Impact of Benchmark Data Set Topology on the Validation of Virtual Screening Methods: Exploration and Quantification by Spatial Statistics

Objective: To determine possible MPro enzyme inhibitors by using structure-based virtual screening methods, in the ZINC Biogenic Data Set containing natural products and natural product-like molecules. Materials and Methods: QVina, an AutoDockVina derivative, was used in virtual screening operations, GROMACS in molecular dynamics studies and SwissAdme server in ADME (Absorption, Distribution, Metabolism, and Excretion) calculations. KNIME (Konstanz Information Miner) and ChemAxon software were used for filtering data and creating three-dimensional structures of the molecules. Results: Seven out of totally screened 51535 natural products or natural products like molecules were identified as possible candidate to be used as SARS–CoV–2 Main Protease (MPro) enzyme inhibitors based on the results obtained from structure based virtual screening and ADME models. Conclusion: Among the seven potent molecules, two of them (ZINC000604382012 and ZINC000514288074) were selected as candidate molecules for further studies according to the results obtained from g_mmpbsa simulations and synthetic accessibility models. In addition, a workflow has been established to identify novel or potent Mpro enzyme inhibitors.

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Critical Comparison of Virtual Screening Methods against the MUV Data Set

Journal of Chemical Information and Modeling ◽

10.1021/ci900249b ◽

2009 ◽

Vol 49 (10) ◽

pp. 2168-2178 ◽

Cited By ~ 34

Author(s):

Pekka Tiikkainen ◽

Patrick Markt ◽

Gerhard Wolber ◽

Johannes Kirchmair ◽

Simona Distinto ◽

...

Keyword(s):

Virtual Screening ◽

Screening Methods ◽

Data Set ◽

Critical Comparison

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Exploration of Novel MTH1 Inhibitors Using Fragment-Based de novo Design, Virtual Screening, and Reverse Virtual Screening Methods

Биоорганическая химия ◽

10.1134/s0132342319040146 ◽

2019 ◽

Vol 45 (4) ◽

pp. 391-391

Author(s):

Xin-yu Zhao ◽

Kai Liu ◽

Xiao-li Wang ◽

Ri-lei Yu ◽

Cong-min Kang

Keyword(s):

Virtual Screening ◽

De Novo ◽

De Novo Design ◽

Screening Methods ◽

Reverse Virtual Screening

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Comprehensive Comparison of Ligand-Based Virtual Screening Tools Against the DUD Data set Reveals Limitations of Current 3D Methods

Journal of Chemical Information and Modeling ◽

10.1021/ci100263p ◽

2010 ◽

Vol 50 (12) ◽

pp. 2079-2093 ◽

Cited By ~ 90

Author(s):

Vishwesh Venkatraman ◽

Violeta I. Pérez-Nueno ◽

Lazaros Mavridis ◽

David W. Ritchie

Keyword(s):

Virtual Screening ◽

Screening Tools ◽

Data Set ◽

Comprehensive Comparison ◽

3D Methods

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Nuclide Composition Benchmark Data Set for Verifying Burnup Codes on Spent Light Water Reactor Fuels

Nuclear Technology ◽

10.13182/nt02-2 ◽

2002 ◽

Vol 137 (2) ◽

pp. 111-126 ◽

Cited By ~ 30

Author(s):

Yoshinori Nakahara ◽

Kenya Suyama ◽

Jun Inagawa ◽

Ryuji Nagaishi ◽

Setsumi Kurosawa ◽

...

Keyword(s):

Light Water Reactor ◽

Data Set ◽

Light Water ◽

Water Reactor ◽

Benchmark Data ◽

Nuclide Composition

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Orbiting Carbon Observatory-2 (OCO-2) cloud screening algorithms; validation against collocated MODIS and CALIOP data

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-8-12663-2015 ◽

2015 ◽

Vol 8 (12) ◽

pp. 12663-12707 ◽

Cited By ~ 2

Author(s):

T. E. Taylor ◽

C. W. O'Dell ◽

C. Frankenberg ◽

P. Partain ◽

H. Q. Cronk ◽

...

Keyword(s):

