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References from Interpretable molecular encodings and representations for machine learning tasks. Local targets link to admitted publications; unresolved targets remain external evidence.
Evaluating molecular representations in machine learning models for drug response prediction and interpretability
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An overview of molecular fingerprint similarity search in virtual screening
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Unresolved reference
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Comparing structural fingerprints using a literature-based similarity benchmark
2016 · External reference
Chemical similarity methods for analyzing secondary metabolite structures
2021 · External reference
Molecular representations in AI-driven drug discovery: a review and practical guide
10.1186/s13321-020-00460-5 · 2020 · External reference
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10.1016/s0378-4274(99)00236-2
10.1016/s0378-4274(99)00236-2 · External reference
Molecular fingerprint-derived similarity measures for toxicological read-across: recommendations for optimal use
10.1016/j.yrtph.2018.11.002 · 2019 · External reference
10.1093/bib/bbab365
10.1093/bib/bbab365 · External reference
Molecular fingerprint-based machine learning assisted qsar model development for prediction of ionic liquid properties
10.1016/j.molliq.2020.115212 · 2021 · External reference
Mutagenpred-gcnns: a graph convolutional neural network-based classification model for mutagenicity prediction with data-driven molecular fingerprints
10.1007/s12539-020-00407-2 · 2021 · External reference
10.1093/nargab/lqab039
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10.1038/s42256-022-00580-7
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Molecular graph convolutions: moving beyond fingerprints
10.1007/s10822-016-9938-8 · 2016 · External reference
10.1038/s41586-021-03819-2
10.1038/s41586-021-03819-2 · External reference
Unresolved reference
External reference
10.1093/nargab/lqac103
10.1093/nargab/lqac103 · External reference
Scikit-learn: machine learning in python
2011 · External reference
Prediction of cell-penetrating peptides using artificial neural networks
10.2174/157340910791202478 · 2010 · External reference
10.3390/ijms23042156
10.3390/ijms23042156 · External reference
Unresolved reference
2011 · External reference
The elements
2000 · External reference
10.1038/s42256-020-00236-4
10.1038/s42256-020-00236-4 · External reference
10.1073/pnas.39.10.1095
10.1073/pnas.39.10.1095 · External reference
Unresolved reference
2020 · External reference
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
10.1371/journal.pone.0130140 · 2015 · External reference
Computer-aided prediction of antigen presenting cell modulators for designing peptide-based vaccine adjuvants
10.1186/s12967-018-1560-1 · 2018 · External reference
10.18632/oncotarget.7815
10.18632/oncotarget.7815 · External reference
10.18632/oncotarget.20365
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In silico models for designing and discovering novel anticancer peptides
10.1038/srep02984 · 2013 · External reference
In silico approach for prediction of antifungal peptides
10.3389/fmicb.2018.00323 · 2018 · External reference
Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou's general pseaac
10.1038/srep42362 · 2017 · External reference
Prediction of anti-inflammatory proteins/peptides: an insilico approach
10.1186/s12967-016-1103-6 · 2017 · External reference
Aippred: sequence-based prediction of anti-inflammatory peptides using random forest
10.3389/fphar.2018.00276 · 2018 · External reference
10.1002/bip.22066
10.1002/bip.22066 · External reference
Antibp2: improved version of antibacterial peptide prediction
10.1186/1471-2105-11-s1-s19 · 2010 · External reference
Analysis and prediction of antibacterial peptides
10.1186/1471-2105-8-263 · 2007 · External reference
10.1093/bioinformatics/btx285
10.1093/bioinformatics/btx285 · External reference
An fpga implementation to detect selective cationic antibacterial peptides
10.1371/journal.pone.0021399 · 2011 · External reference
Cs-amppred: an updated svm model for antimicrobial activity prediction in cysteine-stabilized peptides
10.1371/journal.pone.0051444 · 2012 · External reference
10.1016/j.ab.2013.01.019
10.1016/j.ab.2013.01.019 · External reference
10.1093/bib/bbab240
10.1093/bib/bbab240 · External reference
Efficient computational model for identification of antitubercular peptides by integrating amino acid patterns and properties
10.1002/1873-3468.13536 · 2019 · External reference
Prediction of antitubercular peptides from sequence information using ensemble classifier and hybrid features
10.3389/fphar.2018.00954 · 2018 · External reference
Developing an antiviral peptides predictor with generative adversarial network data augmentation
2021 · External reference
Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou's general pseaac
10.1038/srep42362 · 2017 · External reference
10.1093/nar/gks450
10.1093/nar/gks450 · External reference
10.3389/fimmu.2018.01695
10.3389/fimmu.2018.01695 · External reference
Skipcpp-pred: an improved and promising sequence-based predictor for predicting cell-penetrating peptides
10.1186/s12864-017-4128-1 · 2017 · External reference
O. s. d. d. consortium, in silico approaches for designing highly effective cell penetrating peptides
10.1186/1479-5876-11-74 · 2013 · External reference
Prediction of cell-penetrating peptides
10.1007/s10989-005-9393-1 · 2005 · External reference
Predicting cell-penetrating peptides
10.1016/j.addr.2007.09.003 · 2008 · External reference
Prediction of cell-penetrating potential of modified peptides containing natural and chemically modified residues
10.3389/fmicb.2018.00725 · 2018 · External reference
Machine-learning-based prediction of cell-penetrating peptides and their uptake efficiency with improved accuracy
10.1021/acs.jproteome.8b00148 · 2018 · External reference
Kelm-cpppred: Kernel extreme learning machine based prediction model for cell-penetrating peptides
10.1021/acs.jproteome.8b00322 · 2018 · External reference
Prediction of cell penetrating peptides by support vector machines
10.1371/journal.pcbi.1002101 · 2011 · External reference
Prediction of cell-penetrating peptides with feature selection techniques
10.1016/j.bbrc.2016.06.035 · 2016 · External reference
10.1093/nar/gkz993
10.1093/nar/gkz993 · External reference
10.1038/srep22843
10.1038/srep22843 · External reference
Prediction of co-receptor usage of hiv-1 from genotype
10.1371/journal.pcbi.1000743 · 2010 · External reference
Predicting bevirimat resistance of hiv-1 from genotype
10.1186/1471-2105-11-37 · 2010 · External reference
10.1093/bioinformatics/btz493
10.1093/bioinformatics/btz493 · External reference
10.1093/bioinformatics/btu810
10.1093/bioinformatics/btu810 · External reference
Computer-aided designing of immunosuppressive peptides based on il-10 inducing potential
10.1038/srep42851 · 2017 · External reference
Neuropipred: a tool to predict, design and scan insect neuropeptides
10.1038/s41598-019-41538-x · 2019 · External reference
Cycpeptmpdb: a comprehensive database of membrane permeability of cyclic peptides
10.1021/acs.jcim.2c01573 · 2023 · External reference
10.3389/fimmu.2018.01783
10.3389/fimmu.2018.01783 · External reference
10.1093/bioinformatics/btg255
10.1093/bioinformatics/btg255 · External reference
In silico approach for predicting toxicity of peptides and proteins
10.1371/journal.pone.0073957 · 2013 · External reference
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10.1093/bib/bbac174 · External reference
