A review of deep learning methods for ligand based drug virtual screening
暂无分享,去创建一个
[1] Huan Zhao,et al. Automated 3D Pre-Training for Molecular Property Prediction , 2023, KDD.
[2] Zeming Lin,et al. Evolutionary-scale prediction of atomic level protein structure with a language model , 2022, bioRxiv.
[3] Xing Chen,et al. Predicting drug-target binding affinity through molecule representation block based on multi-head attention and skip connection , 2022, Briefings Bioinform..
[4] Ka-chun Wong,et al. CoaDTI: multi-modal co-attention based framework for drug-target interaction annotation , 2022, Briefings Bioinform..
[5] Xiangrui Cai,et al. Heterogeneous Graph Attention Network for Drug-Target Interaction Prediction , 2022, CIKM.
[6] P. Pan,et al. Boosting Protein-Ligand Binding Pose Prediction and Virtual Screening Based on Residue-Atom Distance Likelihood Potential and Graph Transformer. , 2022, Journal of medicinal chemistry.
[7] Yang Shen,et al. Cross-Modality and Self-Supervised Protein Embedding for Compound–Protein Affinity and Contact Prediction , 2022, bioRxiv.
[8] Craig J. Neal,et al. AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification , 2022, Briefings Bioinform..
[9] J. Guan,et al. Effective drug–target interaction prediction with mutual interaction neural network , 2022, Bioinform..
[10] Yijie Ding,et al. MLapSVM-LBS: Predicting DNA-binding proteins via a multiple Laplacian regularized support vector machine with local behavior similarity , 2022, Knowl. Based Syst..
[11] Martin Ester,et al. Ligand Binding Prediction Using Protein Structure Graphs and Residual Graph Attention Networks , 2022, bioRxiv.
[12] Min Zeng,et al. BridgeDPI: A Novel Graph Neural Network for Predicting Drug-Protein Interactions. , 2022, Bioinformatics.
[13] S. Yiu,et al. Compound-protein interaction prediction by deep learning: databases, descriptors and models. , 2022, Drug discovery today.
[14] Yuedong Yang,et al. Structure-Aware Multimodal Deep Learning for Drug-Protein Interaction Prediction , 2022, J. Chem. Inf. Model..
[15] Evan E. Bolton,et al. PubChem Protein, Gene, Pathway, and Taxonomy Data Collections: Bridging Biology and Chemistry through Target-Centric Views of PubChem Data. , 2022, Journal of molecular biology.
[16] Hualiang Jiang,et al. Graph neural network approaches for drug-target interactions. , 2022, Current opinion in structural biology.
[17] D. Sundar,et al. TransDTI: Transformer-Based Language Models for Estimating DTIs and Building a Drug Recommendation Workflow , 2022, ACS omega.
[18] Lu Zhao,et al. MGraphDTA: deep multiscale graph neural network for explainable drug–target binding affinity prediction , 2022, Chemical science.
[19] Meriem Belguidoum,et al. EA-based hyperparameter optimization of hybrid deep learning models for effective drug-target interactions prediction , 2021, Expert Syst. Appl..
[20] Jike Wang,et al. InteractionGraphNet: A Novel and Efficient Deep Graph Representation Learning Framework for Accurate Protein-Ligand Interaction Predictions. , 2021, Journal of medicinal chemistry.
[21] Jiming Chen,et al. A unified drug–target interaction prediction framework based on knowledge graph and recommendation system , 2021, Nature Communications.
[22] Jijun Tang,et al. Drug-disease associations prediction via Multiple Kernel-based Dual Graph Regularized Least Squares , 2021, Appl. Soft Comput..
[23] T. Nakaguchi,et al. GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug-protein interaction prediction , 2021, Briefings Bioinform..
[24] Qichang Zhao,et al. HyperAttentionDTI: improving drug-protein interaction prediction by sequence-based deep learning with attention mechanism , 2021, Bioinform..
[25] Tie-Yan Liu,et al. Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer , 2021, Briefings Bioinform..
[26] Guohua Wang,et al. Drug-target interaction predication via multi-channel graph neural networks , 2021, Briefings Bioinform..
[27] Zhiqiang Wei,et al. SAG-DTA: Prediction of Drug–Target Affinity Using Self-Attention Graph Network , 2021, International journal of molecular sciences.
