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Zhang WY, Zheng XL, Coghi PS, Chen JH, Dong BJ, Fan XX. Revolutionizing adjuvant development: harnessing AI for next-generation cancer vaccines. Front Immunol 2024; 15:1438030. [PMID: 39206192 PMCID: PMC11349682 DOI: 10.3389/fimmu.2024.1438030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Accepted: 07/23/2024] [Indexed: 09/04/2024] Open
Abstract
With the COVID-19 pandemic, the importance of vaccines has been widely recognized and has led to increased research and development efforts. Vaccines also play a crucial role in cancer treatment by activating the immune system to target and destroy cancer cells. However, enhancing the efficacy of cancer vaccines remains a challenge. Adjuvants, which enhance the immune response to antigens and improve vaccine effectiveness, have faced limitations in recent years, resulting in few novel adjuvants being identified. The advancement of artificial intelligence (AI) technology in drug development has provided a foundation for adjuvant screening and application, leading to a diversification of adjuvants. This article reviews the significant role of tumor vaccines in basic research and clinical treatment and explores the use of AI technology to screen novel adjuvants from databases. The findings of this review offer valuable insights for the development of new adjuvants for next-generation vaccines.
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Affiliation(s)
- Wan-Ying Zhang
- Dr. Neher’s Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macao, Macao SAR, China
| | - Xiao-Li Zheng
- Dr. Neher’s Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macao, Macao SAR, China
| | - Paolo Saul Coghi
- Dr. Neher’s Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macao, Macao SAR, China
| | - Jun-Hui Chen
- Intervention and Cell Therapy Center, Peking University Shenzhen Hospital, Shenzhen, China
| | - Bing-Jun Dong
- Gynecology Department, Zhuhai Hospital of Integrated Traditional Chinese and Western Medicine, Zhuhai, China
| | - Xing-Xing Fan
- Dr. Neher’s Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macao, Macao SAR, China
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Crouzet A, Lopez N, Riss Yaw B, Lepelletier Y, Demange L. The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang? Molecules 2024; 29:2716. [PMID: 38930784 PMCID: PMC11206022 DOI: 10.3390/molecules29122716] [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: 03/29/2024] [Revised: 05/30/2024] [Accepted: 05/31/2024] [Indexed: 06/28/2024] Open
Abstract
The journey of drug discovery (DD) has evolved from ancient practices to modern technology-driven approaches, with Artificial Intelligence (AI) emerging as a pivotal force in streamlining and accelerating the process. Despite the vital importance of DD, it faces challenges such as high costs and lengthy timelines. This review examines the historical progression and current market of DD alongside the development and integration of AI technologies. We analyse the challenges encountered in applying AI to DD, focusing on drug design and protein-protein interactions. The discussion is enriched by presenting models that put forward the application of AI in DD. Three case studies are highlighted to demonstrate the successful application of AI in DD, including the discovery of a novel class of antibiotics and a small-molecule inhibitor that has progressed to phase II clinical trials. These cases underscore the potential of AI to identify new drug candidates and optimise the development process. The convergence of DD and AI embodies a transformative shift in the field, offering a path to overcome traditional obstacles. By leveraging AI, the future of DD promises enhanced efficiency and novel breakthroughs, heralding a new era of medical innovation even though there is still a long way to go.
