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Zeng X, Zhong KY, Meng PY, Li SJ, Lv SQ, Wen ML, Li Y. MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs. BMC Biol 2024; 22:182. [PMID: 39183297 PMCID: PMC11346193 DOI: 10.1186/s12915-024-01981-3] [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: 05/23/2024] [Accepted: 08/13/2024] [Indexed: 08/27/2024] Open
Abstract
BACKGROUND Accurately identifying drug-target affinity (DTA) plays a pivotal role in drug screening, design, and repurposing in pharmaceutical industry. It not only reduces the time, labor, and economic costs associated with biological experiments but also expedites drug development process. However, achieving the desired level of computational accuracy for DTA identification methods remains a significant challenge. RESULTS We proposed a novel multi-view-based graph deep model known as MvGraphDTA for DTA prediction. MvGraphDTA employed a graph convolutional network (GCN) to extract the structural features from original graphs of drugs and targets, respectively. It went a step further by constructing line graphs with edges as vertices based on original graphs of drugs and targets. GCN was also used to extract the relationship features within their line graphs. To enhance the complementarity between the extracted features from original graphs and line graphs, MvGraphDTA fused the extracted multi-view features of drugs and targets, respectively. Finally, these fused features were concatenated and passed through a fully connected (FC) network to predict DTA. CONCLUSIONS During the experiments, we performed data augmentation on all the training sets used. Experimental results showed that MvGraphDTA outperformed the competitive state-of-the-art methods on benchmark datasets for DTA prediction. Additionally, we evaluated the universality and generalization performance of MvGraphDTA on additional datasets. Experimental outcomes revealed that MvGraphDTA exhibited good universality and generalization capability, making it a reliable tool for drug-target interaction prediction.
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Affiliation(s)
- Xin Zeng
- College of Mathematics and Computer Science, Dali University, Dali, 671003, China
| | - Kai-Yang Zhong
- College of Mathematics and Computer Science, Dali University, Dali, 671003, China
| | - Pei-Yan Meng
- College of Mathematics and Computer Science, Dali University, Dali, 671003, China
| | - Shu-Juan Li
- Yunnan Institute of Endemic Diseases Control & Prevention, Dali, 671000, China
| | - Shuang-Qing Lv
- Institute of Surveying and Information Engineering, West Yunnan University of Applied Science, Dali, 671000, China
| | - Meng-Liang Wen
- State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, Kunming, 650000, China
| | - Yi Li
- College of Mathematics and Computer Science, Dali University, Dali, 671003, China.
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Han L, Kang L, Guo Q. ImageDTA: A Simple Model for Drug-Target Binding Affinity Prediction. ACS OMEGA 2024; 9:28485-28493. [PMID: 38973881 PMCID: PMC11223229 DOI: 10.1021/acsomega.4c02308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 06/12/2024] [Accepted: 06/13/2024] [Indexed: 07/09/2024]
Abstract
Predicting the drug-target binding affinity (DTA) is crucial in drug discovery, and an increasing number of researchers are using artificial intelligence techniques to make such predictions. Many effective deep neural network prediction models have been proposed. However, current methods need improvement in accuracy, complexity, and efficiency. In this study, we propose a method based on a multiscale 2-dimensional convolutional neural network (CNN), namely ImageDTA. Many studies have shown that CNN achieves good learning effects with limited data. Therefore, we take a unique perspective by treating the word vector encoded with a simplified molecular input line entry system (SMILES) string as an "image" and processing it like handling images, fully leveraging the efficient processing capabilities of CNN for image data. Furthermore, we show that ImageDTA has higher training and inference efficiency than pretrained large models and outperforms attention-based graph neural network models in accuracy and interpretability. We also use visualization techniques to select appropriate convolutional kernel sizes, thereby increasing the network's interpretability.
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Affiliation(s)
- Li Han
- Software
and Big Data Technology Department, Dalian
Neusoft University of Information, Dalian, Liaoning 116023, China
| | - Ling Kang
- Neusoft
Research Institute, Dalian Neusoft University
of Information, Dalian, Liaoning 116023, China
| | - Quan Guo
- Neusoft
Research Institute, Dalian Neusoft University
of Information, Dalian, Liaoning 116023, China
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Qiu X, Wang H, Tan X, Fang Z. G-K BertDTA: A graph representation learning and semantic embedding-based framework for drug-target affinity prediction. Comput Biol Med 2024; 173:108376. [PMID: 38552281 DOI: 10.1016/j.compbiomed.2024.108376] [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: 12/21/2023] [Revised: 03/21/2024] [Accepted: 03/24/2024] [Indexed: 04/17/2024]
Abstract
Developing new drugs is costly, time-consuming, and risky. Drug-target affinity (DTA), indicating the binding capability between drugs and target proteins, is a crucial indicator for drug development. Accurately predicting interaction strength between new drug-target pairs by analyzing previous experiments aids in screening potential drug molecules, repurposing them, and developing safe and effective medicines. Existing computational models for DTA prediction rely on strings or single-graph neural networks, lacking consideration of protein structure and molecular semantic information, leading to limited accuracy. Our experiments demonstrate that string-based methods may overlook protein conformations, causing a high root mean square error (RMSE) of 3.584 in affinity due to a lack of spatial context. Single graph networks also underperform on topology features, with a 6% lower confidence interval (CI) for activity classification. Absent semantic information also limits generalization across diverse compounds, resulting in 18% increment in RMSE and 5% in misclassifications within quantifications study, restricting potential drug discovery. To address these limitations, we propose G-K BertDTA, a novel framework for accurate DTA prediction incorporating protein features, molecular semantic features, and molecular structural information. In this proposed model, we represent drugs as graphs, with a GIN employed to learn the molecular topological information. For the extraction of protein structural features, we utilize a DenseNet architecture. A knowledge-based BERT semantic model is incorporated to obtain rich pre-trained semantic embeddings, thereby enhancing the feature information. We extensively evaluated our proposed approach on the publicly available benchmark datasets (i.e., KIBA and Davis), and experimental results demonstrate the promising performance of our method, which consistently outperforms previous state-of-the-art approaches. Code is available at https://github.com/AmbitYuki/G-K-BertDTA.
