1
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Shi W, Yang H, Xie L, Yin XX, Zhang Y. A review of machine learning-based methods for predicting drug-target interactions. Health Inf Sci Syst 2024; 12:30. [PMID: 38617016 PMCID: PMC11014838 DOI: 10.1007/s13755-024-00287-6] [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: 09/11/2023] [Accepted: 03/04/2024] [Indexed: 04/16/2024] Open
Abstract
The prediction of drug-target interactions (DTI) is a crucial preliminary stage in drug discovery and development, given the substantial risk of failure and the prolonged validation period associated with in vitro and in vivo experiments. In the contemporary landscape, various machine learning-based methods have emerged as indispensable tools for DTI prediction. This paper begins by placing emphasis on the data representation employed by these methods, delineating five representations for drugs and four for proteins. The methods are then categorized into traditional machine learning-based approaches and deep learning-based ones, with a discussion of representative approaches in each category and the introduction of a novel taxonomy for deep neural network models in DTI prediction. Additionally, we present a synthesis of commonly used datasets and evaluation metrics to facilitate practical implementation. In conclusion, we address current challenges and outline potential future directions in this research field.
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Affiliation(s)
- Wen Shi
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006 China
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004 China
| | - Hong Yang
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006 China
| | - Linhai Xie
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Beijing, 102206 China
| | - Xiao-Xia Yin
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006 China
| | - Yanchun Zhang
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004 China
- Department of New Networks, Peng Cheng Laboratory, Shenzhen, 518000 China
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2
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Liu S, Yu J, Ni N, Wang Z, Chen M, Li Y, Xu C, Ding Y, Zhang J, Yao X, Liu H. Versatile Framework for Drug-Target Interaction Prediction by Considering Domain-Specific Features. J Chem Inf Model 2024. [PMID: 38976879 DOI: 10.1021/acs.jcim.4c00403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Predicting drug-target interactions (DTIs) is one of the crucial tasks in drug discovery, but traditional wet-lab experiments are costly and time-consuming. Recently, deep learning has emerged as a promising tool for accelerating DTI prediction due to its powerful performance. However, the models trained on limited known DTI data struggle to generalize effectively to novel drug-target pairs. In this work, we propose a strategy to train an ensemble of models by capturing both domain-generic and domain-specific features (E-DIS) to learn diverse domain features and adapt them to out-of-distribution data. Multiple experts were trained on different domains to capture and align domain-specific information from various distributions without accessing any data from unseen domains. E-DIS provides a comprehensive representation of proteins and ligands by capturing diverse features. Experimental results on four benchmark data sets in both in-domain and cross-domain settings demonstrated that E-DIS significantly improved model performance and domain generalization compared to existing methods. Our approach presents a significant advancement in DTI prediction by combining domain-generic and domain-specific features, enhancing the generalization ability of the DTI prediction model.
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Affiliation(s)
- Shuo Liu
- School of Pharmacy, Lanzhou University, Gansu 730000, China
- Huawei Technologies Co., Ltd., Hangzhou 310000, China
| | - Jialiang Yu
- Huawei Technologies Co., Ltd., Hangzhou 310000, China
| | - Ningxi Ni
- Huawei Technologies Co., Ltd., Hangzhou 310000, China
| | - Zidong Wang
- Huawei Technologies Co., Ltd., Hangzhou 310000, China
| | - Mengyun Chen
- Huawei Technologies Co., Ltd., Hangzhou 310000, China
| | - Yuquan Li
- College of Chemistry and Chemical Engineering, Lanzhou University, Gansu 730000, China
| | - Chen Xu
- Huawei Technologies Co., Ltd., Hangzhou 310000, China
| | - Yahao Ding
- Huawei Technologies Co., Ltd., Hangzhou 310000, China
| | - Jun Zhang
- Changping Laboratory, Beijing 102200, China
| | - Xiaojun Yao
- Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR 999078, China
| | - Huanxiang Liu
- Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR 999078, China
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3
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Nguyen VTD, Hy TS. Multimodal pretraining for unsupervised protein representation learning. Biol Methods Protoc 2024; 9:bpae043. [PMID: 38983679 PMCID: PMC11233121 DOI: 10.1093/biomethods/bpae043] [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: 04/15/2024] [Revised: 05/30/2024] [Accepted: 06/12/2024] [Indexed: 07/11/2024] Open
Abstract
Proteins are complex biomolecules essential for numerous biological processes, making them crucial targets for advancements in molecular biology, medical research, and drug design. Understanding their intricate, hierarchical structures, and functions is vital for progress in these fields. To capture this complexity, we introduce Multimodal Protein Representation Learning (MPRL), a novel framework for symmetry-preserving multimodal pretraining that learns unified, unsupervised protein representations by integrating primary and tertiary structures. MPRL employs Evolutionary Scale Modeling (ESM-2) for sequence analysis, Variational Graph Auto-Encoders (VGAE) for residue-level graphs, and PointNet Autoencoder (PAE) for 3D point clouds of atoms, each designed to capture the spatial and evolutionary intricacies of proteins while preserving critical symmetries. By leveraging Auto-Fusion to synthesize joint representations from these pretrained models, MPRL ensures robust and comprehensive protein representations. Our extensive evaluation demonstrates that MPRL significantly enhances performance in various tasks such as protein-ligand binding affinity prediction, protein fold classification, enzyme activity identification, and mutation stability prediction. This framework advances the understanding of protein dynamics and facilitates future research in the field. Our source code is publicly available at https://github.com/HySonLab/Protein_Pretrain.
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Affiliation(s)
| | - Truong Son Hy
- FPT Software AI Center, HCMC, Hanoi, Vietnam
- Department of Mathematics and Computer Science, Indiana State University, Terre Haute, IN, 47809, United States
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4
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Amorim AM, Piochi LF, Gaspar AT, Preto A, Rosário-Ferreira N, Moreira IS. Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction. Chem Res Toxicol 2024; 37:827-849. [PMID: 38758610 PMCID: PMC11187637 DOI: 10.1021/acs.chemrestox.3c00352] [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: 11/06/2023] [Revised: 04/29/2024] [Accepted: 05/07/2024] [Indexed: 05/19/2024]
Abstract
The attrition rate of drugs in clinical trials is generally quite high, with estimates suggesting that approximately 90% of drugs fail to make it through the process. The identification of unexpected toxicity issues during preclinical stages is a significant factor contributing to this high rate of failure. These issues can have a major impact on the success of a drug and must be carefully considered throughout the development process. These late-stage rejections or withdrawals of drug candidates significantly increase the costs associated with drug development, particularly when toxicity is detected during clinical trials or after market release. Understanding drug-biological target interactions is essential for evaluating compound toxicity and safety, as well as predicting therapeutic effects and potential off-target effects that could lead to toxicity. This will enable scientists to predict and assess the safety profiles of drug candidates more accurately. Evaluation of toxicity and safety is a critical aspect of drug development, and biomolecules, particularly proteins, play vital roles in complex biological networks and often serve as targets for various chemicals. Therefore, a better understanding of these interactions is crucial for the advancement of drug development. The development of computational methods for evaluating protein-ligand interactions and predicting toxicity is emerging as a promising approach that adheres to the 3Rs principles (replace, reduce, and refine) and has garnered significant attention in recent years. In this review, we present a thorough examination of the latest breakthroughs in drug toxicity prediction, highlighting the significance of drug-target binding affinity in anticipating and mitigating possible adverse effects. In doing so, we aim to contribute to the development of more effective and secure drugs.
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Affiliation(s)
- Ana M.
B. Amorim
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- PhD
Programme in Biosciences, Department of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- PURR.AI,
Rua Pedro Nunes, IPN Incubadora, Ed C, 3030-199 Coimbra, Portugal
| | - Luiz F. Piochi
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
| | - Ana T. Gaspar
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
| | - António
J. Preto
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- PhD Programme
in Experimental Biology and Biomedicine, Institute for Interdisciplinary
Research (IIIUC), University of Coimbra, Casa Costa Alemão, 3030-789 Coimbra, Portugal
| | - Nícia Rosário-Ferreira
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
| | - Irina S. Moreira
- Department
of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CNC-UC—Center
for Neuroscience and Cell Biology, University
of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
- CIBB—Centre
for Innovative Biomedicine and Biotechnology, University of Coimbra, Calçada Martim de Freitas, 3000-456 Coimbra, Portugal
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5
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Zhang Y, Li J, Lin S, Zhao J, Xiong Y, Wei DQ. An end-to-end method for predicting compound-protein interactions based on simplified homogeneous graph convolutional network and pre-trained language model. J Cheminform 2024; 16:67. [PMID: 38849874 PMCID: PMC11162000 DOI: 10.1186/s13321-024-00862-9] [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: 11/29/2023] [Accepted: 05/19/2024] [Indexed: 06/09/2024] Open
Abstract
Identification of interactions between chemical compounds and proteins is crucial for various applications, including drug discovery, target identification, network pharmacology, and elucidation of protein functions. Deep neural network-based approaches are becoming increasingly popular in efficiently identifying compound-protein interactions with high-throughput capabilities, narrowing down the scope of candidates for traditional labor-intensive, time-consuming and expensive experimental techniques. In this study, we proposed an end-to-end approach termed SPVec-SGCN-CPI, which utilized simplified graph convolutional network (SGCN) model with low-dimensional and continuous features generated from our previously developed model SPVec and graph topology information to predict compound-protein interactions. The SGCN technique, dividing the local neighborhood aggregation and nonlinearity layer-wise propagation steps, effectively aggregates K-order neighbor information while avoiding neighbor explosion and expediting training. The performance of the SPVec-SGCN-CPI method was assessed across three datasets and compared against four machine learning- and deep learning-based methods, as well as six state-of-the-art methods. Experimental results revealed that SPVec-SGCN-CPI outperformed all these competing methods, particularly excelling in unbalanced data scenarios. By propagating node features and topological information to the feature space, SPVec-SGCN-CPI effectively incorporates interactions between compounds and proteins, enabling the fusion of heterogeneity. Furthermore, our method scored all unlabeled data in ChEMBL, confirming the top five ranked compound-protein interactions through molecular docking and existing evidence. These findings suggest that our model can reliably uncover compound-protein interactions within unlabeled compound-protein pairs, carrying substantial implications for drug re-profiling and discovery. In summary, SPVec-SGCN demonstrates its efficacy in accurately predicting compound-protein interactions, showcasing potential to enhance target identification and streamline drug discovery processes.Scientific contributionsThe methodology presented in this work not only enables the comparatively accurate prediction of compound-protein interactions but also, for the first time, take sample imbalance which is very common in real world and computation efficiency into consideration simultaneously, accelerating the target identification and drug discovery process.
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Affiliation(s)
- Yufang Zhang
- School of Mathematical Sciences and SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, 200240, China
- Peng Cheng Laboratory, Shenzhen, 518055, Guangdong, China
- Zhongjing Research and Industrialization, Institute of Chinese Medicine, Zhongguancun Scientific Park, Meixi, Nanyang, 473006, Henan, China
| | - Jiayi Li
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, and Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Shanghai JiaoTong University, Shanghai, China
| | - Shenggeng Lin
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, and Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Shanghai JiaoTong University, Shanghai, China
| | - Jianwei Zhao
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, and Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Shanghai JiaoTong University, Shanghai, China
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, and Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Shanghai JiaoTong University, Shanghai, China.
