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Machine learning models for classification tasks related to drug safety
2021
Molecular diversity
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Although one can already find classification models with great performances to hERG-mediated cardiotoxicity and the isoenzymes of the cytochrome P450 enzyme family, these targets are still central to ADMET-related ...
Based on the evaluation of the data, we can say that tree-based algorithms are (still) dominating the field, with consensus modeling being an increasing trend in drug safety predictions. ...
Support vector machine Support vector machines (SVM) are a classical nonlinear algorithm for classification and regression modeling as well. ...
doi:10.1007/s11030-021-10239-x
pmid:34110577
pmcid:PMC8342376
fatcat:iwjei2jzffbzhcow2agowm746i
Pattern recognition approach to classifying CYP 2C19 isoform
2012
Open Medicine
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Presented solution deals with those problems, additionally incorporating a throughout feature selection for improving the stability of received results. ...
First of all analyzed data are characterized by a significant biological noise. Additionally the training set is unbalanced, with objects from negative class outnumbering the positives four times. ...
The pattern recognition algorithm maps the feature space X to the set of class labels M. (1) The mapping (1)
Support Vector Machine Classifier Support vector machine (SVM) is a supervised learning ...
doi:10.2478/s11536-011-0120-3
fatcat:axbhk2pconhg5fp56r4ax3t5ge
GPU-accelerated machine learning techniques enable QSAR modeling of large HTS data
2012
2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
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We also report results of a case study using HTS data for a target of pharmacological and pharmaceutical relevance, cytochrome P450 3A4, for which an enrichment of 94% of the theoretical maximum is achieved ...
Leveraging massively parallel architectures such as graphics processing units (GPUs) to accelerate the training algorithms for these machine learning techniques is a cost-efficient manner in which to combat ...
Support Vector Machine Training For the SVM training algorithm, only the more computationally expensive portions of the algorithm are offloaded to the GPU for acceleration. ...
doi:10.1109/cibcb.2012.6217246
dblp:conf/cibcb/LoweBWM12
fatcat:wvn6gdiuijcmpgcv5k2ubo4myi
A Unified Proteochemometric Model for Prediction of Inhibition of Cytochrome P450 Isoforms
2013
PLoS ONE
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The entire training dataset contained 63 391 interactions and the best PCM model was obtained using signature descriptors of height 1, 2 and 3 and inducing the model with a support vector machine. ...
A unified proteochemometric (PCM) model for the prediction of the ability of drug-like chemicals to inhibit five major drug metabolizing CYP isoforms (i.e. ...
Shown are results from models induced by Support Vector Machine, Random Forest, and k-Nearest Neighbor algorithms. ...
doi:10.1371/journal.pone.0066566
pmid:23799117
pmcid:PMC3684587
fatcat:rnxqtv42kjestgru5pyv3kv72u
Classification of drug molecules considering their IC50 values using mixed-integer linear programming based hyper-boxes method
2008
BMC Bioinformatics
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To further test applicability of this approach we first created dataset for Cytochrome P450 C17 inhibitors and then predicted their activities with 100% accuracy. ...
In this study, we approach the activity prediction problem as a classification problem and, we aim to improve the classification accuracy by introducing an algorithm that combines partial least squares ...
IHK thanks for the support of Turkish National Academy of Science of Turkey for young investigator program (TUBA-GEBIP). ...
doi:10.1186/1471-2105-9-411
pmid:18834515
pmcid:PMC2572625
fatcat:7hza5pkubvgn5l3beyvdot26lq
Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data
2017
Journal of Cheminformatics
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and second their performance was compared to popular methods widely employed in the field of cheminformatics namely Naïve Bayes, k-nearest neighbor, random forest and support vector machines. ...
Background Machine learning techniques have become an integral part of the modern drug discovery process. ...
Acknowledgements The authors thank the Advanced Computing Facility of the University of Kansas (https://corelabs.ku.edu/advanced-computing-facility) for the support throughout this research. ...
doi:10.1186/s13321-017-0226-y
pmid:29086090
pmcid:PMC5489441
fatcat:7mdy7aqlbjfi3kc2s6uzn46kqi
MLViS: A Web Tool for Machine Learning-Based Virtual Screening in Early-Phase of Drug Discovery and Development
2015
PLoS ONE
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Statistical machine learning methods are widely used in drug discovery studies for classification purpose. ...
Besides classification, this application has also ability to create heat map and dendrogram for visual inspection of the molecules through hierarchical cluster analysis. ...
Rose for valuable discussions concerning implementation details and thorough testing of the tool. ...
doi:10.1371/journal.pone.0124600
pmid:25928885
pmcid:PMC4415797
fatcat:ic3r6j2wrbb4lby7sfgxgntd6y
Effect of Dataset Size and Train/Test Split Ratios in QSAR/QSPR Multiclass Classification
2021
Molecules
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We compared several combinations of dataset sizes and split ratios with five different machine learning algorithms to find the differences or similarities and to select the best parameter settings in nonbinary ...
However, the size of the datasets and the train/test split ratios can greatly affect the outcome of the models, and thus the classification performance itself. ...