Surface Pressure ◽

Near Infrared ◽

Optical Absorption Spectroscopy ◽

Retrieval Algorithm ◽

Orthogonal Polarization ◽

Screening Methods ◽

Data Set ◽

Atmospheric Scattering ◽

Carbon Dioxide Co2 ◽

Cloud Screening

Abstract. The objective of the National Aeronautics and Space Administration's (NASA) Orbiting Carbon Observatory-2 (OCO-2) mission is to retrieve the column-averaged carbon dioxide (CO2) dry air mole fraction (XCO2) from satellite measurements of reflected sunlight in the near-infrared. These estimates can be biased by clouds and aerosols within the instrument's field of view (FOV). Screening of the most contaminated soundings minimizes unnecessary calls to the computationally expensive Level 2 (L2) XCO2 retrieval algorithm. Hence, robust cloud screening methods have been an important focus of the OCO-2 algorithm development team. Two distinct, computationally inexpensive cloud screening algorithms have been developed for this application. The A-Band Preprocessor (ABP) retrieves the surface pressure using measurements in the 0.76 μm O2 A-band, neglecting scattering by clouds and aerosols, which introduce photon path-length (PPL) differences that can cause large deviations between the expected and retrieved surface pressure. The Iterative Maximum A-Posteriori (IMAP) Differential Optical Absorption Spectroscopy (DOAS) Preprocessor (IDP) retrieves independent estimates of the CO2 and H2O column abundances using observations taken at 1.61 μm (weak CO2 band) and 2.06 μm (strong CO2 band), while neglecting atmospheric scattering. The CO2 and H2O column abundances retrieved in these two spectral regions differ significantly in the presence of cloud and scattering aerosols. The combination of these two algorithms, which key off of different features in the spectra, provides the basis for cloud screening of the OCO-2 data set. To validate the OCO-2 cloud screening approach, collocated measurements from NASA's Moderate Resolution Imaging Spectrometer (MODIS), aboard the Aqua platform, were compared to results from the two OCO-2 cloud screening algorithms. With tuning to allow throughputs of &simeq; 30 %, agreement between the OCO-2 and MODIS cloud screening methods is found to be &simeq; 85 % over four 16-day orbit repeat cycles in both the winter (December) and spring (April–May) for OCO-2 nadir-land, glint-land and glint-water observations. No major, systematic, spatial or temporal dependencies were found, although slight differences in the seasonal data sets do exist and validation is more problematic with increasing solar zenith angle and when surfaces are covered in snow and ice and have complex topography. To further analyze the performance of the cloud screening algorithms, an initial comparison of OCO-2 observations was made to collocated measurements from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). These comparisons highlight the strength of the OCO-2 cloud screening algorithms in identifying high, thin clouds but suggest some difficulty in identifying some clouds near the surface, even when the optical thicknesses are greater than 1.

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Practical Model Selection for Prospective Virtual Screening

10.1101/337956 ◽

2018 ◽

Cited By ~ 1

Author(s):

Shengchao Liu ◽

Moayad Alnammi ◽

Spencer S. Ericksen ◽

Andrew F. Voter ◽

Gene E. Ananiev ◽

...

Keyword(s):

Random Forest ◽

Virtual Screening ◽

Protein Interactions ◽

High Throughput Screening ◽

Screening Methods ◽

Protein Protein Interactions ◽

Screening Algorithm ◽

Screening Performance ◽

Wide Range ◽

Public Datasets

AbstractVirtual (computational) high-throughput screening provides a strategy for prioritizing compounds for experimental screens, but the choice of virtual screening algorithm depends on the dataset and evaluation strategy. 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 which algorithm is best for prospective compound prioritization. Our workflow identifies a random forest as the best algorithm for these targets over more sophisticated neural network-based models. The top 250 predictions from our selected random forest recover 37 of the 54 active compounds from a library of 22,434 new molecules assayed on PriA-SSB. We show that virtual screening methods that perform well in public datasets and synthetic benchmarks, like multi-task neural networks, may not always translate to prospective screening performance on a specific assay of interest.

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Generating Property-Matched Decoy Molecules Using Deep Learning

10.1101/2020.08.26.268193 ◽

2020 ◽

Author(s):

Fergus Imrie ◽

Anthony R. Bradley ◽

Charlotte M. Deane

Keyword(s):

Deep Learning ◽

Virtual Screening ◽

Method Development ◽

Screening Method ◽

Screening Methods ◽

Additional Risk ◽

Link Type ◽

Screening Performance ◽

And Training ◽

Virtual Screening Performance

An essential step in the development of virtual screening methods is the use of established sets of actives and decoys for benchmarking and training. However, the decoy molecules in commonly used sets are biased meaning that methods often exploit these biases to separate actives and decoys, rather than learning how to perform molecular recognition. This fundamental issue prevents generalisation and hinders virtual screening method development. We have developed a deep learning method (DeepCoy) that generates decoys to a user’s preferred specification in order to remove such biases or construct sets with a defined bias. We validated DeepCoy using two established benchmarks, DUD-E and DEKOIS 2.0. For all DUD-E targets and 80 of the 81 DEKOIS 2.0 targets, our generated decoy molecules more closely matched the active molecules’ physicochemical properties while introducing no discernible additional risk of false negatives. The DeepCoy decoys improved the Deviation from Optimal Embedding (DOE) score by an average of 81% and 66%, respectively, decreasing from 0.163 to 0.032 for DUD-E and from 0.109 to 0.038 for DEKOIS 2.0. Further, the generated decoys are harder to distinguish than the original decoy molecules via docking with Autodock Vina, with virtual screening performance falling from an AUC ROC of 0.71 to 0.63. The code is available at https://github.com/oxpig/DeepCoy. Generated molecules can be downloaded from http://opig.stats.ox.ac.uk/resources.

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