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Hu R, Ge R, Deng G, Fan J, Tang B, Wang C. MultiKD-DTA: Enhancing Drug-Target Affinity Prediction Through Multiscale Feature Extraction. Interdiscip Sci 2025:10.1007/s12539-025-00697-4. [PMID: 40019659 DOI: 10.1007/s12539-025-00697-4] [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: 09/29/2024] [Revised: 02/05/2025] [Accepted: 02/07/2025] [Indexed: 03/01/2025]
Abstract
The discovery and development of novel pharmaceutical agents is characterized by high costs, lengthy timelines, and significant safety concerns. Traditional drug discovery involves pharmacologists manually screening drug molecules against protein targets, focusing on binding within protein cavities. However, this manual process is slow and inherently limited. Given these constraints, the use of deep learning techniques to predict drug-target interaction (DTI) affinities is both significant and promising for future applications. This paper introduces an innovative deep learning architecture designed to enhance the prediction of DTI affinities. The model ingeniously combines graph neural networks, pre-trained large-scale protein models, and attention mechanisms to improve performance. In this framework, molecular structures are represented as graphs and processed through graph neural networks and multiscale convolutional networks to facilitate feature extraction. Simultaneously, protein sequences are encoded using pre-trained ESM-2 large models and processed with bidirectional long short-term memory networks. Subsequently, the molecular and protein embeddings derived from these processes are integrated within a fusion module to compute affinity scores. Experimental results demonstrate that our proposed model outperforms existing methods on two publicly available datasets.
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Affiliation(s)
- Riqian Hu
- Hangzhou Dianzi University, Hangzhou, 310018, China
- University of California, San Diego, La Jolla, 92093, USA
| | - Ruiquan Ge
- Hangzhou Dianzi University, Hangzhou, 310018, China.
| | - Guojian Deng
- Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Jin Fan
- Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Bowen Tang
- MindRank AI Ltd., Hangzhou, 310018, China
| | - Changmiao Wang
- Shenzhen Research Institute of Big Data, Shenzhen, 518172, China.
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2
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Lee J, Kim D, Jun DW, Kim Y. Multimodal Fusion-Based Lightweight Model for Enhanced Generalization in Drug-Target Interaction Prediction. J Chem Inf Model 2024; 64:9215-9226. [PMID: 39626073 DOI: 10.1021/acs.jcim.4c01397] [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: 12/24/2024]
Abstract
Predicting drug-target interactions (DTIs) with precision is a crucial challenge in the quest for efficient and cost-effective drug discovery. Existing DTI prediction models often require significant computational resources because of the intricate and exceptionally lengthy protein target sequences. This study introduces MMF-DTI, a lightweight model that uses multimodal fusion, to improve the generalizability of DTI predictions without sacrificing computational efficiency. The MMF-DTI model combines four distinct modalities: molecular sequence, molecular properties, target sequence, and target function description. This approach is noteworthy because it is the first to use natural language-based target function descriptions in predicting DTIs. The effectiveness of MMF-DTI has been confirmed through benchmark data sets, demonstrating its comparable performance in terms of generalizability, especially in scenarios with limited information about the drug or target. Remarkably, MMF-DTI accomplishes this using only half of the parameters and 17% of the VRAM compared with previous state-of-the-art models. This allows it to function even in constrained computational environments. The combination of performance and efficiency highlights the potential of multimodal data fusion in improving the overall applicability of models, providing promising opportunities for future drug discovery endeavors.