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China.
| | - Dong-Qing Wei
- Peng Cheng Laboratory, Shenzhen, 518055, Guangdong, China.
- Zhongjing Research and Industrialization, Institute of Chinese Medicine, Zhongguancun Scientific Park, Meixi, Nanyang, 473006, Henan, China.
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, and Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Shanghai JiaoTong University, Shanghai, China.
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6
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Yu H, Xu WX, Tan T, Liu Z, Shi JY. Prediction of drug-target binding affinity based on multi-scale feature fusion. Comput Biol Med 2024; 178:108699. [PMID: 38870725 DOI: 10.1016/j.compbiomed.2024.108699] [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: 02/02/2024] [Revised: 05/05/2024] [Accepted: 06/01/2024] [Indexed: 06/15/2024]
Abstract
Accurate prediction of drug-target binding affinity (DTA) plays a pivotal role in drug discovery and repositioning. Although deep learning methods are widely used in DTA prediction, two significant challenges persist: (i) how to effectively represent the complex structural information of proteins and drugs; (ii) how to precisely model the mutual interactions between protein binding sites and key drug substructures. To address these challenges, we propose a MSFFDTA (Multi-scale feature fusion for predicting drug target affinity) model, in which multi-scale encoders effectively capture multi-level structural information of drugs and proteins are designed. And then a Selective Cross Attention (SCA) mechanism is developed to filter out the trivial interactions between drug-protein substructure pairs and retain the important ones, which will make the proposed model better focusing on these key interactions and offering insights into their underlying mechanism. Experimental results on two benchmark datasets demonstrate that MSFFDTA is superior to several state-of-the-art methods across almost all comparison metrics. Finally, we provide the ablation and case studies with visualizations to verify the effectiveness and the interpretability of MSFFDTA. The source code is freely available at https://github.com/whitehat32/MSFF-DTA/.
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Affiliation(s)
- Hui Yu
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China.
| | - Wen-Xin Xu
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China.
| | - Tian Tan
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China.
| | - Zun Liu
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China.
| | - Jian-Yu Shi
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, 710072, China.
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7
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Zhou G, Qin Y, Hong Q, Li H, Chen H, Shen J. GEMF: a novel geometry-enhanced mid-fusion network for PLA prediction. Brief Bioinform 2024; 25:bbae333. [PMID: 38980371 PMCID: PMC11232467 DOI: 10.1093/bib/bbae333] [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: 02/07/2024] [Revised: 06/04/2024] [Accepted: 06/26/2024] [Indexed: 07/10/2024] Open
Abstract
Accurate prediction of protein-ligand binding affinity (PLA) is important for drug discovery. Recent advances in applying graph neural networks have shown great potential for PLA prediction. However, existing methods usually neglect the geometric information (i.e. bond angles), leading to difficulties in accurately distinguishing different molecular structures. In addition, these methods also pose limitations in representing the binding process of protein-ligand complexes. To address these issues, we propose a novel geometry-enhanced mid-fusion network, named GEMF, to learn comprehensive molecular geometry and interaction patterns. Specifically, the GEMF consists of a graph embedding layer, a message passing phase, and a multi-scale fusion module. GEMF can effectively represent protein-ligand complexes as graphs, with graph embeddings based on physicochemical and geometric properties. Moreover, our dual-stream message passing framework models both covalent and non-covalent interactions. In particular, the edge-update mechanism, which is based on line graphs, can fuse both distance and angle information in the covalent branch. In addition, the communication branch consisting of multiple heterogeneous interaction modules is developed to learn intricate interaction patterns. Finally, we fuse the multi-scale features from the covalent, non-covalent, and heterogeneous interaction branches. The extensive experimental results on several benchmarks demonstrate the superiority of GEMF compared with other state-of-the-art methods.
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Affiliation(s)
- Guoqiang Zhou
- School of Computer Science, Nanjing University of Posts and Telecommunications, No.9 Wenyuan Road, Jiangsu 210023, China
| | - Yuke Qin
- School of Computer Science, Nanjing University of Posts and Telecommunications, No.9 Wenyuan Road, Jiangsu 210023, China
| | - Qiansen Hong
- School of Computer Science, Nanjing University of Posts and Telecommunications, No.9 Wenyuan Road, Jiangsu 210023, China
| | - Haoran Li
- School of Computing and Information Technology, University of Wollongong, Northfields Avenue, NSW 2522, Australia
| | - Huaming Chen
- School of Electrical and Computer Engineering, University of Sydney, Camperdown, NSW 2050, Australia
| | - Jun Shen
- School of Computing and Information Technology, University of Wollongong, Northfields Avenue, NSW 2522, Australia
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8
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Rovenchak A, Druchok M. Machine learning-assisted search for novel coagulants: When machine learning can be efficient even if data availability is low. J Comput Chem 2024; 45:937-952. [PMID: 38174834 DOI: 10.1002/jcc.27292] [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: 10/30/2023] [Revised: 12/04/2023] [Accepted: 12/10/2023] [Indexed: 01/05/2024]
Abstract
Design of new drugs is a challenging process: a candidate molecule should satisfy multiple conditions to act properly and make the least side-effect-perfect candidates selectively attach to and influence only targets, leaving off-targets intact. The amount of experimental data about various properties of molecules constantly grows, promoting data-driven approaches. However, the applicability of typical predictive machine learning techniques can be substantially limited by a lack of experimental data about a particular target. For example, there are many known Thrombin inhibitors (acting as anticoagulants), but a very limited number of known Protein C inhibitors (coagulants). In this study, we present our approach to suggest new inhibitor candidates by building an effective representation of chemical space. For this aim, we developed a deep learning model-autoencoder, trained on a large set of molecules in the SMILES format to map the chemical space. Further, we applied different sampling strategies to generate novel coagulant candidates. Symmetrically, we tested our approach on anticoagulant candidates, where we were able to predict their inhibition towards Thrombin. We also compare our approach with MegaMolBART-another deep learning generative model, but exploiting similar principles of navigation in a chemical space.
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Affiliation(s)
- Andrij Rovenchak
- SoftServe, Inc., Lviv, Ukraine
- Professor Ivan Vakarchuk Department for Theoretical Physics, Ivan Franko National University of Lviv, Lviv, Ukraine
| | - Maksym Druchok
- SoftServe, Inc., Lviv, Ukraine
- Institute for Condensed Matter Physics, Lviv, Ukraine
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9
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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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10
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Ma W, Bi X, Jiang H, Zhang S, Wei Z. CollaPPI: A Collaborative Learning Framework for Predicting Protein-Protein Interactions. IEEE J Biomed Health Inform 2024; 28:3167-3177. [PMID: 38466584 DOI: 10.1109/jbhi.2024.3375621] [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: 03/13/2024]
Abstract
Exploring protein-protein interaction (PPI) is of paramount importance for elucidating the intrinsic mechanism of various biological processes. Nevertheless, experimental determination of PPI can be both time-consuming and expensive, motivating the exploration of data-driven deep learning technologies as a viable, efficient, and accurate alternative. Nonetheless, most current deep learning-based methods regarded a pair of proteins to be predicted for possible interaction as two separate entities when extracting PPI features, thus neglecting the knowledge sharing among the collaborative protein and the target protein. Aiming at the above issue, a collaborative learning framework CollaPPI was proposed in this study, where two kinds of collaboration, i.e., protein-level collaboration and task-level collaboration, were incorporated to achieve not only the knowledge-sharing between a pair of proteins, but also the complementation of such shared knowledge between biological domains closely related to PPI (i.e., protein function, and subcellular location). Evaluation results demonstrated that CollaPPI obtained superior performance compared to state-of-the-art methods on two PPI benchmarks. Besides, evaluation results of CollaPPI on the additional PPI type prediction task further proved its excellent generalization ability.
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11
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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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12
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Hong Q, Zhou G, Qin Y, Shen J, Li H. SadNet: a novel multimodal fusion network for protein-ligand binding affinity prediction. Phys Chem Chem Phys 2024; 26:12880-12891. [PMID: 38625412 DOI: 10.1039/d3cp05664c] [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: 04/17/2024]
Abstract
Protein-ligand binding affinity prediction plays an important role in the field of drug discovery. Existing deep learning-based approaches have significantly improved the efficiency of protein-ligand binding affinity prediction through their excellent inductive bias capability. However, these methods only focus on fragmented three-dimensional data, which truncates the integrity of pocket data, leading to the neglect of potential long-range interactions. In this paper, we propose a dual-stream framework, with amino acid sequence assisting the atomic data fusion for graph neural network (termed SadNet), to fuse both 3D atomic data and sequence data for more accurate prediction results. In detail, SadNet consists of a pocket module and a sequence module. The sequence module expands the "receptive field" of the pocket module through a mid-term virtual node fusion. To better integrate sequence-level information from the sequence module and 3D structural information from the pocket module, we incorporate structural information for each amino acid within the sequence module. Besides, to better understand the intrinsic relationship between sequences and 3D atomic information, our SadNet utilizes information stacking from both the early stage and later stage. Experimental results on publicly available benchmark datasets demonstrate the superiority of the proposed dual-stream approach over the state-of-the-art alternatives. The code of this work is available online at https://github.com/wardhong/SadNet.
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Affiliation(s)
- Qiansen Hong
- Nanjing University of Posts and Telecommunications, NanJing, China.
| | - Guoqiang Zhou
- Nanjing University of Posts and Telecommunications, NanJing, China.
| | - Yuke Qin
- Nanjing University of Posts and Telecommunications, NanJing, China.
| | - Jun Shen
- University of Wollongong, Australia
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13
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Zhong KY, Wen ML, Meng FF, Li X, Jiang B, Zeng X, Li Y. MMDTA: A Multimodal Deep Model for Drug-Target Affinity with a Hybrid Fusion Strategy. J Chem Inf Model 2024; 64:2878-2888. [PMID: 37610162 DOI: 10.1021/acs.jcim.3c00866] [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: 08/24/2023]
Abstract
The prediction of the drug-target affinity (DTA) plays an important role in evaluating molecular druggability. Although deep learning-based models for DTA prediction have been extensively attempted, there are rare reports on multimodal models that leverage various fusion strategies to exploit heterogeneous information from multiple different modalities of drugs and targets. In this study, we proposed a multimodal deep model named MMDTA, which integrated the heterogeneous information from various modalities of drugs and targets using a hybrid fusion strategy to enhance DTA prediction. To achieve this, MMDTA first employed convolutional neural networks (CNNs) and graph convolutional networks (GCNs) to extract diverse heterogeneous information from the sequences and structures of drugs and targets. It then utilized a hybrid fusion strategy to combine and complement the extracted heterogeneous information, resulting in the fused modal information for predicting drug-target affinity through the fully connected (FC) layers. Experimental results demonstrated that MMDTA outperformed the competitive state-of-the-art deep learning models on the widely used benchmark data sets, particularly with a significantly improved key evaluation metric, Root Mean Square Error (RMSE). Furthermore, MMDTA exhibited excellent generalization and practical application performance on multiple different data sets. These findings highlighted MMDTA's accuracy and reliability in predicting the drug-target binding affinity. For researchers interested in the source data and code, they are accessible at http://github.com/dldxzx/MMDTA.
