25,892 research outputs found
Fingerprint Recognition Using Translation Invariant Scattering Network
Fingerprint recognition has drawn a lot of attention during last decades.
Different features and algorithms have been used for fingerprint recognition in
the past. In this paper, a powerful image representation called scattering
transform/network, is used for recognition. Scattering network is a
convolutional network where its architecture and filters are predefined wavelet
transforms. The first layer of scattering representation is similar to sift
descriptors and the higher layers capture higher frequency content of the
signal. After extraction of scattering features, their dimensionality is
reduced by applying principal component analysis (PCA). At the end, multi-class
SVM is used to perform template matching for the recognition task. The proposed
scheme is tested on a well-known fingerprint database and has shown promising
results with the best accuracy rate of 98\%.Comment: IEEE Signal Processing in Medicine and Biology Symposium, 201
Pharmacoprint -- a combination of pharmacophore fingerprint and artificial intelligence as a tool for computer-aided drug design
Structural fingerprints and pharmacophore modeling are methodologies that
have been used for at least two decades in various fields of cheminformatics:
from similarity searching to machine learning (ML). Advances in silico
techniques consequently led to combining both these methodologies into a new
approach known as pharmacophore fingerprint. Herein, we propose a
high-resolution, pharmacophore fingerprint called Pharmacoprint that encodes
the presence, types, and relationships between pharmacophore features of a
molecule. Pharmacoprint was evaluated in classification experiments by using ML
algorithms (logistic regression, support vector machines, linear support vector
machines, and neural networks) and outperformed other popular molecular
fingerprints (i.e., Estate, MACCS, PubChem, Substructure, Klekotha-Roth, CDK,
Extended, and GraphOnly) and ChemAxon Pharmacophoric Features fingerprint.
Pharmacoprint consisted of 39973 bits; several methods were applied for
dimensionality reduction, and the best algorithm not only reduced the length of
bit string but also improved the efficiency of ML tests. Further optimization
allowed us to define the best parameter settings for using Pharmacoprint in
discrimination tests and for maximizing statistical parameters. Finally,
Pharmacoprint generated for 3D structures with defined hydrogens as input data
was applied to neural networks with a supervised autoencoder for selecting the
most important bits and allowed to maximize Matthews Correlation Coefficient up
to 0.962. The results show the potential of Pharmacoprint as a new, perspective
tool for computer-aided drug design.Comment: Journal of Chemical Information and Modeling (2021
IMPROVING MOLECULAR FINGERPRINT SIMILARITY VIA ENHANCED FOLDING
Drug discovery depends on scientists finding similarity in molecular fingerprints to the drug target. A new way to improve the accuracy of molecular fingerprint folding is presented. The goal is to alleviate a growing challenge due to excessively long fingerprints. This improved method generates a new shorter fingerprint that is more accurate than the basic folded fingerprint. Information gathered during preprocessing is used to determine an optimal attribute order. The most commonly used blocks of bits can then be organized and used to generate a new improved fingerprint for more optimal folding. We thenapply the widely usedTanimoto similarity search algorithm to benchmark our results. We show an improvement in the final results using this method to generate an improved fingerprint when compared against other traditional folding methods
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