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Affiliation(s)
- Aurore Crouzet
- UMR 8038 CNRS CiTCoM, Team PNAS, Faculté de Pharmacie, Université Paris Cité, 4 Avenue de l’Observatoire, 75006 Paris, France
- W-MedPhys, 128 Rue la Boétie, 75008 Paris, France
| | - Nicolas Lopez
- W-MedPhys, 128 Rue la Boétie, 75008 Paris, France
- ENOES, 62 Rue de Miromesnil, 75008 Paris, France
- Unité Mixte de Recherche «Institut de Physique Théorique (IPhT)» CEA-CNRS, UMR 3681, Bat 774, Route de l’Orme des Merisiers, 91191 St Aubin-Gif-sur-Yvette, France
| | - Benjamin Riss Yaw
- UMR 8038 CNRS CiTCoM, Team PNAS, Faculté de Pharmacie, Université Paris Cité, 4 Avenue de l’Observatoire, 75006 Paris, France
| | - Yves Lepelletier
- W-MedPhys, 128 Rue la Boétie, 75008 Paris, France
- Université Paris Cité, Imagine Institute, 24 Boulevard Montparnasse, 75015 Paris, France
- INSERM UMR 1163, Laboratory of Cellular and Molecular Basis of Normal Hematopoiesis and Hematological Disorders: Therapeutical Implications, 24 Boulevard Montparnasse, 75015 Paris, France
| | - Luc Demange
- UMR 8038 CNRS CiTCoM, Team PNAS, Faculté de Pharmacie, Université Paris Cité, 4 Avenue de l’Observatoire, 75006 Paris, France
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Kairys V, Baranauskiene L, Kazlauskiene M, Zubrienė A, Petrauskas V, Matulis D, Kazlauskas E. Recent advances in computational and experimental protein-ligand affinity determination techniques. Expert Opin Drug Discov 2024; 19:649-670. [PMID: 38715415 DOI: 10.1080/17460441.2024.2349169] [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: 03/18/2024] [Accepted: 04/25/2024] [Indexed: 05/22/2024]
Abstract
INTRODUCTION Modern drug discovery revolves around designing ligands that target the chosen biomolecule, typically proteins. For this, the evaluation of affinities of putative ligands is crucial. This has given rise to a multitude of dedicated computational and experimental methods that are constantly being developed and improved. AREAS COVERED In this review, the authors reassess both the industry mainstays and the newest trends among the methods for protein - small-molecule affinity determination. They discuss both computational affinity predictions and experimental techniques, describing their basic principles, main limitations, and advantages. Together, this serves as initial guide to the currently most popular and cutting-edge ligand-binding assays employed in rational drug design. EXPERT OPINION The affinity determination methods continue to develop toward miniaturization, high-throughput, and in-cell application. Moreover, the availability of data analysis tools has been constantly increasing. Nevertheless, cross-verification of data using at least two different techniques and careful result interpretation remain of utmost importance.
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Affiliation(s)
- Visvaldas Kairys
- Department of Bioinformatics, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Lina Baranauskiene
- Department of Biothermodynamics and Drug Design, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | | | - Asta Zubrienė
- Department of Biothermodynamics and Drug Design, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Vytautas Petrauskas
- Department of Biothermodynamics and Drug Design, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Daumantas Matulis
- Department of Biothermodynamics and Drug Design, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Egidijus Kazlauskas
- Department of Biothermodynamics and Drug Design, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
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Zeng X, Li SJ, Lv SQ, Wen ML, Li Y. A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning. Front Pharmacol 2024; 15:1375522. [PMID: 38628639 PMCID: PMC11019008 DOI: 10.3389/fphar.2024.1375522] [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: 01/24/2024] [Accepted: 03/21/2024] [Indexed: 04/19/2024] Open
Abstract
Accurate calculation of drug-target affinity (DTA) is crucial for various applications in the pharmaceutical industry, including drug screening, design, and repurposing. However, traditional machine learning methods for calculating DTA often lack accuracy, posing a significant challenge in accurately predicting DTA. Fortunately, deep learning has emerged as a promising approach in computational biology, leading to the development of various deep learning-based methods for DTA prediction. To support researchers in developing novel and highly precision methods, we have provided a comprehensive review of recent advances in predicting DTA using deep learning. We firstly conducted a statistical analysis of commonly used public datasets, providing essential information and introducing the used fields of these datasets. We further explored the common representations of sequences and structures of drugs and targets. These analyses served as the foundation for constructing DTA prediction methods based on deep learning. Next, we focused on explaining how deep learning models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer, and Graph Neural Networks (GNNs), were effectively employed in specific DTA prediction methods. We highlighted the unique advantages and applications of these models in the context of DTA prediction. Finally, we conducted a performance analysis of multiple state-of-the-art methods for predicting DTA based on deep learning. The comprehensive review aimed to help researchers understand the shortcomings and advantages of existing methods, and further develop high-precision DTA prediction tool to promote the development of drug discovery.
