Comparative evaluation of eight docking tools for docking and virtual screening accuracy

2004 ◽  
Vol 57 (2) ◽  
pp. 225-242 ◽  
Author(s):  
Esther Kellenberger ◽  
Jordi Rodrigo ◽  
Pascal Muller ◽  
Didier Rognan
Keyword(s):  
Virtual Screening ◽  
Comparative Evaluation ◽  
Screening Accuracy

scholarly journals A Ligand-Based Virtual Screening Method Using Direct Quantification of Generalization Ability

Molecules ◽  
2019 ◽  
Vol 24 (13) ◽  
pp. 2414
Author(s):  
Weixing Dai ◽  
Dianjing Guo
Keyword(s):  
Machine Learning ◽  
Virtual Screening ◽  
Learning Algorithm ◽  
Screening Method ◽  
Chemical Characteristic ◽  
High Dimensional ◽  
Learning Approaches ◽  
Generalization Ability ◽  
Model Interpretation ◽  
Screening Accuracy

Machine learning plays an important role in ligand-based virtual screening. However, conventional machine learning approaches tend to be inefficient when dealing with such problems where the data are imbalanced and features describing the chemical characteristic of ligands are high-dimensional. We here describe a machine learning algorithm LBS (local beta screening) for ligand-based virtual screening. The unique characteristic of LBS is that it quantifies the generalization ability of screening directly by a refined loss function, and thus can assess the risk of over-fitting accurately and efficiently for imbalanced and high-dimensional data in ligand-based virtual screening without the help of resampling methods such as cross validation. The robustness of LBS was demonstrated by a simulation study and tests on real datasets, in which LBS outperformed conventional algorithms in terms of screening accuracy and model interpretation. LBS was then used for screening potential activators of HIV-1 integrase multimerization in an independent compound library, and the virtual screening result was experimentally validated. Of the 25 compounds tested, six were proved to be active. The most potent compound in experimental validation showed an EC50 value of 0.71 µM.

Comparison of Several Molecular Docking Programs: Pose Prediction and Virtual Screening Accuracy

2009 ◽  
Vol 49 (6) ◽  
pp. 1455-1474 ◽  
Author(s):  
Jason B. Cross ◽  
David C. Thompson ◽  
Brajesh K. Rai ◽  
J. Christian Baber ◽  
Kristi Yi Fan ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
Pose Prediction ◽  
Screening Accuracy

Profile-QSAR 2.0: Kinase Virtual Screening Accuracy Comparable to Four-Concentration IC50s for Realistically Novel Compounds

2017 ◽  
Vol 57 (8) ◽  
pp. 2077-2088 ◽  
Author(s):  
Eric J. Martin ◽  
Valery R. Polyakov ◽  
Li Tian ◽  
Rolando C. Perez
Keyword(s):  
Virtual Screening ◽  
Screening Accuracy

scholarly journals Handling Imbalance Classification Virtual Screening Big Data Using Machine Learning Algorithms

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Sahar K. Hussin ◽  
Salah M. Abdelmageid ◽  
Adel Alkhalil ◽  
Yasser M. Omar ◽  
Mahmoud I. Marie ◽  
...  
Keyword(s):  
Machine Learning ◽  
Virtual Screening ◽  
Predictive Accuracy ◽  
Specific Protein ◽  
Machine Learning Algorithms ◽  
Large Set ◽  
High Dimensions ◽  
Screening Process ◽  
Screening Accuracy ◽  
Critical Process

Virtual screening is the most critical process in drug discovery, and it relies on machine learning to facilitate the screening process. It enables the discovery of molecules that bind to a specific protein to form a drug. Despite its benefits, virtual screening generates enormous data and suffers from drawbacks such as high dimensions and imbalance. This paper tackles data imbalance and aims to improve virtual screening accuracy, especially for a minority dataset. For a dataset identified without considering the data’s imbalanced nature, most classification methods tend to have high predictive accuracy for the majority category. However, the accuracy was significantly poor for the minority category. The paper proposes a K-mean algorithm coupled with Synthetic Minority Oversampling Technique (SMOTE) to overcome the problem of imbalanced datasets. The proposed algorithm is named as KSMOTE. Using KSMOTE, minority data can be identified at high accuracy and can be detected at high precision. A large set of experiments were implemented on Apache Spark using numeric PaDEL and fingerprint descriptors. The proposed solution was compared to both no-sampling method and SMOTE on the same datasets. Experimental results showed that the proposed solution outperformed other methods.

Conformational Sampling for Large-Scale Virtual Screening: Accuracy versus Ensemble Size

2009 ◽  
Vol 49 (10) ◽  
pp. 2303-2311 ◽  
Author(s):  
Axel Griewel ◽  
Ole Kayser ◽  
Jochen Schlosser ◽  
Matthias Rarey
Keyword(s):  
Virtual Screening ◽  
Large Scale ◽  
Conformational Sampling ◽  
Ensemble Size ◽  
Screening Accuracy

Docking Ligands into Flexible and Solvated Macromolecules. 7. Impact of Protein Flexibility and Water Molecules on Docking-Based Virtual Screening Accuracy

2014 ◽  
Vol 54 (11) ◽  
pp. 3198-3210 ◽  
Author(s):  
Eric Therrien ◽  
Nathanael Weill ◽  
Anna Tomberg ◽  
Christopher R. Corbeil ◽  
Devin Lee ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Protein Flexibility ◽  
Water Molecules ◽  
Screening Accuracy

Comparison of In-the-Ear and Over-the-Ear Hearing Aid Fittings

1986 ◽  
Vol 51 (4) ◽  
pp. 362-369 ◽  
Author(s):  
Donna M. Risberg ◽  
Robyn M. Cox
Keyword(s):  
Hearing Aids ◽  
High Frequency ◽  
Comparative Evaluation ◽  
Speech Intelligibility ◽  
Hearing Aid ◽  
Hearing Aid Fitting ◽  
The Difference ◽  
Functional Gain ◽  
The Relationship

A custom in-the-ear (ITE) hearing aid fitting was compared to two over-the-ear (OTE) hearing aid fittings for each of 9 subjects with mild to moderately severe hearing losses. Speech intelligibility via the three instruments was compared using the Speech Intelligibility Rating (SIR) test. The relationship between functional gain and coupler gain was compared for the ITE and the higher rated OTE instruments. The difference in input received at the microphone locations of the two types of hearing aids was measured for 10 different subjects and compared to the functional gain data. It was concluded that (a) for persons with mild to moderately severe hearing losses, appropriately adjusted custom ITE fittings typically yield speech intelligibility that is equal to the better OTE fitting identified in a comparative evaluation; and (b) gain prescriptions for ITE hearing aids should be adjusted to account for the high-frequency emphasis associated with in-the-concha microphone placement.

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