[28] Kenli Li,et al. BioERP: biomedical heterogeneous network-based self-supervised representation learning approach for entity relationship predictions , 2021, Bioinform..
[29] Oriol Vinyals,et al. Highly accurate protein structure prediction with AlphaFold , 2021, Nature.
[30] Lei Xu,et al. Application of Machine Learning for Drug–Target Interaction Prediction , 2021, Frontiers in Genetics.
[31] B. Berger,et al. Learning the protein language: Evolution, structure, and function. , 2021, Cell systems.
[32] Jijun Tang,et al. Identification of drug-target interactions via multi-view graph regularized link propagation model , 2021, Neurocomputing.
[33] Yangkun Cao,et al. Autoencoder-based drug-target interaction prediction by preserving the consistency of chemical properties and functions of drugs , 2021, Bioinform..
[34] Jike Wang,et al. Mining Toxicity Information from Large Amounts of Toxicity Data. , 2021, Journal of medicinal chemistry.
[35] Talia B. Kimber,et al. Deep Learning in Virtual Screening: Recent Applications and Developments , 2021, International journal of molecular sciences.
[36] Shirui Pan,et al. GADTI: Graph Autoencoder Approach for DTI Prediction From Heterogeneous Network , 2021, Frontiers in Genetics.
[37] Yaohang Li,et al. DeepDTAF: a deep learning method to predict protein-ligand binding affinity , 2021, Briefings Bioinform..
[38] Xinjiang Lu,et al. GraphMS: Drug Target Prediction Using Graph Representation Learning with Substructures , 2021, Applied Sciences.
[39] Sun Kim,et al. A review on compound-protein interaction prediction methods: Data, format, representation and model , 2021, Computational and structural biotechnology journal.
[40] Jianye Hao,et al. An end-to-end heterogeneous graph representation learning-based framework for drug-target interaction prediction , 2021, Briefings Bioinform..
[41] Jijun Tang,et al. Exploring associations of non-coding RNAs in human diseases via three-matrix factorization with hypergraph-regular terms on center kernel alignment , 2021, Briefings Bioinform..
[42] Jiangning Song,et al. Computational identification of eukaryotic promoters based on cascaded deep capsule neural networks , 2020, Briefings Bioinform..
[43] Shengyu Zhang,et al. TrimNet: learning molecular representation from triplet messages for biomedicine , 2020, Briefings Bioinform..
[44] Truyen Tran,et al. GEFA: Early Fusion Approach in Drug-Target Affinity Prediction , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[45] Jijun Tang,et al. Identification of Drug-Target Interactions via Dual Laplacian Regularized Least Squares with Multiple Kernel Fusion , 2020, Knowl. Based Syst..
[46] B. Rost,et al. ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Deep Learning and High Performance Computing , 2020, bioRxiv.
[47] Qiming Fu,et al. Empirical Potential Energy Function Toward ab Initio Folding G Protein-Coupled Receptors , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[48] Andre Franke,et al. Amino acid encoding for deep learning applications , 2020, BMC Bioinformatics.
[49] Xiaofeng Wang,et al. Drug–target affinity prediction using graph neural network and contact maps , 2020, RSC advances.
[50] Arne Elofsson,et al. TransformerCPI: improving compound-protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments , 2020, Bioinform..
[51] Jiajie Peng,et al. Identifying drug-target interactions based on graph convolutional network and deep neural network , 2020, Briefings Bioinform..
[52] Jimeng Sun,et al. MolTrans: Molecular Interaction Transformer for drug–target interaction prediction , 2020, Bioinform..
[53] M. Aljofan,et al. An overview of drug discovery and development. , 2020, Future medicinal chemistry.
[54] Nicholas Marshall,et al. A Deep Dive into Machine Learning Models for Protein Engineering. , 2020, Journal of chemical information and modeling.
[55] Dan Zhao,et al. MONN: A Multi-objective Neural Network for Predicting Compound-Protein Interactions and Affinities , 2020, Cell Systems.
[56] Hai-Cheng Yi,et al. A deep learning-based method for drug-target interaction prediction based on long short-term memory neural network , 2020, BMC Medical Informatics and Decision Making.
[57] Xiangxiang Zeng,et al. Network-based prediction of drug-target interactions using an arbitrary-order proximity embedded deep forest , 2020, Bioinform..
[58] J. Reymond,et al. SMILES-based deep generative scaffold decorator for de-novo drug design , 2020, Journal of Cheminformatics.
