scholarly journals CompScore: boosting structure-based virtual screening performance by incorporating docking scoring functions components into consensus scoring

2019 ◽  
Author(s):  
Yunierkis Perez-Castillo ◽  
Stellamaris Sotomayor-Burneo ◽  
Karina Jimenes-Vargas ◽  
Mario Gonzalez-Rodriguez ◽  
Maykel Cruz-Monteagudo ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Virtual Screening ◽  
High Performance ◽  
Scoring Function ◽  
Scoring Functions ◽  
Traditional Use ◽  
Screening Performance ◽  
Consensus Scoring ◽  
Improved Performance ◽  
Virtual Screening Performance

AbstractConsensus scoring has become a commonly used strategy within structure-based virtual screening (VS) workflows with improved performance compared to those based in a single scoring function. However, no research has been devoted to analyze the worth of docking scoring functions components in consensus scoring. We implemented and tested a method that incorporates docking scoring functions components into the setting of high performance VS workflows. This method uses genetic algorithms for finding the combination of scoring components that maximizes the VS enrichment for any target. Our methodology was validated using a dataset that contains ligands and decoys for 102 targets that has been widely used in VS validation studies. Results show that our approach outperforms other methods for all targets. It also boosts the initial enrichment performance of the traditional use of whole scoring functions in consensus scoring by an average of 45%. CompScore is freely available at: http://bioquimio.udla.edu.ec/compscore/

scholarly journals CompScore: Boosting Structure-Based Virtual Screening Performance by Incorporating Docking Scoring Function Components into Consensus Scoring

2019 ◽  
Vol 59 (9) ◽  
pp. 3655-3666 ◽  
Author(s):  
Yunierkis Perez-Castillo ◽  
Stellamaris Sotomayor-Burneo ◽  
Karina Jimenes-Vargas ◽  
Mario Gonzalez-Rodriguez ◽  
Maykel Cruz-Monteagudo ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Scoring Function ◽  
Screening Performance ◽  
Consensus Scoring ◽  
Virtual Screening Performance

scholarly journals Correlation between Virtual Screening Performance and Binding Site Descriptors of Protein Targets

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Jamal Shamsara
Keyword(s):  
Virtual Screening ◽  
Binding Site ◽  
Center Of Mass ◽  
Scoring Function ◽  
Scoring Functions ◽  
Autodock Vina ◽  
Protein Targets ◽  
Shape Complementarity ◽  
Screening Performance ◽  
Consensus Scoring

Rescoring is a simple approach that theoretically could improve the original docking results. In this study AutoDock Vina was used as a docked engine and three other scoring functions besides the original scoring function, Vina, as well as their combinations as consensus scoring functions were employed to explore the effect of rescoring on virtual screenings that had been done on diverse targets. Rescoring by DrugScore produces the most number of cases with significant changes in screening power. Thus, the DrugScore results were used to build a simple model based on two binding site descriptors that could predict possible improvement by DrugScore rescoring. Furthermore, generally the screening power of all rescoring approach as well as original AutoDock Vina docking results correlated with the Maximum Theoretical Shape Complementarity (MTSC) and Maximum Distance from Center of Mass and all Alpha spheres (MDCMA). Therefore, it was suggested that, with a more complete set of binding site descriptors, it could be possible to find robust relationship between binding site descriptors and response to certain molecular docking programs and scoring functions. The results could be helpful for future researches aiming to do a virtual screening using AutoDock Vina and/or rescoring using DrugScore.

scholarly journals Virtual Screening with Gnina 1.0

2021 ◽  
Author(s):  
Jocelyn Sunseri ◽  
David Koes
Keyword(s):  
Virtual Screening ◽  
Scoring Function ◽  
Compound Library ◽  
Autodock Vina ◽  
Convolutional Networks ◽  
Development Costs ◽  
Screening Performance ◽  
Computationally Intensive ◽  
Speed And Accuracy ◽  
Virtual Screening Performance