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Affiliation(s)
- Xin Zeng
- College of Mathematics and Computer Science, Dali University, Dali, China
| | - Shu-Juan Li
- Yunnan Institute of Endemic Diseases Control and Prevention, Dali, China
| | - Shuang-Qing Lv
- Institute of Surveying and Information Engineering West Yunnan University of Applied Science, Dali, China
| | - Meng-Liang Wen
- State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, Kunming, China
| | - Yi Li
- College of Mathematics and Computer Science, Dali University, Dali, China
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Liu Y, Xing L, Zhang L, Cai H, Guo M. GEFormerDTA: drug target affinity prediction based on transformer graph for early fusion. Sci Rep 2024; 14:7416. [PMID: 38548825 PMCID: PMC10979032 DOI: 10.1038/s41598-024-57879-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 03/22/2024] [Indexed: 04/01/2024] Open
Abstract
Predicting the interaction affinity between drugs and target proteins is crucial for rapid and accurate drug discovery and repositioning. Therefore, more accurate prediction of DTA has become a key area of research in the field of drug discovery and drug repositioning. However, traditional experimental methods have disadvantages such as long operation cycles, high manpower requirements, and high economic costs, making it difficult to predict specific interactions between drugs and target proteins quickly and accurately. Some methods mainly use the SMILES sequence of drugs and the primary structure of proteins as inputs, ignoring the graph information such as bond encoding, degree centrality encoding, spatial encoding of drug molecule graphs, and the structural information of proteins such as secondary structure and accessible surface area. Moreover, previous methods were based on protein sequences to learn feature representations, neglecting the completeness of information. To address the completeness of drug and protein structure information, we propose a Transformer graph-based early fusion research approach for drug-target affinity prediction (GEFormerDTA). Our method reduces prediction errors caused by insufficient feature learning. Experimental results on Davis and KIBA datasets showed a better prediction of drugtarget affinity than existing affinity prediction methods.
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Affiliation(s)
- Youzhi Liu
- Department of Computer Science and Technology, Shandong University of Technology, Zibo, 255000, China
| | - Linlin Xing
- Department of Computer Science and Technology, Shandong University of Technology, Zibo, 255000, China.
| | - Longbo Zhang
- Department of Computer Science and Technology, Shandong University of Technology, Zibo, 255000, China
| | - Hongzhen Cai
- Department of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, 255000, China
| | - Maozu Guo
- Department of Electrical and Information Engineering, Beijing University of Architecture, Beijing, 102616, China
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Zhou C, Li Z, Song J, Xiang W. TransVAE-DTA: Transformer and variational autoencoder network for drug-target binding affinity prediction. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:108003. [PMID: 38181572 DOI: 10.1016/j.cmpb.2023.108003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 12/29/2023] [Accepted: 12/30/2023] [Indexed: 01/07/2024]
Abstract
BACKGROUND AND OBJECTIVE Recent studies have emphasized the significance of computational in silico drug-target binding affinity (DTA) prediction in the field of drug discovery and drug repurposing. However, existing DTA prediction approaches suffer from two major deficiencies that impede their progress. Firstly, while most methods primarily focus on the feature representations of drug-target binding affinity pairs, they fail to consider the long-distance relationships of proteins. Furthermore, many deep learning-based DTA predictors simply model the interaction of drug-target pairs through concatenation, which hampers the ability to enhance prediction performance. METHODS To address these issues, this study proposes a novel framework named TransVAE-DTA, which combines the transformer and variational autoencoder (VAE). Inspired by the early success of VAEs, we aim to further investigate the feasibility of VAEs for drug structure encoding, while utilizing the transformer architecture for target feature representation. Additionally, an adaptive attention pooling (AAP) module is designed to fuse the drug and target encoded features. Notably, TransVAE-DTA is proven to maximize the lower bound of the joint likelihood of drug, target, and their DTAs. RESULTS Experimental results demonstrate the superiority of TransVAE-DTA in drug-target binding affinity prediction assignments on two public Davis and KIBA datasets. CONCLUSIONS In this research, the developed TransVAE-DTA opens a new avenue for engineering drug-target interactions.
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Affiliation(s)
- Changjian Zhou
- School of life sciences, Northeast Agricultural University, Harbin, PR China; Department of Data and Computing, Northeast Agricultural University, Harbin, PR China
| | - Zhongzheng Li
- Department of Data and Computing, Northeast Agricultural University, Harbin, PR China; School of Engineering, Northeast Agricultural University, Harbin, PR China
| | - Jia Song
- Department of Data and Computing, Northeast Agricultural University, Harbin, PR China; School of Plant Protection, Northeast Agricultural University, Harbin, PR China.
| | - Wensheng Xiang
- Department of Data and Computing, Northeast Agricultural University, Harbin, PR China; School of Plant Protection, Northeast Agricultural University, Harbin, PR China; State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, PR China.
