51
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Wang H. Prediction of protein-ligand binding affinity via deep learning models. Brief Bioinform 2024; 25:bbae081. [PMID: 38446737 PMCID: PMC10939342 DOI: 10.1093/bib/bbae081] [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: 11/27/2023] [Revised: 01/31/2024] [Indexed: 03/08/2024] Open
Abstract
Accurately predicting the binding affinity between proteins and ligands is crucial in drug screening and optimization, but it is still a challenge in computer-aided drug design. The recent success of AlphaFold2 in predicting protein structures has brought new hope for deep learning (DL) models to accurately predict protein-ligand binding affinity. However, the current DL models still face limitations due to the low-quality database, inaccurate input representation and inappropriate model architecture. In this work, we review the computational methods, specifically DL-based models, used to predict protein-ligand binding affinity. We start with a brief introduction to protein-ligand binding affinity and the traditional computational methods used to calculate them. We then introduce the basic principles of DL models for predicting protein-ligand binding affinity. Next, we review the commonly used databases, input representations and DL models in this field. Finally, we discuss the potential challenges and future work in accurately predicting protein-ligand binding affinity via DL models.
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Affiliation(s)
- Huiwen Wang
- School of Physics and Engineering, Henan University of Science and Technology, Luoyang 471023, China
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52
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Li Z, Ren P, Yang H, Zheng J, Bai F. TEFDTA: a transformer encoder and fingerprint representation combined prediction method for bonded and non-bonded drug-target affinities. Bioinformatics 2024; 40:btad778. [PMID: 38141210 PMCID: PMC10777355 DOI: 10.1093/bioinformatics/btad778] [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: 09/25/2023] [Revised: 11/23/2023] [Accepted: 12/22/2023] [Indexed: 12/25/2023] Open
Abstract
MOTIVATION The prediction of binding affinity between drug and target is crucial in drug discovery. However, the accuracy of current methods still needs to be improved. On the other hand, most deep learning methods focus only on the prediction of non-covalent (non-bonded) binding molecular systems, but neglect the cases of covalent binding, which has gained increasing attention in the field of drug development. RESULTS In this work, a new attention-based model, A Transformer Encoder and Fingerprint combined Prediction method for Drug-Target Affinity (TEFDTA) is proposed to predict the binding affinity for bonded and non-bonded drug-target interactions. To deal with such complicated problems, we used different representations for protein and drug molecules, respectively. In detail, an initial framework was built by training our model using the datasets of non-bonded protein-ligand interactions. For the widely used dataset Davis, an additional contribution of this study is that we provide a manually corrected Davis database. The model was subsequently fine-tuned on a smaller dataset of covalent interactions from the CovalentInDB database to optimize performance. The results demonstrate a significant improvement over existing approaches, with an average improvement of 7.6% in predicting non-covalent binding affinity and a remarkable average improvement of 62.9% in predicting covalent binding affinity compared to using BindingDB data alone. At the end, the potential ability of our model to identify activity cliffs was investigated through a case study. The prediction results indicate that our model is sensitive to discriminate the difference of binding affinities arising from small variances in the structures of compounds. AVAILABILITY AND IMPLEMENTATION The codes and datasets of TEFDTA are available at https://github.com/lizongquan01/TEFDTA.
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Affiliation(s)
- Zongquan Li
- School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Pengxuan Ren
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Hao Yang
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Jie Zheng
- School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Fang Bai
- School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China
- Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
- Shanghai Clinical Research and Trial Center, Shanghai, 201210, China
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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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54
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Nath A, Chaube R. Mining Chemogenomic Spaces for Prediction of Drug-Target Interactions. Methods Mol Biol 2024; 2714:155-169. [PMID: 37676598 DOI: 10.1007/978-1-0716-3441-7_9] [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: 09/08/2023]
Abstract
The pipeline of drug discovery consists of a number of processes; drug-target interaction determination is one of the salient steps among them. Computational prediction of drug-target interactions can facilitate in reducing the search space of experimental wet lab-based verifications steps, thus considerably reducing time and other resources dedicated to the drug discovery pipeline. While machine learning-based methods are more widespread for drug-target interaction prediction, network-centric methods are also evolving. In this chapter, we focus on the process of the drug-target interaction prediction from the perspective of using machine learning algorithms and the various stages involved for developing an accurate predictor.
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Affiliation(s)
- Abhigyan Nath
- Department of Biochemistry, Pt. Jawahar Lal Nehru Memorial Medical College, Raipur, India
| | - Radha Chaube
- Department of Zoology, Institute of Science, Banaras Hindu University, Varanasi, India
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55
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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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56
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Stephenson EH, Higgins JMG. Pharmacological approaches to understanding protein kinase signaling networks. Front Pharmacol 2023; 14:1310135. [PMID: 38164473 PMCID: PMC10757940 DOI: 10.3389/fphar.2023.1310135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 11/27/2023] [Indexed: 01/03/2024] Open
Abstract
Protein kinases play vital roles in controlling cell behavior, and an array of kinase inhibitors are used successfully for treatment of disease. Typical drug development pipelines involve biological studies to validate a protein kinase target, followed by the identification of small molecules that effectively inhibit this target in cells, animal models, and patients. However, it is clear that protein kinases operate within complex signaling networks. These networks increase the resilience of signaling pathways, which can render cells relatively insensitive to inhibition of a single kinase, and provide the potential for pathway rewiring, which can result in resistance to therapy. It is therefore vital to understand the properties of kinase signaling networks in health and disease so that we can design effective multi-targeted drugs or combinations of drugs. Here, we outline how pharmacological and chemo-genetic approaches can contribute to such knowledge, despite the known low selectivity of many kinase inhibitors. We discuss how detailed profiling of target engagement by kinase inhibitors can underpin these studies; how chemical probes can be used to uncover kinase-substrate relationships, and how these tools can be used to gain insight into the configuration and function of kinase signaling networks.
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Affiliation(s)
| | - Jonathan M. G. Higgins
- Faculty of Medical Sciences, Biosciences Institute, Newcastle University, Newcastle uponTyne, United Kingdom
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57
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Jiang M, Shao Y, Zhang Y, Zhou W, Pang S. A deep learning method for drug-target affinity prediction based on sequence interaction information mining. PeerJ 2023; 11:e16625. [PMID: 38099302 PMCID: PMC10720480 DOI: 10.7717/peerj.16625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 11/16/2023] [Indexed: 12/17/2023] Open
Abstract
Background A critical aspect of in silico drug discovery involves the prediction of drug-target affinity (DTA). Conducting wet lab experiments to determine affinity is both expensive and time-consuming, making it necessary to find alternative approaches. In recent years, deep learning has emerged as a promising technique for DTA prediction, leveraging the substantial computational power of modern computers. Methods We proposed a novel sequence-based approach, named KC-DTA, for predicting drug-target affinity (DTA). In this approach, we converted the target sequence into two distinct matrices, while representing the molecule compound as a graph. The proposed method utilized k-mers analysis and Cartesian product calculation to capture the interactions and evolutionary information among various residues, enabling the creation of the two matrices for target sequence. For molecule, it was represented by constructing a molecular graph where atoms serve as nodes and chemical bonds serve as edges. Subsequently, the obtained target matrices and molecule graph were utilized as inputs for convolutional neural networks (CNNs) and graph neural networks (GNNs) to extract hidden features, which were further used for the prediction of binding affinity. Results In order to evaluate the effectiveness of the proposed method, we conducted several experiments and made a comprehensive comparison with the state-of-the-art approaches using multiple evaluation metrics. The results of our experiments demonstrated that the KC-DTA method achieves high performance in predicting drug-target affinity (DTA). The findings of this research underscore the significance of the KC-DTA method as a valuable tool in the field of in silico drug discovery, offering promising opportunities for accelerating the drug development process. All the data and code are available for access on https://github.com/syc2017/KCDTA.
