Structure-based virtual screening for PDL1 dimerizers: Evaluating generic scoring functions
Tài liệu tham khảo
Adeshina, 2020, Machine learning classification can reduce false positives in structure-based virtual screening, Proc. Natl. Acad. Sci. Unit. States Am., 117, 18477, 10.1073/pnas.2000585117
Ballester, 2020, Selecting machine-learning scoring functions for structure-based virtual screening, Drug Discov. Today Technol., 32–33, 81
Ballester, 2009, Ultrafast shape recognition : evaluating a new ligand-based virtual screening technology, J. Mol. Graph. Model., 27, 836, 10.1016/j.jmgm.2009.01.001
Durrant, 2015, Neural-network scoring functions identify structurally novel estrogen-receptor ligands, J. Chem. Inf. Model., 55, 1953, 10.1021/acs.jcim.5b00241
Fresnais, 2020, The impact of compound library size on the performance of scoring functions for structure-based virtual screening, Brief. Bioinform. bbaa095
Ghislat, 2021, Recent progress on the prospective application of machine learning to structure-based virtual screening, Curr. Opin. Chem. Biol., 65, 28, 10.1016/j.cbpa.2021.04.009
Imrie, 2021, Generating property-matched decoy molecules using deep learning, Bioinf., 10.1093/bioinformatics/btab080
Koes, 2013, Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise, J. Chem. Inf. Model., 53, 1893, 10.1021/ci300604z
Konieczny, 2020, Di-bromo-Based small-molecule inhibitors of the PD-1/PD-L1 immune checkpoint, J. Med. Chem., 63, 11271, 10.1021/acs.jmedchem.0c01260
Kuang, 2020, Partial least-squares discriminant analysis and ensemble-based flexible docking of PD-1/PD-L1 inhibitors: a pilot study, ACS Omega, 5, 26914, 10.1021/acsomega.0c04149
Li, 2020, Machine-learning scoring functions for structure-based drug lead optimization, Wiley Interdiscip. Rev. Comput. Mol. Sci., 10, 10.1002/wcms.1465
Li, 2021, Machine-learning scoring functions for structure-based virtual screening, WIREs Comput. Mol. Sci., e1478
Meng, 2021, Persistent spectral-based machine learning (PerSpect ML) for protein-ligand binding affinity prediction, Sci. Adv., 7, 10.1126/sciadv.abc5329
Nguyen, 2019, Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges, J. Comput. Aided Mol. Des., 33, 71, 10.1007/s10822-018-0146-6
Ragoza, 2017, Protein–ligand scoring with convolutional neural networks, J. Chem. Inf. Model., 57, 942, 10.1021/acs.jcim.6b00740
Sánchez-Cruz, 2021, Extended connectivity interaction features: improving binding affinity prediction through chemical description, Bioinformatics, 37, 1376, 10.1093/bioinformatics/btaa982
Saito, 2015, The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets, PLoS One, 10, 10.1371/journal.pone.0118432
Shen, 2019, From machine learning to deep learning: advances in scoring functions for protein–ligand docking, Wiley Interdiscip. Rev. Comput. Mol. Sci., e1429
Shen, 2021, Accuracy or novelty: what can we gain from target-specific machine-learning-based scoring functions in virtual screening?, Briefings Bioinf., 22
Shi, 2019, Computational insight into the small molecule intervening PD-L1 dimerization and the potential structure-activity relationship, Front. Chem., 7, 10.3389/fchem.2019.00764
Tran-Nguyen, 2021, True accuracy of fast scoring functions to predict high-throughput screening data from docking poses: the simpler the better, J. Chem. Inf. Model., 61, 2788, 10.1021/acs.jcim.1c00292
Upadhaya, 2022, Challenges and opportunities in the PD1/PDL1 inhibitor clinical trial landscape, Nat. Rev. Drug Discov., 10.1038/d41573-022-00030-4
Wójcikowski, 2017, Performance of machine-learning scoring functions in structure-based virtual screening, Sci. Rep., 7, 10.1038/srep46710
Xiong, 2020, Improving structure-based virtual screening performance via learning from scoring function components, Brief. Bioinform. bbaa094
Yasuo, 2019, An improved method of structure-based virtual screening via interaction-energy-based learning, J. Chem. Inf. Model., 59, 1050, 10.1021/acs.jcim.8b00673