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Affiliation(s)
- Jonghyun Lee
- Institute of Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Dokyoon Kim
- Institute of Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Dae Won Jun
- Department of Medical and Digital Engineering, Hanyang University College of Engineering, Seoul 04763, Republic of Korea
- Department of Internal Medicine, Hanyang University College of Medicine, Seoul 04763, Republic of Korea
| | - Yun Kim
- College of Pharmacy, Daegu Catholic University, Gyeongsan 38430, Republic of Korea
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Huang J, Sun C, Li M, Tang R, Xie B, Wang S, Wei JM. Structure-inclusive similarity based directed GNN: a method that can control information flow to predict drug-target binding affinity. Bioinformatics 2024; 40:btae563. [PMID: 39292540 PMCID: PMC11474107 DOI: 10.1093/bioinformatics/btae563] [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: 03/19/2024] [Revised: 05/21/2024] [Accepted: 09/17/2024] [Indexed: 09/20/2024] Open
Abstract
MOTIVATION Exploring the association between drugs and targets is essential for drug discovery and repurposing. Comparing with the traditional methods that regard the exploration as a binary classification task, predicting the drug-target binding affinity can provide more specific information. Many studies work based on the assumption that similar drugs may interact with the same target. These methods constructed a symmetric graph according to the undirected drug similarity or target similarity. Although these similarities can measure the difference between two molecules, it is unable to analyze the inclusion relationship of their substructure. For example, if drug A contains all the substructures of drug B, then in the message-passing mechanism of the graph neural network, drug A should acquire all the properties of drug B, while drug B should only obtain some of the properties of A. RESULTS To this end, we proposed a structure-inclusive similarity (SIS) which measures the similarity of two drugs by considering the inclusion relationship of their substructures. Based on SIS, we constructed a drug graph and a target graph, respectively, and predicted the binding affinities between drugs and targets by a graph convolutional network-based model. Experimental results show that considering the inclusion relationship of the substructure of two molecules can effectively improve the accuracy of the prediction model. The performance of our SIS-based prediction method outperforms several state-of-the-art methods for drug-target binding affinity prediction. The case studies demonstrate that our model is a practical tool to predict the binding affinity between drugs and targets. AVAILABILITY AND IMPLEMENTATION Source codes and data are available at https://github.com/HuangStomach/SISDTA.
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Affiliation(s)
- Jipeng Huang
- Centre for Bioinformatics and Intelligent Medicine, Nankai University, Tianjin 300071, China
- College of Computer Science, Nankai University, Tianjin 300071, China
- Tianjin Key Laboratory of Network and Data Security, Tianjin 300350, China
| | - Chang Sun
- Centre for Bioinformatics and Intelligent Medicine, Nankai University, Tianjin 300071, China
- College of Computer Science, Nankai University, Tianjin 300071, China
- Tianjin Key Laboratory of Network and Data Security, Tianjin 300350, China
| | - Minglei Li
- Centre for Bioinformatics and Intelligent Medicine, Nankai University, Tianjin 300071, China
- College of Computer Science, Nankai University, Tianjin 300071, China
- Tianjin Key Laboratory of Network and Data Security, Tianjin 300350, China
| | - Rong Tang
- Centre for Bioinformatics and Intelligent Medicine, Nankai University, Tianjin 300071, China
- College of Computer Science, Nankai University, Tianjin 300071, China
- Tianjin Key Laboratory of Network and Data Security, Tianjin 300350, China
| | - Bin Xie
- College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang 050024, China
| | - Shuqin Wang
- College of Computer and Information Engineering, Tianjin Normal University, Tianjin, Xi Qing District 300387, China
| | - Jin-Mao Wei
- Centre for Bioinformatics and Intelligent Medicine, Nankai University, Tianjin 300071, China
- College of Computer Science, Nankai University, Tianjin 300071, 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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Zhang H, Liu X, Cheng W, Wang T, Chen Y. Prediction of drug-target binding affinity based on deep learning models. Comput Biol Med 2024; 174:108435. [PMID: 38608327 DOI: 10.1016/j.compbiomed.2024.108435] [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: 01/29/2024] [Revised: 04/05/2024] [Accepted: 04/07/2024] [Indexed: 04/14/2024]
Abstract
The prediction of drug-target binding affinity (DTA) plays an important role in drug discovery. Computerized virtual screening techniques have been used for DTA prediction, greatly reducing the time and economic costs of drug discovery. However, these techniques have not succeeded in reversing the low success rate of new drug development. In recent years, the continuous development of deep learning (DL) technology has brought new opportunities for drug discovery through the DTA prediction. This shift has moved the prediction of DTA from traditional machine learning methods to DL. The DL frameworks used for DTA prediction include convolutional neural networks (CNN), graph convolutional neural networks (GCN), and recurrent neural networks (RNN), and reinforcement learning (RL), among others. This review article summarizes the available literature on DTA prediction using DL models, including DTA quantification metrics and datasets, and DL algorithms used for DTA prediction (including input representation of models, neural network frameworks, valuation indicators, and model interpretability). In addition, the opportunities, challenges, and prospects of the application of DL frameworks for DTA prediction in the field of drug discovery are discussed.
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Affiliation(s)
- Hao Zhang
- College of Science, Nanjing Agricultural University, Nanjing, 210095, China
| | - Xiaoqian Liu
- College of Science, Nanjing Agricultural University, Nanjing, 210095, China
| | - Wenya Cheng
- College of Science, Nanjing Agricultural University, Nanjing, 210095, China
| | - Tianshi Wang
- College of Science, Nanjing Agricultural University, Nanjing, 210095, China
| | - Yuanyuan Chen
- College of Science, Nanjing Agricultural University, Nanjing, 210095, China.