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Affiliation(s)
- Mingjian Jiang
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao, Shandong, China
| | - Yunchang Shao
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao, Shandong, China
| | - Yuanyuan Zhang
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao, Shandong, China
| | - Wei Zhou
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao, Shandong, China
| | - Shunpeng Pang
- School of Computer Engineering, WeiFang University, Weifang, Shandong, China
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58
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Li H, Wang S, Zheng W, Yu L. Multi-dimensional search for drug-target interaction prediction by preserving the consistency of attention distribution. Comput Biol Chem 2023; 107:107968. [PMID: 37844375 DOI: 10.1016/j.compbiolchem.2023.107968] [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: 08/06/2023] [Revised: 09/27/2023] [Accepted: 10/05/2023] [Indexed: 10/18/2023]
Abstract
Predicting drug-target interaction (DTI) is a crucial step in the process of drug repurposing and new drug development. Although the attention mechanism has been widely used to capture the interactions between drugs and targets, it mainly uses the Simplified Molecular Input Line Entry System (SMILES) and two-dimensional (2D) molecular graph features of drugs. In this paper, we propose a neural network model called MdDTI for DTI prediction. The model searches for binding sites that may interact with the target from the multiple dimensions of drug structure, namely the 2D substructures and the three-dimensional (3D) spatial structure. For the 2D substructures, we have developed a novel substructure decomposition strategy based on drug molecular graphs and compared its performance with the SMILES-based decomposition method. For the 3D spatial structure of drugs, we constructed spatial feature representation matrices for drugs based on the Cartesian coordinates of heavy atoms (without hydrogen atoms) in each drug. Finally, to ensure the search results of the model are consistent across multiple dimensions, we construct a consistency loss function. We evaluate MdDTI on four drug-target interaction datasets and three independent compound-protein affinity test sets. The results indicate that our model surpasses a series of state-of-the-art models. Case studies demonstrate that our model is capable of capturing the potential binding regions between drugs and targets, and it shows efficacy in drug repurposing. Our code is available at https://github.com/lhhu1999/MdDTI.
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Affiliation(s)
- Huaihu Li
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, Yunnan, China
| | - Shunfang Wang
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, Yunnan, China; The Key Lab of Intelligent Systems and Computing of Yunnan Province, Yunnan University, Kunming, Yunnan, China.
| | - Weihua Zheng
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, Yunnan, China
| | - Li Yu
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, Yunnan, China
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59
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Liyaqat T, Ahmad T, Saxena C. TeM-DTBA: time-efficient drug target binding affinity prediction using multiple modalities with Lasso feature selection. J Comput Aided Mol Des 2023; 37:573-584. [PMID: 37777631 DOI: 10.1007/s10822-023-00533-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 09/07/2023] [Indexed: 10/02/2023]
Abstract
Drug discovery, especially virtual screening and drug repositioning, can be accelerated through deeper understanding and prediction of Drug Target Interactions (DTIs). The advancement of deep learning as well as the time and financial costs associated with conventional wet-lab experiments have made computational methods for DTI prediction more popular. However, the majority of these computational methods handle the DTI problem as a binary classification task, ignoring the quantitative binding affinity that determines the drug efficacy to their target proteins. Moreover, computational space as well as execution time of the model is often ignored over accuracy. To address these challenges, we introduce a novel method, called Time-efficient Multimodal Drug Target Binding Affinity (TeM-DTBA), which predicts the binding affinity between drugs and targets by fusing different modalities based on compound structures and target sequences. We employ the Lasso feature selection method, which lowers the dimensionality of feature vectors and speeds up the proposed model training time by more than 50%. The results from two benchmark datasets demonstrate that our method outperforms state-of-the-art methods in terms of performance. The mean squared errors of 18.8% and 23.19%, achieved on the KIBA and Davis datasets, respectively, suggest that our method is more accurate in predicting drug-target binding affinity.
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Affiliation(s)
- Tanya Liyaqat
- Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India.
| | - Tanvir Ahmad
- Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India
| | - Chandni Saxena
- The Chinese University of Hong Kong, Sha Tin, SAR, China
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60
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Wang M, Li W, Yu X, Luo Y, Han K, Wang C, Jin Q. AffinityVAE: A multi-objective model for protein-ligand affinity prediction and drug design. Comput Biol Chem 2023; 107:107971. [PMID: 37852036 DOI: 10.1016/j.compbiolchem.2023.107971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 09/23/2023] [Accepted: 10/08/2023] [Indexed: 10/20/2023]
Abstract
In the prediction of protein-ligand affinity, the traditional methods require a large amount of computing resources, and have certain limitations in predicting and simulating the structural changes. Although employing data-driven approaches can yield favorable outcomes in deep learning, it entails a lack of interpretability. Some methods may require additional structural information or domain knowledge to support the interpretation, which may limit their applicability. This paper proposes an affinity variational autoencoder (AffinityVAE) using interaction feature mapping and a variational autoencoder, which consists of a multi-objective model capable of end-to-end affinity prediction and drug discovery. In this study, the limitations of affinity prediction in terms of interpretability are tackled by proposing the concept of a protein-ligand interaction feature map. This increases the diversity and quantity of protein-ligand binding data by designing an adaptive autoencoder of target chemical properties to generate new ligands similar to known ligands and adding them to the original training set. AffinityVAE is then retrained using this extended training set to further validate the protein-ligand binding affinity prediction. Comparisons were conducted between the AffinityVAE and recent methods to demonstrate the high efficiency of the proposed model. The experimental results show that AffinityVAE has very high prediction performance, and it has the potential to enhance the diversity and the amount of protein-ligand binding data, which promotes the drug development.
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Affiliation(s)
- Mengying Wang
- School of Computer Engineering and Science, Shanghai University, Shanghai, China.
| | - Weimin Li
- School of Computer Engineering and Science, Shanghai University, Shanghai, China.
| | - Xiao Yu
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Yin Luo
- School of Life Sciences, East China Normal University, China
| | - Ke Han
- Medical and Health Center, Liaocheng People's Hospital, LiaoCheng, China.
| | - Can Wang
- School of Information and Communication Technology, Griffith University, Australia
| | - Qun Jin
- Networked Information System Laboratory, Waseda University, Tokyo, Japan
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61
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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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62
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Aly Abdelkader G, Ngnamsie Njimbouom S, Oh TJ, Kim JD. ResBiGAAT: Residual Bi-GRU with attention for protein-ligand binding affinity prediction. Comput Biol Chem 2023; 107:107969. [PMID: 37866117 DOI: 10.1016/j.compbiolchem.2023.107969] [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: 06/19/2023] [Revised: 09/20/2023] [Accepted: 10/05/2023] [Indexed: 10/24/2023]
Abstract
Protein-ligand interaction plays a crucial role in drug discovery, facilitating efficient drug development and enabling drug repurposing. Several computational algorithms, such as Graph Neural Networks and Convolutional Neural Networks, have been proposed to predict the binding affinity using the three-dimensional structure of ligands and proteins. However, there are limitations due to the need for experimental characterization of the three-dimensional structure of protein sequences, which is still lacking for some proteins. Moreover, these models often suffer from unnecessary complexity, resulting in extraneous computations. This study presents ResBiGAAT, a novel deep learning model that combines a deep Residual Bidirectional Gated Recurrent Unit with two-sided self-attention mechanisms. ResBiGAAT leverages protein and ligand sequence-level features and their physicochemical properties to efficiently predict protein-ligand binding affinity. Through rigorous evaluation using 5-fold cross-validation, we demonstrate the performance of our proposed approach. The model exhibits competitive performance on an external dataset, highlighting its generalizability. Our publicly available web interface, located at resbigaat.streamlit.app, allows users to conveniently input protein and ligand sequences to estimate binding affinity.
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Affiliation(s)
- Gelany Aly Abdelkader
- Department of Computer Science and Electronic Engineering, Sun Moon University, Asan 31460, the Republic of Korea
| | - Soualihou Ngnamsie Njimbouom
- Department of Computer Science and Electronic Engineering, Sun Moon University, Asan 31460, the Republic of Korea
| | - Tae-Jin Oh
- Genome‑based BioIT Convergence Institute, Asan 31460, the Republic of Korea; Department of Pharmaceutical Engineering and Biotechnology, Sun Moon University, Asan 31460, the Republic of Korea
| | - Jeong-Dong Kim
- Department of Computer Science and Electronic Engineering, Sun Moon University, Asan 31460, the Republic of Korea; Division of Computer Science and Engineering, Sun Moon University, Asan 31460, the Republic of Korea.
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63
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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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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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65
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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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66
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Zhang L, Wang CC, Zhang Y, Chen X. GPCNDTA: Prediction of drug-target binding affinity through cross-attention networks augmented with graph features and pharmacophores. Comput Biol Med 2023; 166:107512. [PMID: 37788507 DOI: 10.1016/j.compbiomed.2023.107512] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 08/28/2023] [Accepted: 09/19/2023] [Indexed: 10/05/2023]
Abstract
Drug-target affinity prediction is a challenging task in drug discovery. The latest computational models have limitations in mining edge information in molecule graphs, accessing to knowledge in pharmacophores, integrating multimodal data of the same biomolecule and realizing effective interactions between two different biomolecules. To solve these problems, we proposed a method called Graph features and Pharmacophores augmented Cross-attention Networks based Drug-Target binding Affinity prediction (GPCNDTA). First, we utilized the GNN module, the linear projection unit and self-attention layer to correspondingly extract features of drugs and proteins. Second, we devised intramolecular and intermolecular cross-attention to respectively fuse and interact features of drugs and proteins. Finally, the linear projection unit was applied to gain final features of drugs and proteins, and the Multi-Layer Perceptron was employed to predict drug-target binding affinity. Three major innovations of GPCNDTA are as follows: (i) developing the residual CensNet and the residual EW-GCN to correspondingly extract features of drug and protein graphs, (ii) regarding pharmacophores as a new type of priors to heighten drug-target affinity prediction performance, and (iii) devising intramolecular and intermolecular cross-attention, in which the intramolecular cross-attention realizes the effective fusion of different modal data related to the same biomolecule, and the intermolecular cross-attention fulfills the information interaction between two different biomolecules in attention space. The test results on five benchmark datasets imply that GPCNDTA achieves the best performance compared with state-of-the-art computational models. Besides, relying on ablation experiments, we proved effectiveness of GNN modules, pharmacophores and two cross-attention strategies in improving the prediction accuracy, stability and reliability of GPCNDA. In case studies, we applied GPCNDTA to predict binding affinities between 3C-like proteinase and 185 drugs, and observed that most binding affinities predicted by GPCNDTA are close to corresponding experimental measurements.
