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Article
Biochemical Research Methods
Xixi Yang et al.
Summary: Prediction of drug-target affinity is crucial in drug discovery. Existing deep learning methods focus on single modality inputs, while our proposed Modality-DTA leverages the multimodality of drugs and targets for better prediction performance. Experimental results demonstrate the superiority of Modality-DTA over existing methods in all metrics.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Qichang Zhao et al.
Summary: The identification of drug-target relations (DTRs) is crucial in drug development. Traditional methods treating DTRs as drug-target interactions (DTIs) suffer from the lack of reliable negative samples and fail to consider many important aspects of DTRs. With the availability of drug-protein binding affinity data, predicting DTRs as a regression problem of drug-target affinities (DTAs) using deep learning architectures has become feasible. In this study, a deep learning-based model named AttentionDTA is proposed, which utilizes attention mechanism to predict DTAs. The model demonstrates superior performance compared to state-of-the-art methods and successfully extracts protein and drug features to better predict drug-target affinities.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Chemistry, Medicinal
Gregory W. Kyro et al.
Summary: Deep learning and graph theory have greatly advanced protein-ligand binding affinity prediction. This study introduces a novel deep learning architecture consisting of a 3D convolutional neural network and two graph convolutional networks. The model, HAC-Net, achieves state-of-the-art results on the PDBbind v.2016 core set.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Computer Science, Artificial Intelligence
Sandra Steyaert et al.
Summary: Cancer diagnosis and treatment decisions often focus on a single data source. However, there is a need for effective multimodal fusion approaches to integrate complementary data types. The current technological advances and introduction of deep learning have the potential to address the challenges of data integration in cancer research.
NATURE MACHINE INTELLIGENCE
(2023)
Article
Biochemical Research Methods
Min Li et al.
Summary: In this study, an end-to-end neural network model called BACPI is proposed to predict compound-protein interactions (CPIs) and binding affinity. The model combines graph attention network and convolutional neural network (CNN) to learn representations of compounds and proteins, and uses a bi-directional attention neural network to integrate these representations. The results show that the BACPI model outperforms other machine learning methods in predicting CPIs and achieves higher performance in predicting binding affinities compared to other state-of-the-art deep learning methods.
Article
Biochemical Research Methods
Weining Yuan et al.
Summary: The FusionDTA approach, based on deep learning, plays a significant role in drug discovery by utilizing linear attention mechanism and knowledge distillation technique to improve drug-target affinity prediction and enhance the prediction of drug-target interaction.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Mohammad A. Rezaei et al.
Summary: This paper introduces a data-driven framework called DeepAtom for accurately predicting protein-ligand binding affinity. By utilizing a 3D Convolutional Neural Network (3D-CNN) architecture, DeepAtom can automatically extract atomic interaction patterns related to binding. Experiment results demonstrate that the DeepAtom approach outperforms other methods in baseline scoring and can potentially be adopted in computational drug development protocols.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Chemistry, Multidisciplinary
Ziduo Yang et al.
Summary: In this study, a deep multiscale graph neural network called MGraphDTA was proposed for predicting drug-target affinity (DTA). The method combines dense connection and super-deep GNN to capture the local and global structure of compounds simultaneously. A novel visual explanation method, Grad-AAM, was also developed to analyze the model from a chemical perspective.
Article
Biochemical Research Methods
Tri Minh Nguyen et al.
Summary: In this paper, we propose a novel graph-in-graph neural network, called GEFA, for modeling the nested graph between drugs and targets. The method addresses the problem of changes in target representation due to binding effects through an attention mechanism, and also utilizes pre-trained protein representation to improve model performance. Experimental results demonstrate the effectiveness of GEFA in modeling drug-target interactions.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Yu Wang et al.
Summary: In this study, a new featurization method based on 3D convolutional neural network was proposed to generate a scoring function model. By testing four architectures and three featurization methods, and comparing with other scoring functions, the results showed that our model accurately and stably predicted the binding affinity of protein-ligand complexes. This model will contribute towards improving the success rate of virtual screening and accelerating the development of potential drugs or novel biologically active lead compounds.
BMC BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Shaofu Lin et al.
