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 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

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

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

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

Perinatal Depression: Prevalence, Screening Accuracy, and Screening Outcomes: Evidence Report/Technology Assessment, Number 119

2005 ◽  
Author(s):  
Bradley N. Gaynes ◽  
Norma Gavin ◽  
Samantha Meltzer-Brody ◽  
Kathleen N. Lohr ◽  
Tammeka Swinson ◽  
...  
Keyword(s):  
Technology Assessment ◽  
Perinatal Depression ◽  
Depression Prevalence ◽  
Screening Accuracy

Discovery of Antibacterial Agents Inhibiting the Energy-Coupling Factor (ECF) Transporters by Structure-Based Virtual Screening

2020 ◽  
Author(s):  
Eleonora Diamanti ◽  
Inda Setyawati ◽  
Spyridon Bousis ◽  
leticia mojas ◽  
lotteke Swier ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Antibacterial Agents ◽  
Antimicrobial Agents ◽  
Energy Coupling ◽  
Coupling Factor ◽  
Design Synthesis ◽  
Screening Design ◽  
Starting Point ◽  
Wide Range ◽  
Vitamin Uptake

Here, we report on the virtual screening, design, synthesis and structure–activity relationships (SARs) of the first class of selective, antibacterial agents against the energy-coupling factor (ECF) transporters. The ECF transporters are a family of transmembrane proteins involved in the uptake of vitamins in a wide range of bacteria. Inhibition of the activity of these proteins could reduce the viability of pathogens that depend on vitamin uptake. Because of their central role in the metabolism of bacteria and their absence in humans, ECF transporters are novel potential antimicrobial targets to tackle infection. The hit compound’s metabolic and plasma stability, the potency (20, MIC Streptococcus pneumoniae = 2 µg/mL), the absence of cytotoxicity and a lack of resistance development under the conditions tested here suggest that this scaffold may represent a promising starting point for the development of novel antimicrobial agents with an unprecedented mechanism of action.<br>

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