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Affiliation(s)
- Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
| | - Chun-Chun Wang
- School of Science, Jiangnan University, Wuxi, 214122, China
| | - Yong Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
| | - Xing Chen
- School of Science, Jiangnan University, Wuxi, 214122, China.
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Barsbey M, ÖZçelİk R, Bağ A, Atil B, ÖZgür A, Ozkirimli E. A Computational Software for Training Robust Drug-Target Affinity Prediction Models: pydebiaseddta. J Comput Biol 2023; 30:1240-1245. [PMID: 37988394 DOI: 10.1089/cmb.2023.0194] [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: 11/23/2023] Open
Abstract
Robust generalization of drug-target affinity (DTA) prediction models is a notoriously difficult problem in computational drug discovery. In this article, we present pydebiaseddta: a computational software for improving the generalizability of DTA prediction models to novel ligands and/or proteins. pydebiaseddta serves as the practical implementation of the DebiasedDTA training framework, which advocates modifying the training distribution to mitigate the effect of spurious correlations in the training data set that leads to substantially degraded performance for novel ligands and proteins. Written in Python programming language, pydebiaseddta combines a user-friendly streamlined interface with a feature-rich and highly modifiable architecture. With this article we introduce our software, showcase its main functionalities, and describe practical ways for new users to engage with it.
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Affiliation(s)
- Melİh Barsbey
- Department of Computer Engineering, Boğaziçi University, İstanbul, Turkey
| | - Riza ÖZçelİk
- Department of Computer Engineering, Boğaziçi University, İstanbul, Turkey
| | - Alperen Bağ
- Technical University of Munich, Munich, Germany
| | - Berk Atil
- Department of Computer Engineering, Boğaziçi University, İstanbul, Turkey
| | - Arzucan ÖZgür
- Department of Computer Engineering, Boğaziçi University, İstanbul, Turkey
| | - Elif Ozkirimli
- Roche Informatics, F. Hoffmann-La Roche AG, Basel, Switzerland
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68
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ÖZçelİk R, Bağ A, Atil B, Barsbey M, ÖZgür A, Ozkirimli E. A Framework for Improving the Generalizability of Drug-Target Affinity Prediction Models. J Comput Biol 2023; 30:1226-1239. [PMID: 37988395 DOI: 10.1089/cmb.2023.0208] [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: 11/23/2023] Open
Abstract
Statistical models that accurately predict the binding affinity of an input ligand-protein pair can greatly accelerate drug discovery. Such models are trained on available ligand-protein interaction data sets, which may contain biases that lead the predictor models to learn data set-specific, spurious patterns instead of generalizable relationships. This leads the prediction performances of these models to drop dramatically for previously unseen biomolecules. Various approaches that aim to improve model generalizability either have limited applicability or introduce the risk of degrading overall prediction performance. In this article, we present DebiasedDTA, a novel training framework for drug-target affinity (DTA) prediction models that addresses data set biases to improve the generalizability of such models. DebiasedDTA relies on reweighting the training samples to achieve robust generalization, and is thus applicable to most DTA prediction models. Extensive experiments with different biomolecule representations, model architectures, and data sets demonstrate that DebiasedDTA achieves improved generalizability in predicting drug-target affinities.
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Affiliation(s)
- Riza ÖZçelİk
- Department of Computer Engineering, Boğaziçi University, İstanbul, Turkey
| | - Alperen Bağ
- Technical University of Munich, Munich, Germany
| | - Berk Atil
- Department of Computer Engineering, Boğaziçi University, İstanbul, Turkey
| | - Melİh Barsbey
- Department of Computer Engineering, Boğaziçi University, İstanbul, Turkey
| | - Arzucan ÖZgür
- Department of Computer Engineering, Boğaziçi University, İstanbul, Turkey
| | - Elif Ozkirimli
- Roche Informatics, F. Hoffmann-La Roche AG, Basel, Switzerland
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69
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Zhai H, Hou H, Luo J, Liu X, Wu Z, Wang J. DGDTA: dynamic graph attention network for predicting drug-target binding affinity. BMC Bioinformatics 2023; 24:367. [PMID: 37777712 PMCID: PMC10543834 DOI: 10.1186/s12859-023-05497-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 09/23/2023] [Indexed: 10/02/2023] Open
Abstract
BACKGROUND Obtaining accurate drug-target binding affinity (DTA) information is significant for drug discovery and drug repositioning. Although some methods have been proposed for predicting DTA, the features of proteins and drugs still need to be further analyzed. Recently, deep learning has been successfully used in many fields. Hence, designing a more effective deep learning method for predicting DTA remains attractive. RESULTS Dynamic graph DTA (DGDTA), which uses a dynamic graph attention network combined with a bidirectional long short-term memory (Bi-LSTM) network to predict DTA is proposed in this paper. DGDTA adopts drug compound as input according to its corresponding simplified molecular input line entry system (SMILES) and protein amino acid sequence. First, each drug is considered a graph of interactions between atoms and edges, and dynamic attention scores are used to consider which atoms and edges in the drug are most important for predicting DTA. Then, Bi-LSTM is used to better extract the contextual information features of protein amino acid sequences. Finally, after combining the obtained drug and protein feature vectors, the DTA is predicted by a fully connected layer. The source code is available from GitHub at https://github.com/luojunwei/DGDTA . CONCLUSIONS The experimental results show that DGDTA can predict DTA more accurately than some other methods.
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Affiliation(s)
- Haixia Zhai
- School of Software, Henan Polytechnic University, Jiaozuo, 454003, China
| | - Hongli Hou
- School of Software, Henan Polytechnic University, Jiaozuo, 454003, China
| | - Junwei Luo
- School of Software, Henan Polytechnic University, Jiaozuo, 454003, China.
| | - Xiaoyan Liu
- School of Software, Henan Polytechnic University, Jiaozuo, 454003, China
| | - Zhengjiang Wu
- School of Software, Henan Polytechnic University, Jiaozuo, 454003, China
| | - Junfeng Wang
- School of Software, Henan Polytechnic University, Jiaozuo, 454003, China
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70
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Pei Q, Wu L, Zhu J, Xia Y, Xie S, Qin T, Liu H, Liu TY, Yan R. Breaking the barriers of data scarcity in drug-target affinity prediction. Brief Bioinform 2023; 24:bbad386. [PMID: 37903413 DOI: 10.1093/bib/bbad386] [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: 07/25/2023] [Revised: 09/14/2023] [Accepted: 10/05/2023] [Indexed: 11/01/2023] Open
Abstract
Accurate prediction of drug-target affinity (DTA) is of vital importance in early-stage drug discovery, facilitating the identification of drugs that can effectively interact with specific targets and regulate their activities. While wet experiments remain the most reliable method, they are time-consuming and resource-intensive, resulting in limited data availability that poses challenges for deep learning approaches. Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue. To overcome this challenge, we present the Semi-Supervised Multi-task training (SSM) framework for DTA prediction, which incorporates three simple yet highly effective strategies: (1) A multi-task training approach that combines DTA prediction with masked language modeling using paired drug-target data. (2) A semi-supervised training method that leverages large-scale unpaired molecules and proteins to enhance drug and target representations. This approach differs from previous methods that only employed molecules or proteins in pre-training. (3) The integration of a lightweight cross-attention module to improve the interaction between drugs and targets, further enhancing prediction accuracy. Through extensive experiments on benchmark datasets such as BindingDB, DAVIS and KIBA, we demonstrate the superior performance of our framework. Additionally, we conduct case studies on specific drug-target binding activities, virtual screening experiments, drug feature visualizations and real-world applications, all of which showcase the significant potential of our work. In conclusion, our proposed SSM-DTA framework addresses the data limitation challenge in DTA prediction and yields promising results, paving the way for more efficient and accurate drug discovery processes.