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Affiliation(s)
- Kai-Yang Zhong
- College of Mathematics and Computer Science, Dali University, Dali 671003, China
| | - Meng-Liang Wen
- State Key Laboratory for Conservation and Utilization of Bio-Resource in Yunnan, Yunnan University, Kunming 650000, China
| | - Fan-Fang Meng
- College of Mathematics and Computer Science, Dali University, Dali 671003, China
| | - Xin Li
- College of Mathematics and Computer Science, Dali University, Dali 671003, China
| | - Bei Jiang
- Yunnan Key Laboratory of Screening and Research on Anti-pathogenic Plant Resources from Western Yunnan, Dali University, Dali 671000, China
| | - Xin Zeng
- College of Mathematics and Computer Science, Dali University, Dali 671003, China
| | - Yi Li
- College of Mathematics and Computer Science, Dali University, Dali 671003, China
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14
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Svensson E, Hoedt PJ, Hochreiter S, Klambauer G. HyperPCM: Robust Task-Conditioned Modeling of Drug-Target Interactions. J Chem Inf Model 2024; 64:2539-2553. [PMID: 38185877 PMCID: PMC11005051 DOI: 10.1021/acs.jcim.3c01417] [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: 09/04/2023] [Revised: 11/27/2023] [Accepted: 11/27/2023] [Indexed: 01/09/2024]
Abstract
A central problem in drug discovery is to identify the interactions between drug-like compounds and protein targets. Over the past few decades, various quantitative structure-activity relationship (QSAR) and proteo-chemometric (PCM) approaches have been developed to model and predict these interactions. While QSAR approaches solely utilize representations of the drug compound, PCM methods incorporate both representations of the protein target and the drug compound, enabling them to achieve above-chance predictive accuracy on previously unseen protein targets. Both QSAR and PCM approaches have recently been improved by machine learning and deep neural networks, that allow the development of drug-target interaction prediction models from measurement data. However, deep neural networks typically require large amounts of training data and cannot robustly adapt to new tasks, such as predicting interaction for unseen protein targets at inference time. In this work, we propose to use HyperNetworks to efficiently transfer information between tasks during inference and thus to accurately predict drug-target interactions on unseen protein targets. Our HyperPCM method reaches state-of-the-art performance compared to previous methods on multiple well-known benchmarks, including Davis, DUD-E, and a ChEMBL derived data set, and particularly excels at zero-shot inference involving unseen protein targets. Our method, as well as reproducible data preparation, is available at https://github.com/ml-jku/hyper-dti.
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Affiliation(s)
- Emma Svensson
- ELLIS
Unit Linz & Institute for Machine Learning, Johannes Kepler University, Linz 4040, Austria
- Molecular
AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, 431 83, Sweden
| | - Pieter-Jan Hoedt
- ELLIS
Unit Linz & Institute for Machine Learning, Johannes Kepler University, Linz 4040, Austria
| | - Sepp Hochreiter
- ELLIS
Unit Linz & Institute for Machine Learning, Johannes Kepler University, Linz 4040, Austria
- Institute
of Advanced Research in Artificial Intelligence (IARAI), Vienna 1030, Austria
| | - Günter Klambauer
- ELLIS
Unit Linz & Institute for Machine Learning, Johannes Kepler University, Linz 4040, Austria
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15
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Gorantla R, Kubincová A, Weiße AY, Mey ASJS. From Proteins to Ligands: Decoding Deep Learning Methods for Binding Affinity Prediction. J Chem Inf Model 2024; 64:2496-2507. [PMID: 37983381 PMCID: PMC11005465 DOI: 10.1021/acs.jcim.3c01208] [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: 08/02/2023] [Revised: 10/26/2023] [Accepted: 10/27/2023] [Indexed: 11/22/2023]
Abstract
Accurate in silico prediction of protein-ligand binding affinity is important in the early stages of drug discovery. Deep learning-based methods exist but have yet to overtake more conventional methods such as giga-docking largely due to their lack of generalizability. To improve generalizability, we need to understand what these models learn from input protein and ligand data. We systematically investigated a sequence-based deep learning framework to assess the impact of protein and ligand encodings on predicting binding affinities for commonly used kinase data sets. The role of proteins is studied using convolutional neural network-based encodings obtained from sequences and graph neural network-based encodings enriched with structural information from contact maps. Ligand-based encodings are generated from graph-neural networks. We test different ligand perturbations by randomizing node and edge properties. For proteins, we make use of 3 different protein contact generation methods (AlphaFold2, Pconsc4, and ESM-1b) and compare these with a random control. Our investigation shows that protein encodings do not substantially impact the binding predictions, with no statistically significant difference in binding affinity for KIBA in the investigated metrics (concordance index, Pearson's R Spearman's Rank, and RMSE). Significant differences are seen for ligand encodings with random ligands and random ligand node properties, suggesting a much bigger reliance on ligand data for the learning tasks. Using different ways to combine protein and ligand encodings did not show a significant change in performance.
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Affiliation(s)
- Rohan Gorantla
- School
of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, U.K.
- EaStCHEM
School of Chemistry, University of Edinburgh, Edinburgh, EH9 3FJ, U.K.
| | | | - Andrea Y. Weiße
- School
of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, U.K.
- School
of Biological Sciences, University of Edinburgh, Edinburgh, EH9 3FF, U.K.
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16
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Zhang X, Gao H, Wang H, Chen Z, Zhang Z, Chen X, Li Y, Qi Y, Wang R. PLANET: A Multi-objective Graph Neural Network Model for Protein-Ligand Binding Affinity Prediction. J Chem Inf Model 2024; 64:2205-2220. [PMID: 37319418 DOI: 10.1021/acs.jcim.3c00253] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Predicting protein-ligand binding affinity is a central issue in drug design. Various deep learning models have been published in recent years, where many of them rely on 3D protein-ligand complex structures as input and tend to focus on the single task of reproducing binding affinity. In this study, we have developed a graph neural network model called PLANET (Protein-Ligand Affinity prediction NETwork). This model takes the graph-represented 3D structure of the binding pocket on the target protein and the 2D chemical structure of the ligand molecule as input. It was trained through a multi-objective process with three related tasks, including deriving the protein-ligand binding affinity, protein-ligand contact map, and ligand distance matrix. Besides the protein-ligand complexes with known binding affinity data retrieved from the PDBbind database, a large number of non-binder decoys were also added to the training data for deriving the final model of PLANET. When tested on the CASF-2016 benchmark, PLANET exhibited a scoring power comparable to the best result yielded by other deep learning models as well as a reasonable ranking power and docking power. In virtual screening trials conducted on the DUD-E benchmark, PLANET's performance was notably better than several deep learning and machine learning models. As on the LIT-PCBA benchmark, PLANET achieved comparable accuracy as the conventional docking program Glide, but it only spent less than 1% of Glide's computation time to finish the same job because PLANET did not need exhaustive conformational sampling. Considering the decent accuracy and efficiency of PLANET in binding affinity prediction, it may become a useful tool for conducting large-scale virtual screening.
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Affiliation(s)
- Xiangying Zhang
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Haotian Gao
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Haojie Wang
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Zhihang Chen
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Zhe Zhang
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Xinchong Chen
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Yan Li
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Yifei Qi
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
| | - Renxiao Wang
- Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China
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17
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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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18
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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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19
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Metcalf DP, Glick ZL, Bortolato A, Jiang A, Cheney DL, Sherrill CD. Directional Δ G Neural Network (DrΔ G-Net): A Modular Neural Network Approach to Binding Free Energy Prediction. J Chem Inf Model 2024; 64:1907-1918. [PMID: 38470995 DOI: 10.1021/acs.jcim.3c02054] [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: 03/14/2024]
Abstract
The protein-ligand binding free energy is a central quantity in structure-based computational drug discovery efforts. Although popular alchemical methods provide sound statistical means of computing the binding free energy of a large breadth of systems, they are generally too costly to be applied at the same frequency as end point or ligand-based methods. By contrast, these data-driven approaches are typically fast enough to address thousands of systems but with reduced transferability to unseen systems. We introduce DrΔG-Net (or simply Dragnet), an equivariant graph neural network that can blend ligand-based and protein-ligand data-driven approaches. It is based on a 3D fingerprint representation of the ligand alone and in complex with the protein target. Dragnet is a global scoring function to predict the binding affinity of arbitrary protein-ligand complexes, but can be easily tuned via transfer learning to specific systems or end points, performing similarly to common 2D ligand-based approaches in these tasks. Dragnet is evaluated on a total of 28 validation proteins with a set of congeneric ligands derived from the Binding DB and one custom set extracted from the ChEMBL Database. In general, a handful of experimental binding affinities are sufficient to optimize the scoring function for a particular protein and ligand scaffold. When not available, predictions from physics-based methods such as absolute free energy perturbation can be used for the transfer learning tuning of Dragnet. Furthermore, we use our data to illustrate the present limitations of data-driven modeling of binding free energy predictions.
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Affiliation(s)
- Derek P Metcalf
- Center for Computational Molecular Science and Technology, School of Chemistry and Biochemistry and School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0400, United States
| | - Zachary L Glick
- Center for Computational Molecular Science and Technology, School of Chemistry and Biochemistry and School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0400, United States
| | - Andrea Bortolato
- Molecular Structure and Design, Bristol-Myers Squibb Company, P.O. Box 5400, Princeton, New Jersey 08543, United States
| | - Andy Jiang
- Center for Computational Molecular Science and Technology, School of Chemistry and Biochemistry and School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0400, United States
| | - Daniel L Cheney
- Molecular Structure and Design, Bristol-Myers Squibb Company, P.O. Box 5400, Princeton, New Jersey 08543, United States
| | - C David Sherrill
- Center for Computational Molecular Science and Technology, School of Chemistry and Biochemistry and School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0400, United States
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20
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Zhang Y, Li S, Meng K, Sun S. Machine Learning for Sequence and Structure-Based Protein-Ligand Interaction Prediction. J Chem Inf Model 2024; 64:1456-1472. [PMID: 38385768 DOI: 10.1021/acs.jcim.3c01841] [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] [Indexed: 02/23/2024]
Abstract
Developing new drugs is too expensive and time -consuming. Accurately predicting the interaction between drugs and targets will likely change how the drug is discovered. Machine learning-based protein-ligand interaction prediction has demonstrated significant potential. In this paper, computational methods, focusing on sequence and structure to study protein-ligand interactions, are examined. Therefore, this paper starts by presenting an overview of the data sets applied in this area, as well as the various approaches applied for representing proteins and ligands. Then, sequence-based and structure-based classification criteria are subsequently utilized to categorize and summarize both the classical machine learning models and deep learning models employed in protein-ligand interaction studies. Moreover, the evaluation methods and interpretability of these models are proposed. Furthermore, delving into the diverse applications of protein-ligand interaction models in drug research is presented. Lastly, the current challenges and future directions in this field are addressed.
