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

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Meng-yu Wang ◽  
Peng Li ◽  
Pei-li Qiao
Keyword(s):  
Virtual Screening ◽  
Ensemble Learning ◽  
Cathepsin K ◽  
Support Vector ◽  
Training Set ◽  
Screening Process ◽  
Ligand Interaction ◽  
Virtual Screening Experiment ◽  
Protein Ligand Interaction

Using the theory of machine learning to assist the virtual screening (VS) has been an effective plan. However, the quality of the training set may reduce because of mixing with the wrong docking poses and it will affect the screening efficiencies. 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). By combining multiple classifiers, ensemble learning is able to avoid the limitations of the single classifier’s performance and obtain better generalization. According to the research of virtual screening experiment with SRC and Cathepsin K as the target, the results show that the ensemble learning method can effectively reduce the error because the sample quality is not high and improve the effect of the whole virtual screening process.

scholarly journals PyPLIF HIPPOS-Assisted Prediction of Molecular Determinants of Ligand Binding to Receptors

Molecules ◽  
2021 ◽  
Vol 26 (9) ◽  
pp. 2452
Author(s):  
Enade P. Istyastono ◽  
Nunung Yuniarti ◽  
Vivitri D. Prasasty ◽  
Sudi Mungkasi
Keyword(s):  
Virtual Screening ◽  
Drug Design ◽  
Ligand Binding ◽  
G Protein Coupled Receptors ◽  
Ligand Interaction ◽  
Molecular Determinants ◽  
Protein Ligand Interaction ◽  
Quality Of Structure ◽  
G Protein Coupled

Identification of molecular determinants of receptor-ligand binding could significantly increase the quality of structure-based virtual screening protocols. In turn, drug design process, especially the fragment-based approaches, could benefit from the knowledge. Retrospective virtual screening campaigns by employing AutoDock Vina followed by protein-ligand interaction fingerprinting (PLIF) identification by using recently published PyPLIF HIPPOS were the main techniques used here. The ligands and decoys datasets from the enhanced version of the database of useful decoys (DUDE) targeting human G protein-coupled receptors (GPCRs) were employed in this research since the mutation data are available and could be used to retrospectively verify the prediction. The results show that the method presented in this article could pinpoint some retrospectively verified molecular determinants. The method is therefore suggested to be employed as a routine in drug design and discovery.

scholarly journals Protein Ligand Interaction Fingerprints

2017 ◽  
pp. 1072-1091
Author(s):  
Ali HajiEbrahimi ◽  
Hamidreza Ghafouri ◽  
Mohsen Ranjbar ◽  
Amirhossein Sakhteman
Keyword(s):  
Virtual Screening ◽  
Docking Studies ◽  
Scoring Functions ◽  
Ligand Interaction ◽  
Advantages And Disadvantages ◽  
Protein Affinity ◽  
Binding Cavity ◽  
Protein Ligand Interaction ◽  
Interaction Fingerprints ◽  
Activity Information

A most challenging part in docking-based virtual screening is the scoring functions implemented in various docking programs in order to evaluate different poses of the ligands inside the binding cavity of the receptor. Precise and trustable measurement of ligand-protein affinity for Structure-Based Virtual Screening (SB-VS) is therefore, an outstanding problem in docking studies. Empirical post-docking filters can be helpful as a way to provide various types of structure-activity information. Different types of interaction have been presented between the ligands and the receptor so far. Based on the diversity and importance of PLIF methods, this chapter will focus on the comparison of different protocols. The advantages and disadvantages of all methods will be discussed explicitly in this chapter as well as future sights for further progress in this field. Different classifications approaches for the protein-ligand interaction fingerprints were also discussed in this chapter.

scholarly journals Identification of Novel PI3Kδ Selective Inhibitors by SVM-Based Multistage Virtual Screening and Molecular Dynamics Simulations

2019 ◽  
Vol 20 (23) ◽  
pp. 6000 ◽  
Author(s):  
Jing-wei Liang ◽  
Shan Wang ◽  
Ming-yang Wang ◽  
Shi-long Li ◽  
Wan-qiu Li ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Screening Method ◽  
Support Vector ◽  
Selective Inhibitors ◽  
Ligand Interaction ◽  
Lipid Kinase ◽  
Isoform Selectivity ◽  
Phosphoinositide 3 Kinase ◽  
Dynamics Simulations ◽  
Novel Scaffold

