed: 06/14/2024] Open
Abstract
Evaluation of the binding affinities of drugs to proteins is a crucial process for identifying drug pharmacological actions, but it requires three dimensional structures of proteins. Herein, we propose novel computational methods to predict the therapeutic indications and side effects of drug candidate compounds from the binding affinities to human protein structures on a proteome-wide scale. Large-scale docking simulations were performed for 7,582 drugs with 19,135 protein structures revealed by AlphaFold (including experimentally unresolved proteins), and machine learning models on the proteome-wide binding affinity score (PBAS) profiles were constructed. We demonstrated the usefulness of the method for predicting the therapeutic indications for 559 diseases and side effects for 285 toxicities. The method enabled to predict drug indications for which the related protein structures had not been experimentally determined and to successfully extract proteins eliciting the side effects. The proposed method will be useful in various applications in drug discovery.
Collapse
Affiliation(s)
- Ryusuke Sawada
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Japan
- Department of Pharmacology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
| | - Yuko Sakajiri
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Japan
- Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Japan
| | - Tomokazu Shibata
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Japan
- Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Japan
| |
Collapse
|
6
|
Bai H, Lu S, Zhang T, Cui H, Nakaguchi T, Xuan P. Graph reasoning method enhanced by relational transformers and knowledge distillation for drug-related side effect prediction. iScience 2024; 27:109571. [PMID: 38799562 PMCID: PMC11126883 DOI: 10.1016/j.isci.2024.109571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 09/29/2023] [Accepted: 03/22/2024] [Indexed: 05/29/2024] Open
Abstract
Identifying the side effects related to drugs is beneficial for reducing the risk of drug development failure and saving the drug development cost. We proposed a graph reasoning method, RKDSP, to fuse the semantics of multiple connection relationships, the local knowledge within each meta-path, the global knowledge among multiple meta-paths, and the attributes of the drug and side effect node pairs. We constructed drug-side effect heterogeneous graphs consisting of the drugs, side effects, and their similarity and association connections. Multiple relational transformers were established to learn node features from diverse meta-path semantic perspectives. A knowledge distillation module was constructed to learn local and global knowledge of multiple meta-paths. Finally, an adaptive convolutional neural network-based strategy was presented to adaptively encode the attributes of each drug-side effect node pair. The experimental results demonstrated that RKDSP outperforms the compared state-of-the-art prediction approaches.
Collapse
Affiliation(s)
- Honglei Bai
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
| | - Siyuan Lu
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
| | - Tiangang Zhang
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
- School of Mathematical Science, Heilongjiang University, Harbin, China
| | - Hui Cui
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, Australia
| | - Toshiya Nakaguchi
- Center for Frontier Medical Engineering, Chiba University, Chiba, Japan
| | - Ping Xuan
- Department of Computer Science and Technology, Shantou University, Shantou, China
| |
Collapse
|
7
|
Toni E, Ayatollahi H, Abbaszadeh R, Fotuhi Siahpirani A. Machine Learning Techniques for Predicting Drug-Related Side Effects: A Scoping Review. Pharmaceuticals (Basel) 2024; 17:795. [PMID: 38931462 PMCID: PMC11206653 DOI: 10.3390/ph17060795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Revised: 06/11/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND Drug safety relies on advanced methods for timely and accurate prediction of side effects. To tackle this requirement, this scoping review examines machine-learning approaches for predicting drug-related side effects with a particular focus on chemical, biological, and phenotypical features. METHODS This was a scoping review in which a comprehensive search was conducted in various databases from 1 January 2013 to 31 December 2023. RESULTS The results showed the widespread use of Random Forest, k-nearest neighbor, and support vector machine algorithms. Ensemble methods, particularly random forest, emphasized the significance of integrating chemical and biological features in predicting drug-related side effects. CONCLUSIONS This review article emphasized the significance of considering a variety of features, datasets, and machine learning algorithms for predicting drug-related side effects. Ensemble methods and Random Forest showed the best performance and combining chemical and biological features improved prediction. The results suggested that machine learning techniques have some potential to improve drug development and trials. Future work should focus on specific feature types, selection techniques, and graph-based methods for even better prediction.
Collapse
Affiliation(s)
- Esmaeel Toni
- Medical Informatics, Student Research Committee, Iran University of Medical Sciences, Tehran, Iran 14496-14535;
| | - Haleh Ayatollahi
- Medical Informatics, Health Management and Economics Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran 1996-713883
| | - Reza Abbaszadeh
- Pediatric Cardiology, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran 19956-14331;
| | - Alireza Fotuhi Siahpirani
- Systems Biology and Bioinformatics, Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran 14176-14411;
| |
Collapse
|
8
|
Feng BM, Zhang YY, Zhou XC, Wang JL, Feng YF. MolLoG: A Molecular Level Interpretability Model Bridging Local to Global for Predicting Drug Target Interactions. J Chem Inf Model 2024; 64:4348-4358. [PMID: 38709146 DOI: 10.1021/acs.jcim.4c00171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Developing new pharmaceuticals is a costly and time-consuming endeavor fraught with significant safety risks. A critical aspect of drug research and disease therapy is discerning the existence of interactions between drugs and proteins. The evolution of deep learning (DL) in computer science has been remarkably aided in this regard in recent years. Yet, two challenges remain: (i) balancing the extraction of profound, local cohesive characteristics while warding off gradient disappearance and (ii) globally representing and understanding the interactions between the drug and target local attributes, which is vital for delivering molecular level insights indispensable to drug development. In response to these challenges, we propose a DL network structure, MolLoG, primarily comprising two modules: local feature encoders (LFE) and global interactive learning (GIL). Within the LFE module, graph convolution networks and leap blocks capture the local features of drug and protein molecules, respectively. The GIL module enables the efficient amalgamation of feature information, facilitating the global learning of feature structural semantics and procuring multihead attention weights for abstract features stemming from two modalities, providing biologically pertinent explanations for black-box results. Finally, predictive outcomes are achieved by decoding the unified representation via a multilayer perceptron. Our experimental analysis reveals that MolLoG outperforms several cutting-edge baselines across four data sets, delivering superior overall performance and providing satisfactory results when elucidating various facets of drug-target interaction predictions.
Collapse
Affiliation(s)
- Bao-Ming Feng
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao, 266520 Shandong, China
| | - Yuan-Yuan Zhang
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao, 266520 Shandong, China
| | - Xiao-Chen Zhou
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao, 266520 Shandong, China
| | - Jin-Long Wang
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao, 266520 Shandong, China
| | - Yin-Fei Feng
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao, 266520 Shandong, China
| |
Collapse
|
9
|
Xia Y, Pan X, Shen HB. Heterogeneous sampled subgraph neural networks with knowledge distillation to enhance double-blind compound-protein interaction prediction. Structure 2024; 32:611-620.e4. [PMID: 38447575 DOI: 10.1016/j.str.2024.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 12/18/2023] [Accepted: 02/08/2024] [Indexed: 03/08/2024]
Abstract
Identifying binding compounds against a target protein is crucial for large-scale virtual screening in drug development. Recently, network-based methods have been developed for compound-protein interaction (CPI) prediction. However, they are difficult to be applied to unseen (i.e., never-seen-before) proteins and compounds. In this study, we propose SgCPI to incorporate local known interacting networks to predict CPI interactions. SgCPI randomly samples the local CPI network of the query compound-protein pair as a subgraph and applies a heterogeneous graph neural network (HGNN) to embed the active/inactive message of the subgraph. For unseen compounds and proteins, SgCPI-KD takes SgCPI as the teacher model to distillate its knowledge by estimating the potential neighbors. Experimental results indicate: (1) the sampled subgraphs of the CPI network introduce efficient knowledge for unseen molecular prediction with the HGNNs, and (2) the knowledge distillation strategy is beneficial to the double-blind interaction prediction by estimating molecular neighbors and distilling knowledge.
Collapse
Affiliation(s)
- Ying Xia
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China
| | - Xiaoyong Pan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.
| |
Collapse
|
10
|
Zhong Y, Seoighe C, Yang H. Non-Negative matrix factorization combined with kernel regression for the prediction of adverse drug reaction profiles. BIOINFORMATICS ADVANCES 2024; 4:vbae009. [PMID: 38736682 PMCID: PMC11087822 DOI: 10.1093/bioadv/vbae009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 01/11/2024] [Accepted: 01/18/2024] [Indexed: 05/14/2024]
Abstract
Motivation Post-market unexpected Adverse Drug Reactions (ADRs) are associated with significant costs, in both financial burden and human health. Due to the high cost and time required to run clinical trials, there is significant interest in accurate computational methods that can aid in the prediction of ADRs for new drugs. As a machine learning task, ADR prediction is made more challenging due to a high degree of class imbalance and existing methods do not successfully balance the requirement to detect the minority cases (true positives for ADR), as measured by the Area Under the Precision-Recall (AUPR) curve with the ability to separate true positives from true negatives [as measured by the Area Under the Receiver Operating Characteristic (AUROC) curve]. Surprisingly, the performance of most existing methods is worse than a naïve method that attributes ADRs to drugs according to the frequency with which the ADR has been observed over all other drugs. The existing advanced methods applied do not lead to substantial gains in predictive performance. Results We designed a rigorous evaluation to provide an unbiased estimate of the performance of ADR prediction methods: Nested Cross-Validation and a hold-out set were adopted. Among the existing methods, Kernel Regression (KR) performed best in AUPR but had a disadvantage in AUROC, relative to other methods, including the naïve method. We proposed a novel method that combines non-negative matrix factorization with kernel regression, called VKR. This novel approach matched or exceeded the performance of existing methods, overcoming the weakness of the existing methods. Availability Code and data are available on https://github.com/YezhaoZhong/VKR.