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Affiliation(s)
- Yunjiang Zhang
- Beijing Key Laboratory for Green Catalysis and Separation, The Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China
| | - Shuyuan Li
- Beijing Key Laboratory for Green Catalysis and Separation, The Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China
| | - Kong Meng
- Beijing Key Laboratory for Green Catalysis and Separation, The Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China
| | - Shaorui Sun
- Beijing Key Laboratory for Green Catalysis and Separation, The Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, P. R. China
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21
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Ma J, Zhao Z, Li T, Liu Y, Ma J, Zhang R. GraphsformerCPI: Graph Transformer for Compound-Protein Interaction Prediction. Interdiscip Sci 2024:10.1007/s12539-024-00609-y. [PMID: 38457109 DOI: 10.1007/s12539-024-00609-y] [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: 08/07/2023] [Revised: 01/01/2024] [Accepted: 01/08/2024] [Indexed: 03/09/2024]
Abstract
Accurately predicting compound-protein interactions (CPI) is a critical task in computer-aided drug design. In recent years, the exponential growth of compound activity and biomedical data has highlighted the need for efficient and interpretable prediction approaches. In this study, we propose GraphsformerCPI, an end-to-end deep learning framework that improves prediction performance and interpretability. GraphsformerCPI treats compounds and proteins as sequences of nodes with spatial structures, and leverages novel structure-enhanced self-attention mechanisms to integrate semantic and graph structural features within molecules for deep molecule representations. To capture the vital association between compound atoms and protein residues, we devise a dual-attention mechanism to effectively extract relational features through .cross-mapping. By extending the powerful learning capabilities of Transformers to spatial structures and extensively utilizing attention mechanisms, our model offers strong interpretability, a significant advantage over most black-box deep learning methods. To evaluate GraphsformerCPI, extensive experiments were conducted on benchmark datasets including human, C. elegans, Davis and KIBA datasets. We explored the impact of model depth and dropout rate on performance and compared our model against state-of-the-art baseline models. Our results demonstrate that GraphsformerCPI outperforms baseline models in classification datasets and achieves competitive performance in regression datasets. Specifically, on the human dataset, GraphsformerCPI achieves an average improvement of 1.6% in AUC, 0.5% in precision, and 5.3% in recall. On the KIBA dataset, the average improvement in Concordance index (CI) and mean squared error (MSE) is 3.3% and 7.2%, respectively. Molecular docking shows that our model provides novel insights into the intrinsic interactions and binding mechanisms. Our research holds practical significance in effectively predicting CPIs and binding affinities, identifying key atoms and residues, enhancing model interpretability.
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Affiliation(s)
- Jun Ma
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China.
- School of Information Engineering, Lanzhou University of Finance and Economics, Lanzhou, 730020, China.
| | - Zhili Zhao
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China
| | - Tongfeng Li
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China
- Computer College, Qinghai Normal University, Xi'ning, 810016, China
| | - Yunwu Liu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China
| | - Jun Ma
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China
| | - Ruisheng Zhang
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China.
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22
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Sun Y, Li YY, Leung CK, Hu P. iNGNN-DTI: prediction of drug-target interaction with interpretable nested graph neural network and pretrained molecule models. Bioinformatics 2024; 40:btae135. [PMID: 38449285 PMCID: PMC10957515 DOI: 10.1093/bioinformatics/btae135] [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: 07/23/2023] [Revised: 12/31/2023] [Accepted: 03/05/2024] [Indexed: 03/08/2024] Open
Abstract
MOTIVATION Drug-target interaction (DTI) prediction aims to identify interactions between drugs and protein targets. Deep learning can automatically learn discriminative features from drug and protein target representations for DTI prediction, but challenges remain, making it an open question. Existing approaches encode drugs and targets into features using deep learning models, but they often lack explanations for underlying interactions. Moreover, limited labeled DTIs in the chemical space can hinder model generalization. RESULTS We propose an interpretable nested graph neural network for DTI prediction (iNGNN-DTI) using pre-trained molecule and protein models. The analysis is conducted on graph data representing drugs and targets by using a specific type of nested graph neural network, in which the target graphs are created based on 3D structures using Alphafold2. This architecture is highly expressive in capturing substructures of the graph data. We use a cross-attention module to capture interaction information between the substructures of drugs and targets. To improve feature representations, we integrate features learned by models that are pre-trained on large unlabeled small molecule and protein datasets, respectively. We evaluate our model on three benchmark datasets, and it shows a consistent improvement on all baseline models in all datasets. We also run an experiment with previously unseen drugs or targets in the test set, and our model outperforms all of the baselines. Furthermore, the iNGNN-DTI can provide more insights into the interaction by visualizing the weights learned by the cross-attention module. AVAILABILITY AND IMPLEMENTATION The source code of the algorithm is available at https://github.com/syan1992/iNGNN-DTI.
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Affiliation(s)
- Yan Sun
- Department of Biochemistry, Western University, London, ON, N6G 2V4, Canada
- Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada
- Department of Computer Science, Western University, London, ON, N6G 2V4, Canada
| | - Yan Yi Li
- Division of Biostatistics, University of Toronto, Toronto, ON, M5T 3M7, Canada
| | - Carson K Leung
- Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada
| | - Pingzhao Hu
- Department of Biochemistry, Western University, London, ON, N6G 2V4, Canada
- Department of Computer Science, University of Manitoba, Winnipeg, MB, R3T 2N2, Canada
- Department of Computer Science, Western University, London, ON, N6G 2V4, Canada
- Division of Biostatistics, University of Toronto, Toronto, ON, M5T 3M7, Canada
- Department of Oncology, Western University, London, ON, N6G 2V4, Canada
- Department of Epidemiology and Biostatistics, Western University, London, ON, N6G 2V4, Canada
- The Children’s Health Research Institute, Lawson Health Research Institute, London, ON, N6A 4V2, Canada
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23
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Huang Z, Xiao Q, Xiong T, Shi W, Yang Y, Li G. Predicting Drug-Protein Interactions through Branch-Chain Mining and multi-dimensional attention network. Comput Biol Med 2024; 171:108127. [PMID: 38350397 DOI: 10.1016/j.compbiomed.2024.108127] [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: 10/24/2023] [Revised: 01/26/2024] [Accepted: 02/06/2024] [Indexed: 02/15/2024]
Abstract
Identifying drug-protein interactions (DPIs) is crucial in drug discovery and repurposing. Computational methods for precise DPI identification can expedite development timelines and reduce expenses compared with conventional experimental methods. Lately, deep learning techniques have been employed for predicting DPIs, enhancing these processes. Nevertheless, the limitations observed in prior studies, where many extract features from complete drug and protein entities, overlooking the crucial theoretical foundation that pharmacological responses are often correlated with specific substructures, can lead to poor predictive performance. Furthermore, certain substructure-focused research confines its exploration to a solitary fragment category, such as a functional group. In this study, addressing these constraints, we present an end-to-end framework termed BCMMDA for predicting DPIs. The framework considers various substructure types, including branch chains, common substructures, and specific fragments. We designed a specific feature learning module by combining our proposed multi-dimensional attention mechanism with convolutional neural networks (CNNs). Deep CNNs assist in capturing the synergistic effects among these fragment sets, enabling the extraction of relevant features of drugs and proteins. Meanwhile, the multi-dimensional attention mechanism refines the relationship between drug and protein features by assigning attention vectors to each drug compound and amino acid. This mechanism empowers the model to further concentrate on pivotal substructures and elements, thereby improving its ability to identify essential interactions in DPI prediction. We evaluated the performance of BCMMDA on four well-known benchmark datasets. The results indicated that BCMMDA outperformed state-of-the-art baseline models, demonstrating significant improvement in performance.
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Affiliation(s)
- Zhuo Huang
- College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China
| | - Qiu Xiao
- College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China; MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, 410081, China; College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China.
| | - Tuo Xiong
- College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China
| | - Wanwan Shi
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China
| | - Yide Yang
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, 410006, China.
| | - Guanghui Li
- School of Information Engineering, East China Jiaotong University, Nanchang, 330013, China.
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24
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Qi H, Yu T, Yu W, Liu C. Drug-target affinity prediction with extended graph learning-convolutional networks. BMC Bioinformatics 2024; 25:75. [PMID: 38365583 PMCID: PMC10874073 DOI: 10.1186/s12859-024-05698-6] [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: 01/15/2024] [Accepted: 02/12/2024] [Indexed: 02/18/2024] Open
Abstract
BACKGROUND High-performance computing plays a pivotal role in computer-aided drug design, a field that holds significant promise in pharmaceutical research. The prediction of drug-target affinity (DTA) is a crucial stage in this process, potentially accelerating drug development through rapid and extensive preliminary compound screening, while also minimizing resource utilization and costs. Recently, the incorporation of deep learning into DTA prediction and the enhancement of its accuracy have emerged as key areas of interest in the research community. Drugs and targets can be characterized through various methods, including structure-based, sequence-based, and graph-based representations. Despite the progress in structure and sequence-based techniques, they tend to provide limited feature information. Conversely, graph-based approaches have risen to prominence, attracting considerable attention for their comprehensive data representation capabilities. Recent studies have focused on constructing protein and drug molecular graphs using sequences and SMILES, subsequently deriving representations through graph neural networks. However, these graph-based approaches are limited by the use of a fixed adjacent matrix of protein and drug molecular graphs for graph convolution. This limitation restricts the learning of comprehensive feature representations from intricate compound and protein structures, consequently impeding the full potential of graph-based feature representation in DTA prediction. This, in turn, significantly impacts the models' generalization capabilities in the complex realm of drug discovery. RESULTS To tackle these challenges, we introduce GLCN-DTA, a model specifically designed for proficiency in DTA tasks. GLCN-DTA innovatively integrates a graph learning module into the existing graph architecture. This module is designed to learn a soft adjacent matrix, which effectively and efficiently refines the contextual structure of protein and drug molecular graphs. This advancement allows for learning richer structural information from protein and drug molecular graphs via graph convolution, specifically tailored for DTA tasks, compared to the conventional fixed adjacent matrix approach. A series of experiments have been conducted to validate the efficacy of the proposed GLCN-DTA method across diverse scenarios. The results demonstrate that GLCN-DTA possesses advantages in terms of robustness and high accuracy. CONCLUSIONS The proposed GLCN-DTA model enhances DTA prediction performance by introducing a novel framework that synergizes graph learning operations with graph convolution operations, thereby achieving richer representations. GLCN-DTA does not distinguish between different protein classifications, including structurally ordered and intrinsically disordered proteins, focusing instead on improving feature representation. Therefore, its applicability scope may be more effective in scenarios involving structurally ordered proteins, while potentially being limited in contexts with intrinsically disordered proteins.
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Affiliation(s)
- Haiou Qi
- Nursing Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China
| | - Ting Yu
- Operating Room Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China.
| | - Wenwen Yu
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Chenxi Liu
- School of Medicine and Health Management, Tongji Medical School, Huazhong University of Science and Technology, Wuhan, 430030, China
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25
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Iliadis D, De Baets B, Pahikkala T, Waegeman W. A comparison of embedding aggregation strategies in drug-target interaction prediction. BMC Bioinformatics 2024; 25:59. [PMID: 38321386 PMCID: PMC10845509 DOI: 10.1186/s12859-024-05684-y] [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: 09/25/2023] [Accepted: 01/30/2024] [Indexed: 02/08/2024] Open
Abstract
The prediction of interactions between novel drugs and biological targets is a vital step in the early stage of the drug discovery pipeline. Many deep learning approaches have been proposed over the last decade, with a substantial fraction of them sharing the same underlying two-branch architecture. Their distinction is limited to the use of different types of feature representations and branches (multi-layer perceptrons, convolutional neural networks, graph neural networks and transformers). In contrast, the strategy used to combine the outputs (embeddings) of the branches has remained mostly the same. The same general architecture has also been used extensively in the area of recommender systems, where the choice of an aggregation strategy is still an open question. In this work, we investigate the effectiveness of three different embedding aggregation strategies in the area of drug-target interaction (DTI) prediction. We formally define these strategies and prove their universal approximator capabilities. We then present experiments that compare the different strategies on benchmark datasets from the area of DTI prediction, showcasing conditions under which specific strategies could be the obvious choice.