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Affiliation(s)
- Xihe Qiu
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, China
| | - Haoyu Wang
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, China
| | - Xiaoyu Tan
- INF Technology (Shanghai) Co., Ltd., Shanghai, China
| | - Zhijun Fang
- School of Computer Science and Technology, Donghua University, Shanghai, China.
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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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Zhang Y, Liu C, Liu M, Liu T, Lin H, Huang CB, Ning L. Attention is all you need: utilizing attention in AI-enabled drug discovery. Brief Bioinform 2023; 25:bbad467. [PMID: 38189543 PMCID: PMC10772984 DOI: 10.1093/bib/bbad467] [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: 09/30/2023] [Revised: 11/03/2023] [Accepted: 11/25/2023] [Indexed: 01/09/2024] Open
Abstract
Recently, attention mechanism and derived models have gained significant traction in drug development due to their outstanding performance and interpretability in handling complex data structures. This review offers an in-depth exploration of the principles underlying attention-based models and their advantages in drug discovery. We further elaborate on their applications in various aspects of drug development, from molecular screening and target binding to property prediction and molecule generation. Finally, we discuss the current challenges faced in the application of attention mechanisms and Artificial Intelligence technologies, including data quality, model interpretability and computational resource constraints, along with future directions for research. Given the accelerating pace of technological advancement, we believe that attention-based models will have an increasingly prominent role in future drug discovery. We anticipate that these models will usher in revolutionary breakthroughs in the pharmaceutical domain, significantly accelerating the pace of drug development.
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Affiliation(s)
- Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Caiqi Liu
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, No.150 Haping Road, Nangang District, Harbin, Heilongjiang 150081, China
- Key Laboratory of Molecular Oncology of Heilongjiang Province, No.150 Haping Road, Nangang District, Harbin, Heilongjiang 150081, China
| | - Mujiexin Liu
- Chongqing Key Laboratory of Sichuan-Chongqing Co-construction for Diagnosis and Treatment of Infectious Diseases Integrated Traditional Chinese and Western Medicine, College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Tianyuan Liu
- Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Japan
| | - Hao Lin
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Cheng-Bing Huang
- School of Computer Science and Technology, Aba Teachers University, Aba, China
| | - Lin Ning
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu 611844, China
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Su Y, Hu Z, Wang F, Bin Y, Zheng C, Li H, Chen H, Zeng X. AMGDTI: drug-target interaction prediction based on adaptive meta-graph learning in heterogeneous network. Brief Bioinform 2023; 25:bbad474. [PMID: 38145949 PMCID: PMC10749791 DOI: 10.1093/bib/bbad474] [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: 09/12/2023] [Revised: 11/10/2023] [Accepted: 11/30/2023] [Indexed: 12/27/2023] Open
Abstract
Prediction of drug-target interactions (DTIs) is essential in medicine field, since it benefits the identification of molecular structures potentially interacting with drugs and facilitates the discovery and reposition of drugs. Recently, much attention has been attracted to network representation learning to learn rich information from heterogeneous data. Although network representation learning algorithms have achieved success in predicting DTI, several manually designed meta-graphs limit the capability of extracting complex semantic information. To address the problem, we introduce an adaptive meta-graph-based method, termed AMGDTI, for DTI prediction. In the proposed AMGDTI, the semantic information is automatically aggregated from a heterogeneous network by training an adaptive meta-graph, thereby achieving efficient information integration without requiring domain knowledge. The effectiveness of the proposed AMGDTI is verified on two benchmark datasets. Experimental results demonstrate that the AMGDTI method overall outperforms eight state-of-the-art methods in predicting DTI and achieves the accurate identification of novel DTIs. It is also verified that the adaptive meta-graph exhibits flexibility and effectively captures complex fine-grained semantic information, enabling the learning of intricate heterogeneous network topology and the inference of potential drug-target relationship.
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Affiliation(s)
- Yansen Su
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, 230601, China
| | - Zhiyang Hu
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, 230601, China
| | - Fei Wang
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, 230601, China
| | - Yannan Bin
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, 230601, China
| | - Chunhou Zheng
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, 230601, China
| | - Haitao Li
- Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei, 230601, China
| | - Haowen Chen
- College of Computer Science and Electronic Engineering, Hunan University, Hunan, 410082, China
| | - Xiangxiang Zeng
- College of Computer Science and Electronic Engineering, Hunan University, Hunan, 410082, China
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