Collapse
Affiliation(s)
- Yezhao Zhong
- School of Mathematical & Statistical Sciences, University of Galway, Galway H91 TK33, Ireland
| | - Cathal Seoighe
- School of Mathematical & Statistical Sciences, University of Galway, Galway H91 TK33, Ireland
| | - Haixuan Yang
- School of Mathematical & Statistical Sciences, University of Galway, Galway H91 TK33, Ireland
| |
Collapse
|
11
|
Ding Y, Zhou H, Zou Q, Yuan L. Identification of drug-side effect association via correntropy-loss based matrix factorization with neural tangent kernel. Methods 2023; 219:73-81. [PMID: 37783242 DOI: 10.1016/j.ymeth.2023.09.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 09/18/2023] [Accepted: 09/20/2023] [Indexed: 10/04/2023] Open
Abstract
Adverse drug reactions include side effects, allergic reactions, and secondary infections. Severe adverse reactions can cause cancer, deformity, or mutation. The monitoring of drug side effects is an important support for post marketing safety supervision of drugs, and an important basis for revising drug instructions. Its purpose is to timely detect and control drug safety risks. Traditional methods are time-consuming. To accelerate the discovery of side effects, we propose a machine learning based method, called correntropy-loss based matrix factorization with neural tangent kernel (CLMF-NTK), to solve the prediction of drug side effects. Our method and other computational methods are tested on three benchmark datasets, and the results show that our method achieves the best predictive performance.
Collapse
Affiliation(s)
- Yijie Ding
- Key Laboratory of Computational Science and Application of Hainan Province, Hainan Normal University, Haikou 571158, China; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China; School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Hongmei Zhou
- Beidahuang Industry Group General Hospital, Harbin 150001, China
| | - Quan Zou
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China.
| | - Lei Yuan
- Department of Hepatobiliary Surgery, Quzhou People's Hospital, 100# Minjiang Main Road, Quzhou 324000, China.
| |
Collapse
|
12
|
Xuan P, Li P, Cui H, Wang M, Nakaguchi T, Zhang T. Learning Multi-Types of Neighbor Node Attributes and Semantics by Heterogeneous Graph Transformer and Multi-View Attention for Drug-Related Side-Effect Prediction. Molecules 2023; 28:6544. [PMID: 37764319 PMCID: PMC10537290 DOI: 10.3390/molecules28186544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 09/01/2023] [Accepted: 09/06/2023] [Indexed: 09/29/2023] Open
Abstract
Since side-effects of drugs are one of the primary reasons for their failure in clinical trials, predicting their side-effects can help reduce drug development costs. We proposed a method based on heterogeneous graph transformer and capsule networks for side-effect-drug-association prediction (TCSD). The method encodes and integrates attributes from multiple types of neighbor nodes, connection semantics, and multi-view pairwise information. In each drug-side-effect heterogeneous graph, a target node has two types of neighbor nodes, the drug nodes and the side-effect ones. We proposed a new heterogeneous graph transformer-based context representation learning module. The module is able to encode specific topology and the contextual relations among multiple kinds of nodes. There are similarity and association connections between the target node and its various types of neighbor nodes, and these connections imply semantic diversity. Therefore, we designed a new strategy to measure the importance of a neighboring node to the target node and incorporate different semantics of the connections between the target node and its multi-type neighbors. Furthermore, we designed attentions at the neighbor node type level and at the graph level, respectively, to obtain enhanced informative neighbor node features and multi-graph features. Finally, a pairwise multi-view feature learning module based on capsule networks was built to learn the pairwise attributes from the heterogeneous graphs. Our prediction model was evaluated using a public dataset, and the cross-validation results showed it achieved superior performance to several state-of-the-art methods. Ablation experiments undertaken demonstrated the effectiveness of heterogeneous graph transformer-based context encoding, the position enhanced pairwise attribute learning, and the neighborhood node category-level attention. Case studies on five drugs further showed TCSD's ability in retrieving potential drug-related side-effect candidates, and TCSD inferred the candidate side-effects for 708 drugs.
Collapse
Affiliation(s)
- Ping Xuan
- School of Computer Science and Technology, Heilongjiang University, Harbin 130407, China
- Department of Computer Science, School of Engineering, Shantou University, Shantou 515000, China
| | - Peiru Li
- School of Computer Science and Technology, Heilongjiang University, Harbin 130407, China
| | - Hui Cui
- Department of Computer Science and Information Technology, La Trobe University, Melbourne 3086, Australia
| | - Meng Wang
- School of Computer Science and Technology, Heilongjiang University, Harbin 130407, China
| | - Toshiya Nakaguchi
- Center for Frontier Medical Engineering, Chiba University, Chiba 263-8522, Japan
| | - Tiangang Zhang
- School of Computer Science and Technology, Heilongjiang University, Harbin 130407, China
- School of Mathematical Science, Heilongjiang University, Harbin 130407, China
| |
Collapse
|
13
|
Xuan P, Xu K, Cui H, Nakaguchi T, Zhang T. Graph generative and adversarial strategy-enhanced node feature learning and self-calibrated pairwise attribute encoding for prediction of drug-related side effects. Front Pharmacol 2023; 14:1257842. [PMID: 37731739 PMCID: PMC10507253 DOI: 10.3389/fphar.2023.1257842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 08/17/2023] [Indexed: 09/22/2023] Open
Abstract
Background: Inferring drug-related side effects is beneficial for reducing drug development cost and time. Current computational prediction methods have concentrated on graph reasoning over heterogeneous graphs comprising the drug and side effect nodes. However, the various topologies and node attributes within multiple drug-side effect heterogeneous graphs have not been completely exploited. Methods: We proposed a new drug-side effect association prediction method, GGSC, to deeply integrate the diverse topologies and attributes from multiple heterogeneous graphs and the self-calibration attributes of each drug-side effect node pair. First, we created two heterogeneous graphs comprising the drug and side effect nodes and their related similarity and association connections. Since each heterogeneous graph has its specific topology and node attributes, a node feature learning strategy was designed and the learning for each graph was enhanced from a graph generative and adversarial perspective. We constructed a generator based on a graph convolutional autoencoder to encode the topological structure and node attributes from the whole heterogeneous graph and then generate the node features embedding the graph topology. A discriminator based on multilayer perceptron was designed to distinguish the generated topological features from the original ones. We also designed representation-level attention to discriminate the contributions of topological representations from multiple heterogeneous graphs and adaptively fused them. Finally, we constructed a self-calibration module based on convolutional neural networks to guide pairwise attribute learning through the features of the small latent space. Results: The comparison experiment results showed that GGSC had higher prediction performance than several state-of-the-art prediction methods. The ablation experiments demonstrated the effectiveness of topological enhancement learning, representation-level attention, and self-calibrated pairwise attribute learning. In addition, case studies over five drugs demonstrated GGSC's ability in discovering the potential drug-related side effect candidates. Conclusion: We proposed a drug-side effect association prediction method, and the method is beneficial for screening the reliable association candidates for the biologists to discover the actual associations.
Collapse
Affiliation(s)
- Ping Xuan
- Department of Computer Science, School of Engineering, Shantou University, Shantou, China
| | - Kai Xu
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
| | - Hui Cui
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, VI, Australia
| | - Toshiya Nakaguchi
- Center for Frontier Medical Engineering, Chiba University, Chiba, Japan
| | - Tiangang Zhang
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
- School of Mathematical Science, Heilongjiang University, Harbin, China
| |
Collapse
|
14
|
Ngo HM, Thai MT, Kahveci T. QuTIE: quantum optimization for target identification by enzymes. BIOINFORMATICS ADVANCES 2023; 3:vbad112. [PMID: 37786534 PMCID: PMC10541652 DOI: 10.1093/bioadv/vbad112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 06/26/2023] [Accepted: 08/18/2023] [Indexed: 10/04/2023]
Abstract
Summary Target identification by enzymes (TIE) problem aims to identify the set of enzymes in a given metabolic network, such that their inhibition eliminates a given set of target compounds associated with a disease while incurring minimum damage to the rest of the compounds. This is a NP-hard problem, and thus optimal solutions using classical computers fail to scale to large metabolic networks. In this article, we develop the first quantum optimization solution, called QuTIE (quantum optimization for target identification by enzymes), to this NP-hard problem. We do that by developing an equivalent formulation of the TIE problem in quadratic unconstrained binary optimization form. We then map it to a logical graph, and embed the logical graph on a quantum hardware graph. Our experimental results on 27 metabolic networks from Escherichia coli, Homo sapiens, and Mus musculus show that QuTIE yields solutions that are optimal or almost optimal. Our experiments also demonstrate that QuTIE can successfully identify enzyme targets already verified in wet-lab experiments for 14 major disease classes. Availability and implementation Code and sample data are available at: https://github.com/ngominhhoang/Quantum-Target-Identification-by-Enzymes.
Collapse
Affiliation(s)
- Hoang M Ngo
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, United States
| | - My T Thai
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, United States
| | - Tamer Kahveci
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, United States
| |
Collapse
|
15
|
Kim G, Lee D. Reverse tracking from drug-induced transcriptomes through multilayer molecular networks reveals hidden drug targets. Comput Biol Med 2023; 158:106881. [PMID: 37028141 DOI: 10.1016/j.compbiomed.2023.106881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 03/03/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023]
Abstract
Identifying molecular targets of a drug is an essential process for drug discovery and development. The recent in-silico approaches are usually based on the structure information of chemicals and proteins. However, 3D structure information is hard to obtain and machine-learning methods using 2D structure suffer from data imbalance problem. Here, we present a reverse tracking method from genes to target proteins using drug-perturbed gene transcriptional profiles and multilayer molecular networks. We scored how well the protein explains gene expression changes perturbed by a drug. We validated the protein scores of our method in predicting known targets of drugs. Our method performs better than other methods using the gene transcriptional profiles and shows the ability to suggest the molecular mechanism of drugs. Furthermore, our method has the potential to predict targets for objects that do not have rigid structural information, such as coronavirus.