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Affiliation(s)
- Dimitrios Iliadis
- Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000, Ghent, Belgium.
| | - Bernard De Baets
- Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000, Ghent, Belgium
| | - Tapio Pahikkala
- Department of Computing, University of Turku, 20500, Turku, Finland
| | - Willem Waegeman
- Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000, Ghent, Belgium
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26
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Zhang Y, Chu Y, Lin S, Xiong Y, Wei DQ. ReHoGCNES-MDA: prediction of miRNA-disease associations using homogenous graph convolutional networks based on regular graph with random edge sampler. Brief Bioinform 2024; 25:bbae103. [PMID: 38517693 PMCID: PMC10959163 DOI: 10.1093/bib/bbae103] [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: 11/07/2023] [Revised: 02/04/2024] [Accepted: 02/23/2024] [Indexed: 03/24/2024] Open
Abstract
Numerous investigations increasingly indicate the significance of microRNA (miRNA) in human diseases. Hence, unearthing associations between miRNA and diseases can contribute to precise diagnosis and efficacious remediation of medical conditions. The detection of miRNA-disease linkages via computational techniques utilizing biological information has emerged as a cost-effective and highly efficient approach. Here, we introduced a computational framework named ReHoGCNES, designed for prospective miRNA-disease association prediction (ReHoGCNES-MDA). This method constructs homogenous graph convolutional network with regular graph structure (ReHoGCN) encompassing disease similarity network, miRNA similarity network and known MDA network and then was tested on four experimental tasks. A random edge sampler strategy was utilized to expedite processes and diminish training complexity. Experimental results demonstrate that the proposed ReHoGCNES-MDA method outperforms both homogenous graph convolutional network and heterogeneous graph convolutional network with non-regular graph structure in all four tasks, which implicitly reveals steadily degree distribution of a graph does play an important role in enhancement of model performance. Besides, ReHoGCNES-MDA is superior to several machine learning algorithms and state-of-the-art methods on the MDA prediction. Furthermore, three case studies were conducted to further demonstrate the predictive ability of ReHoGCNES. Consequently, 93.3% (breast neoplasms), 90% (prostate neoplasms) and 93.3% (prostate neoplasms) of the top 30 forecasted miRNAs were validated by public databases. Hence, ReHoGCNES-MDA might serve as a dependable and beneficial model for predicting possible MDAs.
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Affiliation(s)
- Yufang Zhang
- School of Mathematical Sciences and SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai 200240, China
- Peng Cheng Laboratory, Shenzhen, Guangdong 518055, China
- Zhongjing Research and Industrialization Institute of Chinese Medicine, Zhongguancun Scientific Park, Meixi, Nanyang, Henan, 473006, China
| | - Yanyi Chu
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Shenggeng Lin
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, and Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, and Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
- Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China
| | - Dong-Qing Wei
- Peng Cheng Laboratory, Shenzhen, Guangdong 518055, China
- Zhongjing Research and Industrialization Institute of Chinese Medicine, Zhongguancun Scientific Park, Meixi, Nanyang, Henan, 473006, China
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, and Joint Laboratory of International Cooperation in Metabolic and Developmental Sciences, Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
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27
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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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28
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Qiu W, Liang Q, Yu L, Xiao X, Qiu W, Lin W. LSTM-SAGDTA: Predicting Drug-target Binding Affinity with an Attention Graph Neural Network and LSTM Approach. Curr Pharm Des 2024; 30:468-476. [PMID: 38323613 PMCID: PMC11071654 DOI: 10.2174/0113816128282837240130102817] [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: 10/18/2023] [Revised: 01/14/2024] [Accepted: 01/19/2024] [Indexed: 02/08/2024]
Abstract
INTRODUCTION Drug development is a challenging and costly process, yet it plays a crucial role in improving healthcare outcomes. Drug development requires extensive research and testing to meet the demands for economic efficiency, cures, and pain relief. METHODS Drug development is a vital research area that necessitates innovation and collaboration to achieve significant breakthroughs. Computer-aided drug design provides a promising avenue for drug discovery and development by reducing costs and improving the efficiency of drug design and testing. RESULTS In this study, a novel model, namely LSTM-SAGDTA, capable of accurately predicting drug-target binding affinity, was developed. We employed SeqVec for characterizing the protein and utilized the graph neural networks to capture information on drug molecules. By introducing self-attentive graph pooling, the model achieved greater accuracy and efficiency in predicting drug-target binding affinity. CONCLUSION Moreover, LSTM-SAGDTA obtained superior accuracy over current state-of-the-art methods only by using less training time. The results of experiments suggest that this method represents a highprecision solution for the DTA predictor.
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Affiliation(s)
- Wenjing Qiu
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen 333000, China
| | - Qianle Liang
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen 333000, China
| | - Liyi Yu
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen 333000, China
| | - Xuan Xiao
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen 333000, China
| | - Wangren Qiu
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen 333000, China
| | - Weizhong Lin
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen 333000, China
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29
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Zeng X, Zhong KY, Jiang B, Li Y. Fusing Sequence and Structural Knowledge by Heterogeneous Models to Accurately and Interpretively Predict Drug-Target Affinity. Molecules 2023; 28:8005. [PMID: 38138496 PMCID: PMC10745601 DOI: 10.3390/molecules28248005] [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: 10/21/2023] [Revised: 12/06/2023] [Accepted: 12/06/2023] [Indexed: 12/24/2023] Open
Abstract
Drug-target affinity (DTA) prediction is crucial for understanding molecular interactions and aiding drug discovery and development. While various computational methods have been proposed for DTA prediction, their predictive accuracy remains limited, failing to delve into the structural nuances of interactions. With increasingly accurate and accessible structure prediction of targets, we developed a novel deep learning model, named S2DTA, to accurately predict DTA by fusing sequence features of drug SMILES, targets, and pockets and their corresponding graph structural features using heterogeneous models based on graph and semantic networks. Experimental findings underscored that complex feature representations imparted negligible enhancements to the model's performance. However, the integration of heterogeneous models demonstrably bolstered predictive accuracy. In comparison to three state-of-the-art methodologies, such as DeepDTA, GraphDTA, and DeepDTAF, S2DTA's performance became more evident. It exhibited a 25.2% reduction in mean absolute error (MAE) and a 20.1% decrease in root mean square error (RMSE). Additionally, S2DTA showed some improvements in other crucial metrics, including Pearson Correlation Coefficient (PCC), Spearman, Concordance Index (CI), and R2, with these metrics experiencing increases of 19.6%, 17.5%, 8.1%, and 49.4%, respectively. Finally, we conducted an interpretability analysis on the effectiveness of S2DTA by bidirectional self-attention mechanism. The analysis results supported that S2DTA was an effective and accurate tool for predicting DTA.
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Affiliation(s)
- Xin Zeng
- College of Mathematics and Computer Science, Dali University, Dali 671003, China; (X.Z.); (K.-Y.Z.)
| | - Kai-Yang Zhong
- College of Mathematics and Computer Science, Dali University, Dali 671003, China; (X.Z.); (K.-Y.Z.)
| | - Bei Jiang
- Yunnan Key Laboratory of Screening and Research on Anti-Pathogenic Plant Resources from Western Yunnan, Dali University, Dali 671000, China;
| | - Yi Li
- College of Mathematics and Computer Science, Dali University, Dali 671003, China; (X.Z.); (K.-Y.Z.)
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30
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Zhu Z, Yao Z, Zheng X, Qi G, Li Y, Mazur N, Gao X, Gong Y, Cong B. Drug-target affinity prediction method based on multi-scale information interaction and graph optimization. Comput Biol Med 2023; 167:107621. [PMID: 37907030 DOI: 10.1016/j.compbiomed.2023.107621] [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: 07/18/2023] [Revised: 10/16/2023] [Accepted: 10/23/2023] [Indexed: 11/02/2023]
Abstract
Drug-target affinity (DTA) prediction as an emerging and effective method is widely applied to explore the strength of drug-target interactions in drug development research. By predicting these interactions, researchers can assess the potential efficacy and safety of candidate drugs at an early stage, narrowing down the search space for therapeutic targets and accelerating the discovery and development of new drugs. However, existing DTA prediction models mainly use graphical representations of drug molecules, which lack information on interactions between individual substructures, thus affecting prediction accuracy and model interpretability. Therefore, transformer and diffusion on drug graphs in DTA prediction (TDGraphDTA) are introduced to predict drug-target interactions using multi-scale information interaction and graph optimization. An interactive module is integrated into feature extraction of drug and target features at different granularity levels. A diffusion model-based graph optimization module is proposed to improve the representation of molecular graph structures and enhance the interpretability of graph representations while obtaining optimal feature representations. In addition, TDGraphDTA improves the accuracy and reliability of predictions by capturing relationships and contextual information between molecular substructures. The performance of the proposed TDGraphDTA in DTA prediction was verified on three publicly available benchmark datasets (Davis, Metz, and KIBA). Compared with state-of-the-art baseline models, it achieved better results in terms of consistency index, R-squared, etc. Furthermore, compared with some existing methods, the proposed TDGraphDTA is demonstrated to have better structure capturing capabilities by visualizing the feature capturing capabilities of the model using Grad-AAM toxicity labels in the ToxCast dataset. The corresponding source codes are available at https://github.com/Lamouryz/TDGraph.
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Affiliation(s)
- Zhiqin Zhu
- College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
| | - Zheng Yao
- College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
| | - Xin Zheng
- College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
| | - Guanqiu Qi
- Computer Information Systems Department, State University of New York at Buffalo State, Buffalo, NY 14222, USA.
| | - Yuanyuan Li
- College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
| | - Neal Mazur
- Computer Information Systems Department, State University of New York at Buffalo State, Buffalo, NY 14222, USA.
| | - Xinbo Gao
- College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
| | - Yifei Gong
- Faculty of applied science & engineering, the Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto at Toronto, ON M5S, Canada.
| | - Baisen Cong
- Diagnostics Digital, DH(Shanghai) Diagnostics Co, Ltd, a Danaher company, Shanghai, 200335, China.