Summary: This study proposes a new DTA prediction model, GeneralizedDTA, which enhances the generalization capability in unknown drug discovery by combining self-supervised pre-training and multi-task learning. Experimental results show that the model has higher generalization capability in predicting DTA of unknown drugs.
BMC BIOINFORMATICS
(2022)
Article
Biology
Jiaqi Liao et al.
Summary: Identifying drug-target affinity (DTA) is crucial for designing effective drugs. In this study, a deep learning framework called GSAML-DTA is proposed, which integrates self-attention and graph neural networks to build representations of drugs and target proteins. Mutual information is introduced to filter out redundant information. Experimental results demonstrate the superior performance of GSAML-DTA for DTA prediction and its interpretability for analyzing binding atoms and residues.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Chemistry, Physical
Huiwen Wang et al.
Summary: This study proposes a novel deep learning-based approach called DLSSAffinity to accurately predict protein-ligand binding affinity. Unlike existing methods, DLSSAffinity combines pocket-ligand structural pairs, full-length protein sequences, and ligand SMILES to improve prediction accuracy.
PHYSICAL CHEMISTRY CHEMICAL PHYSICS
(2022)
Article
Chemistry, Physical
Dong Chen et al.
Summary: The study introduces a self-supervised learning approach to pretrain models from unlabeled molecules and extract predictive representations for specific tasks. Extensive validation indicates that the proposed method shows superb performance.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
(2021)
Article
Biochemical Research Methods
Kaili Wang et al.
Summary: The study developed a deep learning method DeepDTAF for predicting protein-ligand binding affinity by integrating local and global contextual features. DeepDTAF showed significant accuracy improvement compared to state-of-art methods, making it a reliable tool for affinity prediction and drug discovery acceleration.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Chemistry, Medicinal
Derek Jones et al.
Summary: Accurately predicting protein-ligand binding affinities is crucial in drug discovery. While current methods face challenges, fusion models that combine features and inference from complementary representations show improved prediction accuracy. Comparative analysis reveals that fusion models perform better than individual neural network models, docking scoring, and MM/GBSA calculations, with the added benefit of greater computational efficiency.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2021)
Article
Biochemistry & Molecular Biology
Shugang Zhang et al.
Summary: The study introduced a new graph-based drug-target affinity prediction model named SAG-DTA, which utilized self-attention mechanisms on drug molecular graphs to obtain effective representations. Different self-attention scoring methods were compared, and two pooling architectures were evaluated. Results demonstrated that SAG-DTA outperformed previous methods and exhibited good generalization ability.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Article
Biochemical Research Methods
Asad Ahmed et al.
Summary: Protein-ligand binding prediction is biologically significant, with binding affinity being a key indicator of the degree of protein-ligand interactions. Recent advances in deep learning have enabled the prediction of binding affinity by identifying spatial relationships in complex data sets.
BIOINFORMATICS AND BIOLOGY INSIGHTS
(2021)
Article
Biochemical Research Methods
Thin Nguyen et al.
Summary: Drug repurposing, finding new uses for already approved drugs, is a cost-effective approach to drug development. The GraphDTA model represents drugs as graphs and utilizes graph neural networks to predict drug-target affinity more effectively than non-deep learning models. This demonstrates the potential of deep learning models in predicting drug-target binding affinity and the benefits of representing drugs as graphs.
Article
Genetics & Heredity
Lingling Zhao et al.
FRONTIERS IN GENETICS
(2020)
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Biochemistry & Molecular Biology
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Article
Biochemical Research Methods
Kexin Huang et al.
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Chemistry, Multidisciplinary
Mingjian Jiang et al.
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Biochemical Research Methods
Mostafa Karimi et al.
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Chemistry, Multidisciplinary
Liangzhen Zheng et al.
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Biochemical Research Methods
Marta M. Stepniewska-Dziubinska et al.
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Biochemical Research Methods
Hakime Ozturk et al.
Article
Chemistry, Medicinal
Jose Jimenez et al.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2018)
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Biochemistry & Molecular Biology
Tiqing Liu et al.
NUCLEIC ACIDS RESEARCH
(2007)
Article
Chemistry, Medicinal
RX Wang et al.
JOURNAL OF MEDICINAL CHEMISTRY
(2004)