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Affiliation(s)
- Qizhi Pei
- Gaoling School of Artificial Intelligence, Renmin University of China, No.59, Zhong Guan Cun Avenue, Haidian District, 100872, Beijing, China
| | - Lijun Wu
- Microsoft Research AI4Science, No.5, Dan Ling Street, Haidian District, 100080, Beijing, China
| | - Jinhua Zhu
- CAS Key Laboratory of GIPAS, EEIS Department, University of Science and Technology of China, No.96, JinZhai Road, Baohe District, 230026, Hefei, Anhui Province, China
| | - Yingce Xia
- Microsoft Research AI4Science, No.5, Dan Ling Street, Haidian District, 100080, Beijing, China
| | - Shufang Xie
- Gaoling School of Artificial Intelligence, Renmin University of China, No.59, Zhong Guan Cun Avenue, Haidian District, 100872, Beijing, China
| | - Tao Qin
- Engineering Research Center of Next-Generation Intelligent Search and Recommendation, Ministry of Education
| | - Haiguang Liu
- Microsoft Research AI4Science, No.5, Dan Ling Street, Haidian District, 100080, Beijing, China
| | - Tie-Yan Liu
- Microsoft Research AI4Science, No.5, Dan Ling Street, Haidian District, 100080, Beijing, China
| | - Rui Yan
- Beijing Key Laboratory of Big Data Management and Analysis Methods
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71
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Xiaolin X, Xiaozhi L, Guoping H, Hongwei L, Jinkuo G, Xiyun B, Zhen T, Xiaofang M, Yanxia L, Na X, Chunyan Z, Rui G, Kuan W, Cheng Z, Cuancuan W, Mingyong L, Xinping D. Overfit deep neural network for predicting drug-target interactions. iScience 2023; 26:107646. [PMID: 37680476 PMCID: PMC10480310 DOI: 10.1016/j.isci.2023.107646] [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: 02/12/2022] [Revised: 06/28/2023] [Accepted: 08/11/2023] [Indexed: 09/09/2023] Open
Abstract
Drug-target interactions (DTIs) prediction is an important step in drug discovery. As traditional biological experiments or high-throughput screening are high cost and time-consuming, many deep learning models have been developed. Overfitting must be avoided when training deep learning models. We propose a simple framework, called OverfitDTI, for DTI prediction. In OverfitDTI, a deep neural network (DNN) model is overfit to sufficiently learn the features of the chemical space of drugs and the biological space of targets. The weights of trained DNN model form an implicit representation of the nonlinear relationship between drugs and targets. Performance of OverfitDTI on three public datasets showed that the overfit DNN models fit the nonlinear relationship with high accuracy. We identified fifteen compounds that interacted with TEK, a receptor tyrosine kinase contributing to vascular homeostasis, and the predicted AT9283 and dorsomorphin were experimentally demonstrated as inhibitors of TEK in human umbilical vein endothelial cells (HUVECs).
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Affiliation(s)
- Xiao Xiaolin
- Department of Cardiology, Tianjin Fifth Central Hospital, Tianjin, China
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- Central Laboratory, Tianjin Fifth Central Hospital, Tianjin, China
| | - Liu Xiaozhi
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- Central Laboratory, Tianjin Fifth Central Hospital, Tianjin, China
| | - He Guoping
- Geriatrics Department, Traditional Chinese Medicine Hospital of Binhai New Area, Tianjin, China
| | - Liu Hongwei
- School of Clinical Medicine, North China University of Science and Technology, Tangshan, Hebei, China
- Department of Anesthesiology, Tangshan Maternal and Child Health Hospital, Tangshan, Hebei, China
| | - Guo Jinkuo
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- College of Food Science and Engineering, Tianjin University of Science & Technology, Tianjin, China
| | - Bian Xiyun
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- Central Laboratory, Tianjin Fifth Central Hospital, Tianjin, China
| | - Tian Zhen
- Deepwater Technology Research Institute, China National Offshore Oil Corporation, Tianjin, China
| | - Ma Xiaofang
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- Central Laboratory, Tianjin Fifth Central Hospital, Tianjin, China
| | - Li Yanxia
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- Central Laboratory, Tianjin Fifth Central Hospital, Tianjin, China
| | - Xue Na
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- Central Laboratory, Tianjin Fifth Central Hospital, Tianjin, China
| | - Zhang Chunyan
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- Central Laboratory, Tianjin Fifth Central Hospital, Tianjin, China
| | - Gao Rui
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
| | - Wang Kuan
- Department of Cardiology, Tianjin Fifth Central Hospital, Tianjin, China
| | - Zhang Cheng
- Department of Cardiology, Tianjin Fifth Central Hospital, Tianjin, China
| | - Wang Cuancuan
- Department of Cardiology, Tianjin Fifth Central Hospital, Tianjin, China
| | - Liu Mingyong
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- Department of Urology, Tianjin Fifth Central Hospital, Tianjin, China
| | - Du Xinping
- Department of Cardiology, Tianjin Fifth Central Hospital, Tianjin, China
- Tianjin Key Laboratory of Epigenetics for Organ Development of Premature Infants, Tianjin Fifth Central Hospital, Tianjin, China
- College of Food Science and Engineering, Tianjin University of Science & Technology, Tianjin, China
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72
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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: 6] [Impact Index Per Article: 6.0] [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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Ma J, Li C, Zhang Y, Wang Z, Li S, Guo Y, Zhang L, Liu H, Gao X, Song J. MULGA, a unified multi-view graph autoencoder-based approach for identifying drug-protein interaction and drug repositioning. Bioinformatics 2023; 39:btad524. [PMID: 37610353 PMCID: PMC10518077 DOI: 10.1093/bioinformatics/btad524] [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: 05/27/2023] [Revised: 07/26/2023] [Accepted: 08/22/2023] [Indexed: 08/24/2023] Open
Abstract
MOTIVATION Identifying drug-protein interactions (DPIs) is a critical step in drug repositioning, which allows reuse of approved drugs that may be effective for treating a different disease and thereby alleviates the challenges of new drug development. Despite the fact that a great variety of computational approaches for DPI prediction have been proposed, key challenges, such as extendable and unbiased similarity calculation, heterogeneous information utilization, and reliable negative sample selection, remain to be addressed. RESULTS To address these issues, we propose a novel, unified multi-view graph autoencoder framework, termed MULGA, for both DPI and drug repositioning predictions. MULGA is featured by: (i) a multi-view learning technique to effectively learn authentic drug affinity and target affinity matrices; (ii) a graph autoencoder to infer missing DPI interactions; and (iii) a new "guilty-by-association"-based negative sampling approach for selecting highly reliable non-DPIs. Benchmark experiments demonstrate that MULGA outperforms state-of-the-art methods in DPI prediction and the ablation studies verify the effectiveness of each proposed component. Importantly, we highlight the top drugs shortlisted by MULGA that target the spike glycoprotein of severe acute respiratory syndrome coronavirus 2 (SAR-CoV-2), offering additional insights into and potentially useful treatment option for COVID-19. Together with the availability of datasets and source codes, we envision that MULGA can be explored as a useful tool for DPI prediction and drug repositioning. AVAILABILITY AND IMPLEMENTATION MULGA is publicly available for academic purposes at https://github.com/jianiM/MULGA/.
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Affiliation(s)
- Jiani Ma
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Chen Li
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
| | - Yiwen Zhang
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Zhikang Wang
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
| | - Shanshan Li
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Yuming Guo
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Lin Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Hui Liu
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Xin Gao
- KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
- Wenzhou Medical University-Monash Biomedicine Discovery Institute (BDI) Alliance in Clinical and Experimental Biomedicine, Wenzhou 325035, China
- Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
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74
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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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75
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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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76
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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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77
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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: 1] [Impact Index Per Article: 1.0] [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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78
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Oršolić D, Šmuc T. Dynamic applicability domain (dAD): compound-target binding affinity estimates with local conformal prediction. Bioinformatics 2023; 39:btad465. [PMID: 37594752 PMCID: PMC10457664 DOI: 10.1093/bioinformatics/btad465] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 04/26/2023] [Accepted: 08/17/2023] [Indexed: 08/19/2023] Open
Abstract
MOTIVATION Increasing efforts are being made in the field of machine learning to advance the learning of robust and accurate models from experimentally measured data and enable more efficient drug discovery processes. The prediction of binding affinity is one of the most frequent tasks of compound bioactivity modelling. Learned models for binding affinity prediction are assessed by their average performance on unseen samples, but point predictions are typically not provided with a rigorous confidence assessment. Approaches, such as the conformal predictor framework equip conventional models with a more rigorous assessment of confidence for individual point predictions. In this article, we extend the inductive conformal prediction framework for interaction data, in particular the compound-target binding affinity prediction task. The new framework is based on dynamically defined calibration sets that are specific for each testing pair and provides prediction assessment in the context of calibration pairs from its compound-target neighbourhood, enabling improved estimates based on the local properties of the prediction model. RESULTS The effectiveness of the approach is benchmarked on several publicly available datasets and tested in realistic use-case scenarios with increasing levels of difficulty on a complex compound-target binding affinity space. We demonstrate that in such scenarios, novel approach combining applicability domain paradigm with conformal prediction framework, produces superior confidence assessment with valid and more informative prediction regions compared to other 'state-of-the-art' conformal prediction approaches. AVAILABILITY AND IMPLEMENTATION Dataset and the code are available on GitHub (https://github.com/mlkr-rbi/dAD).