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31
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Zhang L, Ouyang C, Liu Y, Liao Y, Gao Z. Multimodal contrastive representation learning for drug-target binding affinity prediction. Methods 2023; 220:126-133. [PMID: 37952703 DOI: 10.1016/j.ymeth.2023.11.005] [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: 09/20/2023] [Revised: 10/28/2023] [Accepted: 11/06/2023] [Indexed: 11/14/2023] Open
Abstract
In the biomedical field, the efficacy of most drugs is demonstrated by their interactions with targets, meanwhile, accurate prediction of the strength of drug-target binding is extremely important for drug development efforts. Traditional bioassay-based drug-target binding affinity (DTA) prediction methods cannot meet the needs of drug R&D in the era of big data. Recent years we have witnessed significant success on deep learning-based models for drug-target binding affinity prediction task. However, these models only considered a single modality of drug and target information, and some valuable information was not fully utilized. In fact, the information of different modalities of drug and target can complement each other, and more valuable information can be obtained by fusing the information of different modalities. In this paper, we introduce a multimodal information fusion model for DTA prediction that is called FMDTA, which fully considers drug/target information in both string and graph modalities and balances the feature representations of different modalities by a contrastive learning approach. In addition, we exploited the alignment information of drug atoms and target residues to capture the positional information of string patterns, which can extract more useful feature information in SMILES and target sequences. Experimental results on two benchmark datasets show that FMDTA outperforms the state-of-the-art model, demonstrating the feasibility and excellent feature capture capability of FMDTA. The code of FMDTA and the data are available at: https://github.com/bestdoubleLin/FMDTA.
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Affiliation(s)
- Linlin Zhang
- School of Computer, University of South China, Hengyang, China
| | - Chunping Ouyang
- School of Computer, University of South China, Hengyang, China.
| | - Yongbin Liu
- School of Computer, University of South China, Hengyang, China
| | - Yiming Liao
- The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
| | - Zheng Gao
- Department of Information and Library Science, Indiana University Bloomington, Bloomington, United States
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32
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Luo Y, Liu Y, Peng J. Calibrated geometric deep learning improves kinase-drug binding predictions. NAT MACH INTELL 2023; 5:1390-1401. [PMID: 38962391 PMCID: PMC11221792 DOI: 10.1038/s42256-023-00751-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 09/29/2023] [Indexed: 07/05/2024]
Abstract
Protein kinases regulate various cellular functions and hold significant pharmacological promise in cancer and other diseases. Although kinase inhibitors are one of the largest groups of approved drugs, much of the human kinome remains unexplored but potentially druggable. Computational approaches, such as machine learning, offer efficient solutions for exploring kinase-compound interactions and uncovering novel binding activities. Despite the increasing availability of three-dimensional (3D) protein and compound structures, existing methods predominantly focus on exploiting local features from one-dimensional protein sequences and two-dimensional molecular graphs to predict binding affinities, overlooking the 3D nature of the binding process. Here we present KDBNet, a deep learning algorithm that incorporates 3D protein and molecule structure data to predict binding affinities. KDBNet uses graph neural networks to learn structure representations of protein binding pockets and drug molecules, capturing the geometric and spatial characteristics of binding activity. In addition, we introduce an algorithm to quantify and calibrate the uncertainties of KDBNet's predictions, enhancing its utility in model-guided discovery in chemical or protein space. Experiments demonstrated that KDBNet outperforms existing deep learning models in predicting kinase-drug binding affinities. The uncertainties estimated by KDBNet are informative and well-calibrated with respect to prediction errors. When integrated with a Bayesian optimization framework, KDBNet enables data-efficient active learning and accelerates the exploration and exploitation of diverse high-binding kinase-drug pairs.
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Affiliation(s)
- Yunan Luo
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
- These authors contributed equally: Yunan Luo, Yang Liu
| | - Yang Liu
- Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL, USA
- These authors contributed equally: Yunan Luo, Yang Liu
| | - Jian Peng
- Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL, USA
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33
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Li Y, Fan Z, Rao J, Chen Z, Chu Q, Zheng M, Li X. An overview of recent advances and challenges in predicting compound-protein interaction (CPI). MEDICAL REVIEW (2021) 2023; 3:465-486. [PMID: 38282802 PMCID: PMC10808869 DOI: 10.1515/mr-2023-0030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 08/30/2023] [Indexed: 01/30/2024]
Abstract
Compound-protein interactions (CPIs) are critical in drug discovery for identifying therapeutic targets, drug side effects, and repurposing existing drugs. Machine learning (ML) algorithms have emerged as powerful tools for CPI prediction, offering notable advantages in cost-effectiveness and efficiency. This review provides an overview of recent advances in both structure-based and non-structure-based CPI prediction ML models, highlighting their performance and achievements. It also offers insights into CPI prediction-related datasets and evaluation benchmarks. Lastly, the article presents a comprehensive assessment of the current landscape of CPI prediction, elucidating the challenges faced and outlining emerging trends to advance the field.
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Affiliation(s)
- Yanbei Li
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhehuan Fan
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jingxin Rao
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhiyi Chen
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Qinyu Chu
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Mingyue Zheng
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
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Wang J, Xiao Y, Shang X, Peng J. Predicting drug-target binding affinity with cross-scale graph contrastive learning. Brief Bioinform 2023; 25:bbad516. [PMID: 38221904 PMCID: PMC10788681 DOI: 10.1093/bib/bbad516] [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: 07/03/2023] [Revised: 12/04/2023] [Accepted: 12/07/2023] [Indexed: 01/16/2024] Open
Abstract
Identifying the binding affinity between a drug and its target is essential in drug discovery and repurposing. Numerous computational approaches have been proposed for understanding these interactions. However, most existing methods only utilize either the molecular structure information of drugs and targets or the interaction information of drug-target bipartite networks. They may fail to combine the molecule-scale and network-scale features to obtain high-quality representations. In this study, we propose CSCo-DTA, a novel cross-scale graph contrastive learning approach for drug-target binding affinity prediction. The proposed model combines features learned from the molecular scale and the network scale to capture information from both local and global perspectives. We conducted experiments on two benchmark datasets, and the proposed model outperformed existing state-of-art methods. The ablation experiment demonstrated the significance and efficacy of multi-scale features and cross-scale contrastive learning modules in improving the prediction performance. Moreover, we applied the CSCo-DTA to predict the novel potential targets for Erlotinib and validated the predicted targets with the molecular docking analysis.
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Affiliation(s)
- Jingru Wang
- School of Computer Science, Northwestern Polytechnical University, Xi’an, 710072, China
- Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi’an, 710072, China
- The National Engineering Laboratory for Integrated Aerospace-Ground-Ocean Big Data Application Technology, Xi’an, 710072, China
| | - Yihang Xiao
- School of Computer Science, Northwestern Polytechnical University, Xi’an, 710072, China
- Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi’an, 710072, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi’an, 710072, China
- Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi’an, 710072, China
- The National Engineering Laboratory for Integrated Aerospace-Ground-Ocean Big Data Application Technology, Xi’an, 710072, China
| | - Jiajie Peng
- School of Computer Science, Northwestern Polytechnical University, Xi’an, 710072, China
- Key Laboratory of Big Data Storage and Management, Northwestern Polytechnical University, Ministry of Industry and Information Technology, Xi’an, 710072, China
- The National Engineering Laboratory for Integrated Aerospace-Ground-Ocean Big Data Application Technology, Xi’an, 710072, China
- Research and Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, 518000, China
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35
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Song N, Dong R, Pu Y, Wang E, Xu J, Guo F. Pmf-cpi: assessing drug selectivity with a pretrained multi-functional model for compound-protein interactions. J Cheminform 2023; 15:97. [PMID: 37838703 PMCID: PMC10576287 DOI: 10.1186/s13321-023-00767-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 09/28/2023] [Indexed: 10/16/2023] Open
Abstract
Compound-protein interactions (CPI) play significant roles in drug development. To avoid side effects, it is also crucial to evaluate drug selectivity when binding to different targets. However, most selectivity prediction models are constructed for specific targets with limited data. In this study, we present a pretrained multi-functional model for compound-protein interaction prediction (PMF-CPI) and fine-tune it to assess drug selectivity. This model uses recurrent neural networks to process the protein embedding based on the pretrained language model TAPE, extracts molecular information from a graph encoder, and produces the output from dense layers. PMF-CPI obtained the best performance compared to outstanding approaches on both the binding affinity regression and CPI classification tasks. Meanwhile, we apply the model to analyzing drug selectivity after fine-tuning it on three datasets related to specific targets, including human cytochrome P450s. The study shows that PMF-CPI can accurately predict different drug affinities or opposite interactions toward similar targets, recognizing selective drugs for precise therapeutics.Kindly confirm if corresponding authors affiliations are identified correctly and amend if any.Yes, it is correct.
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Affiliation(s)
- Nan Song
- School of New Media and Communication, Tianjin University, Tianjin, Tianjin, 300072, China
- College of Intelligence and Computing, Tianjin University, Tianjin, Tianjin, 300350, China
| | - Ruihan Dong
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, Beijing, 100871, China
| | - Yuqian Pu
- College of Intelligence and Computing, Tianjin University, Tianjin, Tianjin, 300350, China
| | - Ercheng Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
- Zhejiang Laboratory, Hangzhou, 311100, Zhejiang, China.
| | - Junhai Xu
- School of New Media and Communication, Tianjin University, Tianjin, Tianjin, 300072, China.
- College of Intelligence and Computing, Tianjin University, Tianjin, Tianjin, 300350, China.
| | - Fei Guo
- School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China.
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36
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Brahma R, Shin JM, Cho KH. KinScan: AI-based rapid profiling of activity across the kinome. Brief Bioinform 2023; 24:bbad396. [PMID: 37985454 DOI: 10.1093/bib/bbad396] [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: 07/30/2023] [Revised: 09/22/2023] [Accepted: 10/14/2023] [Indexed: 11/22/2023] Open
Abstract
Kinases play a vital role in regulating essential cellular processes, including cell cycle progression, growth, apoptosis, and metabolism, by catalyzing the transfer of phosphate groups from adenosing triphosphate to substrates. Their dysregulation has been closely associated with numerous diseases, including cancer development, making them attractive targets for drug discovery. However, accurately predicting the binding affinity between chemical compounds and kinase targets remains challenging due to the highly conserved structural similarities across the kinome. To address this limitation, we present KinScan, a novel computational approach that leverages large-scale bioactivity data and integrates the Multi-Scale Context Aware Transformer framework to construct a virtual profiling model encompassing 391 protein kinases. The developed model demonstrates exceptional prediction capability, distinguishing between kinases by utilizing structurally aligned kinase binding site features derived from multiple sequence alignment for fast and accurate predictions. Through extensive validation and benchmarking, KinScan demonstrated its robust predictive power and generalizability for large-scale kinome-wide profiling and selectivity, uncovering associations with specific diseases and providing valuable insights into kinase activity profiles of compounds. Furthermore, we deployed a web platform for end-to-end profiling and selectivity analysis, accessible at https://kinscan.drugonix.com/softwares/kinscan.
