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Evaluation of different machine learning methods for ligand-based virtual screening

R Kurczab, S Smusz, AJ Bojarski
2011 Journal of Cheminformatics  
In recent years, many comparative studies of different machine learning methods performance in ligandbased virtual screening have been reported [2, 3] .  ...  In order to extend these studies, we have evaluated over 60 different machine learning methods, such as: support vector machines (with and without parameter optimization), naïve Bayesian, decision trees  ...  In recent years, many comparative studies of different machine learning methods performance in ligandbased virtual screening have been reported [2, 3] .  ... 
doi:10.1186/1758-2946-3-s1-p41 pmcid:PMC3083598 fatcat:qji3n7zxvjbifp5hkrxf3imgny

Automated discovery of GPCR bioactive ligands

Sebastian Raschka
2019 Current Opinion in Structural Biology  
This review describes the most recent and successful applications of machine learning for bioactive ligand discovery, concluding with an outlook on deep learning methods that are capable of automatically  ...  ligands using machine learning.  ...  Virtual screening has become a major approach for computer-aided ligand discovery and is traditionally categorized as either ligand-or structure-based virtual screening.  ... 
doi:10.1016/ pmid:30909105 fatcat:tm5pfuy4z5dexdsnnlca7rzmgm

Prediction Methods of Herbal Compounds in Chinese Medicinal Herbs

Ke Han, Lei Zhang, Miao Wang, Rui Zhang, Chunyu Wang, Chengzhi Zhang
2018 Molecules  
The methods for predicting the drug-like properties of Chinese herbal compounds include the virtual screening method, pharmacophore model method and machine learning method.  ...  We analyze the advantages and disadvantages of the above three methods, and then introduce the specific steps of the virtual screening method.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/molecules23092303 pmid:30201875 pmcid:PMC6225236 fatcat:juaipuzpova7pjy5ipcom37f4u

Traditional machine learning and big data analytics in virtual screening: a comparative study

Sahar K. Hussin, Yasser M. Omar, Salah M. Abdelmageid, Mahmoud I. Marie
2020 International Journal of Advanced Computer Research  
Conflicts of interest The authors have no conflicts of interest to declare.  ...  used in VS. it reviews traditional machine learning methods and machine learning along with big data frameworks used for VS. 3.1Traditional machine learning based solution Virtual Screening is typically  ...  Figure 1Taxonomy for 1Taxonomy 3D structure of virtual screening methods Figure 2 2 Taxonomy of virtual screening solutions Figure 3 3 Support vector machine Figure 4 4 Artificial comparison of  ... 
doi:10.19101/ijacr.2019.940150 fatcat:zzdudmniuvaytcqwhvicn4kg64

Virtual screening approach to identifying influenza virus neuraminidase inhibitors using molecular docking combined with machine-learning-based scoring function

Li Zhang, Hai-Xin Ai, Shi-Meng Li, Meng-Yuan Qi, Jian Zhao, Qi Zhao, Hong-Sheng Liu
2017 OncoTarget  
To develop novel anti-influenza therapies, we have constructed a machine-learning-based scoring function (RF-NA-Score) for the effective virtual screening of lead compounds targeting the viral neuraminidase  ...  The performance of RF-NA-Score in a docking-based virtual screening of NA inhibitors was evaluated with a dataset containing 281 NA inhibitors and 322 noninhibitors.  ...  The effectiveness of various virtual screening strategies using this docking-rescoring method, with different docking software tools combined with different rescoring functions, was evaluated based on  ... 
doi:10.18632/oncotarget.20915 pmid:29137330 pmcid:PMC5669956 fatcat:opeazl4s2zg37i466qk232h2ga

Implicit-descriptor ligand-based virtual screening by means of collaborative filtering

Raghuram Srinivas, Pavel V Klimovich, Eric C Larson
2018 Journal of Cheminformatics  
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  ...  We show that implicit methods significantly outperform traditional machine learning methods, and the main strengths of implicit methods are their resilience to target-ligand sparsity and high potential  ...  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

Practical Model Selection for Prospective Virtual Screening

Shengchao Liu, Moayad Alnammi, Spencer S. Ericksen, Andrew F. Voter, Gene E. Ananiev, James L. Keck, F. Michael Hoffmann, Scott A. Wildman, Anthony Gitter
2018 Journal of Chemical Information and Modeling  
We consider a wide range of ligand-based machine learning and docking-based approaches for virtual screening on two protein-protein interactions, PriA-SSB and RMI-FANCM, and present a strategy for choosing  ...  Virtual (computational) high-throughput screening provides a strategy for prioritizing compounds for experimental screens, but the choice of virtual screening algorithm depends on the data set and evaluation  ...  , whereas the ligand-based machine learning methods do.  ... 
doi:10.1021/acs.jcim.8b00363 pmid:30500183 pmcid:PMC6351977 fatcat:4cqp6nqkfrf3hdetp42rmwgaey