Collapse
|
16
|
Bongini P, Scarselli F, Bianchini M, Dimitri GM, Pancino N, Lio P. Modular Multi-Source Prediction of Drug Side-Effects With DruGNN. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1211-1220. [PMID: 35576419 DOI: 10.1109/tcbb.2022.3175362] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Drug Side-Effects (DSEs) have a high impact on public health, care system costs, and drug discovery processes. Predicting the probability of side-effects, before their occurrence, is fundamental to reduce this impact, in particular on drug discovery. Candidate molecules could be screened before undergoing clinical trials, reducing the costs in time, money, and health of the participants. Drug side-effects are triggered by complex biological processes involving many different entities, from drug structures to protein-protein interactions. To predict their occurrence, it is necessary to integrate data from heterogeneous sources. In this work, such heterogeneous data is integrated into a graph dataset, expressively representing the relational information between different entities, such as drug molecules and genes. The relational nature of the dataset represents an important novelty for drug side-effect predictors. Graph Neural Networks (GNNs) are exploited to predict DSEs on our dataset with very promising results. GNNs are deep learning models that can process graph-structured data, with minimal information loss, and have been applied on a wide variety of biological tasks. Our experimental results confirm the advantage of using relationships between data entities, suggesting interesting future developments in this scope. The experimentation also shows the importance of specific subsets of data in determining associations between drugs and side-effects.
Collapse
|
17
|
Das P, Mazumder DH. An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects. Artif Intell Rev 2023; 56:1-28. [PMID: 36819660 PMCID: PMC9930028 DOI: 10.1007/s10462-023-10413-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/01/2023] [Indexed: 02/19/2023]
Abstract
Approved drugs for sale must be effective and safe, implying that the drug's advantages outweigh its known harmful side effects. Side effects (SE) of drugs are one of the common reasons for drug failure that may halt the whole drug discovery pipeline. The side effects might vary from minor concerns like a runny nose to potentially life-threatening issues like liver damage, heart attack, and death. Therefore, predicting the side effects of the drug is vital in drug development, discovery, and design. Supervised machine learning-based side effects prediction task has recently received much attention since it reduces time, chemical waste, design complexity, risk of failure, and cost. The advancement of supervised learning approaches for predicting side effects have emerged as essential computational tools. Supervised machine learning technique provides early information on drug side effects to develop an effective drug based on drug properties. Still, there are several challenges to predicting drug side effects. Thus, a near-exhaustive survey is carried out in this paper on the use of supervised machine learning approaches employed in drug side effects prediction tasks in the past two decades. In addition, this paper also summarized the drug descriptor required for the side effects prediction task, commonly utilized drug properties sources, computational models, and their performances. Finally, the research gap, open problems, and challenges for the further supervised learning-based side effects prediction task have been discussed.
Collapse
Affiliation(s)
- Pranab Das
- Department of Computer Science and Engineering, National Institute of Technology Nagaland, Chumukedima, Dimapur, Nagaland 797103 India
| | - Dilwar Hussain Mazumder
- Department of Computer Science and Engineering, National Institute of Technology Nagaland, Chumukedima, Dimapur, Nagaland 797103 India
| |
Collapse
|
18
|
Hyperbolic matrix factorization improves prediction of drug-target associations. Sci Rep 2023; 13:959. [PMID: 36653463 PMCID: PMC9849222 DOI: 10.1038/s41598-023-27995-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 01/11/2023] [Indexed: 01/19/2023] Open
Abstract
Past research in computational systems biology has focused more on the development and applications of advanced statistical and numerical optimization techniques and much less on understanding the geometry of the biological space. By representing biological entities as points in a low dimensional Euclidean space, state-of-the-art methods for drug-target interaction (DTI) prediction implicitly assume the flat geometry of the biological space. In contrast, recent theoretical studies suggest that biological systems exhibit tree-like topology with a high degree of clustering. As a consequence, embedding a biological system in a flat space leads to distortion of distances between biological objects. Here, we present a novel matrix factorization methodology for drug-target interaction prediction that uses hyperbolic space as the latent biological space. When benchmarked against classical, Euclidean methods, hyperbolic matrix factorization exhibits superior accuracy while lowering embedding dimension by an order of magnitude. We see this as additional evidence that the hyperbolic geometry underpins large biological networks.
Collapse
|
19
|
Uner OC, Kuru HI, Cinbis RG, Tastan O, Cicek AE. DeepSide: A Deep Learning Approach for Drug Side Effect Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:330-339. [PMID: 34995191 DOI: 10.1109/tcbb.2022.3141103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Drug failures due to unforeseen adverse effects at clinical trials pose health risks for the participants and lead to substantial financial losses. Side effect prediction algorithms have the potential to guide the drug design process. LINCS L1000 dataset provides a vast resource of cell line gene expression data perturbed by different drugs and creates a knowledge base for context specific features. The state-of-the-art approach that aims at using context specific information relies on only the high-quality experiments in LINCS L1000 and discards a large portion of the experiments. In this study, our goal is to boost the prediction performance by utilizing this data to its full extent. We experiment with 5 deep learning architectures. We find that a multi-modal architecture produces the best predictive performance among multi-layer perceptron-based architectures when drug chemical structure (CS), and the full set of drug perturbed gene expression profiles (GEX) are used as modalities. Overall, we observe that the CS is more informative than the GEX. A convolutional neural network-based model that uses only SMILES string representation of the drugs achieves the best results and provides 13.0% macro-AUC and 3.1% micro-AUC improvements over the state-of-the-art. We also show that the model is able to predict side effect-drug pairs that are reported in the literature but was missing in the ground truth side effect dataset. DeepSide is available at http://github.com/OnurUner/DeepSide.
Collapse
|
20
|
Liang X, Fu Y, Qu L, Zhang P, Chen Y. Prediction of drug side effects with transductive matrix co-completion. Bioinformatics 2023; 39:btad006. [PMID: 36655793 PMCID: PMC9869719 DOI: 10.1093/bioinformatics/btad006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 12/06/2022] [Accepted: 01/18/2023] [Indexed: 01/20/2023] Open
Abstract
MOTIVATION Side effects of drugs could cause severe health problems and the failure of drug development. Drug-target interactions are the basis for side effect production and are important for side effect prediction. However, the information on the known targets of drugs is incomplete. Furthermore, there could be also some missing data in the existing side effect profile of drugs. As a result, new methods are needed to deal with the missing features and missing labels in the problem of side effect prediction. RESULTS We propose a novel computational method based on transductive matrix co-completion and leverage the low-rank structure in the side effects and drug-target data. Positive-unlabelled learning is incorporated into the model to handle the impact of unobserved data. We also introduce graph regularization to integrate the drug chemical information for side effect prediction. We collect the data on side effects, drug targets, drug-associated proteins and drug chemical structures to train our model and test its performance for side effect prediction. The experiment results show that our method outperforms several other state-of-the-art methods under different scenarios. The case study and additional analysis illustrate that the proposed method could not only predict the side effects of drugs but also could infer the missing targets of drugs. AVAILABILITY AND IMPLEMENTATION The data and the code for the proposed method are available at https://github.com/LiangXujun/GTMCC. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Xujun Liang
- NHC Key Laboratory of Cancer Proteomics, Department of Oncology
- National Clinical Research Center for Gerontology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Ying Fu
- NHC Key Laboratory of Cancer Proteomics, Department of Oncology
| | - Lingzhi Qu
- NHC Key Laboratory of Cancer Proteomics, Department of Oncology
| | - Pengfei Zhang
- NHC Key Laboratory of Cancer Proteomics, Department of Oncology
| | - Yongheng Chen
- NHC Key Laboratory of Cancer Proteomics, Department of Oncology
- National Clinical Research Center for Gerontology, Xiangya Hospital, Central South University, Changsha 410008, China
| |
Collapse
|
21
|
Chen YH, Shih YT, Chien CS, Tsai CS. Predicting adverse drug effects: A heterogeneous graph convolution network with a multi-layer perceptron approach. PLoS One 2022; 17:e0266435. [PMID: 36516131 PMCID: PMC9750037 DOI: 10.1371/journal.pone.0266435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 11/19/2022] [Indexed: 12/15/2022] Open
Abstract
We apply a heterogeneous graph convolution network (GCN) combined with a multi-layer perceptron (MLP) denoted by GCNMLP to explore the potential side effects of drugs. Here the SIDER, OFFSIDERS, and FAERS are used as the datasets. We integrate the drug information with similar characteristics from the datasets of known drugs and side effect networks. The heterogeneous graph networks explore the potential side effects of drugs by inferring the relationship between similar drugs and related side effects. This novel in silico method will shorten the time spent in uncovering the unseen side effects within routine drug prescriptions while highlighting the relevance of exploring drug mechanisms from well-documented drugs. In our experiments, we inquire about the drugs Vancomycin, Amlodipine, Cisplatin, and Glimepiride from a trained model, where the parameters are acquired from the dataset SIDER after training. Our results show that the performance of the GCNMLP on these three datasets is superior to the non-negative matrix factorization method (NMF) and some well-known machine learning methods with respect to various evaluation scales. Moreover, new side effects of drugs can be obtained using the GCNMLP.
Collapse
Affiliation(s)
- Y.-H. Chen
- Dept. of Nephrology, Taichung Tzu Chi Hospital, Taichung, Taiwan
- School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Y.-T. Shih
- Dept. of Applied Mathematics, National Chung Hsing University, Taichung, Taiwan
| | - C.-S. Chien
- Dept. of Applied Mathematics, National Chung Hsing University, Taichung, Taiwan
| | - C.-S. Tsai
- Dept. of Management of Information Systems, National Chung Hsing University, Taichung, Taiwan
| |
Collapse
|
22
|
Quality of life among patients with the common chronic disease during COVID-19 pandemic in Northwest Ethiopia: A structural equation modelling. PLoS One 2022; 17:e0278557. [PMID: 36472997 PMCID: PMC9725128 DOI: 10.1371/journal.pone.0278557] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 11/19/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Improving Quality of Life (QoL) for patients with chronic diseases is a critical step in controlling disease progression and preventing complications. The COVID-19 pandemic has hampered chronic disease management, lowering patients' quality of life. Thus, we aimed to assess the quality of life and its determinants in patients with common chronic diseases, in Northwest Ethiopia during the COVID-19 pandemic. METHODS A cross-sectional study was conducted among 1815 randomly selected chronic patients with common chronic diseases. A standardized WHOQOL BREF tool was used, and electronic data collection was employed with the kobo toolbox data collection server. Overall QoL and the domains of Health-Related Quality of life (HRQoL) were determined. Structural equation modelling was done to estimate independent variables' direct and indirect effects. Path coefficients with a 95% confidence interval were reported. RESULTS About one in third, (33.35%) and 11.43% of the study participants had co-morbid conditions and identified complications, respectively. The mean score of QoL was 56.3 ranging from 14.59 and 98.95. The environmental domain was the most affected domain of HRQoL with a mean score of 52.18. Age, psychological, and environmental domains of HRQoL had a direct positive effect on the overall QoL while the physical and social relationships domains had an indirect positive effect. On the other hand, the number of medications taken, the presence of comorbidity, and complications had a direct negative impact on overall QoL. Furthermore, both rural residency and the presence of complications had an indirect negative effect on overall QoL via the mediator variables of environmental and physical health, respectively. CONCLUSION The quality of life was compromised in chronic disease patients. During the COVID-19 pandemic, the environmental domain of HRQoL was the most affected. Several socio-demographic and clinical factors had an impact on QoL, either directly or indirectly. These findings highlighted the importance of paying special attention to rural residents, patients with complications, patients taking a higher number of medications, and patients with comorbidity.