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Affiliation(s)
- Rahul Brahma
- School of Systems Biomedical Science, Soongsil University, Seoul, Republic of Korea
| | - Jae-Min Shin
- AzothBio, Rm. DA724 Hyundai Knowledge Industry Center, Hanam-si, Gyeonggi-do, Republic of Korea
| | - Kwang-Hwi Cho
- School of Systems Biomedical Science, Soongsil University, Seoul, Republic of Korea
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37
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Ru Z, Wu Y, Shao J, Yin J, Qian L, Miao X. A dual-modal graph learning framework for identifying interaction events among chemical and biotech drugs. Brief Bioinform 2023; 24:bbad271. [PMID: 37507113 DOI: 10.1093/bib/bbad271] [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: 03/28/2023] [Revised: 06/18/2023] [Accepted: 07/06/2023] [Indexed: 07/30/2023] Open
Abstract
Drug-drug interaction (DDI) identification is essential to clinical medicine and drug discovery. The two categories of drugs (i.e. chemical drugs and biotech drugs) differ remarkably in molecular properties, action mechanisms, etc. Biotech drugs are up-to-comers but highly promising in modern medicine due to higher specificity and fewer side effects. However, existing DDI prediction methods only consider chemical drugs of small molecules, not biotech drugs of large molecules. Here, we build a large-scale dual-modal graph database named CB-DB and customize a graph-based framework named CB-TIP to reason event-aware DDIs for both chemical and biotech drugs. CB-DB comprehensively integrates various interaction events and two heterogeneous kinds of molecular structures. It imports endogenous proteins founded on the fact that most drugs take effects by interacting with endogenous proteins. In the modality of molecular structure, drugs and endogenous proteins are two heterogeneous kinds of graphs, while in the modality of interaction, they are nodes connected by events (i.e. edges of different relationships). CB-TIP employs graph representation learning methods to generate drug representations from either modality and then contrastively mixes them to predict how likely an event occurs when a drug meets another in an end-to-end manner. Experiments demonstrate CB-TIP's great superiority in DDI prediction and the promising potential of uncovering novel DDIs.
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Affiliation(s)
- Zhongying Ru
- Center for Data Science, Zhejiang University, 866 Yuhangtang Rd, 310058, Hangzhou, P.R. China
- Polytechnic Institute, Zhejiang University, 866 Yuhangtang Rd, 310058, Hangzhou, P.R. China
| | - Yangyang Wu
- Center for Data Science, Zhejiang University, 866 Yuhangtang Rd, 310058, Hangzhou, P.R. China
| | - Jinning Shao
- Institute of Drug Metabolism and Pharmaceutical Analysis, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Cancer Center, & Hangzhou Institute of Innovative Medicine, Zhejiang University, 866 Yuhangtang Rd, 310058, Hangzhou, P.R. China
| | - Jianwei Yin
- Center for Data Science, Zhejiang University, 866 Yuhangtang Rd, 310058, Hangzhou, P.R. China
- College of Computer Science, Zhejiang University, 866 Yuhangtang Rd, 310058, Hangzhou, P.R. China
| | - Linghui Qian
- Institute of Drug Metabolism and Pharmaceutical Analysis, Zhejiang Province Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Cancer Center, & Hangzhou Institute of Innovative Medicine, Zhejiang University, 866 Yuhangtang Rd, 310058, Hangzhou, P.R. China
| | - Xiaoye Miao
- Center for Data Science, Zhejiang University, 866 Yuhangtang Rd, 310058, Hangzhou, P.R. China
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38
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Dong L, Shi S, Qu X, Luo D, Wang B. Ligand binding affinity prediction with fusion of graph neural networks and 3D structure-based complex graph. Phys Chem Chem Phys 2023; 25:24110-24120. [PMID: 37655493 DOI: 10.1039/d3cp03651k] [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: 09/02/2023]
Abstract
Accurate prediction of protein-ligand binding affinity is pivotal for drug design and discovery. Here, we proposed a novel deep fusion graph neural networks framework named FGNN to learn the protein-ligand interactions from the 3D structures of protein-ligand complexes. Unlike 1D sequences for proteins or 2D graphs for ligands, the 3D graph of protein-ligand complex enables the more accurate representations of the protein-ligand interactions. Benchmark studies have shown that our fusion models FGNN can achieve more accurate prediction of binding affinity than any individual algorithm. The advantages of fusion strategies have been demonstrated in terms of expressive power of data, learning efficiency and model interpretability. Our fusion models show satisfactory performances on diverse data sets, demonstrating their generalization ability. Given the good performances in both binding affinity prediction and virtual screening, our fusion models are expected to be practically applied for drug screening and design. Our work highlights the potential of the fusion graph neural network algorithm in solving complex prediction problems in computational biology and chemistry. The fusion graph neural networks (FGNN) model is freely available in https://github.com/LinaDongXMU/FGNN.
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Affiliation(s)
- Lina Dong
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China.
| | - Shuai Shi
- Department of Algorithm, TuringQ Co., Ltd., Shanghai, 200240, China
| | - Xiaoyang Qu
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China.
| | - Ding Luo
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China.
| | - Binju Wang
- State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China.
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen, 361005, China
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Pan S, Xia L, Xu L, Li Z. SubMDTA: drug target affinity prediction based on substructure extraction and multi-scale features. BMC Bioinformatics 2023; 24:334. [PMID: 37679724 PMCID: PMC10485962 DOI: 10.1186/s12859-023-05460-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 08/31/2023] [Indexed: 09/09/2023] Open
Abstract
BACKGROUND Drug-target affinity (DTA) prediction is a critical step in the field of drug discovery. In recent years, deep learning-based methods have emerged for DTA prediction. In order to solve the problem of fusion of substructure information of drug molecular graphs and utilize multi-scale information of protein, a self-supervised pre-training model based on substructure extraction and multi-scale features is proposed in this paper. RESULTS For drug molecules, the model obtains substructure information through the method of probability matrix, and the contrastive learning method is implemented on the graph-level representation and subgraph-level representation to pre-train the graph encoder for downstream tasks. For targets, a BiLSTM method that integrates multi-scale features is used to capture long-distance relationships in the amino acid sequence. The experimental results showed that our model achieved better performance for DTA prediction. CONCLUSIONS The proposed model improves the performance of the DTA prediction, which provides a novel strategy based on substructure extraction and multi-scale features.
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Affiliation(s)
- Shourun Pan
- College of Computer Science and Technology, Qingdao University, Qingdao, China
| | - Leiming Xia
- College of Computer Science and Technology, Qingdao University, Qingdao, China
| | - Lei Xu
- College of Computer Science and Technology, Qingdao University, Qingdao, China
| | - Zhen Li
- College of Computer Science and Technology, Qingdao University, Qingdao, China.
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40
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Sharma AK, Kukreja R, Kharbanda M, Chakraborty T. Node injection for class-specific network poisoning. Neural Netw 2023; 166:236-247. [PMID: 37517358 DOI: 10.1016/j.neunet.2023.07.025] [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: 01/06/2023] [Revised: 06/13/2023] [Accepted: 07/15/2023] [Indexed: 08/01/2023]
Abstract
Graph Neural Networks (GNNs) are powerful in learning rich network representations that aid the performance of downstream tasks. However, recent studies showed that GNNs are vulnerable to adversarial attacks involving node injection and network perturbation. Among these, node injection attacks are more practical as they do not require manipulation in the existing network and can be performed more realistically. In this paper, we propose a novel problem statement - a class-specific poison attack on graphs in which the attacker aims to misclassify specific nodes in the target class into a different class using node injection. Additionally, nodes are injected in such a way that they camouflage as benign nodes. We propose NICKI, a novel attacking strategy that utilizes an optimization-based approach to sabotage the performance of GNN-based node classifiers. NICKI works in two phases - it first learns the node representation and then generates the features and edges of the injected nodes. Extensive experiments and ablation studies on four benchmark networks show that NICKI is consistently better than four baseline attacking strategies for misclassifying nodes in the target class. We also show that the injected nodes are properly camouflaged as benign, thus making the poisoned graph indistinguishable from its clean version w.r.t various topological properties.
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Affiliation(s)
| | - Rahul Kukreja
- Indraprastha Institute of Information Technology, Delhi, India
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41
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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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Lei C, Lu Z, Wang M, Li M. StackCPA: A stacking model for compound-protein binding affinity prediction based on pocket multi-scale features. Comput Biol Med 2023; 164:107131. [PMID: 37494820 DOI: 10.1016/j.compbiomed.2023.107131] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 05/10/2023] [Accepted: 06/01/2023] [Indexed: 07/28/2023]
Abstract
Accurately predicting compound-protein binding affinity is a crucial task in drug discovery. Computational models offer the advantages of short time, low cost and safety compared to traditional drug development. Pocket is the key binding region of the protein, which provides invaluable information for drug repositioning and drug design. In this study, we propose an ensemble learning model, called StackCPA, to predict the compound-protein binding affinity. The model integrates multi-scale features of protein pocket and compound through a transfer learning strategy. The protein pocket is described in a fine-grained way by atomic level, residue level and subdomain level. The proposed model StackCPA is evaluated on three binding affinity benchmark datasets. The experiment results show that StackCPA achieves the best performance on all the three datasets in comparison with other state-of-the-art deep learning models. The ablation study shows that the protein pocket can provide sufficient information for affinity prediction and its multi-scale features enable the model to further improve the prediction performance. In addition, the case study for epidermal growth factor receptor erbB1 (EGFR) indicates that StackCPA could serve as an effective tool for drug repurposing. Source codes and data of StackCPA are available at https://github.com/CSUBioGroup/StackCPA.
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Affiliation(s)
- Chuqi Lei
- School of Computer Science and Engineering, Central South University, 410083, Changsha, PR China
| | - Zhangli Lu
- School of Computer Science and Engineering, Central South University, 410083, Changsha, PR China
| | - Meng Wang
- School of Computer Science and Engineering, Central South University, 410083, Changsha, PR China
| | - Min Li
- School of Computer Science and Engineering, Central South University, 410083, Changsha, PR 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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Suviriyapaisal N, Wichadakul D. iEdgeDTA: integrated edge information and 1D graph convolutional neural networks for binding affinity prediction. RSC Adv 2023; 13:25218-25228. [PMID: 37636509 PMCID: PMC10448119 DOI: 10.1039/d3ra03796g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 08/14/2023] [Indexed: 08/29/2023] Open
Abstract
Artificial intelligence has become more prevalent in broad fields, including drug discovery, in which the process is costly and time-consuming when conducted through wet experiments. As a result, drug repurposing, which tries to utilize approved and low-risk drugs for a new purpose, becomes more attractive. However, screening candidates from many drugs for specific protein targets is still expensive and tedious. This study aims to leverage computational resources to aid drug discovery by utilizing drug-protein interaction data and estimating their interaction strength, so-called binding affinity. Our estimation approach addresses multiple challenges encountered in the field. First, we employed a graph-based deep learning technique to overcome the limitations of drug compounds represented in string format by incorporating background knowledge of node and edge information as separate multi-dimensional features. Second, we tackled the complexities associated with extracting the representation and structure of proteins by utilizing a pre-trained model for feature extraction. Also, we employed graph operations over the 1D representation of a protein sequence to overcome the fixed-length problem typically encountered in language model tasks. In addition, we conducted a comparative analysis with a baseline model that creates a protein graph from a contact map prediction model, giving valuable insights into the performance and effectiveness of our proposed method. We evaluated the performance of our model using the same benchmark datasets with a variety of matrices as other previous work, and the results show that our model achieved the best prediction results while requiring no contact map information compared to other graph-based methods.
