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Evaluation of different machine learning methods for ligand-based virtual screening
2011
Journal of Cheminformatics
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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
2019
Current Opinion in Structural Biology
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2018 pre-print arXiv:1812.06400v1 fatcat:2noehybncfhyndkpn56sr6ipu4Other Versions
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/j.sbi.2019.02.011
pmid:30909105
fatcat:tm5pfuy4z5dexdsnnlca7rzmgm
Prediction Methods of Herbal Compounds in Chinese Medicinal Herbs
2018
Molecules
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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
2020
International Journal of Advanced Computer Research
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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
2017
OncoTarget
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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
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 ...
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
2018
Journal of Chemical Information and Modeling
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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
2021
Molecules
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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
2016
Computational and Mathematical Methods in Medicine
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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
2018
Computers in Biology and Medicine
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2017 pre-print arXiv:1610.07187v2 fatcat:6x4dk4qudbg5fdyonsc3v54rliOther Versions
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
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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]
2020
arXiv
pre-print
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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
APPLICATIONS OF SUPPORT VECTOR MACHINES AS A ROBUST TOOL IN HIGH THROUGHPUT VIRTUAL SCREENING
2012
International Journal for Computational Biology
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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
2018
Journal of Cheminformatics
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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
2020
RSC Advances
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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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