Collapse
|
23
|
Huang L, Lin J, Liu R, Zheng Z, Meng L, Chen X, Li X, Wong KC. CoaDTI: multi-modal co-attention based framework for drug-target interaction annotation. Brief Bioinform 2022; 23:6770087. [PMID: 36274236 DOI: 10.1093/bib/bbac446] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 08/26/2022] [Accepted: 09/18/2022] [Indexed: 12/14/2022] Open
Abstract
MOTIVATION The identification of drug-target interactions (DTIs) plays a vital role for in silico drug discovery, in which the drug is the chemical molecule, and the target is the protein residues in the binding pocket. Manual DTI annotation approaches remain reliable; however, it is notoriously laborious and time-consuming to test each drug-target pair exhaustively. Recently, the rapid growth of labelled DTI data has catalysed interests in high-throughput DTI prediction. Unfortunately, those methods highly rely on the manual features denoted by human, leading to errors. RESULTS Here, we developed an end-to-end deep learning framework called CoaDTI to significantly improve the efficiency and interpretability of drug target annotation. CoaDTI incorporates the Co-attention mechanism to model the interaction information from the drug modality and protein modality. In particular, CoaDTI incorporates transformer to learn the protein representations from raw amino acid sequences, and GraphSage to extract the molecule graph features from SMILES. Furthermore, we proposed to employ the transfer learning strategy to encode protein features by pre-trained transformer to address the issue of scarce labelled data. The experimental results demonstrate that CoaDTI achieves competitive performance on three public datasets compared with state-of-the-art models. In addition, the transfer learning strategy further boosts the performance to an unprecedented level. The extended study reveals that CoaDTI can identify novel DTIs such as reactions between candidate drugs and severe acute respiratory syndrome coronavirus 2-associated proteins. The visualization of co-attention scores can illustrate the interpretability of our model for mechanistic insights. AVAILABILITY Source code are publicly available at https://github.com/Layne-Huang/CoaDTI.
Collapse
Affiliation(s)
- Lei Huang
- Department of Computer Science, City University of Hong Kong, Hong Kong SAR
| | - Jiecong Lin
- Department of Pathology, Harvard Medical School, Boston, USA.,Department of Computer Science, The University of Hong Kong, Hong Kong SAR
| | - Rui Liu
- Department of Computer Science, City University of Hong Kong, Hong Kong SAR
| | - Zetian Zheng
- Department of Computer Science, City University of Hong Kong, Hong Kong SAR
| | - Lingkuan Meng
- Department of Computer Science, City University of Hong Kong, Hong Kong SAR
| | - Xingjian Chen
- Department of Computer Science, City University of Hong Kong, Hong Kong SAR
| | - Xiangtao Li
- School of Artificial Intelligence, Jilin University, China
| | - Ka-Chun Wong
- Department of Computer Science, City University of Hong Kong, Hong Kong SAR.,Hong Kong Institute for Data Science, City University of Hong Kong, Hong Kong SAR
| |
Collapse
|
24
|
Iwata M, Mutsumine H, Nakayama Y, Suita N, Yamanishi Y. Pathway trajectory analysis with tensor imputation reveals drug-induced single-cell transcriptomic landscape. NATURE COMPUTATIONAL SCIENCE 2022; 2:758-770. [PMID: 38177364 PMCID: PMC10768635 DOI: 10.1038/s43588-022-00352-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 10/11/2022] [Indexed: 01/06/2024]
Abstract
Genome-wide identification of single-cell transcriptomic responses of drugs in various human cells is a challenging issue in medical and pharmaceutical research. Here we present a computational method, tensor-based imputation of gene-expression data at the single-cell level (TIGERS), which reveals the drug-induced single-cell transcriptomic landscape. With this algorithm, we predict missing drug-induced single-cell gene-expression data with tensor imputation, and identify trajectories of regulated pathways considering intercellular heterogeneity. Tensor imputation outperformed existing imputation methods for data completion, and provided cell-type-specific transcriptomic responses for unobserved drugs. For example, TIGERS correctly predicted the cell-type-specific expression of maker genes for pancreatic islets. Pathway trajectory analysis of the imputed gene-expression profiles of all combinations of drugs and human cells identified single-cell-specific drug activities and pathway trajectories that reflect drug-induced changes in pathway regulation. The proposed method is expected to expand our understanding of the single-cell mechanisms of drugs at the pathway level.
Collapse
Affiliation(s)
- Michio Iwata
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan
| | - Hiroaki Mutsumine
- Tsukuba Research Institute, Ono Pharmaceutical Co., Ltd, Tsukuba, Ibaraki, Japan
| | - Yusuke Nakayama
- Tsukuba Research Institute, Ono Pharmaceutical Co., Ltd, Tsukuba, Ibaraki, Japan
| | - Naomasa Suita
- Tsukuba Research Institute, Ono Pharmaceutical Co., Ltd, Tsukuba, Ibaraki, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan.
| |
Collapse
|
25
|
Alpay BA, Gosink M, Aguiar D. Evaluating molecular fingerprint-based models of drug side effects against a statistical control. Drug Discov Today 2022; 27:103364. [PMID: 36115633 DOI: 10.1016/j.drudis.2022.103364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 08/24/2022] [Accepted: 09/09/2022] [Indexed: 11/16/2022]
Abstract
There are many machine learning models that use molecular fingerprints of drugs to predict side effects. Characterizing their skill is necessary for understanding their usefulness in pharmaceutical development. Here, we analyze a statistical control of side effect prediction skill, develop a pipeline for benchmarking models, and evaluate how well existing models predict side effects identified in pharmaceutical documentation. We demonstrate that molecular fingerprints are useful for ranking drugs by their likelihood to cause a given side effect. However, the predictions for one or more drugs overall benefit only marginally from molecular fingerprints when ranking the likelihoods of many possible side effects, and display at most modest overall skill at identifying the side effects that do and do not occur.
Collapse
Affiliation(s)
- Berk A Alpay
- Systems, Synthetic, and Quantitative Biology Program, Harvard University, Cambridge, MA 02138, USA.
| | | | - Derek Aguiar
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, USA
| |
Collapse
|
26
|
Zare Harofte S, Soltani M, Siavashy S, Raahemifar K. Recent Advances of Utilizing Artificial Intelligence in Lab on a Chip for Diagnosis and Treatment. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2203169. [PMID: 36026569 DOI: 10.1002/smll.202203169] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 07/16/2022] [Indexed: 05/14/2023]
Abstract
Nowadays, artificial intelligence (AI) creates numerous promising opportunities in the life sciences. AI methods can be significantly advantageous for analyzing the massive datasets provided by biotechnology systems for biological and biomedical applications. Microfluidics, with the developments in controlled reaction chambers, high-throughput arrays, and positioning systems, generate big data that is not necessarily analyzed successfully. Integrating AI and microfluidics can pave the way for both experimental and analytical throughputs in biotechnology research. Microfluidics enhances the experimental methods and reduces the cost and scale, while AI methods significantly improve the analysis of huge datasets obtained from high-throughput and multiplexed microfluidics. This review briefly presents a survey of the role of AI and microfluidics in biotechnology. Also, the incorporation of AI with microfluidics is comprehensively investigated. Specifically, recent studies that perform flow cytometry cell classification, cell isolation, and a combination of them by gaining from both AI methods and microfluidic techniques are covered. Despite all current challenges, various fields of biotechnology can be remarkably affected by the combination of AI and microfluidic technologies. Some of these fields include point-of-care systems, precision, personalized medicine, regenerative medicine, prognostics, diagnostics, and treatment of oncology and non-oncology-related diseases.
Collapse
Affiliation(s)
- Samaneh Zare Harofte
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, 19967-15433, Iran
| | - Madjid Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, 19967-15433, Iran
- Department of Electrical and Computer Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
- Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, N2L 3G1, Canada
- Advanced Bioengineering Initiative Center, Multidisciplinary International Complex, K. N. Toosi University of Technology, Tehran, 14176-14411, Iran
- Cancer Biology Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, 14197-33141, Iran
| | - Saeed Siavashy
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, 19967-15433, Iran
| | - Kaamran Raahemifar
- Data Science and Artificial Intelligence Program, College of Information Sciences and Technology (IST), Penn State University, State College, PA, 16801, USA
- School of Optometry and Vision Science, Faculty of Science, University of Waterloo, 200 University Ave. W, Waterloo, ON, N2L 3G1, Canada
- Department of Chemical Engineering, Faculty of Engineering, University of Waterloo, 200 University Ave. W, Waterloo, ON, N2L 3G1, Canada
| |
Collapse
|
27
|
Decoding kinase-adverse event associations for small molecule kinase inhibitors. Nat Commun 2022; 13:4349. [PMID: 35896580 PMCID: PMC9329312 DOI: 10.1038/s41467-022-32033-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 07/14/2022] [Indexed: 11/08/2022] Open
Abstract
Small molecule kinase inhibitors (SMKIs) are being approved at a fast pace under expedited programs for anticancer treatment. In this study, we construct a multi-domain dataset from a total of 4638 patients in the registrational trials of 16 FDA-approved SMKIs and employ a machine-learning model to examine the relationships between kinase targets and adverse events (AEs). Internal and external (datasets from two independent SMKIs) validations have been conducted to verify the usefulness of the established model. We systematically evaluate the potential associations between 442 kinases with 2145 AEs and made publicly accessible an interactive web application “Identification of Kinase-Specific Signal” (https://gongj.shinyapps.io/ml4ki). The developed model (1) provides a platform for experimentalists to identify and verify undiscovered KI-AE pairs, (2) serves as a precision-medicine tool to mitigate individual patient safety risks by forecasting clinical safety signals and (3) can function as a modern drug development tool to screen and compare SMKI target therapies from the safety perspective. Small molecule kinase inhibitors (SMKIs) are being approved at a fast pace under expedited programs for anticancer treatment. Here, the authors employ a machine-learning model to examine the relationships between kinase targets and adverse events in the trials of 16 FDA-approved SMKIs.