Virtual Screening with Gnina 1.0

Jocelyn Sunseri, David Ryan Koes
2021 Molecules  
Here we investigate the virtual screening performance of the recently released Gnina molecular docking software, which uses deep convolutional networks to score protein-ligand structures.  ...  However, we also find that issues of bias linger in these sets, even when not used directly to train models, and this bias obfuscates to what extent machine learning models are achieving their performance  ...  Virtual screening methods can be broadly classified as ligand-based or structure-based.  ... 
doi:10.3390/molecules26237369 pmid:34885952 pmcid:PMC8659095 fatcat:izosxicennbilesme7ne33s6ma

The Virtual Screening of the Drug Protein with a Few Crystal Structures Based on the Adaboost-SVM

Meng-yu Wang, Peng Li, Pei-li Qiao
2016 Computational and Mathematical Methods in Medicine  
Using the theory of machine learning to assist the virtual screening (VS) has been an effective plan.  ...  To solve this problem, we present a method using the ensemble learning to improve the support vector machine to process the generated protein-ligand interaction fingerprint (IFP).  ...  The Evaluation Index of the Machine Learning Combined with Virtual Screening. At present, the virtual screening and machine learning have their own evaluation index [17] .  ... 
doi:10.1155/2016/4809831 pmid:27127534 pmcid:PMC4834164 fatcat:gdn2ltxidnegflqverndnaenwu

Interaction prediction in structure-based virtual screening using deep learning

Adam Gonczarek, Jakub M. Tomczak, Szymon Zaręba, Joanna Kaczmar, Piotr Dąbrowski, Michał J. Walczak
2018 Computers in Biology and Medicine  
Moreover, we show that widely used benchmark datasets may be insufficient for testing structure-based virtual screening methods that utilize machine learning.  ...  We introduce a deep learning architecture for structure-based virtual screening that generates fixed-sized fingerprints of proteins and small molecules by applying learnable atom convolution and softmax  ...  Results The efficacy of machine learning methods for virtual screening are typically evaluated with one of the renowned benchmarks, e.g., DUD-E [8] .  ... 
doi:10.1016/j.compbiomed.2017.09.007 pmid:28941550 fatcat:5guo54dm4fc3bhiarvl2k65zxm

Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace

2020 Briefings in Bioinformatics  
Virtual screening methods are among the most popular computational approaches in pharmaceutical research.  ...  Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments.  ...  Machine learning-based virtual screening tool (MLViS) is a tool that attempts to classify molecules as drug-like and nondrug-like based on various machine learning methods, including discriminant, tree-based  ... 
doi:10.1093/bib/bbaa034 pmid:32187356 pmcid:PMC7986591 fatcat:mq3csvzjkfekhpjfti2wmyipxi

Research and development of MolAICal for drug design via deep learning and classical programming [article]

Qifeng Bai
2020 arXiv   pre-print
The MolAICal invokes the deep learning generative model and molecular docking for drug virtual screening automatically.  ...  Deep learning methods have permeated into the research area of computer-aided drug design.  ...  Deep learning and virtual screening method for drug design In the second module, the deep learning generative model and virtual screening method are introduced for drug screening in the MolAICal.  ... 
arXiv:2006.09747v1 fatcat:q6j5x4uy35fvhm73nmzqf6df24


Renu Vyas, S.S. Tambe, B.D. Kulkarni
2012 International Journal for Computational Biology  
As the principles of drug design are also applicable for agrochemicals, SVM methods are being applied for virtual screening for pesticides too.  ...  Virtual screening methods act as knowledge-based filters to discover the coveted novel lead molecules possessing desired pharmacological properties.  ...  ACKNOWLEDGEMENTS Renu Vyas thanks Department of Science & Technology, Govt of India, New Delhi, for the award of "Women Scientist Fellowship  ... 
doi:10.34040/ijcb.1.1.2012.12 fatcat:xccm5mwc4bcdvb3bqkuttdmfqe

Efficient iterative virtual screening with Apache Spark and conformal prediction

Laeeq Ahmed, Valentin Georgiev, Marco Capuccini, Salman Toor, Wesley Schaal, Erwin Laure, Ola Spjuth
2018 Journal of Cheminformatics  
Docking and scoring large libraries of ligands against target proteins forms the basis of structure-based virtual screening.  ...  Results: We show on 4 different targets that conformal prediction based virtual screening (CPVS) is able to reduce the number of docked molecules by 62.61% while retaining an accuracy for the top 30 hits  ...  HPC computations were performed on resources provided by SNIC through Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) [40] under Project b2015245.  ... 
doi:10.1186/s13321-018-0265-z pmid:29492726 pmcid:PMC5833896 fatcat:jk3vh6ha2rbsppvmuqjgrxjvuy

Improved method of structure-based virtual screening based on ensemble learning

Jin Li, WeiChao Liu, Yongping Song, JiYi Xia
2020 RSC Advances  
This paper proposed a target-specific virtual screening method based on ensemble learning named ENS-VS.  ...  Virtual screening has become a successful alternative and complementary technique to experimental high-throughput screening technologies for drug design.  ...  , namely, ligand-based virtual screening (LBVS) and structurebased virtual screening (SBVS).  ... 
doi:10.1039/c9ra09211k pmid:35492172 pmcid:PMC9049841 fatcat:3gx6wpveefcbdlaek25dk56ahi
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