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Affiliation(s)
- Davor Oršolić
- Division of Electronics, Ruđer Bošković Institute, Bijenička cesta 54, Zagreb 10000, Croatia
| | - Tomislav Šmuc
- Division of Electronics, Ruđer Bošković Institute, Bijenička cesta 54, Zagreb 10000, Croatia
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79
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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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80
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Park H, Hong S, Lee M, Kang S, Brahma R, Cho KH, Shin JM. AiKPro: deep learning model for kinome-wide bioactivity profiling using structure-based sequence alignments and molecular 3D conformer ensemble descriptors. Sci Rep 2023; 13:10268. [PMID: 37355672 PMCID: PMC10290719 DOI: 10.1038/s41598-023-37456-8] [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: 04/10/2023] [Accepted: 06/22/2023] [Indexed: 06/26/2023] Open
Abstract
The discovery of selective and potent kinase inhibitors is crucial for the treatment of various diseases, but the process is challenging due to the high structural similarity among kinases. Efficient kinome-wide bioactivity profiling is essential for understanding kinase function and identifying selective inhibitors. In this study, we propose AiKPro, a deep learning model that combines structure-validated multiple sequence alignments and molecular 3D conformer ensemble descriptors to predict kinase-ligand binding affinities. Our deep learning model uses an attention-based mechanism to capture complex patterns in the interactions between the kinase and the ligand. To assess the performance of AiKPro, we evaluated the impact of descriptors, the predictability for untrained kinases and compounds, and kinase activity profiling based on odd ratios. Our model, AiKPro, shows good Pearson's correlation coefficients of 0.88 and 0.87 for the test set and for the untrained sets of compounds, respectively, which also shows the robustness of the model. AiKPro shows good kinase-activity profiles across the kinome, potentially facilitating the discovery of novel interactions and selective inhibitors. Our approach holds potential implications for the discovery of novel, selective kinase inhibitors and guiding rational drug design.
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Affiliation(s)
- Hyejin Park
- AZothBio Inc., Rm. DA724 Hyundai Knowledge Industry Center, Hanam-si, Gyeonggi-do, Republic of Korea
| | - Sujeong Hong
- AZothBio Inc., Rm. DA724 Hyundai Knowledge Industry Center, Hanam-si, Gyeonggi-do, Republic of Korea
| | - Myeonghun Lee
- AZothBio Inc., Rm. DA724 Hyundai Knowledge Industry Center, Hanam-si, Gyeonggi-do, Republic of Korea
| | - Sungil Kang
- AZothBio Inc., Rm. DA724 Hyundai Knowledge Industry Center, Hanam-si, Gyeonggi-do, Republic of Korea
| | - Rahul Brahma
- School of Systems Biomedical Science, Soongsil University, Seoul, Republic of Korea
| | - Kwang-Hwi Cho
- School of Systems Biomedical Science, Soongsil University, Seoul, Republic of Korea
| | - Jae-Min Shin
- AZothBio Inc., Rm. DA724 Hyundai Knowledge Industry Center, Hanam-si, Gyeonggi-do, Republic of Korea.
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81
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Luukkonen S, Meijer E, Tricarico GA, Hofmans J, Stouten PFW, van Westen GJP, Lenselink EB. Large-Scale Modeling of Sparse Protein Kinase Activity Data. J Chem Inf Model 2023. [PMID: 37294674 DOI: 10.1021/acs.jcim.3c00132] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Protein kinases are a protein family that plays an important role in several complex diseases such as cancer and cardiovascular and immunological diseases. Protein kinases have conserved ATP binding sites, which when targeted can lead to similar activities of inhibitors against different kinases. This can be exploited to create multitarget drugs. On the other hand, selectivity (lack of similar activities) is desirable in order to avoid toxicity issues. There is a vast amount of protein kinase activity data in the public domain, which can be used in many different ways. Multitask machine learning models are expected to excel for these kinds of data sets because they can learn from implicit correlations between tasks (in this case activities against a variety of kinases). However, multitask modeling of sparse data poses two major challenges: (i) creating a balanced train-test split without data leakage and (ii) handling missing data. In this work, we construct a protein kinase benchmark set composed of two balanced splits without data leakage, using random and dissimilarity-driven cluster-based mechanisms, respectively. This data set can be used for benchmarking and developing protein kinase activity prediction models. Overall, the performance on the dissimilarity-driven cluster-based split is lower than on random split-based sets for all models, indicating poor generalizability of models. Nevertheless, we show that multitask deep learning models, on this very sparse data set, outperform single-task deep learning and tree-based models. Finally, we demonstrate that data imputation does not improve the performance of (multitask) models on this benchmark set.
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Affiliation(s)
- Sohvi Luukkonen
- Leiden Academic Centre of Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands
| | - Erik Meijer
- Leiden Academic Centre of Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands
| | | | - Johan Hofmans
- Galapagos NV, Generaal De Wittelaan L11 A3, 2800 Mechelen, Belgium
| | - Pieter F W Stouten
- Leiden Academic Centre of Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands
- Galapagos NV, Generaal De Wittelaan L11 A3, 2800 Mechelen, Belgium
- Stouten Pharma Consultancy BV, Kempenarestraat 47, 2860 Sint-Katelijne-Waver, Belgium
| | - Gerard J P van Westen
- Leiden Academic Centre of Drug Research, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands
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82
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He H, Chen G, Chen CYC. NHGNN-DTA: a node-adaptive hybrid graph neural network for interpretable drug-target binding affinity prediction. Bioinformatics 2023; 39:btad355. [PMID: 37252835 PMCID: PMC10287904 DOI: 10.1093/bioinformatics/btad355] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 04/21/2023] [Accepted: 05/30/2023] [Indexed: 06/01/2023] Open
Abstract
MOTIVATION Large-scale prediction of drug-target affinity (DTA) plays an important role in drug discovery. In recent years, machine learning algorithms have made great progress in DTA prediction by utilizing sequence or structural information of both drugs and proteins. However, sequence-based algorithms ignore the structural information of molecules and proteins, while graph-based algorithms are insufficient in feature extraction and information interaction. RESULTS In this article, we propose NHGNN-DTA, a node-adaptive hybrid neural network for interpretable DTA prediction. It can adaptively acquire feature representations of drugs and proteins and allow information to interact at the graph level, effectively combining the advantages of both sequence-based and graph-based approaches. Experimental results have shown that NHGNN-DTA achieved new state-of-the-art performance. It achieved the mean squared error (MSE) of 0.196 on the Davis dataset (below 0.2 for the first time) and 0.124 on the KIBA dataset (3% improvement). Meanwhile, in the case of cold start scenario, NHGNN-DTA proved to be more robust and more effective with unseen inputs than baseline methods. Furthermore, the multi-head self-attention mechanism endows the model with interpretability, providing new exploratory insights for drug discovery. The case study on Omicron variants of SARS-CoV-2 illustrates the efficient utilization of drug repurposing in COVID-19. AVAILABILITY AND IMPLEMENTATION The source code and data are available at https://github.com/hehh77/NHGNN-DTA.
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Affiliation(s)
- Haohuai He
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, P.R. China
| | - Guanxing Chen
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, P.R. China
| | - Calvin Yu-Chian Chen
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, P.R. China
- Department of Medical Research, China Medical University Hospital, Taichung 40447, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
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83
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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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84
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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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85
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Lien ST, Lin TE, Hsieh JH, Sung TY, Chen JH, Hsu KC. Establishment of extensive artificial intelligence models for kinase inhibitor prediction: Identification of novel PDGFRB inhibitors. Comput Biol Med 2023; 156:106722. [PMID: 36878123 DOI: 10.1016/j.compbiomed.2023.106722] [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: 11/21/2022] [Revised: 02/16/2023] [Accepted: 02/26/2023] [Indexed: 03/06/2023]
Abstract
Identifying hit compounds is an important step in drug development. Unfortunately, this process continues to be a challenging task. Several machine learning models have been generated to aid in simplifying and improving the prediction of candidate compounds. Models tuned for predicting kinase inhibitors have been established. However, an effective model can be limited by the size of the chosen training dataset. In this study, we tested several machine learning models to predict potential kinase inhibitors. A dataset was curated from a number of publicly available repositories. This resulted in a comprehensive dataset covering more than half of the human kinome. More than 2,000 kinase models were established using different model approaches. The performances of the models were compared, and the Keras-MLP model was determined to be the best performing model. The model was then used to screen a chemical library for potential inhibitors targeting platelet-derived growth factor receptor-β (PDGFRB). Several PDGFRB candidates were selected, and in vitro assays confirmed four compounds with PDGFRB inhibitory activity and IC50 values in the nanomolar range. These results show the effectiveness of machine learning models trained on the reported dataset. This report would aid in the establishment of machine learning models as well as in the discovery of novel kinase inhibitors.
