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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-蛋白质亲和力预测
| 1 | Hierarchical graph representation learning for the prediction of drug-target binding affinity. INFORMATION SCIENCES. 2022 pdf |
| 2 | BridgeDPI: a novel Graph Neural Network for predicting drug-protein interactions. BIOINFORMATICS. 2022 |
| 3 | BACPI: a bi-directional attention neural network for compound-protein interaction and binding affinity prediction. BIOINFORMATICS .2022 |
| 4 | Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2. CHEMICAL REVIEWS. 2022 |
| 5 | DeepDTA: deep drug–target binding affinity prediction. Bioinformatics. 2018. Link GitHub（含KIBA, Davis数据） |
| 6 | Co-VAE: Drug-Target Binding Affinity Prediction by Co-Regularized Variational Autoencoders. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE. 2022 |
| 7 | DeepDTA: deep drug-target binding affinity prediction. Bioinformatics. 2018 Link |
| 8 | MultiscaleDTA: A multiscale-based method with a self-attention mechanism for drug-target binding affinity prediction. Methods. 2022 pdf |
| 9 | GSAML-DTA: An interpretable drug-target binding affinity prediction model based on graph neural networks with self-attention mechanism and mutual information. COMPUTERS IN BIOLOGY AND MEDICINE. 2022 pdf |
| 10 | Graph-sequence attention and transformer for predicting drug-target affinity. RSC ADVANCES 2022 Link |
| 11 | PLA-MoRe: A Protein-Ligand Binding Affinity Prediction Model via Comprehensive Molecular Representations. JOURNAL OF CHEMICAL INFORMATION AND MODELING. 2022 |
| 12 | Sequence-based drug-target affinity prediction using weighted graph neural networks. BMC GENOMICS. 2022 Link |
| 13 | CSatDTA: Prediction of Drug-Target Binding Affinity Using Convolution Model with Self-Attention. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES. 2022. pdf |
| 14 | MGraphDTA: deep multiscale graph neural network for explainable drug-target binding affinity prediction. CHEMICAL SCIENCE. Jan 19 2022. DOI 10.1039/d1sc05180f pdf 引用已经很多了 |
| 15 | Prediction of protein-ligand binding affinity from sequencing data with interpretable machine learning. NATURE BIOTECHNOLOGY. 2022. pdf |
| 16 | GEFA: Early Fusion Approach in Drug-Target Affinity Prediction. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS. 2022 |
| 17 | NerLTR-DTA: drug-target binding affinity prediction based on neighbor relationship and learning to rank. BIOINFORMATICS. 2022 |
| 18 | ELECTRA-DTA: a new compound-protein binding affinity prediction model based on the contextualized sequence encoding. JOURNAL OF CHEMINFORMATICS. 2022 pdf |
| 19 | SAM-DTA: a sequence-agnostic model for drug-target binding affinity prediction.. BRIEFINGS IN BIOINFORMATICS. 2022 |
| 20 | Predicting drug-target binding affinity through molecule representation block based on multi-head attention and skip connection. BRIEFINGS IN BIOINFORMATICS. 2022 |
| 21 | DTITR: End-to-end drug-target binding affinity prediction with transformers. COMPUTERS IN BIOLOGY AND MEDICINE. 2022 pdf GitHub |
| 22 | DeepDTA: deep drug-target binding affinity prediction. Bioinformatics. 2018. 309次引用. pdf |
| 23 | GSAML-DTA: An interpretable drug-target binding affinity prediction model based on graph neural networks with self-attention mechanism and mutual information. COMPUTERS IN BIOLOGY AND MEDICINE. 2022 pdf |
| 24 | BatchDTA: implicit batch alignment enhances deep learning-based drug-target affinity estimation. BRIEFINGS IN BIOINFORMATICS. 2022 |
| 25 | DeepFusionDTA: Drug-Target Binding Affinity Prediction With Information Fusion and Hybrid Deep-Learning Ensemble Model. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS. 2022 |
| 26 | AttentionDTA: drug-target binding affinity prediction by sequence-based deep learning with attention mechanism. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS. 2022 |
| 27 | DeepDTAF: a deep learning method to predict protein-ligand binding affinity. BRIEFINGS IN BIOINFORMATICS. 2021 |
| 28 | MDeePred: novel multi-channel protein featurization for deep learning-based binding affinity prediction in drug discovery. BIOINFORMATICS. 2021 |
| 29 | GraphDTA: predicting drug-target binding affinity with graph neural networks. BIOINFORMATICS. 2021 大量引用了 |
| 30 | Deep drug-target binding affinity prediction with multiple attention blocks. BRIEFINGS IN BIOINFORMATICS. 2021 pdf |
| 31 | FusionDTA: attention-based feature polymerizer and knowledge distillation for drug-target binding affinity prediction. BRIEFINGS IN BIOINFORMATICS. 2021 |
| 32 | GANsDTA: Predicting Drug-Target Binding Affinity Using GANs. FRONTIERS IN GENETICS. 2020. Link |
| 33 | SAG-DTA: Prediction of Drug-Target Affinity Using Self-Attention Graph Network. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES. 2021. Link |
| 34 | Graph-sequence attention and transformer for predicting drug-target affinity. RSC ADVANCES. 2022 pdf |
| 35 | Ligand Binding Prediction Using Protein Structure Graphs and Residual Graph Attention Networks. MOLECULES. 2022 pdf || GitHub |
| 36 | Affinity2Vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning.SCIENTIFIC REPORTS. 2022. pdf |
| 37 | A brief review of protein-ligand interaction prediction. COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL. 2022. pdf |
| 38 | Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model.COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL. 2021. pdf |
| A New Hybrid Neural Network Deep Learning Method for Protein-Ligand Binding Affinity Prediction and De Novo Drug Design. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES. 2022 | |
关联文献
| 1 | SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines. JOURNAL OF CHEMINFORMATICS. 2017 pdf 数据：KIBA dataset |
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关联网站或代码地址
| 1 | HGRL-DTA （华中农大开发的）：GitHub，文献(上面第一篇）:pdf 开发和运行环境：2个Intel（R）Xeon（R）Gold 6146 3.20 GHz CPU、128 GB RAM和2个NVIDIA 1080 Ti GPU 数据： Davis dataset，KIBA dataset： KIBA, Davis数据（GitHub） ，所用图框架： Pytorch Geometric |
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