Collapse
|
28
|
Galletti C, Aguirre-Plans J, Oliva B, Fernandez-Fuentes N. Prediction of Adverse Drug Reaction Linked to Protein Targets Using Network-Based Information and Machine Learning. FRONTIERS IN BIOINFORMATICS 2022; 2:906644. [PMID: 36304303 PMCID: PMC9580901 DOI: 10.3389/fbinf.2022.906644] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 06/02/2022] [Indexed: 11/17/2022] Open
Abstract
Drug discovery attrition rates, particularly at advanced clinical trial stages, are high because of unexpected adverse drug reactions (ADR) elicited by novel drug candidates. Predicting undesirable ADRs produced by the modulation of certain protein targets would contribute to developing safer drugs, thereby reducing economic losses associated with high attrition rates. As opposed to the more traditional drug-centric approach, we propose a target-centric approach to predict associations between protein targets and ADRs. The implementation of the predictor is based on a machine learning classifier that integrates a set of eight independent network-based features. These include a network diffusion-based score, identification of protein modules based on network clustering algorithms, functional similarity among proteins, network distance to proteins that are part of safety panels used in preclinical drug development, set of network descriptors in the form of degree and betweenness centrality measurements, and conservation. This diverse set of descriptors were used to generate predictors based on different machine learning classifiers ranging from specific models for individual ADR to higher levels of abstraction as per MEDDRA hierarchy such as system organ class. The results obtained from the different machine-learning classifiers, namely, support vector machine, random forest, and neural network were further analyzed as a meta-predictor exploiting three different voting systems, namely, jury vote, consensus vote, and red flag, obtaining different models for each of the ADRs in analysis. The level of accuracy of the predictors justifies the identification of problematic protein targets both at the level of individual ADR as well as a set of related ADRs grouped in common system organ classes. As an example, the prediction of ventricular tachycardia achieved an accuracy and precision of 0.83 and 0.90, respectively, and a Matthew correlation coefficient of 0.70. We believe that this approach is a good complement to the existing methodologies devised to foresee potential liabilities in preclinical drug discovery. The method is available through the DocTOR utility at GitHub (https://github.com/cristian931/DocTOR).
Collapse
Affiliation(s)
- Cristiano Galletti
- Department of Biosciences, U Science Tech, Universitat de Vic-Universitat Central de Catalunya, Barcelona, Spain
| | - Joaquim Aguirre-Plans
- Department of Physics, Network Science Institute, Northeastern University, Boston, MA, United States
| | - Baldo Oliva
- Department of Experimental and Health Sciences, Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Spain
| | - Narcis Fernandez-Fuentes
- Department of Biosciences, U Science Tech, Universitat de Vic-Universitat Central de Catalunya, Barcelona, Spain
- *Correspondence: Narcis Fernandez-Fuentes,
| |
Collapse
|
29
|
Liang X, Li J, Fu Y, Qu L, Tan Y, Zhang P. A novel machine learning model based on sparse structure learning with adaptive graph regularization for predicting drug side effects. J Biomed Inform 2022; 132:104131. [PMID: 35840061 DOI: 10.1016/j.jbi.2022.104131] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 06/08/2022] [Accepted: 06/29/2022] [Indexed: 10/17/2022]
Abstract
Drug side effects are closely related to the success and failure of drug development. Here we present a novel machine learning method for side effect prediction. The proposed method treats side effect prediction as a multi-label learning problem and uses sparse structure learning to model the relationships between side effects. Additionally, the proposed method adopts the adaptive graph regularization strategy to explore the local structure in drug data and fuse multiple types of drug features. An alternating optimization algorithm is proposed to solve the optimization problem. We collected chemical structures and biological pathway features of drugs as the inputs of our method to predict drug side effects. The results of the cross-validation experiment showed that our method could significantly improve the prediction performance compared to the other state-of-the-art methods. Besides, our model is highly interpretable. It could learn the drug neighbourhood relationships, side effect relationships, and drug features related to side effects. We systematically validated the information extracted by the model with independent data. Some prediction results could also be supported by literature reports. The proposed method could be applied to integrate both chemical and biological data to predict side effects and helps improve drug safety.
Collapse
Affiliation(s)
- Xujun Liang
- NHC Key Laboratory of Cancer Proteomics, Department of Oncology, PR China; National Clinical Research Center for Gerontology, Xiangya Hospital, Central South University, PR China.
| | - Jun Li
- NHC Key Laboratory of Cancer Proteomics, Department of Oncology, PR China
| | - Ying Fu
- NHC Key Laboratory of Cancer Proteomics, Department of Oncology, PR China
| | - Lingzhi Qu
- NHC Key Laboratory of Cancer Proteomics, Department of Oncology, PR China
| | - Yuying Tan
- NHC Key Laboratory of Cancer Proteomics, Department of Oncology, PR China
| | - Pengfei Zhang
- NHC Key Laboratory of Cancer Proteomics, Department of Oncology, PR China; National Clinical Research Center for Gerontology, Xiangya Hospital, Central South University, PR China
| |
Collapse
|
30
|
Xu X, Xuan P, Zhang T, Chen B, Sheng N. Inferring Drug-Target Interactions Based on Random Walk and Convolutional Neural Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2294-2304. [PMID: 33729947 DOI: 10.1109/tcbb.2021.3066813] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Computational strategies for identifying new drug-target interactions (DTIs) can guide the process of drug discovery, reduce the cost and time of drug development, and thus promote drug development. Most recently proposed methods predict DTIs via integration of heterogeneous data related to drugs and proteins. However, previous methods have failed to deeply integrate these heterogeneous data and learn deep feature representations of multiple original similarities and interactions related to drugs and proteins. We therefore constructed a heterogeneous network by integrating a variety of connection relationships about drugs and proteins, including drugs, proteins, and drug side effects, as well as their similarities, interactions, and associations. A DTI prediction method based on random walk and convolutional neural network was proposed and referred to as DTIPred. DTIPred not only takes advantage of various original features related to drugs and proteins, but also integrates the topological information of heterogeneous networks. The prediction model is composed of two sides and learns the deep feature representation of a drug-protein pair. On the left side, random walk with restart is applied to learn the topological vectors of drug and protein nodes. The topological representation is further learned by the constructed deep learning frame based on convolutional neural network. The right side of the model focuses on integrating multiple original similarities and interactions of drugs and proteins to learn the original representation of the drug-protein pair. The results of cross-validation experiments demonstrate that DTIPred achieves better prediction performance than several state-of-the-art methods. During the validation process, DTIPred can retrieve more actual drug-protein interactions within the top part of the predicted results, which may be more helpful to biologists. In addition, case studies on five drugs further demonstrate the ability of DTIPred to discover potential drug-protein interactions.
Collapse
|
31
|
Li Y, Qiao G, Gao X, Wang G. Supervised graph co-contrastive learning for drug-target interaction prediction. Bioinformatics 2022; 38:2847-2854. [PMID: 35561181 DOI: 10.1093/bioinformatics/btac164] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 02/05/2022] [Accepted: 03/20/2022] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Identification of Drug-Target Interactions (DTIs) is an essential step in drug discovery and repositioning. DTI prediction based on biological experiments is time-consuming and expensive. In recent years, graph learning-based methods have aroused widespread interest and shown certain advantages on this task, where the DTI prediction is often modeled as a binary classification problem of the nodes composed of drug and protein pairs (DPPs). Nevertheless, in many real applications, labeled data are very limited and expensive to obtain. With only a few thousand labeled data, models could hardly recognize comprehensive patterns of DPP node representations, and are unable to capture enough commonsense knowledge, which is required in DTI prediction. Supervised contrastive learning gives an aligned representation of DPP node representations with the same class label. In embedding space, DPP node representations with the same label are pulled together, and those with different labels are pushed apart. RESULTS We propose an end-to-end supervised graph co-contrastive learning model for DTI prediction directly from heterogeneous networks. By contrasting the topology structures and semantic features of the drug-protein-pair network, as well as the new selection strategy of positive and negative samples, SGCL-DTI generates a contrastive loss to guide the model optimization in a supervised manner. Comprehensive experiments on three public datasets demonstrate that our model outperforms the SOTA methods significantly on the task of DTI prediction, especially in the case of cold start. Furthermore, SGCL-DTI provides a new research perspective of contrastive learning for DTI prediction. AVAILABILITY AND IMPLEMENTATION The research shows that this method has certain applicability in the discovery of drugs, the identification of drug-target pairs and so on.