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Affiliation(s)
- Ssu-Ting Lien
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Tony Eight Lin
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Ph.D. Program for Cancer Molecular Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Jui-Hua Hsieh
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, National Institutes of Health, Durham, NC, USA
| | - Tzu-Ying Sung
- Biomedical Translation Research Center, Academia Sinica, Taipei, Taiwan
| | - Jun-Hong Chen
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Kai-Cheng Hsu
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Ph.D. Program for Cancer Molecular Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Ph.D. Program in Drug Discovery and Development Industry, College of Pharmacy, Taipei Medical University, Taipei, Taiwan; Cancer Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan; TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei, Taiwan; TMU Research Center of Drug Discovery, Taipei Medical University, Taipei, Taiwan.
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86
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Kalemati M, Zamani Emani M, Koohi S. BiComp-DTA: Drug-target binding affinity prediction through complementary biological-related and compression-based featurization approach. PLoS Comput Biol 2023; 19:e1011036. [PMID: 37000857 PMCID: PMC10096306 DOI: 10.1371/journal.pcbi.1011036] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 04/12/2023] [Accepted: 03/20/2023] [Indexed: 04/03/2023] Open
Abstract
Drug-target binding affinity prediction plays a key role in the early stage of drug discovery. Numerous experimental and data-driven approaches have been developed for predicting drug-target binding affinity. However, experimental methods highly rely on the limited structural-related information from drug-target pairs, domain knowledge, and time-consuming assays. On the other hand, learning-based methods have shown an acceptable prediction performance. However, most of them utilize several simple and complex types of proteins and drug compounds data, ranging from the protein sequences to the topology of a graph representation of drug compounds, employing multiple deep neural networks for encoding and feature extraction, and so, leads to the computational overheads. In this study, we propose a unified measure for protein sequence encoding, named BiComp, which provides compression-based and evolutionary-related features from the protein sequences. Specifically, we employ Normalized Compression Distance and Smith-Waterman measures for capturing complementary information from the algorithmic information theory and biological domains, respectively. We utilize the proposed measure to encode the input proteins feeding a new deep neural network-based method for drug-target binding affinity prediction, named BiComp-DTA. BiComp-DTA is evaluated utilizing four benchmark datasets for drug-target binding affinity prediction. Compared to the state-of-the-art methods, which employ complex models for protein encoding and feature extraction, BiComp-DTA provides superior efficiency in terms of accuracy, runtime, and the number of trainable parameters. The latter achievement facilitates execution of BiComp-DTA on a normal desktop computer in a fast fashion. As a comparative study, we evaluate BiComp’s efficiency against its components for drug-target binding affinity prediction. The results have shown superior accuracy of BiComp due to the orthogonality and complementary nature of Smith-Waterman and Normalized Compression Distance measures for protein sequences. Such a protein sequence encoding provides efficient representation with no need for multiple sources of information, deep domain knowledge, and complex neural networks.
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Affiliation(s)
- Mahmood Kalemati
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Mojtaba Zamani Emani
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Somayyeh Koohi
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
- * E-mail:
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87
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Voitsitskyi T, Stratiichuk R, Koleiev I, Popryho L, Ostrovsky Z, Henitsoi P, Khropachov I, Vozniak V, Zhytar R, Nechepurenko D, Yesylevskyy S, Nafiiev A, Starosyla S. 3DProtDTA: a deep learning model for drug-target affinity prediction based on residue-level protein graphs. RSC Adv 2023; 13:10261-10272. [PMID: 37006369 PMCID: PMC10065141 DOI: 10.1039/d3ra00281k] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 03/26/2023] [Indexed: 04/03/2023] Open
Abstract
Accurate prediction of the drug-target affinity (DTA) in silico is of critical importance for modern drug discovery. Computational methods of DTA prediction, applied in the early stages of drug development, are able to speed it up and cut its cost significantly. A wide range of approaches based on machine learning were recently proposed for DTA assessment. The most promising of them are based on deep learning techniques and graph neural networks to encode molecular structures. The recent breakthrough in protein structure prediction made by AlphaFold made an unprecedented amount of proteins without experimentally defined structures accessible for computational DTA prediction. In this work, we propose a new deep learning DTA model 3DProtDTA, which utilises AlphaFold structure predictions in conjunction with the graph representation of proteins. The model is superior to its rivals on common benchmarking datasets and has potential for further improvement.
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Affiliation(s)
- Taras Voitsitskyi
- Receptor.AI Inc. 20-22 Wenlock Road London N1 7GU UK
- Department of Physics of Biological Systems, Institute of Physics of The National Academy of Sciences of Ukraine Nauky Ave. 46 03038 Kyiv Ukraine
| | - Roman Stratiichuk
- Receptor.AI Inc. 20-22 Wenlock Road London N1 7GU UK
- Department of Biophysics and Medical Informatics, Educational and Scientific Centre "Institute of Biology and Medicine", Taras Shevchenko National University of Kyiv 64 Volodymyrska Str. 01601 Kyiv Ukraine
| | - Ihor Koleiev
- Receptor.AI Inc. 20-22 Wenlock Road London N1 7GU UK
| | | | | | | | | | | | - Roman Zhytar
- Receptor.AI Inc. 20-22 Wenlock Road London N1 7GU UK
| | | | - Semen Yesylevskyy
- Receptor.AI Inc. 20-22 Wenlock Road London N1 7GU UK
- Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences CZ-166 10 Prague 6 Czech Republic
- Department of Physics of Biological Systems, Institute of Physics of The National Academy of Sciences of Ukraine Nauky Ave. 46 03038 Kyiv Ukraine
| | - Alan Nafiiev
- Receptor.AI Inc. 20-22 Wenlock Road London N1 7GU UK
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88
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D’Souza S, Prema KV, Balaji S, Shah R. Deep Learning-Based Modeling of Drug–Target Interaction Prediction Incorporating Binding Site Information of Proteins. INTERDISCIPLINARY SCIENCES: COMPUTATIONAL LIFE SCIENCES 2023; 15:306-315. [PMID: 36967455 PMCID: PMC10148762 DOI: 10.1007/s12539-023-00557-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 02/22/2023] [Accepted: 02/22/2023] [Indexed: 03/29/2023]
Abstract
AbstractChemogenomics, also known as proteochemometrics, covers various computational methods for predicting interactions between related drugs and targets on large-scale data. Chemogenomics is used in the early stages of drug discovery to predict the off-target effects of proteins against therapeutic candidates. This study aims to predict unknown ligand–target interactions using one-dimensional SMILES as inputs for ligands and binding site residues for proteins in a computationally efficient manner. We first formulate a Deep learning CNN model using one-dimensional SMILES for drugs and motif-rich binding pocket subsequences of proteins as inputs. We evaluate and compare the proposed deep learning model trained on expert-based features against shallow feature-based machine learning methods. The proposed method achieved better or similar performance on the MSE and AUPR metrics than the shallow methods. Additionally, We show that our deep learning model, DeepPS is computationally more efficient than the deep learning model trained on full-length raw sequences of proteins. We conclude that a beneficial research approach would be to integrate structural information of proteins for modeling drug-target interaction prediction of large datasets for more interpretability, high throughput, and broad applicability.
Graphical abstract
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Affiliation(s)
- Sofia D’Souza
- Department of Computer Science and Engineering, Manipal Academy of Higher Education, Manipal, India
| | - K. V. Prema
- Department of Computer Science and Engineering, Manipal Academy of Higher Education, Bengaluru, India
| | - S. Balaji
- Department of Biotechnology, Manipal Academy of Higher Education, Manipal, India
| | - Ronak Shah
- Department of Computer Science and Engineering, Manipal Academy of Higher Education, Manipal, India
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89
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Yang X, Niu Z, Liu Y, Song B, Lu W, Zeng L, Zeng X. Modality-DTA: Multimodality Fusion Strategy for Drug-Target Affinity Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1200-1210. [PMID: 36083952 DOI: 10.1109/tcbb.2022.3205282] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Prediction of the drug-target affinity (DTA) plays an important role in drug discovery. Existing deep learning methods for DTA prediction typically leverage a single modality, namely simplified molecular input line entry specification (SMILES) or amino acid sequence to learn representations. SMILES or amino acid sequences can be encoded into different modalities. Multimodality data provide different kinds of information, with complementary roles for DTA prediction. We propose Modality-DTA, a novel deep learning method for DTA prediction that leverages the multimodality of drugs and targets. A group of backward propagation neural networks is applied to ensure the completeness of the reconstruction process from the latent feature representation to original multimodality data. The tag between the drug and target is used to reduce the noise information in the latent representation from multimodality data. Experiments on three benchmark datasets show that our Modality-DTA outperforms existing methods in all metrics. Modality-DTA reduces the mean square error by 15.7% and improves the area under the precisionrecall curve by 12.74% in the Davis dataset. We further find that the drug modality Morgan fingerprint and the target modality generated by one-hot-encoding play the most significant roles. To the best of our knowledge, Modality-DTA is the first method to explore multimodality for DTA prediction.