[59] Kayvan Najarian,et al. Machine learning approaches and databases for prediction of drug–target interaction: a survey paper , 2020, Briefings Bioinform..
[60] Yang Liu,et al. GANsDTA: Predicting Drug-Target Binding Affinity Using GANs , 2020, Frontiers in Genetics.
[61] Mostafa Karimi,et al. Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts , 2019, bioRxiv.
[62] Bin Yu,et al. Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure. , 2019, Genomics.
[63] Wenyu Chen,et al. Prediction of drug-target interaction based on protein features using undersampling and feature selection techniques with boosting. , 2019, Analytical biochemistry.
[64] Emile R Chimusa,et al. Computational/in silico methods in drug target and lead prediction , 2019, Briefings Bioinform..
[65] Yaohang Li,et al. AttentionDTA: prediction of drug–target binding affinity using attention model , 2019, 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).
[66] Mohammed AlQuraishi,et al. AlphaFold at CASP13 , 2019, Bioinform..
[67] Fei Guo,et al. Identification of drug–target interactions via fuzzy bipartite local model , 2019, Neural Computing and Applications.
[68] Ying Fu,et al. Identification of novel inhibitors of p-hydroxyphenylpyruvate dioxygenase using receptor-based virtual screening , 2019, Journal of the Taiwan Institute of Chemical Engineers.
[69] Priya Singh,et al. Exploring the role of water molecules in the ligand binding domain of PDE4B and PDE4D:Virtual screening based molecular docking of some active scaffolds. , 2019, Current computer-aided drug design.
[70] Xiaoli Li,et al. Computational prediction of drug-target interactions using chemogenomic approaches: an empirical survey , 2019, Briefings Bioinform..
[71] John Canny,et al. Evaluating Protein Transfer Learning with TAPE , 2019, bioRxiv.
[72] Heather A Carlson,et al. Updates to Binding MOAD (Mother of All Databases): Polypharmacology Tools and Their Utility in Drug Repurposing. , 2019, Journal of molecular biology.
[73] Li Li,et al. Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees , 2019, Scientific Reports.
[74] Myle Ott,et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences , 2019, Proceedings of the National Academy of Sciences.
[75] Bin Li,et al. Applications of machine learning in drug discovery and development , 2019, Nature Reviews Drug Discovery.
[76] Yongjian Li,et al. Predicting drug–protein interaction using quasi-visual question answering system , 2019, Nature Machine Intelligence.
[77] Volkan Atalay,et al. DEEPScreen: high performance drug–target interaction prediction with convolutional neural networks using 2-D structural compound representations , 2018, bioRxiv.
[78] Russ B. Altman,et al. Graph Convolutional Neural Networks for Predicting Drug-Target Interactions , 2018, bioRxiv.
[79] Andrew R. Leach,et al. ChEMBL: towards direct deposition of bioassay data , 2018, Nucleic Acids Res..
[80] Hojung Nam,et al. DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences , 2018, PLoS Comput. Biol..
[81] The UniProt Consortium,et al. UniProt: a worldwide hub of protein knowledge , 2018, Nucleic Acids Res..
[82] Antje Chang,et al. BRENDA in 2019: a European ELIXIR core data resource , 2018, Nucleic Acids Res..
[83] Tudor I. Oprea,et al. DrugCentral 2018: an update , 2018, Nucleic Acids Res..
[84] Stefano E. Rensi,et al. Machine learning in chemoinformatics and drug discovery. , 2018, Drug discovery today.
[85] Jun Sese,et al. Compound‐protein interaction prediction with end‐to‐end learning of neural networks for graphs and sequences , 2018, Bioinform..
[86] Ping Zhang,et al. Interpretable Drug Target Prediction Using Deep Neural Representation , 2018, IJCAI.
[87] Juho Rousu,et al. Learning with multiple pairwise kernels for drug bioactivity prediction , 2018, Bioinform..
[88] Di Wu,et al. DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks , 2018, bioRxiv.
[89] Namrata Anand,et al. Generative modeling for protein structures , 2018, NeurIPS.
[90] Arzucan Özgür,et al. DeepDTA: deep drug–target binding affinity prediction , 2018, Bioinform..
[91] Sabrina Jaeger,et al. Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition , 2018, J. Chem. Inf. Model..
