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Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets.
- Published in:
- Molecular Informatics, 2015, v. 34, n. 2/3, p. 115, doi. 10.1002/minf.201400132
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- Article
MolTarPred: A web tool for comprehensive target prediction with reliability estimation.
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- Chemical Biology & Drug Design, 2019, v. 94, n. 1, p. 1390, doi. 10.1111/cbdd.13516
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- Article
Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest.
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- Molecules, 2015, v. 20, n. 6, p. 10947, doi. 10.3390/molecules200610947
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- Article
Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitors.
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- Journal of Cheminformatics, 2024, v. 16, n. 1, p. 1, doi. 10.1186/s13321-024-00832-1
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- Article
Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study.
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- BMC Bioinformatics, 2014, v. 15, n. 1, p. 1, doi. 10.1186/1471-2105-15-291
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- Article
Correcting the impact of docking pose generation error on binding affinity prediction.
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- BMC Bioinformatics, 2016, v. 17, p. 1, doi. 10.1186/s12859-016-1169-4
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- Article
Artificial intelligence for the next generation of precision oncology.
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- NPJ Precision Oncology, 2021, v. 5, n. 1, p. 1, doi. 10.1038/s41698-021-00216-w
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- Article
Interpretable Machine Learning Models to Predict the Resistance of Breast Cancer Patients to Doxorubicin from Their microRNA Profiles.
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- Advanced Science, 2022, v. 9, n. 24, p. 1, doi. 10.1002/advs.202201501
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- Article
Using Stochastic Computing for Virtual Screening Acceleration.
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- Electronics (2079-9292), 2021, v. 10, n. 23, p. 2981, doi. 10.3390/electronics10232981
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- Article
Artificial intelligence for drug response prediction in disease models.
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- Briefings in Bioinformatics, 2022, v. 23, n. 1, p. 1, doi. 10.1093/bib/bbab450
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- Article
gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab312
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- Article
The impact of compound library size on the performance of scoring functions for structure-based virtual screening.
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- Briefings in Bioinformatics, 2021, v. 22, n. 3, p. 1, doi. 10.1093/bib/bbaa095
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- Article
Editorial: Intelligent Systems for Genome Functional Annotations.
- Published in:
- 2020
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- Publication type:
- Editorial
A practical guide to machine-learning scoring for structure-based virtual screening.
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- Nature Protocols, 2023, v. 18, n. 11, p. 3460, doi. 10.1038/s41596-023-00885-w
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- Article
Classical scoring functions for docking are unable to exploit large volumes of structural and interaction data.
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- Bioinformatics, 2019, v. 35, n. 20, p. 3989, doi. 10.1093/bioinformatics/btz183
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- Article
Machine‐learning scoring functions for structure‐based virtual screening.
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- WIREs: Computational Molecular Science, 2021, v. 11, n. 1, p. 1, doi. 10.1002/wcms.1478
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- Article
Machine‐learning scoring functions for structure‐based drug lead optimization.
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- WIREs: Computational Molecular Science, 2020, v. 10, n. 5, p. 1, doi. 10.1002/wcms.1465
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- Publication type:
- Article
Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening.
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- WIREs: Computational Molecular Science, 2015, v. 5, n. 6, p. 405, doi. 10.1002/wcms.1225
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- Article
istar: A Web Platform for Large-Scale Protein-Ligand Docking.
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- PLoS ONE, 2014, v. 9, n. 1, p. 1, doi. 10.1371/journal.pone.0085678
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- Article
Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties.
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- PLoS ONE, 2013, v. 8, n. 4, p. 1, doi. 10.1371/journal.pone.0061318
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- Article
Performance of machine-learning scoring functions in structure-based virtual screening.
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- Scientific Reports, 2017, p. 46710, doi. 10.1038/srep46710
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- Article
On the Best Way to Cluster NCI-60 Molecules.
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- Biomolecules (2218-273X), 2023, v. 13, n. 3, p. 498, doi. 10.3390/biom13030498
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- Article
Identification and Validation of Carbonic Anhydrase II as the First Target of the Anti-Inflammatory Drug Actarit.
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- Biomolecules (2218-273X), 2020, v. 10, n. 11, p. 1570, doi. 10.3390/biom10111570
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- Publication type:
- Article
Concise Polygenic Models for Cancer-Specific Identification of Drug-Sensitive Tumors from Their Multi-Omics Profiles.
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- Biomolecules (2218-273X), 2020, v. 10, n. 6, p. 963, doi. 10.3390/biom10060963
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- Article
Machine Learning for Molecular Modelling in Drug Design.
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- 2019
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- Publication type:
- Editorial
The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction.
- Published in:
- Biomolecules (2218-273X), 2018, v. 8, n. 1, p. 12, doi. 10.3390/biom8010012
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- Publication type:
- Article
Unearthing new genomic markers of drug response by improved measurement of discriminative power.
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- BMC Medical Genomics, 2018, v. 11, p. 1, doi. 10.1186/s12920-018-0336-z
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- Article
Ultrafast shape recognition to search compound databases for similar molecular shapes.
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- Journal of Computational Chemistry, 2007, v. 28, n. 10, p. 1711, doi. 10.1002/jcc.20681
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- Article
USR-VS: a web server for large-scale prospective virtual screening using ultrafast shape recognition techniques.
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- Nucleic Acids Research, 2016, v. 44, n. W1, p. W436, doi. 10.1093/nar/gkw320
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- Article
A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking.
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- Bioinformatics, 2010, v. 26, n. 9, p. 1169, doi. 10.1093/bioinformatics/btq112
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- Article
NF-κB–dependent IRF1 activation programs cDC1 dendritic cells to drive antitumor immunity.
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- Science Immunology, 2021, v. 6, n. 61, p. 1, doi. 10.1126/sciimmunol.abg3570
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- Article
Predicting Cancer Drug Response In Vivo by Learning an Optimal Feature Selection of Tumour Molecular Profiles.
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- Biomedicines, 2021, v. 9, n. 10, p. 1319, doi. 10.3390/biomedicines9101319
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- Article
Drug repurposing for aging research using model organisms.
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- Aging Cell, 2017, v. 16, n. 5, p. 1006, doi. 10.1111/acel.12626
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- Article