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90
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Zhao Q, Duan G, Yang M, Cheng Z, Li Y, Wang J. AttentionDTA: Drug-Target Binding Affinity Prediction by Sequence-Based Deep Learning With Attention Mechanism. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:852-863. [PMID: 35471889 DOI: 10.1109/tcbb.2022.3170365] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The identification of drug-target relations (DTRs) is substantial in drug development. A large number of methods treat DTRs as drug-target interactions (DTIs), a binary classification problem. The main drawback of these methods are the lack of reliable negative samples and the absence of many important aspects of DTR, including their dose dependence and quantitative affinities. With increasing number of publications of drug-protein binding affinity data recently, DTRs prediction can be viewed as a regression problem of drug-target affinities (DTAs) which reflects how tightly the drug binds to the target and can present more detailed and specific information than DTIs. The growth of affinity data enables the use of deep learning architectures, which have been shown to be among the state-of-the-art methods in binding affinity prediction. Although relatively effective, due to the black-box nature of deep learning, these models are less biologically interpretable. In this study, we proposed a deep learning-based model, named AttentionDTA, which uses attention mechanism to predict DTAs. Different from the models using 3D structures of drug-target complexes or graph representation of drugs and proteins, the novelty of our work is to use attention mechanism to focus on key subsequences which are important in drug and protein sequences when predicting its affinity. We use two separate one-dimensional Convolution Neural Networks (1D-CNNs) to extract the semantic information of drug's SMILES string and protein's amino acid sequence. Furthermore, a two-side multi-head attention mechanism is developed and embedded to our model to explore the relationship between drug features and protein features. We evaluate our model on three established DTA benchmark datasets, Davis, Metz, and KIBA. AttentionDTA outperforms the state-of-the-art deep learning methods under different evaluation metrics. The results show that the attention-based model can effectively extract protein features related to drug information and drug features related to protein information to better predict drug target affinities. It is worth mentioning that we test our model on IC50 dataset, which provides the binding sites between drugs and proteins, to evaluate the ability of our model to locate binding sites. Finally, we visualize the attention weight to demonstrate the biological significance of the model. The source code of AttentionDTA can be downloaded from https://github.com/zhaoqichang/AttentionDTA_TCBB.
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91
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Prediction of drug protein interactions based on variable scale characteristic pyramid convolution network. Methods 2023; 211:42-47. [PMID: 36804213 DOI: 10.1016/j.ymeth.2023.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 12/31/2022] [Accepted: 02/13/2023] [Indexed: 02/17/2023] Open
Abstract
MOTIVATION In the process of drug screening, it is significant to improve the accuracy of drug-target binding affinity prediction. A multilayer convolutional neural network is one of the most popular existing methods for predicting affinity based on deep learning. It uses multiple convolution layers to extract features from the simplified molecular input system (SMILES) strings of the compounds and amino acid sequences of proteins and then performs affinity prediction analysis. However, the semantic information contained in low-level features can gradually be lost due to the increasing network depth, which affects the prediction performance. RESULT We propose a novel method called the Pyramid Network Convolution Drug-Target Binding Affinity (PCNN-DTA) method for drug-target binding affinity prediction. The PCNN-DTA method, which is based on a feature pyramid network (FPN), fuses the features extracted from each layer of a multilayer convolution network to retain more low-level feature information, thus improving the prediction accuracy. PCNN-DTA is compared with other typical algorithms on three benchmark datasets, namely, the KIBA, Davis, and Binding DB datasets. Experimental results show that the PCNN-DTA method is superior to existing regression prediction methods using convolutional neural networks, which further demonstrates its effectiveness.
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92
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DoubleSG-DTA: Deep Learning for Drug Discovery: Case Study on the Non-Small Cell Lung Cancer with EGFRT790M Mutation. Pharmaceutics 2023; 15:pharmaceutics15020675. [PMID: 36839996 PMCID: PMC9965659 DOI: 10.3390/pharmaceutics15020675] [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: 01/04/2023] [Revised: 02/05/2023] [Accepted: 02/14/2023] [Indexed: 02/19/2023] Open
Abstract
Drug-targeted therapies are promising approaches to treating tumors, and research on receptor-ligand interactions for discovering high-affinity targeted drugs has been accelerating drug development. This study presents a mechanism-driven deep learning-based computational model to learn double drug sequences, protein sequences, and drug graphs to project drug-target affinities (DTAs), which was termed the DoubleSG-DTA. We deployed lightweight graph isomorphism networks to aggregate drug graph representations and discriminate between molecular structures, and stacked multilayer squeeze-and-excitation networks to selectively enhance spatial features of drug and protein sequences. What is more, cross-multi-head attentions were constructed to further model the non-covalent molecular docking behavior. The multiple cross-validation experimental evaluations on various datasets indicated that DoubleSG-DTA consistently outperformed all previously reported works. To showcase the value of DoubleSG-DTA, we applied it to generate promising hit compounds of Non-Small Cell Lung Cancer harboring EGFRT790M mutation from natural products, which were consistent with reported laboratory studies. Afterward, we further investigated the interpretability of the graph-based "black box" model and highlighted the active structures that contributed the most. DoubleSG-DTA thus provides a powerful and interpretable framework that extrapolates for potential chemicals to modulate the systemic response to disease.
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93
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Wang L, Song Y, Wang H, Zhang X, Wang M, He J, Li S, Zhang L, Li K, Cao L. Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade. Pharmaceuticals (Basel) 2023; 16:253. [PMID: 37259400 PMCID: PMC9963982 DOI: 10.3390/ph16020253] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/25/2023] [Accepted: 02/06/2023] [Indexed: 10/13/2023] Open
Abstract
Anti-cancer drug design has been acknowledged as a complicated, expensive, time-consuming, and challenging task. How to reduce the research costs and speed up the development process of anti-cancer drug designs has become a challenging and urgent question for the pharmaceutical industry. Computer-aided drug design methods have played a major role in the development of cancer treatments for over three decades. Recently, artificial intelligence has emerged as a powerful and promising technology for faster, cheaper, and more effective anti-cancer drug designs. This study is a narrative review that reviews a wide range of applications of artificial intelligence-based methods in anti-cancer drug design. We further clarify the fundamental principles of these methods, along with their advantages and disadvantages. Furthermore, we collate a large number of databases, including the omics database, the epigenomics database, the chemical compound database, and drug databases. Other researchers can consider them and adapt them to their own requirements.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Kang Li
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin 150081, China
| | - Lei Cao
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin 150081, China
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94
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Hua Y, Song X, Feng Z, Wu X. MFR-DTA: a multi-functional and robust model for predicting drug-target binding affinity and region. Bioinformatics 2023; 39:7008321. [PMID: 36708000 PMCID: PMC9900210 DOI: 10.1093/bioinformatics/btad056] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 12/31/2022] [Accepted: 01/25/2023] [Indexed: 01/29/2023] Open
Abstract
MOTIVATION Recently, deep learning has become the mainstream methodology for drug-target binding affinity prediction. However, two deficiencies of the existing methods restrict their practical applications. On the one hand, most existing methods ignore the individual information of sequence elements, resulting in poor sequence feature representations. On the other hand, without prior biological knowledge, the prediction of drug-target binding regions based on attention weights of a deep neural network could be difficult to verify, which may bring adverse interference to biological researchers. RESULTS We propose a novel Multi-Functional and Robust Drug-Target binding Affinity prediction (MFR-DTA) method to address the above issues. Specifically, we design a new biological sequence feature extraction block, namely BioMLP, that assists the model in extracting individual features of sequence elements. Then, we propose a new Elem-feature fusion block to refine the extracted features. After that, we construct a Mix-Decoder block that extracts drug-target interaction information and predicts their binding regions simultaneously. Last, we evaluate MFR-DTA on two benchmarks consistently with the existing methods and propose a new dataset, sc-PDB, to better measure the accuracy of binding region prediction. We also visualize some samples to demonstrate the locations of their binding sites and the predicted multi-scale interaction regions. The proposed method achieves excellent performance on these datasets, demonstrating its merits and superiority over the state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION https://github.com/JU-HuaY/MFR.
