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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 |
免疫治疗（含CAR-T）
| 1. A Machine learning model trained on dual-energy CT radiomics significantly improves immunotherapy response prediction for patients with stage IV melanoma. Journal for Immunotherapy of Cancer 2021, 9. 2. Artificial Intelligence-Assisted Score Analysis for Predicting the Expression of the Immunotherapy Biomarker PD-L1 in Lung Cancer. Frontiers in Immunology 2022, 13. 3. Artificial intelligence-powered programmed death ligand 1 analyser reduces interobserver variation in tumour proportion score for non-small cell lung cancer with better prediction of immunotherapy response. European Journal of Cancer 2022, 170, 17-26. 4. The diagnosis, classification, and treatment of sarcoma in this era of artificial intelligence and immunotherapy. Cancer Communications 2022, 42, 1288-313. 5.Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy. Journal for Immunotherapy of Cancer 2022, 10. 6. DeePaN: deep patient graph convolutional network integrating clinico-genomic evidence to stratify lung cancers for immunotherapy. Npj Digital Medicine 2021, 4. 7. A machine learning approach to predict response to immunotherapy in type 1 diabetes. Cellular & Molecular Immunology 2021, 18, 515-7. 8. Facts and Hopes on the Use of Artificial Intelligence for Predictive Immunotherapy Biomarkers in Cancer. Clinical cancer research : an official journal of the American Association for Cancer Research 2023, 29, 316-23. 9. Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker. Journal for Immunotherapy of Cancer 2020, 8. 10. Artificial Intelligence for Histology-Based Detection of Microsatellite Instability and Prediction of Response to Immunotherapy in Colorectal Cancer. Cancers 2021, 13. 11.Deep-learning-based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells. Elife 2020, 9. 12. Machine learning for prediction of cutaneous adverse events in patients receiving antiePD-1 immunotherapy. Journal of the American Academy of Dermatology 2021, 84, 183-5. 13. Deep learning reveals cuproptosis features assist in predict prognosis and guide immunotherapy in lung adenocarcinoma. Frontiers in Endocrinology 2022, 13. 14. Immune landscape-based machine-learning-assisted subclassification, prognosis, and immunotherapy prediction for glioblastoma. Frontiers in Immunology 2022, 13. 15.Assessing the efficacy of immunotherapy in lung squamous carcinoma using artificial intelligence neural network. Frontiers in Immunology 2022, 13. 16. Integrating machine learning to construct aberrant alternative splicing event related classifiers to predict prognosis and immunotherapy response in patients with hepatocellular carcinoma. Frontiers in Pharmacology 2022, 13. 17. Predictive mutation signature of immunotherapy benefits in NSCLC based on machine learning algorithms. Frontiers in immunology 2022, 13, 989275-. 18. Epigenome signature as an immunophenotype indicator prompts durable clinical immunotherapy benefits in lung adenocarcinoma. Briefings in Bioinformatics 2022, 23. 19. Trustworthy artificial intelligence models using real-world and circulating genomics data for the prediction of immunotherapy efficacy in non-small cell lung cancer patients. Annals of Oncology 2022, 33, S1043-S. 20. Deep learning signature from chest CT and association with immunotherapy outcomes in EGFR/ALK-negative NSCLC. Journal of Clinical Oncology 2022, 40. 21. Machine learning-aided quantification of antibody-based cancer immunotherapy by natural killer cells in microfluidic droplets. Lab on a Chip 2020, 20, 2317-27. 22.Deep learning reveals predictive sequence concepts within immune repertoires to immunotherapy. Science Advances 2022, 8. 23. Multimodal data integration improves immunotherapy response prediction. Nature Cancer 2022. 24. Identification and validation of novel biomarkers affecting bladder cancer immunotherapy via machine learning and its association with M2 macrophages. Frontiers in Immunology 2022, 13. 25. Machine learning revealed stemness features and a novel stemness-based classification with appealing implications in discriminating the prognosis, immunotherapy and temozolomide responses of 906 glioblastoma patients. Briefings in Bioinformatics 2021, 22. 26. Machine learning reveals a PD-L1-independent prediction of response to immunotherapy of non-small cell lung cancer by gene expression context. European Journal of Cancer 2020, 140, 76-85. 27. Applying artificial intelligence for cancer immunotherapy. Acta Pharmaceutica Sinica B 2021, 11, 3393-405. 28. Applicability analysis of immunotherapy for lung cancer patients based on deep learning. Methods 2022, 205, 149-56. 29. Artificial intelligence for prediction of response to cancer immunotherapy. Seminars in Cancer Biology 2022, 87, 137-47. 30. Artificial intelligence-based prediction of clinical outcome in immunotherapy and targeted therapy of lung cancer. Seminars in Cancer Biology 2022, 86, 146-59. 31. Machine learning on syngeneic mouse tumor profiles to model clinical immunotherapy response. Science advances 2022, 8, eabm8564-eabm. 32. Machine learning-based tumor-infiltrating immune cell-associated lncRNAs for predicting prognosis and immunotherapy response in patients with glioblastoma. Briefings in Bioinformatics 2022. 33.RCMNet: A deep learning model assists CAR-T therapy for leukemia. Computers in Biology and Medicine 2022, 150. 34. Screening Cancer Immunotherapy: When Engineering Approaches Meet Artificial Intelligence. Advanced Science 2020, 7. |