Research graph
References from DeepSCM: An efficient convolutional neural network surrogate model for the screening of therapeutic antibody viscosity. Local targets link to admitted publications; unresolved targets remain external evidence.
Recent progresses of understanding the viscosity of concentrated protein solutions
10.1016/j.coche.2017.04.001 · 2017 · External reference
10.1016/j.jconrel.2018.08.001
10.1016/j.jconrel.2018.08.001 · External reference
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10.1002/jps.22190
10.1002/jps.22190 · External reference
10.1080/19420862.2017.1285479
10.1080/19420862.2017.1285479 · External reference
In silico selection of therapeutic antibodies for development: viscosity, clearance, and chemical stability
10.1073/pnas.1421779112 · 2014 · External reference
10.1021/acs.molpharmaceut.0c01073
10.1021/acs.molpharmaceut.0c01073 · External reference
10.1080/19420862.2021.1991256
10.1080/19420862.2021.1991256 · External reference
10.1080/19420862.2022.2026208
10.1080/19420862.2022.2026208 · External reference
Computational tool for the early screening of monoclonal antibodies for their viscosities
10.1080/19420862.2015.1099773 · 2015 · External reference
Coarse-grained modeling of the self-association of therapeutic monoclonal antibodies
10.1021/jp301140u · 2012 · External reference
Coarse-grained molecular dynamics simulations for understanding the impact of short-range anisotropic attractions on structure and viscosity of concentrated monoclonal antibody solutions
10.1021/acs.molpharmaceut.9b00960 · 2020 · External reference
Multiscale coarse-grained approach to investigate self-association of antibodies
10.1016/j.bpj.2020.04.022 · 2020 · External reference
10.1080/19420862.2021.1907882
10.1080/19420862.2021.1907882 · External reference
10.1021/acs.jpcb.7b11053
10.1021/acs.jpcb.7b11053 · External reference
Cluster formation and entanglement in the rheology of antibody solutions
10.1021/acs.jpcb.9b01511 · 2019 · External reference
Machine learning feature selection for predicting high concentration therapeutic antibody aggregation
10.1016/j.xphs.2020.12.014 · 2021 · External reference
10.3389/frai.2020.00004
10.3389/frai.2020.00004 · External reference
A review of deep learning methods for antibodies
2020 · External reference
10.1093/bioinformatics/btaa457
10.1093/bioinformatics/btaa457 · External reference
Antibody structure prediction using interpretable deep learning
2021 · External reference
10.1038/s41551-021-00699-9
10.1038/s41551-021-00699-9 · External reference
DRREP: deep ridge regressed epitope predictor
10.1186/s12864-017-4024-8 · 2017 · External reference
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10.1093/nar/gkt1043
10.1093/nar/gkt1043 · External reference
10.1016/j.jmb.2016.08.019
10.1016/j.jmb.2016.08.019 · External reference
10.1093/bioinformatics/btv552
10.1093/bioinformatics/btv552 · External reference
10.1080/19420862.2016.1205773
10.1080/19420862.2016.1205773 · External reference
Comparative protein structure modeling using MODELLER
10.1002/cpbi.3 · 2016 · External reference
10.1063/1.445869
10.1063/1.445869 · External reference
10.1016/0263-7855(96)00018-5
10.1016/0263-7855(96)00018-5 · External reference
10.1002/jcc.20289
10.1002/jcc.20289 · External reference
10.1021/jp101759q
10.1021/jp101759q · External reference
10.1038/nmeth.4067
10.1038/nmeth.4067 · External reference
10.1063/1.470117
10.1063/1.470117 · External reference
Scikit-learn: machine learning in Python
2011 · External reference
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Five computational developability guidelines for therapeutic antibody profiling
10.1073/pnas.1810576116 · 2019 · External reference
10.1080/19420862.2019.1692764
10.1080/19420862.2019.1692764 · External reference
10.1073/pnas.0904191106
10.1073/pnas.0904191106 · External reference
Prediction of delayed retention of antibodies in hydrophobic interaction chromatography from sequence using machine learning
10.1093/bioinformatics/btx519 · 2017 · External reference
10.1021/acs.molpharmaceut.1c00280
10.1021/acs.molpharmaceut.1c00280 · External reference