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7
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Wu H, Liu J, Jiang T, Zou Q, Qi S, Cui Z, Tiwari P, Ding Y. AttentionMGT-DTA: A multi-modal drug-target affinity prediction using graph transformer and attention mechanism. Neural Netw 2024; 169:623-636. [PMID: 37976593 DOI: 10.1016/j.neunet.2023.11.018] [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: 06/26/2023] [Revised: 09/29/2023] [Accepted: 11/07/2023] [Indexed: 11/19/2023]
Abstract
The accurate prediction of drug-target affinity (DTA) is a crucial step in drug discovery and design. Traditional experiments are very expensive and time-consuming. Recently, deep learning methods have achieved notable performance improvements in DTA prediction. However, one challenge for deep learning-based models is appropriate and accurate representations of drugs and targets, especially the lack of effective exploration of target representations. Another challenge is how to comprehensively capture the interaction information between different instances, which is also important for predicting DTA. In this study, we propose AttentionMGT-DTA, a multi-modal attention-based model for DTA prediction. AttentionMGT-DTA represents drugs and targets by a molecular graph and binding pocket graph, respectively. Two attention mechanisms are adopted to integrate and interact information between different protein modalities and drug-target pairs. The experimental results showed that our proposed model outperformed state-of-the-art baselines on two benchmark datasets. In addition, AttentionMGT-DTA also had high interpretability by modeling the interaction strength between drug atoms and protein residues. Our code is available at https://github.com/JK-Liu7/AttentionMGT-DTA.
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Affiliation(s)
- Hongjie Wu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China.
| | - Junkai Liu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China; Yangtze Delta Region Institute(Quzhou), University of Electronic Science and Technology of China, Quzhou, 324003, China.
| | - Tengsheng Jiang
- Gusu School, Nanjing Medical University, Suzhou, 215009, China.
| | - Quan Zou
- Yangtze Delta Region Institute(Quzhou), University of Electronic Science and Technology of China, Quzhou, 324003, China.
| | - Shujie Qi
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China.
| | - Zhiming Cui
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China.
| | - Prayag Tiwari
- School of Information Technology, Halmstad University, Sweden.
| | - Yijie Ding
- Yangtze Delta Region Institute(Quzhou), University of Electronic Science and Technology of China, Quzhou, 324003, China.
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Wang M, Li W, Yu X, Luo Y, Han K, Wang C, Jin Q. AffinityVAE: A multi-objective model for protein-ligand affinity prediction and drug design. Comput Biol Chem 2023; 107:107971. [PMID: 37852036 DOI: 10.1016/j.compbiolchem.2023.107971] [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: 07/13/2023] [Revised: 09/23/2023] [Accepted: 10/08/2023] [Indexed: 10/20/2023]
Abstract
In the prediction of protein-ligand affinity, the traditional methods require a large amount of computing resources, and have certain limitations in predicting and simulating the structural changes. Although employing data-driven approaches can yield favorable outcomes in deep learning, it entails a lack of interpretability. Some methods may require additional structural information or domain knowledge to support the interpretation, which may limit their applicability. This paper proposes an affinity variational autoencoder (AffinityVAE) using interaction feature mapping and a variational autoencoder, which consists of a multi-objective model capable of end-to-end affinity prediction and drug discovery. In this study, the limitations of affinity prediction in terms of interpretability are tackled by proposing the concept of a protein-ligand interaction feature map. This increases the diversity and quantity of protein-ligand binding data by designing an adaptive autoencoder of target chemical properties to generate new ligands similar to known ligands and adding them to the original training set. AffinityVAE is then retrained using this extended training set to further validate the protein-ligand binding affinity prediction. Comparisons were conducted between the AffinityVAE and recent methods to demonstrate the high efficiency of the proposed model. The experimental results show that AffinityVAE has very high prediction performance, and it has the potential to enhance the diversity and the amount of protein-ligand binding data, which promotes the drug development.