Collapse
Affiliation(s)
- Yang Li
- College of information and Computer Engineering, Northeast Forestry University, Harbin 150004, China
| | - Guanyu Qiao
- College of information and Computer Engineering, Northeast Forestry University, Harbin 150004, China
| | - Xin Gao
- Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology, Mathematical and Computer Sciences and Engineering, Thuwal 23955, Kingdom of Saudi Arabia
| | - Guohua Wang
- College of information and Computer Engineering, Northeast Forestry University, Harbin 150004, China
| |
Collapse
|
32
|
Zhao Y, Yu Y, Wang H, Li Y, Deng Y, Jiang G, Luo Y. Machine Learning in Causal Inference: Application in Pharmacovigilance. Drug Saf 2022; 45:459-476. [PMID: 35579811 PMCID: PMC9114053 DOI: 10.1007/s40264-022-01155-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/09/2022] [Indexed: 01/28/2023]
Abstract
Monitoring adverse drug events or pharmacovigilance has been promoted by the World Health Organization to assure the safety of medicines through a timely and reliable information exchange regarding drug safety issues. We aim to discuss the application of machine learning methods as well as causal inference paradigms in pharmacovigilance. We first reviewed data sources for pharmacovigilance. Then, we examined traditional causal inference paradigms, their applications in pharmacovigilance, and how machine learning methods and causal inference paradigms were integrated to enhance the performance of traditional causal inference paradigms. Finally, we summarized issues with currently mainstream correlation-based machine learning models and how the machine learning community has tried to address these issues by incorporating causal inference paradigms. Our literature search revealed that most existing data sources and tasks for pharmacovigilance were not designed for causal inference. Additionally, pharmacovigilance was lagging in adopting machine learning-causal inference integrated models. We highlight several currently trending directions or gaps to integrate causal inference with machine learning in pharmacovigilance research. Finally, our literature search revealed that the adoption of causal paradigms can mitigate known issues with machine learning models. We foresee that the pharmacovigilance domain can benefit from the progress in the machine learning field.
Collapse
Affiliation(s)
- Yiqing Zhao
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 750 N Lake Shore Drive, Room 11-189, Chicago, IL, 60611, USA
| | - Yue Yu
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, 55902, USA
| | - Hanyin Wang
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 750 N Lake Shore Drive, Room 11-189, Chicago, IL, 60611, USA
| | - Yikuan Li
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 750 N Lake Shore Drive, Room 11-189, Chicago, IL, 60611, USA
| | - Yu Deng
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 750 N Lake Shore Drive, Room 11-189, Chicago, IL, 60611, USA
| | - Guoqian Jiang
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, 55902, USA
| | - Yuan Luo
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 750 N Lake Shore Drive, Room 11-189, Chicago, IL, 60611, USA.
| |
Collapse
|
33
|
DTIP-TC2A: An analytical framework for drug-target interactions prediction methods. Comput Biol Chem 2022; 99:107707. [DOI: 10.1016/j.compbiolchem.2022.107707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 05/01/2022] [Accepted: 05/26/2022] [Indexed: 11/18/2022]
|
34
|
Xuan P, Wang M, Liu Y, Wang D, Zhang T, Nakaguchi T. Integrating specific and common topologies of heterogeneous graphs and pairwise attributes for drug-related side effect prediction. Brief Bioinform 2022; 23:6573962. [PMID: 35470853 DOI: 10.1093/bib/bbac126] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 02/15/2022] [Accepted: 03/15/2022] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Computerized methods for drug-related side effect identification can help reduce costs and speed up drug development. Multisource data about drug and side effects are widely used to predict potential drug-related side effects. Heterogeneous graphs are commonly used to associate multisourced data of drugs and side effects which can reflect similarities of the drugs from different perspectives. Effective integration and formulation of diverse similarities, however, are challenging. In addition, the specific topology of each heterogeneous graph and the common topology of multiple graphs are neglected. RESULTS We propose a drug-side effect association prediction model, GCRS, to encode and integrate specific topologies, common topologies and pairwise attributes of drugs and side effects. First, multiple drug-side effect heterogeneous graphs are constructed using various kinds of similarities and associations related to drugs and side effects. As each heterogeneous graph has its specific topology, we establish separate module based on graph convolutional autoencoder (GCA) to learn the particular topology representation of each drug node and each side effect node, respectively. Since multiple graphs reflect the complex relationships among the drug and side effect nodes and contain common topologies, we construct a module based on GCA with sharing parameters to learn the common topology representations of each node. Afterwards, we design an attention mechanism to obtain more informative topology representations at the representation level. Finally, multi-layer convolutional neural networks with attribute-level attention are constructed to deeply integrate the similarity and association attributes of a pair of drug-side effect nodes. Comprehensive experiments show that GCRS's prediction performance is superior to other comparing state-of-the-art methods for predicting drug-side effect associations. The recall rates in top-ranked candidates and case studies on five drugs further demonstrate GCRS's ability in discovering potential drug-related side effects. CONTACT zhang@hlju.edu.cn.
Collapse
Affiliation(s)
- Ping Xuan
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China.,School of Computer Science, Shaanxi Normal University, Xi'an 710062, China
| | - Meng Wang
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
| | - Yong Liu
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
| | - Dong Wang
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
| | - Tiangang Zhang
- School of Mathematical Science, Heilongjiang University, Harbin 150080, China
| | - Toshiya Nakaguchi
- Center for Frontier Medical Engineering, Chiba University, Chiba 2638522, Japan
| |
Collapse
|
35
|
Yu L, Qiu W, Lin W, Cheng X, Xiao X, Dai J. HGDTI: predicting drug-target interaction by using information aggregation based on heterogeneous graph neural network. BMC Bioinformatics 2022; 23:126. [PMID: 35413800 PMCID: PMC9004085 DOI: 10.1186/s12859-022-04655-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 03/28/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In research on new drug discovery, the traditional wet experiment has a long period. Predicting drug-target interaction (DTI) in silico can greatly narrow the scope of search of candidate medications. Excellent algorithm model may be more effective in revealing the potential connection between drug and target in the bioinformatics network composed of drugs, proteins and other related data. RESULTS In this work, we have developed a heterogeneous graph neural network model, named as HGDTI, which includes a learning phase of network node embedding and a training phase of DTI classification. This method first obtains the molecular fingerprint information of drugs and the pseudo amino acid composition information of proteins, then extracts the initial features of nodes through Bi-LSTM, and uses the attention mechanism to aggregate heterogeneous neighbors. In several comparative experiments, the overall performance of HGDTI significantly outperforms other state-of-the-art DTI prediction models, and the negative sampling technology is employed to further optimize the prediction power of model. In addition, we have proved the robustness of HGDTI through heterogeneous network content reduction tests, and proved the rationality of HGDTI through other comparative experiments. These results indicate that HGDTI can utilize heterogeneous information to capture the embedding of drugs and targets, and provide assistance for drug development. CONCLUSIONS The HGDTI based on heterogeneous graph neural network model, can utilize heterogeneous information to capture the embedding of drugs and targets, and provide assistance for drug development. For the convenience of related researchers, a user-friendly web-server has been established at http://bioinfo.jcu.edu.cn/hgdti .
Collapse
Affiliation(s)
- Liyi Yu
- School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Wangren Qiu
- School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Weizhong Lin
- School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Xiang Cheng
- School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen, China
| | - Xuan Xiao
- School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen, China.
| | - Jiexia Dai
- School of Foreign Languages, Jingdezhen University, Jingdezhen, China
| |
Collapse
|
36
|
Shang Y, Ye X, Futamura Y, Yu L, Sakurai T. Multiview network embedding for drug-target Interactions prediction by consistent and complementary information preserving. Brief Bioinform 2022; 23:6544850. [PMID: 35262678 DOI: 10.1093/bib/bbac059] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 02/01/2022] [Accepted: 02/06/2022] [Indexed: 01/02/2023] Open
Abstract
Accurate prediction of drug-target interactions (DTIs) can reduce the cost and time of drug repositioning and drug discovery. Many current methods integrate information from multiple data sources of drug and target to improve DTIs prediction accuracy. However, these methods do not consider the complex relationship between different data sources. In this study, we propose a novel computational framework, called MccDTI, to predict the potential DTIs by multiview network embedding, which can integrate the heterogenous information of drug and target. MccDTI learns high-quality low-dimensional representations of drug and target by preserving the consistent and complementary information between multiview networks. Then MccDTI adopts matrix completion scheme for DTIs prediction based on drug and target representations. Experimental results on two datasets show that the prediction accuracy of MccDTI outperforms four state-of-the-art methods for DTIs prediction. Moreover, literature verification for DTIs prediction shows that MccDTI can predict the reliable potential DTIs. These results indicate that MccDTI can provide a powerful tool to predict new DTIs and accelerate drug discovery. The code and data are available at: https://github.com/ShangCS/MccDTI.
Collapse
Affiliation(s)
- Yifan Shang
- Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan
| | - Xiucai Ye
- Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan
| | - Yasunori Futamura
- Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan
| | - Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, 710071, China
| | - Tetsuya Sakurai
- Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan
| |
Collapse
|
37
|
Chen H, Zhang Z, Zhang J. In silico drug repositioning based on integrated drug targets and canonical correlation analysis. BMC Med Genomics 2022; 15:48. [PMID: 35249529 PMCID: PMC8898485 DOI: 10.1186/s12920-022-01203-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 03/02/2022] [Indexed: 01/21/2023] Open
Abstract
Background Besides binding to proteins, the most recent advances in pharmacogenomics indicate drugs can regulate the expression of non-coding RNAs (ncRNAs). The polypharmacological feature in drugs enables us to find new uses for existing drugs (namely drug repositioning). However, current computational methods for drug repositioning mainly consider proteins as drug targets. Meanwhile, these methods identify only statistical relationships between drugs and diseases. They provide little information about how drug-disease associations are formed at the molecular target level. Methods Herein, we first comprehensively collect proteins and two categories of ncRNAs as drug targets from public databases to construct drug–target interactions. Experimentally confirmed drug-disease associations are downloaded from an established database. A canonical correlation analysis (CCA) based method is then applied to the two datasets to extract correlated sets of targets and diseases. The correlated sets are regarded as canonical components, and they are used to investigate drug’s mechanism of actions. We finally develop a strategy to predict novel drug-disease associations for drug repositioning by combining all the extracted correlated sets. Results We receive 400 canonical components which correlate targets with diseases in our study. We select 4 components for analysis and find some top-ranking diseases in an extracted set might be treated by drugs interfacing with the top-ranking targets in the same set. Experimental results from 10-fold cross-validations show integrating different categories of target information results in better prediction performance than only using proteins or ncRNAs as targets. When compared with 3 state-of-the-art approaches, our method receives the highest AUC value 0.8576. We use our method to predict new indications for 789 drugs and confirm 24 predictions in the top 1 predictions. Conclusions To the best of our knowledge, this is the first computational effort which combines both proteins and ncRNAs as drug targets for drug repositioning. Our study provides a biologically relevant interpretation regarding the forming of drug-disease associations, which is useful for guiding future biomedical tests. Supplementary Information The online version contains supplementary material available at 10.1186/s12920-022-01203-1.
