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The influence of hashed fingerprints density on the machine learning methods performance
2013
Journal of Cheminformatics
Preserved Fulltext
The aim of our study was to examine the impact of such fingerprint density on the performance of machine learning methods. ...
Both length and density (the percentage of 1's) can be modified during hashed fingerprint generation, which (as it was already proved) influence the similarity searching process [3] . ...
Acknowledgements The study was supported by a grant PRELUDIUM 2011/03/N/NZ2/02478 financed by the National Science Centre. ...
doi:10.1186/1758-2946-5-s1-p25
pmcid:PMC3606238
fatcat:yopjg5jmfrbvtbko35ck3mbkxy
Ensemble Machine Learning and Applicability Domain Estimation for Fluorescence Properties and its Application to Structural Design
2019
Journal of Computer Aided Chemistry
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The performance of the AD models was shown better than the OCSVM-based model. ...
Fluorescent substances are used in a wide range of applications, and the method that effectively design molecules having desirable absorption and emission wavelength is required. ...
We compared the predictive performance with other machine learning methods and also with quantum computation. ...
doi:10.2751/jcac.20.7
fatcat:xwtvpd64xzfq3ouop5ts72eroa
LigEGFR: Spatial graph embedding and molecular descriptors assisted bioactivity prediction of ligand molecules for epidermal growth factor receptor on a cell line-based dataset
[article]
2020
bioRxiv
pre-print
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Our model was notable for higher performance in hit compound classification, compared to molecular docking and machine learning approaches. ...
One of the leading mechanisms underlying the development of lung cancer in nonsmokers is an amplification of the epidermal growth factor receptor (EGFR) gene. ...
These results were obtained by comparing baseline machine 389 learning algorithms and molecular docking methods. 390 Nowadays, research is focused on machine learning development for pIC 50 with the human ...
doi:10.1101/2020.12.24.423424
fatcat:4fq47wu64fdhhhkhpuvquul45i
Reaction Classification and Yield Prediction using the Differential Reaction Fingerprint DRFP
2022
Digital Discovery
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The recent application of deep learning-based learned fingerprints to reaction classification and... ...
Predicting the nature and outcome of reactions using computational methods is a crucial tool to accelerate chemical research. ...
Acknowledgements This work was supported financially by the Swiss National Science Foundation, NCCR TransCure. ...
doi:10.1039/d1dd00006c
pmid:35515081
pmcid:PMC8996827
fatcat:5mtvpkg6erdgznll464pitm4by
Towards Automated Classification of Firmware Images and Identification of Embedded Devices
[chapter]
2017
IFIP Advances in Information and Communication Technology
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[19] introduced the area of remote physical device fingerprinting. Desmond et al. ...
Shah [29] presented early techniques to fingerprint and identify web applications at the HTTP level. Similar, the BlindElephant [1] attempts to discover the version of a web application. ...
The accuracy of the fuzzy hashing can be influenced by the file size and various other factors. ...
doi:10.1007/978-3-319-58469-0_16
fatcat:5cptmxpbmvgo5mycdcmp3c22ge
Learned Feature Generation for Molecules
[chapter]
2018
Lecture Notes in Computer Science
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The most common methods use a static algorithm that has been created based on domain knowledge to perform this generation of features. ...
We propose an approach where this conversion is learned by a convolutional neural network finding features that are useful for the task at hand based on the available data. ...
This work was partially funded by the Konstanz Research School Chemical Biology and KNIME AG. ...
doi:10.1007/978-3-030-01768-2_31
fatcat:4sklkmnvdnealm4bf3haz5pay4
A Text Book Of Research Papers On Fingerprint Recognition & Hash Code Techniques
2018
Zenodo
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This book contains research articles related to Fingerprint image enhancement, recognition and Hash code generation methods. ...
This Book has written with an intention to get all papers together under one roof, which will benefit all the researchers of related areas. ...
Segmentation algorithm generally falls under two categories of machine learning techniques as supervised learning and unsupervised learning. ...
doi:10.5281/zenodo.1409464
fatcat:243353fegrfxji7jvxzmqg5sge
Implicit-descriptor ligand-based virtual screening by means of collaborative filtering
2018
Journal of Cheminformatics
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Current ligand-based machine learning methods in virtual screening rely heavily on molecular fingerprinting for preprocessing, i.e., explicit description of ligands' structural and physicochemical properties ...
Our implicit descriptor method does not require any fingerprint similarity search, which makes the method free of the bias arising from the empirical nature of the fingerprint models. ...