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Affiliation(s)
- Yang Hua
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
| | - Xiaoning Song
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
| | - Zhenhua Feng
- School of Computer Science and Electronic Engineering, University of Surrey, Guildford GU2 7XH, UK
| | - Xiaojun Wu
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
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95
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Hyperbolic matrix factorization improves prediction of drug-target associations. Sci Rep 2023; 13:959. [PMID: 36653463 PMCID: PMC9849222 DOI: 10.1038/s41598-023-27995-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 01/11/2023] [Indexed: 01/19/2023] Open
Abstract
Past research in computational systems biology has focused more on the development and applications of advanced statistical and numerical optimization techniques and much less on understanding the geometry of the biological space. By representing biological entities as points in a low dimensional Euclidean space, state-of-the-art methods for drug-target interaction (DTI) prediction implicitly assume the flat geometry of the biological space. In contrast, recent theoretical studies suggest that biological systems exhibit tree-like topology with a high degree of clustering. As a consequence, embedding a biological system in a flat space leads to distortion of distances between biological objects. Here, we present a novel matrix factorization methodology for drug-target interaction prediction that uses hyperbolic space as the latent biological space. When benchmarked against classical, Euclidean methods, hyperbolic matrix factorization exhibits superior accuracy while lowering embedding dimension by an order of magnitude. We see this as additional evidence that the hyperbolic geometry underpins large biological networks.
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96
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Chandra A, Tünnermann L, Löfstedt T, Gratz R. Transformer-based deep learning for predicting protein properties in the life sciences. eLife 2023; 12:e82819. [PMID: 36651724 PMCID: PMC9848389 DOI: 10.7554/elife.82819] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 01/06/2023] [Indexed: 01/19/2023] Open
Abstract
Recent developments in deep learning, coupled with an increasing number of sequenced proteins, have led to a breakthrough in life science applications, in particular in protein property prediction. There is hope that deep learning can close the gap between the number of sequenced proteins and proteins with known properties based on lab experiments. Language models from the field of natural language processing have gained popularity for protein property predictions and have led to a new computational revolution in biology, where old prediction results are being improved regularly. Such models can learn useful multipurpose representations of proteins from large open repositories of protein sequences and can be used, for instance, to predict protein properties. The field of natural language processing is growing quickly because of developments in a class of models based on a particular model-the Transformer model. We review recent developments and the use of large-scale Transformer models in applications for predicting protein characteristics and how such models can be used to predict, for example, post-translational modifications. We review shortcomings of other deep learning models and explain how the Transformer models have quickly proven to be a very promising way to unravel information hidden in the sequences of amino acids.
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Affiliation(s)
- Abel Chandra
- Department of Computing Science, Umeå UniversityUmeåSweden
| | - Laura Tünnermann
- Umeå Plant Science Centre (UPSC), Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural SciencesUmeåSweden
| | - Tommy Löfstedt
- Department of Computing Science, Umeå UniversityUmeåSweden
| | - Regina Gratz
- Umeå Plant Science Centre (UPSC), Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural SciencesUmeåSweden
- Department of Forest Ecology and Management, Swedish University of Agricultural SciencesUmeåSweden
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97
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Kanakala G, Aggarwal R, Nayar D, Priyakumar UD. Latent Biases in Machine Learning Models for Predicting Binding Affinities Using Popular Data Sets. ACS OMEGA 2023; 8:2389-2397. [PMID: 36687059 PMCID: PMC9850481 DOI: 10.1021/acsomega.2c06781] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 11/21/2022] [Indexed: 06/17/2023]
Abstract
Drug design involves the process of identifying and designing molecules that bind well to a given receptor. A vital computational component of this process is the protein-ligand interaction scoring functions that evaluate the binding ability of various molecules or ligands with a given protein receptor binding pocket reasonably accurately. With the publicly available protein-ligand binding affinity data sets in both sequential and structural forms, machine learning methods have gained traction as a top choice for developing such scoring functions. While the performance shown by these models is optimistic, there are several hidden biases present in these data sets themselves that affect the utility of such models for practical purposes such as virtual screening. In this work, we use published methods to systematically investigate several such factors or biases present in these data sets. In our analysis, we highlight the importance of considering sequence, protein-ligand interaction, and pocket structure similarity while constructing data splits and provide an explanation for good protein-only and ligand-only performances in some data sets. Through this study, we provide to the community several pointers for the design of binding affinity predictors and data sets for reliable applicability.
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Affiliation(s)
| | - Rishal Aggarwal
- International
Institute of Information Technology, Hyderabad500 032, India
| | - Divya Nayar
- Department
of Materials Science and Engineering, Indian
Institute of Technology Delhi, Hauz Khas, New Delhi110016, India
| | - U. Deva Priyakumar
- International
Institute of Information Technology, Hyderabad500 032, India
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98
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Béquignon OJM, Bongers BJ, Jespers W, IJzerman AP, van der Water B, van Westen GJP. Papyrus: a large-scale curated dataset aimed at bioactivity predictions. J Cheminform 2023; 15:3. [PMID: 36609528 PMCID: PMC9824924 DOI: 10.1186/s13321-022-00672-x] [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: 10/10/2022] [Accepted: 12/17/2022] [Indexed: 01/07/2023] Open
Abstract
With the ongoing rapid growth of publicly available ligand-protein bioactivity data, there is a trove of valuable data that can be used to train a plethora of machine-learning algorithms. However, not all data is equal in terms of size and quality and a significant portion of researchers' time is needed to adapt the data to their needs. On top of that, finding the right data for a research question can often be a challenge on its own. To meet these challenges, we have constructed the Papyrus dataset. Papyrus is comprised of around 60 million data points. This dataset contains multiple large publicly available datasets such as ChEMBL and ExCAPE-DB combined with several smaller datasets containing high-quality data. The aggregated data has been standardised and normalised in a manner that is suitable for machine learning. We show how data can be filtered in a variety of ways and also perform some examples of quantitative structure-activity relationship analyses and proteochemometric modelling. Our ambition is that this pruned data collection constitutes a benchmark set that can be used for constructing predictive models, while also providing an accessible data source for research.
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Affiliation(s)
- O. J. M. Béquignon
- grid.5132.50000 0001 2312 1970Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - B. J. Bongers
- grid.5132.50000 0001 2312 1970Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - W. Jespers
- grid.5132.50000 0001 2312 1970Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - A. P. IJzerman
- grid.5132.50000 0001 2312 1970Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - B. van der Water
- grid.5132.50000 0001 2312 1970Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - G. J. P. van Westen
- grid.5132.50000 0001 2312 1970Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
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Bao L, Wang Z, Wu Z, Luo H, Yu J, Kang Y, Cao D, Hou T. Kinome-wide polypharmacology profiling of small molecules by multi-task graph isomorphism network approach. Acta Pharm Sin B 2023; 13:54-67. [PMID: 36815050 PMCID: PMC9939366 DOI: 10.1016/j.apsb.2022.05.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/15/2022] [Accepted: 04/30/2022] [Indexed: 11/18/2022] Open
Abstract
Prediction of the interactions between small molecules and their targets play important roles in various applications of drug development, such as lead discovery, drug repurposing and elucidation of potential drug side effects. Therefore, a variety of machine learning-based models have been developed to predict these interactions. In this study, a model called auxiliary multi-task graph isomorphism network with uncertainty weighting (AMGU) was developed to predict the inhibitory activities of small molecules against 204 different kinases based on the multi-task Graph Isomorphism Network (MT-GIN) with the auxiliary learning and uncertainty weighting strategy. The calculation results illustrate that the AMGU model outperformed the descriptor-based models and state-of-the-art graph neural networks (GNN) models on the internal test set. Furthermore, it also exhibited much better performance on two external test sets, suggesting that the AMGU model has enhanced generalizability due to its great transfer learning capacity. Then, a naïve model-agnostic interpretable method for GNN called edges masking was devised to explain the underlying predictive mechanisms, and the consistency of the interpretability results for 5 typical epidermal growth factor receptor (EGFR) inhibitors with their structure‒activity relationships could be observed. Finally, a free online web server called KIP was developed to predict the kinome-wide polypharmacology effects of small molecules (http://cadd.zju.edu.cn/kip).
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Affiliation(s)
- Lingjie Bao
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Zhe Wang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Zhenxing Wu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Hao Luo
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jiahui Yu
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yu Kang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Corresponding authors. Tel./fax: +86 571 88208412.
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, China
- Corresponding authors. Tel./fax: +86 571 88208412.
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058, China
- Corresponding authors. Tel./fax: +86 571 88208412.
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100
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Combining machine‐learning and molecular‐modeling methods for drug‐target affinity predictions. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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