Collapse
|
38
|
Present and future challenges in therapeutic designing using computational approaches. COMPUTATIONAL APPROACHES FOR NOVEL THERAPEUTIC AND DIAGNOSTIC DESIGNING TO MITIGATE SARS-COV-2 INFECTION 2022. [PMCID: PMC9300749 DOI: 10.1016/b978-0-323-91172-6.00020-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Currently, various computational methods are being used for the purpose of therapeutic design. The advent of the Coronavirus disease-2019 (COVID-19) pandemic has created a lot of problems due to which the development of effective treatment options is urgently needed. Computational intelligence is used in the control, prevention, prediction, diagnosis, and treatment of the disease. Several important drug targets have been identified in severe acute respiratory syndrome-Coronavirus-2 using in silico methods. Computer-aided drug design includes a variety of theoretical and computational approaches that are part of modern drug discovery. Advances in machine learning methods and their applications speed up the drug discovery process. Exploration of nucleic acid-based therapeutics is playing an important role in healthcare also. But a lot of challenges have also been seen that complicate the therapeutic design. Therefore, investigation of challenges associated with therapeutic design is important, and the present chapter is aimed to cover various therapeutic design approaches and challenges associated with them. Moreover, the role of computational strategies in the exploration of potential therapeutics against COVID-19 has been investigated.
Collapse
|
39
|
Ye Q, Hsieh CY, Yang Z, Kang Y, Chen J, Cao D, He S, Hou T. A unified drug-target interaction prediction framework based on knowledge graph and recommendation system. Nat Commun 2021; 12:6775. [PMID: 34811351 PMCID: PMC8635420 DOI: 10.1038/s41467-021-27137-3] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 11/05/2021] [Indexed: 02/06/2023] Open
Abstract
Prediction of drug-target interactions (DTI) plays a vital role in drug development in various areas, such as virtual screening, drug repurposing and identification of potential drug side effects. Despite extensive efforts have been invested in perfecting DTI prediction, existing methods still suffer from the high sparsity of DTI datasets and the cold start problem. Here, we develop KGE_NFM, a unified framework for DTI prediction by combining knowledge graph (KG) and recommendation system. This framework firstly learns a low-dimensional representation for various entities in the KG, and then integrates the multimodal information via neural factorization machine (NFM). KGE_NFM is evaluated under three realistic scenarios, and achieves accurate and robust predictions on four benchmark datasets, especially in the scenario of the cold start for proteins. Our results indicate that KGE_NFM provides valuable insight to integrate KG and recommendation system-based techniques into a unified framework for novel DTI discovery.
Collapse
Affiliation(s)
- Qing Ye
- grid.13402.340000 0004 1759 700XInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang China ,grid.13402.340000 0004 1759 700XCollege of Control Science and Engineering, Zhejiang University, Hangzhou, 310027 Zhejiang China ,grid.13402.340000 0004 1759 700XState Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang 310058 China
| | - Chang-Yu Hsieh
- Tencent Quantum Laboratory, Shenzhen, 518057 Guangdong China
| | - Ziyi Yang
- Tencent Quantum Laboratory, Shenzhen, 518057 Guangdong China
| | - Yu Kang
- grid.13402.340000 0004 1759 700XInnovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang China
| | - Jiming Chen
- grid.13402.340000 0004 1759 700XCollege of Control Science and Engineering, Zhejiang University, Hangzhou, 310027 Zhejiang China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, Hunan, China.
| | - Shibo He
- College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, Zhejiang, China.
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China. .,State Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
| |
Collapse
|
40
|
Li Y, Qiao G, Wang K, Wang G. Drug-target interaction predication via multi-channel graph neural networks. Brief Bioinform 2021; 23:6363570. [PMID: 34661237 DOI: 10.1093/bib/bbab346] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 07/21/2021] [Accepted: 08/12/2021] [Indexed: 12/15/2022] Open
Abstract
Drug-target interaction (DTI) is an important step in drug discovery. Although there are many methods for predicting drug targets, these methods have limitations in using discrete or manual feature representations. In recent years, deep learning methods have been used to predict DTIs to improve these defects. However, most of the existing deep learning methods lack the fusion of topological structure and semantic information in DPP representation learning process. Besides, when learning the DPP node representation in the DPP network, the different influences between neighboring nodes are ignored. In this paper, a new model DTI-MGNN based on multi-channel graph convolutional network and graph attention is proposed for DTI prediction. We use two independent graph attention networks to learn the different interactions between nodes for the topology graph and feature graph with different strengths. At the same time, we use a graph convolutional network with shared weight matrices to learn the common information of the two graphs. The DTI-MGNN model combines topological structure and semantic features to improve the representation learning ability of DPPs, and obtain the state-of-the-art results on public datasets. Specifically, DTI-MGNN has achieved a high accuracy in identifying DTIs (the area under the receiver operating characteristic curve is 0.9665).
Collapse
Affiliation(s)
- Yang Li
- College of Information and Computer Engineering, Northeast Forestry University, 150004, Harbin, China
| | - Guanyu Qiao
- College of Information and Computer Engineering, Northeast Forestry University, 150004, Harbin, China
| | - Keqi Wang
- College of Information and Computer Engineering, Northeast Forestry University, 150004, Harbin, China
| | - Guohua Wang
- College of Information and Computer Engineering, Northeast Forestry University, 150004, Harbin, China
| |
Collapse
|
41
|
Prediction of Drug-Target Interactions by Combining Dual-Tree Complex Wavelet Transform with Ensemble Learning Method. Molecules 2021; 26:molecules26175359. [PMID: 34500792 PMCID: PMC8433937 DOI: 10.3390/molecules26175359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 08/27/2021] [Accepted: 08/30/2021] [Indexed: 11/17/2022] Open
Abstract
Identification of drug–target interactions (DTIs) is vital for drug discovery. However, traditional biological approaches have some unavoidable shortcomings, such as being time consuming and expensive. Therefore, there is an urgent need to develop novel and effective computational methods to predict DTIs in order to shorten the development cycles of new drugs. In this study, we present a novel computational approach to identify DTIs, which uses protein sequence information and the dual-tree complex wavelet transform (DTCWT). More specifically, a position-specific scoring matrix (PSSM) was performed on the target protein sequence to obtain its evolutionary information. Then, DTCWT was used to extract representative features from the PSSM, which were then combined with the drug fingerprint features to form the feature descriptors. Finally, these descriptors were sent to the Rotation Forest (RoF) model for classification. A 5-fold cross validation (CV) was adopted on four datasets (Enzyme, Ion Channel, GPCRs (G-protein-coupled receptors), and NRs (Nuclear Receptors)) to validate the proposed model; our method yielded high average accuracies of 89.21%, 85.49%, 81.02%, and 74.44%, respectively. To further verify the performance of our model, we compared the RoF classifier with two state-of-the-art algorithms: the support vector machine (SVM) and the k-nearest neighbor (KNN) classifier. We also compared it with some other published methods. Moreover, the prediction results for the independent dataset further indicated that our method is effective for predicting potential DTIs. Thus, we believe that our method is suitable for facilitating drug discovery and development.
Collapse
|
42
|
Zhao H, Zheng K, Li Y, Wang J. A novel graph attention model for predicting frequencies of drug-side effects from multi-view data. Brief Bioinform 2021; 22:6312959. [PMID: 34213525 DOI: 10.1093/bib/bbab239] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 05/30/2021] [Accepted: 06/04/2021] [Indexed: 12/15/2022] Open
Abstract
Identifying the frequencies of the drug-side effects is a very important issue in pharmacological studies and drug risk-benefit. However, designing clinical trials to determine the frequencies is usually time consuming and expensive, and most existing methods can only predict the drug-side effect existence or associations, not their frequencies. Inspired by the recent progress of graph neural networks in the recommended system, we develop a novel prediction model for drug-side effect frequencies, using a graph attention network to integrate three different types of features, including the similarity information, known drug-side effect frequency information and word embeddings. In comparison, the few available studies focusing on frequency prediction use only the known drug-side effect frequency scores. One novel approach used in this work first decomposes the feature types in drug-side effect graph to extract different view representation vectors based on three different type features, and then recombines these latent view vectors automatically to obtain unified embeddings for prediction. The proposed method demonstrates high effectiveness in 10-fold cross-validation. The computational results show that the proposed method achieves the best performance in the benchmark dataset, outperforming the state-of-the-art matrix decomposition model. In addition, some ablation experiments and visual analyses are also supplied to illustrate the usefulness of our method for the prediction of the drug-side effect frequencies. The codes of MGPred are available at https://github.com/zhc940702/MGPred and https://zenodo.org/record/4449613.