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Affiliation(s)
- Mengying Wang
- School of Computer Engineering and Science, Shanghai University, Shanghai, China.
| | - Weimin Li
- School of Computer Engineering and Science, Shanghai University, Shanghai, China.
| | - Xiao Yu
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Yin Luo
- School of Life Sciences, East China Normal University, China
| | - Ke Han
- Medical and Health Center, Liaocheng People's Hospital, LiaoCheng, China.
| | - Can Wang
- School of Information and Communication Technology, Griffith University, Australia
| | - Qun Jin
- Networked Information System Laboratory, Waseda University, Tokyo, Japan
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9
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Wang L, Sun C, Xu X, Li J, Zhang W. A neighborhood-regularization method leveraging multiview data for predicting the frequency of drug-side effects. Bioinformatics 2023; 39:btad532. [PMID: 37647657 PMCID: PMC10491955 DOI: 10.1093/bioinformatics/btad532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 08/24/2023] [Accepted: 08/28/2023] [Indexed: 09/01/2023] Open
Abstract
MOTIVATION A critical issue in drug benefit-risk assessment is to determine the frequency of side effects, which is performed by randomized controlled trails. Computationally predicted frequencies of drug side effects can be used to effectively guide the randomized controlled trails. However, it is more challenging to predict drug side effect frequencies, and thus only a few studies cope with this problem. RESULTS In this work, we propose a neighborhood-regularization method (NRFSE) that leverages multiview data on drugs and side effects to predict the frequency of side effects. First, we adopt a class-weighted non-negative matrix factorization to decompose the drug-side effect frequency matrix, in which Gaussian likelihood is used to model unknown drug-side effect pairs. Second, we design a multiview neighborhood regularization to integrate three drug attributes and two side effect attributes, respectively, which makes most similar drugs and most similar side effects have similar latent signatures. The regularization can adaptively determine the weights of different attributes. We conduct extensive experiments on one benchmark dataset, and NRFSE improves the prediction performance compared with five state-of-the-art approaches. Independent test set of post-marketing side effects further validate the effectiveness of NRFSE. AVAILABILITY AND IMPLEMENTATION Source code and datasets are available at https://github.com/linwang1982/NRFSE or https://codeocean.com/capsule/4741497/tree/v1.
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Affiliation(s)
- Lin Wang
- College of Artificial Intelligence, Tianjin University of Science and Technology, No. 9, 13th Street, Tianjin Economic-Technological Development Area, Tianjin 300457, China
| | - Chenhao Sun
- College of Artificial Intelligence, Tianjin University of Science and Technology, No. 9, 13th Street, Tianjin Economic-Technological Development Area, Tianjin 300457, China
| | - Xianyu Xu
- College of Artificial Intelligence, Tianjin University of Science and Technology, No. 9, 13th Street, Tianjin Economic-Technological Development Area, Tianjin 300457, China
| | - Jia Li
- College of Artificial Intelligence, Tianjin University of Science and Technology, No. 9, 13th Street, Tianjin Economic-Technological Development Area, Tianjin 300457, China
| | - Wenjuan Zhang
- College of General Education, Tianjin Foreign Studies University, No. 117, Machang Road, Hexi District, Tianjin 300204, China
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Zhang Y, Hu Y, Han N, Yang A, Liu X, Cai H. A survey of drug-target interaction and affinity prediction methods via graph neural networks. Comput Biol Med 2023; 163:107136. [PMID: 37329615 DOI: 10.1016/j.compbiomed.2023.107136] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 05/29/2023] [Accepted: 06/04/2023] [Indexed: 06/19/2023]
Abstract
The tasks of drug-target interaction (DTI) and drug-target affinity (DTA) prediction play important roles in the field of drug discovery. However, biological experiment-based methods are time-consuming and expensive. Recently, computational-based approaches have accelerated the process of drug-target relationship prediction. Drug and target features are represented in structure-based, sequence-based, and graph-based ways. Although some achievements have been made regarding structure-based representations and sequence-based representations, the acquired feature information is not sufficiently rich. Molecular graph-based representations are some of the more popular approaches, and they have also generated a great deal of interest. In this article, we provide an overview of the DTI prediction and DTA prediction tasks based on graph neural networks (GNNs). We briefly discuss the molecular graphs of drugs, the primary sequences of target proteins, and the graph reSLBpresentations of target proteins. Meanwhile, we conducted experiments on various fundamental datasets to substantiate the plausibility of DTI and DTA utilizing graph neural networks.
