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药物设计

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材料科学
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经济学与金融学
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药物设计论文


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药物-蛋白质亲和力预测 药物-靶标相互作用预测 药物重定向 分子性质预测 蛋白质-蛋白质亲和力预测
药物代谢 药物毒理学 药物安全 抗原表位预测 药物-药物相互作用预测
基于配体的从头药物设计 基于受体的从头药物设计 药物知识图谱 药物-靶标的分子对接 分子逆合成设计
AI分子生成 抗体药物发现 免疫治疗(含CAR-T) 制药公司论文 AI4Drug-Papers

药物-靶标相互作用预测

1 A geometric deep learning model for display and prediction of potential drug-virus interactions against SARS-CoV-2. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS. 2022. pdf
2 MultiscaleDTA: A multiscale-based method with a self-attention mechanism for drug-target binding affinity prediction . METHODS. Nov 2022. DOI 10.1016/j.ymeth.2022.09.006 pdf
3 MHADTI: predicting drug-target interactions via multiview heterogeneous information network embedding with hierarchical attention mechanisms. BRIEFINGS IN BIOINFORMATICS. OCT 2022. DOI 10.1093/bib/bbac434
4 Machine learning approaches and databases for prediction of drug-target interaction: a survey paper. BRIEFINGS IN BIOINFORMATICS. 2021
5 PreDTIs: prediction of drug-target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques. BRIEFINGS IN BIOINFORMATICS. 2021
6 Predicting Protein-Ligand Docking Structure with Graph Neural Network. JOURNAL OF CHEMICAL INFORMATION AND MODELING. 2022
7 D3AI-CoV: a deep learning platform for predicting drug targets and for virtual screening against COVID-19. BRIEFINGS IN BIOINFORMATICS. 2022
8 An end-to-end heterogeneous graph representation learning-based framework for drug-target interaction prediction. BRIEFINGS IN BIOINFORMATICS. 2021
9 A Novel Deep Neural Network Technique for Drug-Target Interaction. PHARMACEUTICS.2022 pdf
10 Machine learning approaches and databases for prediction of drug-target interaction: a survey paper. BRIEFINGS IN BIOINFORMATICS. JAN 2021. DOI 10.1093/bib/bbz157 pdf
11 Predicting target-ligand interactions with graph convolutional networks for interpretable pharmaceutical discovery. SCIENTIFIC REPORTS. 2022 pdf
12 Drug-target interaction predication via multi-channel graph neural networks. BRIEFINGS IN BIOINFORMATICS. 2022
13 Predicting Drug-Target Interactions via Dual-Stream Graph Neural Network. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS. 2022
14 MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm.. BRIEFINGS IN BIOINFORMATICS. 2021
15 CoaDTI: multi-modal co-attention based framework for drug-target interaction annotation. BRIEFINGS IN BIOINFORMATICS. 2022
16 BETA: a comprehensive benchmark for computational drug-target prediction. BRIEFINGS IN BIOINFORMATICS. 2022
17 Drug-target interactions prediction via deep collaborative filtering with multiembeddings. BRIEFINGS IN BIOINFORMATICS. 2022
18 A brief review of protein-ligand interaction prediction. COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL. 2022 pdf
19 IIFDTI: predicting drug-target interactions through interactive and independent features based on attention mechanism. Bioinformatics. 2022
20 AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification. BRIEFINGS IN BIOINFORMATICS. 2022
21 Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation. JOURNAL OF CHEMICAL INFORMATION AND MODELING. 2019
22 GCRNN: graph convolutional recurrent neural network for compound-protein interaction prediction. BMC BIOINFORMATICS.2022
23 DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method.. BRIEFINGS IN BIOINFORMATICS. 2021
24 Drug target inference by mining transcriptional data using a novel graph convolutional network framework. PROTEIN & CELL. 2021 pdf
25 Deep learning in target prediction and drug repositioning: Recent advances and challenges. DRUG DISCOVERY TODAY. 2022 pdf
26 D3AI-CoV: a deep learning platform for predicting drug targets and for virtual screening against COVID-19. BRIEFINGS IN BIOINFORMATICS. 2022
27 Identifying drug-target interactions based on graph convolutional network and deep neural network. BRIEFINGS IN BIOINFORMATICS. 2021
28 DTi2Vec: Drug-target interaction prediction using network embedding and ensemble learning. JOURNAL OF CHEMINFORMATICS. 2021 pdf
29 MolTrans: Molecular Interaction Transformer for drug-target interaction prediction. Bioinformatics.2021
30 Graph Convolutional Autoencoder and Generative Adversarial Network-Based Method for Predicting Drug-Target Interactions. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS. 2022
31 Sequence-based prediction of protein binding regions and drug-target interactions. JOURNAL OF CHEMINFORMATICS. 2022 pdf
32 Prediction of drug-target interactions based on multi-layer network representation learning. NEUROCOMPUTING.2021 pdf
33 Graph neural network approaches for drug-target interactions. CURRENT OPINION IN STRUCTURAL BIOLOGY.2022 pdf
34 SSGraphCPI: A Novel Model for Predicting Compound-Protein Interactions Based on Deep Learning. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES.2022 pdf
35 Improved drug-target interaction prediction with intermolecular graph transformer. BRIEFINGS IN BIOINFORMATICS. 2022
36 GNN-SubNet: disease subnetwork detection with explainable graph neural networks. Bioinformatics.2022
37 DeepMGT-DTI: Transformer network incorporating multilayer graph information for Drug-Target interaction prediction. COMPUTERS IN BIOLOGY AND MEDICINE. 2022 pdf
38 Fine-tuning of BERT Model to Accurately Predict Drug-Target Interactions.PHARMACEUTICS.2022 pdf
39 DrugAI: a multi-view deep learning model for predicting drug-target activating/inhibiting mechanisms.. BRIEFINGS IN BIOINFORMATICS. 2022
40 PIGNet: a physics-informed deep learning model toward generalized drug-target interaction predictions. CHEMICAL SCIENCE. 2022 pdf
41 Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework. IScience.2022 pdf
42 DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences. PLOS COMPUTATIONAL BIOLOGY. 2019 高引用 pdf
43 Graph Convolutional Neural Networks for Predicting Drug-Target Interactions. JOURNAL OF CHEMICAL INFORMATION AND MODELING. 高引用. 2019
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   

 


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