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Affiliation(s)
- Natchanon Suviriyapaisal
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University Bangkok 10330 Thailand
| | - Duangdao Wichadakul
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University Bangkok 10330 Thailand
- Center of Excellence in Systems Biology, Faculty of Medicine, Chulalongkorn University Bangkok 10330 Thailand
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45
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Tsui LI, Hsu TC, Lin C. NG-DTA: Drug-target affinity prediction with n-gram molecular graphs. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082648 DOI: 10.1109/embc40787.2023.10339968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Drug-target affinity (DTA) prediction is crucial to speed up drug development. The advance in deep learning allows accurate DTA prediction. However, most deep learning methods treat protein as a 1D string which is not informative to models compared to a graph representation. In this paper, we present a deep-learning-based DTA prediction method called N-gram Graph DTA (NG-DTA) that takes molecular graphs of drugs and n-gram molecular sub-graphs of proteins as inputs which are then processed by graph neural networks (GNNs). Without using any prediction tool for protein structure, NG-DTA performs better than other methods on two datasets in terms of concordance index (CI) and mean square error (MSE) (CI: 0.905, MSE: 0.196 for the Davis dataset; CI: 0.904, MSE: 0.120 for Kiba dataset). Our results showed that using n-gram molecular sub-graphs of proteins as input improves deep learning models' performance in DTA prediction.
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Sun J, Xu M, Ru J, James-Bott A, Xiong D, Wang X, Cribbs AP. Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications. Eur J Med Chem 2023; 257:115500. [PMID: 37262996 DOI: 10.1016/j.ejmech.2023.115500] [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: 03/28/2023] [Revised: 05/05/2023] [Accepted: 05/15/2023] [Indexed: 06/03/2023]
Abstract
Small molecules have been providing medical breakthroughs for human diseases for more than a century. Recently, identifying small molecule inhibitors that target microRNAs (miRNAs) has gained importance, despite the challenges posed by labour-intensive screening experiments and the significant efforts required for medicinal chemistry optimization. Numerous experimentally-verified cases have demonstrated the potential of miRNA-targeted small molecule inhibitors for disease treatment. This new approach is grounded in their posttranscriptional regulation of the expression of disease-associated genes. Reversing dysregulated gene expression using this mechanism may help control dysfunctional pathways. Furthermore, the ongoing improvement of algorithms has allowed for the integration of computational strategies built on top of laboratory-based data, facilitating a more precise and rational design and discovery of lead compounds. To complement the use of extensive pharmacogenomics data in prioritising potential drugs, our previous work introduced a computational approach based on only molecular sequences. Moreover, various computational tools for predicting molecular interactions in biological networks using similarity-based inference techniques have been accumulated in established studies. However, there are a limited number of comprehensive reviews covering both computational and experimental drug discovery processes. In this review, we outline a cohesive overview of both biological and computational applications in miRNA-targeted drug discovery, along with their disease implications and clinical significance. Finally, utilizing drug-target interaction (DTIs) data from DrugBank, we showcase the effectiveness of deep learning for obtaining the physicochemical characterization of DTIs.
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Affiliation(s)
- Jianfeng Sun
- Botnar Research Centre, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK.
| | - Miaoer Xu
- Department of Biology, Emory University, Atlanta, GA, 30322, USA
| | - Jinlong Ru
- Chair of Prevention of Microbial Diseases, School of Life Sciences Weihenstephan, Technical University of Munich, Freising, 85354, Germany
| | - Anna James-Bott
- Botnar Research Centre, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
| | - Dapeng Xiong
- Department of Computational Biology, Cornell University, Ithaca, NY, 14853, USA; Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY, 14853, USA
| | - Xia Wang
- College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China.
| | - Adam P Cribbs
- Botnar Research Centre, Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK.
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Choo HY, Wee J, Shen C, Xia K. Fingerprint-Enhanced Graph Attention Network (FinGAT) Model for Antibiotic Discovery. J Chem Inf Model 2023; 63:2928-2935. [PMID: 37167016 DOI: 10.1021/acs.jcim.3c00045] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Artificial Intelligence (AI) techniques are of great potential to fundamentally change antibiotic discovery industries. Efficient and effective molecular featurization is key to all highly accurate learning models for antibiotic discovery. In this paper, we propose a fingerprint-enhanced graph attention network (FinGAT) model by the combination of sequence-based 2D fingerprints and structure-based graph representation. In our feature learning process, sequence information is transformed into a fingerprint vector, and structural information is encoded through a GAT module into another vector. These two vectors are concatenated and input into a multilayer perceptron (MLP) for antibiotic activity classification. Our model is extensively tested and compared with existing models. It has been found that our FinGAT can outperform various state-of-the-art GNN models in antibiotic discovery.
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Affiliation(s)
- Hou Yee Choo
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences Nanyang Technological University, Singapore 637371
| | - JunJie Wee
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences Nanyang Technological University, Singapore 637371
| | - Cong Shen
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences Nanyang Technological University, Singapore 637371
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410083, China
| | - Kelin Xia
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences Nanyang Technological University, Singapore 637371
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Wang S, Song X, Zhang Y, Zhang K, Liu Y, Ren C, Pang S. MSGNN-DTA: Multi-Scale Topological Feature Fusion Based on Graph Neural Networks for Drug-Target Binding Affinity Prediction. Int J Mol Sci 2023; 24:ijms24098326. [PMID: 37176031 PMCID: PMC10179712 DOI: 10.3390/ijms24098326] [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: 04/17/2023] [Revised: 05/03/2023] [Accepted: 05/04/2023] [Indexed: 05/15/2023] Open
Abstract
The accurate prediction of drug-target binding affinity (DTA) is an essential step in drug discovery and drug repositioning. Although deep learning methods have been widely adopted for DTA prediction, the complexity of extracting drug and target protein features hampers the accuracy of these predictions. In this study, we propose a novel model for DTA prediction named MSGNN-DTA, which leverages a fused multi-scale topological feature approach based on graph neural networks (GNNs). To address the challenge of accurately extracting drug and target protein features, we introduce a gated skip-connection mechanism during the feature learning process to fuse multi-scale topological features, resulting in information-rich representations of drugs and proteins. Our approach constructs drug atom graphs, motif graphs, and weighted protein graphs to fully extract topological information and provide a comprehensive understanding of underlying molecular interactions from multiple perspectives. Experimental results on two benchmark datasets demonstrate that MSGNN-DTA outperforms the state-of-the-art models in all evaluation metrics, showcasing the effectiveness of the proposed approach. Moreover, the study conducts a case study based on already FDA-approved drugs in the DrugBank dataset to highlight the potential of the MSGNN-DTA framework in identifying drug candidates for specific targets, which could accelerate the process of virtual screening and drug repositioning.
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Affiliation(s)
- Shudong Wang
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China
| | - Xuanmo Song
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China
| | - Yuanyuan Zhang
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266525, China
| | - Kuijie Zhang
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China
| | - Yingye Liu
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China
| | - Chuanru Ren
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China
| | - Shanchen Pang
- Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China
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Yousefi N, Yazdani-Jahromi M, Tayebi A, Kolanthai E, Neal CJ, Banerjee T, Gosai A, Balasubramanian G, Seal S, Ozmen Garibay O. BindingSite-AugmentedDTA: enabling a next-generation pipeline for interpretable prediction models in drug repurposing. Brief Bioinform 2023; 24:7140297. [PMID: 37096593 DOI: 10.1093/bib/bbad136] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 03/02/2022] [Accepted: 03/16/2023] [Indexed: 04/26/2023] Open
Abstract
While research into drug-target interaction (DTI) prediction is fairly mature, generalizability and interpretability are not always addressed in the existing works in this field. In this paper, we propose a deep learning (DL)-based framework, called BindingSite-AugmentedDTA, which improves drug-target affinity (DTA) predictions by reducing the search space of potential-binding sites of the protein, thus making the binding affinity prediction more efficient and accurate. Our BindingSite-AugmentedDTA is highly generalizable as it can be integrated with any DL-based regression model, while it significantly improves their prediction performance. Also, unlike many existing models, our model is highly interpretable due to its architecture and self-attention mechanism, which can provide a deeper understanding of its underlying prediction mechanism by mapping attention weights back to protein-binding sites. The computational results confirm that our framework can enhance the prediction performance of seven state-of-the-art DTA prediction algorithms in terms of four widely used evaluation metrics, including concordance index, mean squared error, modified squared correlation coefficient ($r^2_m$) and the area under the precision curve. We also contribute to three benchmark drug-traget interaction datasets by including additional information on 3D structure of all proteins contained in those datasets, which include the two most commonly used datasets, namely Kiba and Davis, as well as the data from IDG-DREAM drug-kinase binding prediction challenge. Furthermore, we experimentally validate the practical potential of our proposed framework through in-lab experiments. The relatively high agreement between computationally predicted and experimentally observed binding interactions supports the potential of our framework as the next-generation pipeline for prediction models in drug repurposing.
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Affiliation(s)
- Niloofar Yousefi
- Industrial Engineering and Management Systems, University of Central Florida, 32816, 4000 Central Florida Blvd., Orlando, FL, USA
| | - Mehdi Yazdani-Jahromi
- Computer Science, University of Central Florida, 32816, 4000 Central Florida Blvd., Orlando, FL, USA
| | - Aida Tayebi
- Industrial Engineering and Management Systems, University of Central Florida, 32816, 4000 Central Florida Blvd., Orlando, FL, USA
| | - Elayaraja Kolanthai
- College of Medicine, Bionix Cluster, University of Central Florida, 4000 Central Florida Blvd., Orlando 32816, FL, USA
| | - Craig J Neal
- College of Medicine, Bionix Cluster, University of Central Florida, 4000 Central Florida Blvd., Orlando 32816, FL, USA
| | - Tanumoy Banerjee
- Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem 18015, PA, USA
| | | | - Ganesh Balasubramanian
- Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem 18015, PA, USA
| | - Sudipta Seal
- College of Medicine, Bionix Cluster, University of Central Florida, 4000 Central Florida Blvd., Orlando 32816, FL, USA
- Advanced Materials Processing and Analysis Center, Department of Materials Science and Engineering, University of Central Florida, 4000 Central Florida Blvd., Orlando 32816, FL, USA
| | - Ozlem Ozmen Garibay
- Industrial Engineering and Management Systems, University of Central Florida, 32816, 4000 Central Florida Blvd., Orlando, FL, USA
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Veličković P. Everything is connected: Graph neural networks. Curr Opin Struct Biol 2023; 79:102538. [PMID: 36764042 DOI: 10.1016/j.sbi.2023.102538] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 12/28/2022] [Accepted: 01/03/2023] [Indexed: 02/11/2023]
Abstract
In many ways, graphs are the main modality of data we receive from nature. This is due to the fact that most of the patterns we see, both in natural and artificial systems, are elegantly representable using the language of graph structures. Prominent examples include molecules (represented as graphs of atoms and bonds), social networks and transportation networks. This potential has already been seen by key scientific and industrial groups, with already-impacted application areas including traffic forecasting, drug discovery, social network analysis and recommender systems. Further, some of the most successful domains of application for machine learning in previous years-images, text and speech processing-can be seen as special cases of graph representation learning, and consequently there has been significant exchange of information between these areas. The main aim of this short survey is to enable the reader to assimilate the key concepts in the area, and position graph representation learning in a proper context with related fields.
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Affiliation(s)
- Petar Veličković
- DeepMind, 6 Pancras Square, London, N1C 4AG, Greater London, UK; Department of Computer Science and Technology, University of Cambridge, 15 JJ Thomson Avenue, Cambridge, CB3 0FD, Cambridgeshire, UK.
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