Collapse
Affiliation(s)
- Haochen Zhao
- School of Computer Science and Engineering, Central South University, Changsha 410083, China.,Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China
| | - Kai Zheng
- School of Computer Science and Engineering, Central South University, Changsha 410083, China.,Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China
| | - Yaohang Li
- Department of Computer Science, Old Dominion University, Norfolk, VA 23529-0001, United States
| | - Jianxin Wang
- School of Computer Science and Engineering, Central South University, Changsha 410083, China.,Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China
| |
Collapse
|
43
|
Kim QH, Ko JH, Kim S, Park N, Jhe W. Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction. Bioinformatics 2021; 37:3428-3435. [PMID: 33978713 PMCID: PMC8545317 DOI: 10.1093/bioinformatics/btab346] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 04/26/2021] [Accepted: 05/05/2021] [Indexed: 11/25/2022] Open
Abstract
Motivation Characterizing drug–protein interactions (DPIs) is crucial to the high-throughput screening for drug discovery. The deep learning-based approaches have attracted attention because they can predict DPIs without human trial and error. However, because data labeling requires significant resources, the available protein data size is relatively small, which consequently decreases model performance. Here, we propose two methods to construct a deep learning framework that exhibits superior performance with a small labeled dataset. Results At first, we use transfer learning in encoding protein sequences with a pretrained model, which trains general sequence representations in an unsupervised manner. Second, we use a Bayesian neural network to make a robust model by estimating the data uncertainty. Our resulting model performs better than the previous baselines at predicting interactions between molecules and proteins. We also show that the quantified uncertainty from the Bayesian inference is related to confidence and can be used for screening DPI data points. Availability and implementation The code is available at https://github.com/QHwan/PretrainDPI. Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- QHwan Kim
- Department of Physics and Astronomy, Institute of Applied Physics, Seoul National University, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Joon-Hyuk Ko
- Department of Physics and Astronomy, Institute of Applied Physics, Seoul National University, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Sunghoon Kim
- Department of Physics and Astronomy, Institute of Applied Physics, Seoul National University, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Nojun Park
- Department of Physics and Astronomy, Institute of Applied Physics, Seoul National University, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Wonho Jhe
- Department of Physics and Astronomy, Institute of Applied Physics, Seoul National University, Gwanak-gu, Seoul 08826, Republic of Korea
| |
Collapse
|
44
|
Abstract
INTRODUCTION Knowledge graphs have proven to be promising systems of information storage and retrieval. Due to the recent explosion of heterogeneous multimodal data sources generated in the biomedical domain, and an industry shift toward a systems biology approach, knowledge graphs have emerged as attractive methods of data storage and hypothesis generation. AREAS COVERED In this review, the author summarizes the applications of knowledge graphs in drug discovery. They evaluate their utility; differentiating between academic exercises in graph theory, and useful tools to derive novel insights, highlighting target identification and drug repurposing as two areas showing particular promise. They provide a case study on COVID-19, summarizing the research that used knowledge graphs to identify repurposable drug candidates. They describe the dangers of degree and literature bias, and discuss mitigation strategies. EXPERT OPINION Whilst knowledge graphs and graph-based machine learning have certainly shown promise, they remain relatively immature technologies. Many popular link prediction algorithms fail to address strong biases in biomedical data, and only highlight biological associations, failing to model causal relationships in complex dynamic biological systems. These problems need to be addressed before knowledge graphs reach their true potential in drug discovery.
Collapse
Affiliation(s)
- Finlay MacLean
- Target Identification., BenevolentAI, United Kingdom of Great Britain and Northern Ireland
| |
Collapse
|
45
|
Wu G, Yang M, Li Y, Wang J. De Novo Prediction of Drug-Target Interactions Using Laplacian Regularized Schatten p-Norm Minimization. J Comput Biol 2021; 28:660-673. [PMID: 33481664 DOI: 10.1089/cmb.2020.0538] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
In pharmaceutical sciences, a crucial step of the drug discovery is the identification of drug-target interactions (DTIs). However, only a small portion of the DTIs have been experimentally validated. Moreover, it is an extremely laborious, expensive, and time-consuming procedure to capture new interactions between drugs and targets through traditional biochemical experiments. Therefore, designing computational methods for predicting potential interactions to guide the experimental verification is of practical significance, especially for de novo situation. In this article, we propose a new algorithm, namely Laplacian regularized Schatten p-norm minimization (LRSpNM), to predict potential target proteins for novel drugs and potential drugs for new targets where there are no known interactions. Specifically, we first take advantage of the drug and target similarity information to dynamically prefill the partial unknown interactions. Then based on the assumption that the interaction matrix is low-rank, we use Schatten p-norm minimization model combined with Laplacian regularization terms to improve prediction performance in the new drug/target cases. Finally, we numerically solve the LRSpNM model by an efficient alternating direction method of multipliers algorithm. We evaluate LRSpNM on five data sets and an extensive set of numerical experiments show that LRSpNM achieves better and more robust performance than five state-of-the-art DTIs prediction algorithms. In addition, we conduct two case studies for new drug and new target prediction, which illustrates that LRSpNM can successfully predict most of the experimental validated DTIs.
Collapse
Affiliation(s)
- Gaoyan Wu
- The Hunan Provincial Key Lab of Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China
| | - Mengyun Yang
- School of Science, Shaoyang University, Shaoyang, China
| | - Yaohang Li
- Department of Computer Science, Old Dominion University, Norfolk, Virginia, USA
| | - Jianxin Wang
- The Hunan Provincial Key Lab of Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China
| |
Collapse
|
46
|
Gong L, Xu Y, Liu G, Zheng M, Zhang X, Hang Y, Kang J. Profiling
Drug‐Protein
Interactions by Micro Column Affinity Purification Combined with Label Free Quantification Proteomics
†. CHINESE J CHEM 2020. [DOI: 10.1002/cjoc.202000353] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Li Gong
- State Key Laboratory of Bioorganic and Natural Products Chemistry, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences 345 Lingling Road Shanghai 200032 China
| | - Yao Xu
- State Key Laboratory of Bioorganic and Natural Products Chemistry, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences 345 Lingling Road Shanghai 200032 China
| | - Guizhen Liu
- State Key Laboratory of Bioorganic and Natural Products Chemistry, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences 345 Lingling Road Shanghai 200032 China
- School of Physical Science and Technology, ShanghaiTech University Haike Road 100 Shanghai 200120 China
| | - Mengmeng Zheng
- State Key Laboratory of Bioorganic and Natural Products Chemistry, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences 345 Lingling Road Shanghai 200032 China
| | - Xuepei Zhang
- State Key Laboratory of Bioorganic and Natural Products Chemistry, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences 345 Lingling Road Shanghai 200032 China
| | - Ying Hang
- School of Life Science and Technology, ShanghaiTech University Haike Road 100 Shanghai 200120 China
| | - Jingwu Kang
- State Key Laboratory of Bioorganic and Natural Products Chemistry, Center for Excellence in Molecular Synthesis, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences 345 Lingling Road Shanghai 200032 China
- School of Physical Science and Technology, ShanghaiTech University Haike Road 100 Shanghai 200120 China
| |
Collapse
|
47
|
NDDSA: A network- and domain-based method for predicting drug-side effect associations. Inf Process Manag 2020. [DOI: 10.1016/j.ipm.2020.102357] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|
48
|
Esaki T, Horinouchi T, Natsume-Kitatani Y, Nojima Y, Sakane I, Matsui H. Estimation of relationships between chemical substructures and antibiotic resistance-related gene expression in bacteria: Adapting a canonical correlation analysis for small sample data of gathered features using consensus clustering. CHEM-BIO INFORMATICS JOURNAL 2020. [DOI: 10.1273/cbij.20.58] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Tsuyoshi Esaki
- The Center for Data Science Education and Research, Shiga University, 1-1-1 Banba, Hikone, Shiga 522-8522, Japan
| | - Takaaki Horinouchi
- Center for Biosystems Dynamics Research, RIKEN, 6-2-3 Furuedai, Suita, Osaka 565-0874, Japan
| | - Yayoi Natsume-Kitatani
- The Center for Data Science Education and Research, Shiga University, 1-1-1 Banba, Hikone, Shiga 522-8522, Japan
- Center of Drug Design Research, National Institutes of Biomedical Innovation, Health and Nutrition, 7-8-6 Saito
| | - Yosui Nojima
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation,
| | - Iwao Sakane
- Central Research Institute, ITO EN Ltd., 21 Megami, Makinohara, Shizuoka 421-0516, Japan
| | - Hidetoshi Matsui
- The Center for Data Science Education and Research, Shiga University, 1-1-1 Banba, Hikone, Shiga 522-8522, Japan
- Faculty of Data Science, Shiga University, 1-1-1 Banba, Hikone, Shiga 522-8522, Japan
| |
Collapse
|
49
|
Deng S, Sun Y, Zhao T, Hu Y, Zang T. A Review of Drug Side Effect Identification Methods. Curr Pharm Des 2020; 26:3096-3104. [PMID: 32532187 DOI: 10.2174/1381612826666200612163819] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 05/18/2020] [Indexed: 11/22/2022]
Abstract
Drug side effects have become an important indicator for evaluating the safety of drugs. There are two main factors in the frequent occurrence of drug safety problems; on the one hand, the clinical understanding of drug side effects is insufficient, leading to frequent adverse drug reactions, while on the other hand, due to the long-term period and complexity of clinical trials, side effects of approved drugs on the market cannot be reported in a timely manner. Therefore, many researchers have focused on developing methods to identify drug side effects. In this review, we summarize the methods of identifying drug side effects and common databases in this field. We classified methods of identifying side effects into four categories: biological experimental, machine learning, text mining and network methods. We point out the key points of each kind of method. In addition, we also explain the advantages and disadvantages of each method. Finally, we propose future research directions.
Collapse
Affiliation(s)
- Shuai Deng
- College of Science, Beijing Forestry University, Beijing, China
| | - Yige Sun
- Microbiology Department, Harbin Medical University, Harbin, 150081, China
| | - Tianyi Zhao
- School of Life Science and Technology, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Yang Hu
- School of Life Science and Technology, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Tianyi Zang
- School of Life Science and Technology, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| |
Collapse
|
50
|
A Novel Triple Matrix Factorization Method for Detecting Drug-Side Effect Association Based on Kernel Target Alignment. BIOMED RESEARCH INTERNATIONAL 2020; 2020:4675395. [PMID: 32596314 PMCID: PMC7275954 DOI: 10.1155/2020/4675395] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Accepted: 04/08/2020] [Indexed: 01/01/2023]
Abstract
All drugs usually have side effects, which endanger the health of patients. To identify potential side effects of drugs, biological and pharmacological experiments are done but are expensive and time-consuming. So, computation-based methods have been developed to accurately and quickly predict side effects. To predict potential associations between drugs and side effects, we propose a novel method called the Triple Matrix Factorization- (TMF-) based model. TMF is built by the biprojection matrix and latent feature of kernels, which is based on Low Rank Approximation (LRA). LRA could construct a lower rank matrix to approximate the original matrix, which not only retains the characteristics of the original matrix but also reduces the storage space and computational complexity of the data. To fuse multivariate information, multiple kernel matrices are constructed and integrated via Kernel Target Alignment-based Multiple Kernel Learning (KTA-MKL) in drug and side effect space, respectively. Compared with other methods, our model achieves better performance on three benchmark datasets. The values of the Area Under the Precision-Recall curve (AUPR) are 0.677, 0.685, and 0.680 on three datasets, respectively.
Collapse
|