Acknowledgments: The authors highly appreciate the invitation from the editorial board.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/molecules26041111
pmid:33669834
pmcid:PMC7922354
fatcat:hayozumujjbyxgdvd4or7bovhu
Machine learning in chemoinformatics and drug discovery
2018
Drug Discovery Today
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With the rapid explosion of chemical 'big' data from HTS and combinatorial synthesis, machine learning has become an indispensable tool for drug designers to mine chemical information from large compound ...
To process the chemical data, we first reviewed multiple processing layers in the chemoinformatics pipeline followed by the introduction of commonly used machine learning models in drug discovery and QSAR ...
The project was supported by Stanford Dean's Postdoctoral Fellowship, Genentech, Pfizer and the following funding sources: NIH GM102365 and FDA U01FD004979. ...
doi:10.1016/j.drudis.2018.05.010
pmid:29750902
pmcid:PMC6078794
fatcat:ckxznjxuujajle6iqycgi74d7i
Machine-learning approaches in drug discovery: methods and applications
2015
Drug Discovery Today
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Here, I focus on machine-learning techniques within the context of ligand-based VS (LBVS). ...
In addition, I analyze several relevant VS studies from recent publications, providing a detailed view of the current state-of-the-art in this field and highlighting not only the problematic issues, but ...
This research was supported by the Ministero dell'Istruzione, Università e Ricerca (MIUR-PRIN 2010-2011, prot. 2010W7YRLZ_003). ...
doi:10.1016/j.drudis.2014.10.012
pmid:25448759
fatcat:6hm6fyziq5f5zg3ejvluclyqvq
Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure
2021
Biology Direct
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Support Vector Machine (SVM) and Random Forest (RF) classifiers showed comparable performance to previously published models with a mean balanced accuracy over models generated using 5-fold LOCO-CV inside ...
In order to identify DILI early in drug development, a better understanding of the injury and models with better predictivity are urgently needed. ...
Acknowledgments We thank CAMDA and the FDA for organizing the CAMDA CMap Drug Safety Challenge 2019 and for providing us with the opportunity to present our contributions at the CAMDA/ISMB conference. ...
doi:10.1186/s13062-020-00285-0
pmid:33461600
fatcat:zb2xdv3erzeejkymeh62aztr5m
Ensemble Methods for Classification in Cheminformatics
2004
Journal of chemical information and computer sciences
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Several variants of single and ensembles models of k-nearest neighbors classifiers, support vector machines (SVMs), and single ridge regression models are compared. ...
On two data sets dealing with specific properties of drug-like substances (cytochrome P450 inhibition and "Frequent Hitters", i.e., unspecific protein inhibition), we achieve classification rates above ...
ACKNOWLEDGMENT The authors would like to thank the inventor of the libSVM, Chih-Jen Lin, for stimulating comments and discussions. ...
doi:10.1021/ci049850e
pmid:15554666
fatcat:si6lvnwygradzfyuspxlin5cqe
Target Fishing: A Single-Label or Multi-Label Problem?
[article]
2014
arXiv
pre-print
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More often than not, a drug-like compound (ligand) can be promiscuous - that is, it can interact with more than one target protein. ...
With a few exceptions, the target-fishing approaches that are currently ubiquitous in cheminformatics literature can be essentially viewed as single-label multi-classification schemes; these approaches ...
Acknowledgements AMA would like to thank the Centre for Molecular Informatics for its support. HYM, AK, AB and RCG acknowledge support by Unilever. ...
arXiv:1411.6285v1
fatcat:xt4w3dx5djhflain7qkpcwkemq
Cytochrome P450 site of metabolism prediction from 2D topological fingerprints using GPU accelerated probabilistic classifiers
2014
Journal of Cheminformatics
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The cytochrome P450s (CYPs) are a family of hemecontaining enzymes involved in the phase-I metabolism of over 90% of drugs on the market [1,2]. ...
The RASCAL algorithm is shown to give equivalent classification performance to PRW but at lower computational expense allowing it to be applied more efficiently in the ensemble scheme. of new drugs or ...
Acknowledgements The authors would like to thank Unilever for funding. We thank Dr. Guus Duchateau, Leo van Buren and Prof. Werner Klaffke for useful discussions in the development of this work. ...
doi:10.1186/1758-2946-6-29
pmid:24959208
pmcid:PMC4047555
fatcat:a2ijlrtctjdevhpjjuyhds3dky
Advances in the Application of Machine Learning Techniques in Drug Discovery, Design and Development
[chapter]
2006
Advances in Intelligent and Soft Computing
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Machine learning tools, in particular support vector machines (SVM), Particle Swarm Optimisation (PSO) and Genetic Programming (GP), are increasingly used in pharmaceuticals research and development. ...
These aspects are demonstrated via review of their current usage and future prospects in context with drug discovery activities. ...
The authors wish to thank GSK colleagues, past and present, for their efforts in expressing the nature of their research. ...
doi:10.1007/978-3-540-36266-1_10
fatcat:gdq5kxmbfjfbbaccdcjpsopg4e
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