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Affiliation(s)
- Yue Zhang
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, 510665, China.
| | - Yuqing Hu
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, 510665, China
| | - Na Han
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, 510665, China
| | - Aqing Yang
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, 510665, China
| | - Xiaoyong Liu
- School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, 510665, China
| | - Hongmin Cai
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China
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Li X, Yang Q, Luo G, Xu L, Dong W, Wang W, Dong S, Wang K, Xuan P, Gao X. SAGDTI: self-attention and graph neural network with multiple information representations for the prediction of drug-target interactions. BIOINFORMATICS ADVANCES 2023; 3:vbad116. [PMID: 38282612 PMCID: PMC10818136 DOI: 10.1093/bioadv/vbad116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/31/2023] [Accepted: 08/24/2023] [Indexed: 01/30/2024]
Abstract
Motivation Accurate identification of target proteins that interact with drugs is a vital step in silico, which can significantly foster the development of drug repurposing and drug discovery. In recent years, numerous deep learning-based methods have been introduced to treat drug-target interaction (DTI) prediction as a classification task. The output of this task is binary identification suggesting the absence or presence of interactions. However, existing studies often (i) neglect the unique molecular attributes when embedding drugs and proteins, and (ii) determine the interaction of drug-target pairs without considering biological interaction information. Results In this study, we propose an end-to-end attention-derived method based on the self-attention mechanism and graph neural network, termed SAGDTI. The aim of this method is to overcome the aforementioned drawbacks in the identification of DTI. SAGDTI is the first method to sufficiently consider the unique molecular attribute representations for both drugs and targets in the input form of the SMILES sequences and three-dimensional structure graphs. In addition, our method aggregates the feature attributes of biological information between drugs and targets through multi-scale topologies and diverse connections. Experimental results illustrate that SAGDTI outperforms existing prediction models, which benefit from the unique molecular attributes embedded by atom-level attention and biological interaction information representation aggregated by node-level attention. Moreover, a case study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shows that our model is a powerful tool for identifying DTIs in real life. Availability and implementation The data and codes underlying this article are available in Github at https://github.com/lixiaokun2020/SAGDTI.
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Affiliation(s)
- Xiaokun Li
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
- Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Harbin 150090, China
| | - Qiang Yang
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
- Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Harbin 150090, China
| | - Gongning Luo
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Long Xu
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
- Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Harbin 150090, China
| | - Weihe Dong
- Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Harbin 150090, China
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Wei Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Suyu Dong
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Kuanquan Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Ping Xuan
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
- Department of Computer Science, School of Engineering, Shantou University, Shantou 515063, China
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 KAUST, Thuwal 23955, Saudi Arabia
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Guo Y, Zhou D, Ruan X, Cao J. Variational gated autoencoder-based feature extraction model for inferring disease-miRNA associations based on multiview features. Neural Netw 2023; 165:491-505. [PMID: 37336034 DOI: 10.1016/j.neunet.2023.05.052] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 05/19/2023] [Accepted: 05/28/2023] [Indexed: 06/21/2023]
Abstract
MicroRNAs (miRNA) play critical roles in diverse biological processes of diseases. Inferring potential disease-miRNA associations enable us to better understand the development and diagnosis of complex human diseases via computational algorithms. The work presents a variational gated autoencoder-based feature extraction model to extract complex contextual features for inferring potential disease-miRNA associations. Specifically, our model fuses three different similarities of miRNAs into a comprehensive miRNA network and then combines two various similarities of diseases into a comprehensive disease network, respectively. Then, a novel graph autoencoder is designed to extract multilevel representations based on variational gate mechanisms from heterogeneous networks of miRNAs and diseases. Finally, a gate-based association predictor is devised to combine multiscale representations of miRNAs and diseases via a novel contrastive cross-entropy function, and then infer disease-miRNA associations. Experimental results indicate that our proposed model achieves remarkable association prediction performance, proving the efficacy of the variational gate mechanism and contrastive cross-entropy loss for inferring disease-miRNA associations.
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Affiliation(s)
- Yanbu Guo
- College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China.
| | - Dongming Zhou
- School of Information Science and Engineering, Yunnan University, Kunming 650500, China.
| | - Xiaoli Ruan
- State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China.
| | - Jinde Cao
- School of Mathematics, Southeast University, Nanjing 211189, China; Yonsei Frontier Lab, Yonsei University, Seoul 03722, South Korea.