Acknowledgements RS thanks the support of DataScience@SMU. PVK acknowledges the support from the SMU Office of Research and Graduate Studies. ...
doi:10.1186/s13321-018-0310-y
pmid:30467684
pmcid:PMC6755561
fatcat:zt2n6qiiezfenbchld4rw32gf4
A STUDY ON MULTIPLE METHODS OF FINGERPRINT HASH CODE GENERATION BASED ON MD5 ALGORITHM USING MODIFIED FILTERING TECHNIQUES AND MINUTIAE DETAILS
2021
Zenodo
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A STUDY ON MULTIPLE METHODS OF FINGERPRINT HASH CODE GENERATION BASED ON MD5 ALGORITHM USING MODIFIED FILTERING TECHNIQUES AND MINUTIAE DETAILS - Thesis ...
The concept of machine learning and neural network are efficiently used in order to learn or train the various features of the enrolled fingerprint. ...
METHODS USED FOR FINGERPRINT HASH CODE GENERATION BASED ON MD5 ALGORITHM For the purpose of finding performance and efficiency of the hash function based fingerprint matching, we have used four methods ...
doi:10.5281/zenodo.5140714
fatcat:steqwl6bcrg3volizjwqk2weuu
MULTI-MODAL RETRIEVAL IN NEWS FEED APP USING GCDL TECHNIQUE
2017
International Journal of Recent Trends in Engineering and Research
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Existing methods proposed to use Canonical Correlation Analysis (CCA), manifolds learning, dual-wing harmoniums, deep autoencoder, and deep Boltzmann machine to approach the task. ...
Experimental results show that the proposed method achieves significantly better performance than state-of-the-art approaches. ...
Previous works studied the problem from different aspects such as fingerprint extraction methods with or without linguistic knowledge, hash codes learning methods, different granularities, and so on. ...
doi:10.23883/ijrter.2017.3365.aeikk
fatcat:6dmfmfsmtbaejale6t63ts7may
Android Malware Clustering using Community Detection on Android Packages Similarity Network
[article]
2020
arXiv
pre-print
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Furthermore, we propose a novel fingerprinting technique, namely community fingerprint, based on a one-class machine learning model for each malicious community. ...
performance of the framework. ...
Instead of employing a hash or fuzzy hash-based signature of the app, the One-Class Support Vector Machine learning model (OC-SVM) [9] is used to compute the community fingerprint of the Android malware ...
arXiv:2005.06075v1
fatcat:43wg2wbvejg35ix6yz6tskmie4
Deep Learning Insights into Lanthanides Complexation Chemistry
2021
Molecules
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It was shown that the main influence on the constants had a mutual location of the binding centers. ...
Here we present an example of deep learning usage not only to build a model but also to determine key structural fragments of ligands influencing metal complexation. ...
Acknowledgments: The authors would like to express gratitude to A. Mitrofanova for illustrations, and A. Varnek and Yu. Ustynuyk for discussion on the topic of QSPR and metal complexation. ...
doi:10.3390/molecules26113237
pmid:34072262
fatcat:e7nmiw4gvre2je2uzw76nceu5e
DeepFrag: A Deep Convolutional Neural Network for Fragment-based Lead Optimization
[article]
2021
bioRxiv
pre-print
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Machine learning has been increasingly applied to the field of computer-aided drug discovery in recent years, leading to notable advances in binding-affinity prediction, virtual screening, and QSAR. ...
In an independent benchmark of known ligands with missing (deleted) fragments, our DeepFrag model selected the known (correct) fragment from a set over 6,500 about 58% of the time. ...
The voxelation method controls the shape of atomic densities 2 and the atom-influence radius controls the size of atomic densities (Tables 2 and S1). ...
doi:10.1101/2021.01.07.425790
fatcat:zqipo5zybbcqdhs2h3nq3kdovm
Editorial
2019
Intelligent Data Analysis
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Editorial Dear Colleague: Welcome to volume 23(5) of Intelligent Data Analysis (IDA) Journal. ...
The authors analyze one of the means to increase the performances of machine learning algorithms which is exploiting data locality. ...
reuse in some selected machine learning algorithms. ...
doi:10.3233/ida-190005
fatcat:x3vz7qnegvgwbeulzzqgup7nvy
MoleculeNet: A Benchmark for Molecular Machine Learning
[article]
2018
arXiv
pre-print
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Improved methods and the presence of larger datasets have enabled machine learning algorithms to make increasingly accurate predictions about molecular properties. ...
to gauge the quality of proposed methods. ...
We investigated how the performance of machine learning methods on FreeSolv changes with the volume of training data. ...
arXiv:1703.00564v3
fatcat:pmhnvly7qfhrxkel5a6tctowv4
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