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Zeng X, Su GP, Li SJ, Lv SQ, Wen ML, Li Y. Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning. BMC Bioinformatics 2024; 25:156. [PMID: 38641811 PMCID: PMC11031932 DOI: 10.1186/s12859-024-05783-w] [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: 02/03/2024] [Accepted: 04/12/2024] [Indexed: 04/21/2024] Open
Abstract
BACKGROUND Accurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites (DTS) is crucial for drug screening, repositioning, and design, as well as for understanding the functions of target. Although there are a few online platforms based on deep learning for drug-target interaction, affinity, and binding sites identification, there is currently no integrated online platforms for all three aspects. RESULTS Our solution, the novel integrated online platform Drug-Online, has been developed to facilitate drug screening, target identification, and understanding the functions of target in a progressive manner of "interaction-affinity-binding sites". Drug-Online platform consists of three parts: the first part uses the drug-target interaction identification method MGraphDTA, based on graph neural networks (GNN) and convolutional neural networks (CNN), to identify whether there is a drug-target interaction. If an interaction is identified, the second part employs the drug-target affinity identification method MMDTA, also based on GNN and CNN, to calculate the strength of drug-target interaction, i.e., affinity. Finally, the third part identifies drug-target binding sites, i.e., pockets. The method pt-lm-gnn used in this part is also based on GNN. CONCLUSIONS Drug-Online is a reliable online platform that integrates drug-target interaction, affinity, and binding sites identification. It is freely available via the Internet at http://39.106.7.26:8000/Drug-Online/ .
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Affiliation(s)
- Xin Zeng
- College of Mathematics and Computer Science, Dali University, Dali, 671003, China
| | - Guang-Peng Su
- College of Mathematics and Computer Science, Dali University, Dali, 671003, China
| | - Shu-Juan Li
- Yunnan Institute of Endemic Diseases Control and 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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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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Abdelkader GA, Kim JD. Advances in Protein-Ligand Binding Affinity Prediction via Deep Learning: A Comprehensive Study of Datasets, Data Preprocessing Techniques, and Model Architectures. Curr Drug Targets 2024; 25:1041-1065. [PMID: 39318214 PMCID: PMC11774311 DOI: 10.2174/0113894501330963240905083020] [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: 06/07/2024] [Revised: 08/11/2024] [Accepted: 08/19/2024] [Indexed: 09/26/2024]
Abstract
BACKGROUND Drug discovery is a complex and expensive procedure involving several timely and costly phases through which new potential pharmaceutical compounds must pass to get approved. One of these critical steps is the identification and optimization of lead compounds, which has been made more accessible by the introduction of computational methods, including deep learning (DL) techniques. Diverse DL model architectures have been put forward to learn the vast landscape of interaction between proteins and ligands and predict their affinity, helping in the identification of lead compounds. OBJECTIVE This survey fills a gap in previous research by comprehensively analyzing the most commonly used datasets and discussing their quality and limitations. It also offers a comprehensive classification of the most recent DL methods in the context of protein-ligand binding affinity prediction (BAP), providing a fresh perspective on this evolving field. METHODS We thoroughly examine commonly used datasets for BAP and their inherent characteristics. Our exploration extends to various preprocessing steps and DL techniques, including graph neural networks, convolutional neural networks, and transformers, which are found in the literature. We conducted extensive literature research to ensure that the most recent deep learning approaches for BAP were included by the time of writing this manuscript. RESULTS The systematic approach used for the present study highlighted inherent challenges to BAP via DL, such as data quality, model interpretability, and explainability, and proposed considerations for future research directions. We present valuable insights to accelerate the development of more effective and reliable DL models for BAP within the research community. CONCLUSION The present study can considerably enhance future research on predicting affinity between protein and ligand molecules, hence further improving the overall drug development process.
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Affiliation(s)
- Gelany Aly Abdelkader
- Department of Computer Science and Electronic Engineering, Sun Moon University, Asan 31460, Republic of Korea
| | - Jeong-Dong Kim
- Department of Computer Science and Electronic Engineering, Sun Moon University, Asan 31460, Republic of Korea
- Division of Computer Science and Engineering, Sun Moon University, Asan 31460, Republic of Korea
- Genome-based BioIT Convergence Institute, Sun Moon University, Asan 31460, Korea
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