[92] Saskia Preissner,et al. SuperDRUG2: a one stop resource for approved/marketed drugs , 2017, Nucleic Acids Res..
[93] David S. Wishart,et al. DrugBank 5.0: a major update to the DrugBank database for 2018 , 2017, Nucleic Acids Res..
[94] Juho Rousu,et al. Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors , 2017, PLoS Comput. Biol..
[95] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[96] Yanli Wang,et al. High-Throughput Screening Assay Datasets from the PubChem Database , 2017, Chemical informatics.
[97] Artem Cherkasov,et al. SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines , 2017, Journal of Cheminformatics.
[98] Peng Chen,et al. DrugRPE: Random projection ensemble approach to drug-target interaction prediction , 2017, Neurocomputing.
[99] Vijay S. Pande,et al. MoleculeNet: a benchmark for molecular machine learning , 2017, Chemical science.
[100] David Ryan Koes,et al. Protein-Ligand Scoring with Convolutional Neural Networks , 2016, Journal of chemical information and modeling.
[101] Minoru Kanehisa,et al. KEGG: new perspectives on genomes, pathways, diseases and drugs , 2016, Nucleic Acids Res..
[102] Rajarshi Guha,et al. Pharos: Collating protein information to shed light on the druggable genome , 2016, Nucleic Acids Res..
[103] Keith C. C. Chan,et al. Large-scale prediction of drug-target interactions from deep representations , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).
[104] Lukasz Kurgan,et al. PDID: database of molecular-level putative protein-drug interactions in the structural human proteome , 2016, Bioinform..
[105] Damian Szklarczyk,et al. STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data , 2015, Nucleic Acids Res..
[106] Shuigeng Zhou,et al. Boosting compound-protein interaction prediction by deep learning , 2015, 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).
[107] Michael K. Gilson,et al. BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology , 2015, Nucleic Acids Res..
[108] Jie Li,et al. PDB-wide collection of binding data: current status of the PDBbind database , 2015, Bioinform..
[109] Jack A Tuszynski,et al. A simple method for finding a protein’s ligand-binding pockets , 2014, BMC Structural Biology.
[110] Tao Xu,et al. Making Sense of Large-Scale Kinase Inhibitor Bioactivity Data Sets: A Comparative and Integrative Analysis , 2014, J. Chem. Inf. Model..
[111] Yasuo Tabei,et al. Identification of chemogenomic features from drug–target interaction networks using interpretable classifiers , 2012, Bioinform..
[112] Michael M. Mysinger,et al. Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking , 2012, Journal of medicinal chemistry.
[113] Hua Yu,et al. A Systematic Prediction of Multiple Drug-Target Interactions from Chemical, Genomic, and Pharmacological Data , 2012, PloS one.
[114] Andreas Bender,et al. Recognizing Pitfalls in Virtual Screening: A Critical Review , 2012, J. Chem. Inf. Model..
[115] Mindy I. Davis,et al. Comprehensive analysis of kinase inhibitor selectivity , 2011, Nature Biotechnology.
[116] Yoshihiro Yamanishi,et al. Extracting Sets of Chemical Substructures and Protein Domains Governing Drug-Target Interactions , 2011, J. Chem. Inf. Model..
[117] Sebastian G. Rohrer,et al. Maximum Unbiased Validation (MUV) Data Sets for Virtual Screening Based on PubChem Bioactivity Data , 2009, J. Chem. Inf. Model..
[118] Robert B. Russell,et al. SuperTarget and Matador: resources for exploring drug-target relationships , 2007, Nucleic Acids Res..
[119] Natasja Brooijmans,et al. Molecular recognition and docking algorithms. , 2003, Annual review of biophysics and biomolecular structure.
[120] David Weininger,et al. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules , 1988, J. Chem. Inf. Comput. Sci..
[121] OUP accepted manuscript , 2022, Bioinformatics.
[122] Karim Abbasi. Deep Learning in Drug Target Interaction Prediction: Current and Future Perspectives , 2021, Current Medicinal Chemistry.
[123] OUP accepted manuscript , 2021, Briefings In Bioinformatics.
[124] OUP accepted manuscript , 2021, Briefings In Bioinformatics.
[125] D. Salazar,et al. Chapter 41 – Modern Drug Discovery and Development , 2017 .
[126] T. N. Bhat,et al. The Protein Data Bank , 2000, Nucleic Acids Res..