Molecular fingerprint similarity search in virtual screening.

By Adrià Cereto-Massagué, María José Ojeda, Cristina Valls, Miquel Mulero, Santiago Garcia-Vallvé, Gerard Pujadas. Methods, 2015

770 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e54398c62c5b3d3b587818a98828bc78d29c57be

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Citations in the corpus

Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases (2018, methodology): “These studies showed that each fingerprint type represents different aspects of compounds; therefore, selection of fingerprints is crucial for VS [62].”
Predicting protein–ligand affinity with a random matrix framework (2016, background): “, within some radius of every atom of the structure) and then mapping these bond paths to a bit string of defined length [a molecular “fingerprint” (27)] through a hash function.”
Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? (2015, background): “Bender and coworkers [5] or Cereto-Massagué and coworkers [7].”
Comparing structural fingerprints using a literature-based similarity benchmark (2016, background): “As many molecular fingerprints are in widespread use [6], an important question to ask is which are better at measuring structural similarity.”
Translation between Molecules and Natural Language (2022, methodology): “Traditionally, ﬁngerprinting methods have been a preferred technique to featurize molecule structural representations (Rogers and Hahn, 2010; Cereto-Massagué et al., 2015).”
ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation (2015, background): “Both molecular descriptors and fingerprints play a fundamental role in QSAR/SAR analysis, virtual molecule screening, similarity-based compound search, target molecule ranking, drug ADME/T prediction and the other drug discovery processes [2–12].”
Benchmarking platform for ligand-based virtual screening (2016, background): “There are two main variants of virtual screening: structure-based (SBVS) [4] and ligand-based virtual screening (LBVS) [5].”
DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences (2018, methodology): “For feature-based DTI prediction models, a fingerprint is the most commonly used descriptor of the substructure of a drug [12].”
SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules (2017, neutral): “A major advantage of FP is the efficiency by which computers handle such bit strings 10 , allowing for instance large-scale virtual screening or the rapid estimation of synthetic accessibility of molecules 11 .”
Geometry-enhanced molecular representation learning for property prediction (2021, methodology): “Molecular fingerprints, such as ECFP [39] and MACCS [12], are commonly used for molecular representations by traditional machine learning methods [6, 8, 13, 24], which encode a molecule into a sequence of bits according to the molecules’ topological substructures.”
Artificial Intelligence For Natural Product Drug Discovery (2023, methodology): “The most ubiquitous method for featurizing a molecule is to convert its molecular structure into a sequence of bits or counts 80 .”