Phosphoinositide 3 kinase delta (PI3Kδ) is a lipid kinase that has been implicated in a variety of immune mediated disorders. The research on isoform selectivity was crucial for reducing side effects. In the current study, an optimized hierarchical multistage virtual screening method was utilized for screening the PI3Kδ selective inhibitors. The method sequentially applied a support vector machine (SVM), a protein ligand interaction fingerprint (PLIF) pharmacophore, and a molecular docking approach. The evaluation of the validation set showed a high hit rate and a high enrichment factor of 75.1% and 301.66, respectively. This multistage virtual screening method was then utilized to screen the NCI database. From the final hit list, Compound 10 has great potential as the PI3Kδ inhibitor with micromolar inhibition in the PI3Kδ kinase activity assay. This compound also shows selectivity against PI3Kδ kinase. The method combining SVM, pharmacophore, and docking was capable of screening out the compounds with potential PI3Kδ selective inhibitors. Moreover, structural modification of Compound 10 will contribute to investigating the novel scaffold and designing novel PI3Kδ inhibitors.

scholarly journals Detecting Maturity in Fresh Lycium barbarum L. Fruit Using Color Information

Horticulturae ◽  
2021 ◽  
Vol 7 (5) ◽  
pp. 108
Author(s):  
Jian Zhao ◽  
Jun Chen
Keyword(s):  
Field Experiments ◽  
Support Vector ◽  
Color Information ◽  
Training Set ◽  
Ripe Fruit ◽  
Lycium Barbarum ◽  
Detection Model ◽  
Svm Model ◽  
The Relationship

The accurate quantitative maturity detection of fresh Lycium barbarum L. (L. barbarum) fruit is the key to determine whether fruit are suitable for harvesting or not and can also be helpful to improve the quality of post-harvest processing. To achieve this goal, abnormal samples were eliminated by the Mahalanobis Distance (MD), and nine components (i.e., R, G, B, H, S, V, L, a, and b) of the ripe fruit, half-ripe fruit, and unripe fruit were extracted, firstly. Then, significant component combinations of the three fruits beneficial to the extraction of their areas were determined. Through binary processing, morphology processing, and other image processing methods, a quantitative maturity detection model of fruit was established based on the support vector machine (SVM) model. On this basis, field experiments were conducted to verify and compare the relationship between the prediction results of the model and the picking forces of fruit. Field experiments showed that the accuracies of both the training set and prediction set were 100% and the prediction results of the model were consistent with the picking forces of fruit. Findings provided a theoretical basis for the accurate quantitative maturity detection of fresh L. barbarum fruit.

scholarly journals Virtual Screening of Conjugated Polymers for Organic Photovoltaic Devices Using Support Vector Machines and Ensemble Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Fang-Chung Chen
Keyword(s):  
Virtual Screening ◽  
Conjugated Polymers ◽  
Ensemble Learning ◽  
High Performance ◽  
Learning Algorithm ◽  
Predictive Performance ◽  
Machine Learning Algorithms ◽  
Organic Photovoltaic ◽  
Support Vector ◽  
Learning Approaches

Herein, we report virtual screening of potential semiconductor polymers for high-performance organic photovoltaic (OPV) devices using various machine learning algorithms. We particularly focus on support vector machine (SVM) and ensemble learning approaches. We found that the power conversion efficiencies of the device prepared with the polymer candidates can be predicted with their structure fingerprints as the only inputs. In other words, no preliminary knowledge about material properties was required. Additionally, the predictive performance could be further improved by “blending” the results of the SVM and random forest models. The resulting ensemble learning algorithm might open up a new opportunity for more precise, high-throughput virtual screening of conjugated polymers for OPV devices.

Protein Ligand Interaction Fingerprints

2016 ◽  
pp. 128-147
Author(s):  
Ali HajiEbrahimi ◽  
Hamidreza Ghafouri ◽  
Mohsen Ranjbar ◽  
Amirhossein Sakhteman
Keyword(s):  
Virtual Screening ◽  
Docking Studies ◽  
Scoring Functions ◽  
Ligand Interaction ◽  
Advantages And Disadvantages ◽  
Protein Affinity ◽  
Binding Cavity ◽  
Protein Ligand Interaction ◽  
Interaction Fingerprints ◽  
Activity Information

A most challenging part in docking-based virtual screening is the scoring functions implemented in various docking programs in order to evaluate different poses of the ligands inside the binding cavity of the receptor. Precise and trustable measurement of ligand-protein affinity for Structure-Based Virtual Screening (SB-VS) is therefore, an outstanding problem in docking studies. Empirical post-docking filters can be helpful as a way to provide various types of structure-activity information. Different types of interaction have been presented between the ligands and the receptor so far. Based on the diversity and importance of PLIF methods, this chapter will focus on the comparison of different protocols. The advantages and disadvantages of all methods will be discussed explicitly in this chapter as well as future sights for further progress in this field. Different classifications approaches for the protein-ligand interaction fingerprints were also discussed in this chapter.

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