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Machine learning models for classification tasks related to drug safety

Anita Rácz, Dávid Bajusz, Ramón Alain Miranda-Quintana, Károly Héberger
2021 Molecular diversity  
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

Bartosz Krawczyk
2012 Open Medicine  
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

E. W. Lowe, M. Butkiewicz, N. Woetzel, J. Meiler
2012 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)  
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

Maris Lapins, Apilak Worachartcheewan, Ola Spjuth, Valentin Georgiev, Virapong Prachayasittikul, Chanin Nantasenamat, Jarl E. S. Wikberg, Daniel S. Sem
2013 PLoS ONE  
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

Pelin Armutlu, Muhittin E Ozdemir, Fadime Uney-Yuksektepe, I Halil Kavakli, Metin Turkay
2008 BMC Bioinformatics  
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

Alexios Koutsoukas, Keith J. Monaghan, Xiaoli Li, Jun Huan
2017 Journal of Cheminformatics  
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

Selcuk Korkmaz, Gokmen Zararsiz, Dincer Goksuluk, L. Michel Espinoza-Fonseca
2015 PLoS ONE  
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

Anita Rácz, Dávid Bajusz, Károly Héberger
2021 Molecules  
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

Yu-Chen Lo, Stefano E. Rensi, Wen Torng, Russ B. Altman
2018 Drug Discovery Today  
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

Antonio Lavecchia
2015 Drug Discovery Today  
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

Anika Liu, Moritz Walter, Peter Wright, Aleksandra Bartosik, Daniela Dolciami, Abdurrahman Elbasir, Hongbin Yang, Andreas Bender
2021 Biology Direct  
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

Christian Merkwirth, Harald Mauser, Tanja Schulz-Gasch, Olivier Roche, Martin Stahl, Thomas Lengauer
2004 Journal of chemical information and computer sciences  
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]

Avid M. Afzal, Hamse Y. Mussa, Richard E. Turner, Andreas Bender, Robert C. Glen
2014 arXiv   pre-print
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

Jonathan D Tyzack, Hamse Y Mussa, Mark J Williamson, Johannes Kirchmair, Robert C Glen
2014 Journal of Cheminformatics  
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]

S. J. Barrett, W. B. Langdon
2006 Advances in Intelligent and Soft Computing  
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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