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13
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Prediction of drug protein interactions based on variable scale characteristic pyramid convolution network. Methods 2023; 211:42-47. [PMID: 36804213 DOI: 10.1016/j.ymeth.2023.02.007] [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: 10/26/2022] [Revised: 12/31/2022] [Accepted: 02/13/2023] [Indexed: 02/17/2023] Open
Abstract
MOTIVATION In the process of drug screening, it is significant to improve the accuracy of drug-target binding affinity prediction. A multilayer convolutional neural network is one of the most popular existing methods for predicting affinity based on deep learning. It uses multiple convolution layers to extract features from the simplified molecular input system (SMILES) strings of the compounds and amino acid sequences of proteins and then performs affinity prediction analysis. However, the semantic information contained in low-level features can gradually be lost due to the increasing network depth, which affects the prediction performance. RESULT We propose a novel method called the Pyramid Network Convolution Drug-Target Binding Affinity (PCNN-DTA) method for drug-target binding affinity prediction. The PCNN-DTA method, which is based on a feature pyramid network (FPN), fuses the features extracted from each layer of a multilayer convolution network to retain more low-level feature information, thus improving the prediction accuracy. PCNN-DTA is compared with other typical algorithms on three benchmark datasets, namely, the KIBA, Davis, and Binding DB datasets. Experimental results show that the PCNN-DTA method is superior to existing regression prediction methods using convolutional neural networks, which further demonstrates its effectiveness.
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14
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Zhu Z, Yao Z, Qi G, Mazur N, Yang P, Cong B. Associative learning mechanism for drug‐target interaction prediction. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2023. [DOI: 10.1049/cit2.12194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023] Open
Affiliation(s)
- Zhiqin Zhu
- College of Automation Chongqing University of Posts and Telecommunications Chongqing China
| | - Zheng Yao
- College of Automation Chongqing University of Posts and Telecommunications Chongqing China
| | - Guanqiu Qi
- Computer Information Systems Department State University of New York at Buffalo State Buffalo New York USA
| | - Neal Mazur
- Computer Information Systems Department State University of New York at Buffalo State Buffalo New York USA
| | - Pan Yang
- Department of Cardiovascular Surgery Chongqing General Hospital University of Chinese Academy of Sciences Chongqing China
- Emergency Department The Second Affiliated Hospital of Chongqing Medical University Chongqing China
| | - Baisen Cong
- Data Scientist Diagnostics Digital DH (Shanghai) Diagnostics Co., Ltd. Danaher Company Shanghai China
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15
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Vigil-Vásquez C, Schüller A. De Novo Prediction of Drug Targets and Candidates by Chemical Similarity-Guided Network-Based Inference. Int J Mol Sci 2022; 23:ijms23179666. [PMID: 36077062 PMCID: PMC9455815 DOI: 10.3390/ijms23179666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/12/2022] [Accepted: 08/21/2022] [Indexed: 12/01/2022] Open
Abstract
Identifying drug–target interactions is a crucial step in discovering novel drugs and for drug repositioning. Network-based methods have shown great potential thanks to the straightforward integration of information from different sources and the possibility of extracting novel information from the graph topology. However, despite recent advances, there is still an urgent need for efficient and robust prediction methods. Here, we present SimSpread, a novel method that combines network-based inference with chemical similarity. This method employs a tripartite drug–drug–target network constructed from protein–ligand interaction annotations and drug–drug chemical similarity on which a resource-spreading algorithm predicts potential biological targets for both known or failed drugs and novel compounds. We describe small molecules as vectors of similarity indices to other compounds, thereby providing a flexible means to explore diverse molecular representations. We show that our proposed method achieves high prediction performance through multiple cross-validation and time-split validation procedures over a series of datasets. In addition, we demonstrate that our method performed a balanced exploration of both chemical ligand space (scaffold hopping) and biological target space (target hopping). Our results suggest robust and balanced performance, and our method may be useful for predicting drug targets, virtual screening, and drug repositioning.
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Affiliation(s)
- Carlos Vigil-Vásquez
- Department of Molecular Genetics and Microbiology, School of Biological Sciences, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile
| | - Andreas Schüller
- Department of Molecular Genetics and Microbiology, School of Biological Sciences, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile
- Correspondence:
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