Virtual screening - predicting which compounds within a specified compound library bind to a target molecule, typically a protein - is a fundamental task in the field of drug discovery. Doing virtual screening well provides tangible practical benefits, including reduced drug development costs, faster time to therapeutic viability, and fewer unforeseen side effects. As with most applied computational tasks, the algorithms currently used to perform virtual screening feature inherent tradeoffs between speed and accuracy. Furthermore, even theoretically rigorous, computationally intensive methods may fail to account for important effects relevant to whether a given compound will ultimately be usable as a drug. 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. We find, on average, that Gnina outperforms conventional empirical scoring. The default scoring in Gnina outperforms the empirical AutoDock Vina scoring function on 89 of the 117 targets of the DUD-E and LIT-PCBA virtual screening benchmarks with a median 1% early enrichment factor that is more than twice that of Vina. 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 through a sophisticated interpretation of molecular interactions versus fitting to non-informative simplistic property distributions.

scholarly journals A Review on Parallel Virtual Screening Softwares for High-Performance Computers

2022 ◽  
Vol 15 (1) ◽  
pp. 63
Author(s):  
Natarajan Arul Murugan ◽  
Artur Podobas ◽  
Davide Gadioli ◽  
Emanuele Vitali ◽  
Gianluca Palermo ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
High Throughput Screening ◽  
High Performance ◽  
Parallel Implementation ◽  
Scoring Function ◽  
Scoring Functions ◽  
Screening Programs ◽  
Lead Compounds ◽  
High Performance Computers

Drug discovery is the most expensive, time-demanding, and challenging project in biopharmaceutical companies which aims at the identification and optimization of lead compounds from large-sized chemical libraries. The lead compounds should have high-affinity binding and specificity for a target associated with a disease, and, in addition, they should have favorable pharmacodynamic and pharmacokinetic properties (grouped as ADMET properties). Overall, drug discovery is a multivariable optimization and can be carried out in supercomputers using a reliable scoring function which is a measure of binding affinity or inhibition potential of the drug-like compound. The major problem is that the number of compounds in the chemical spaces is huge, making the computational drug discovery very demanding. However, it is cheaper and less time-consuming when compared to experimental high-throughput screening. As the problem is to find the most stable (global) minima for numerous protein–ligand complexes (on the order of 106 to 1012), the parallel implementation of in silico virtual screening can be exploited to ensure drug discovery in affordable time. In this review, we discuss such implementations of parallelization algorithms in virtual screening programs. The nature of different scoring functions and search algorithms are discussed, together with a performance analysis of several docking softwares ported on high-performance computing architectures.

Improving structure-based virtual screening performance via learning from scoring function components

2020 ◽  
Author(s):  
Guo-Li Xiong ◽  
Wen-Ling Ye ◽  
Chao Shen ◽  
Ai-Ping Lu ◽  
Ting-Jun Hou ◽  
...  
Keyword(s):  
Scoring Function ◽  
Scoring Functions ◽  
Ligand Interaction ◽  
Promising Alternative ◽  
Screening Performance ◽  
Comparable Performance ◽  
Protein Ligand Interaction ◽  
New Protein ◽  
Virtual Screening Performance ◽  
Energy Components

Abstract Scoring functions (SFs) based on complex machine learning (ML) algorithms have gradually emerged as a promising alternative to overcome the weaknesses of classical SFs. However, extensive efforts have been devoted to the development of SFs based on new protein–ligand interaction representations and advanced alternative ML algorithms instead of the energy components obtained by the decomposition of existing SFs. Here, we propose a new method named energy auxiliary terms learning (EATL), in which the scoring components are extracted and used as the input for the development of three levels of ML SFs including EATL SFs, docking-EATL SFs and comprehensive SFs with ascending VS performance. The EATL approach not only outperforms classical SFs for the absolute performance (ROC) and initial enrichment (BEDROC) but also yields comparable performance compared with other advanced ML-based methods on the diverse subset of Directory of Useful Decoys: Enhanced (DUD-E). The test on the relatively unbiased actives as decoys (AD) dataset also proved the effectiveness of EATL. Furthermore, the idea of learning from SF components to yield improved screening power can also be extended to other docking programs and SFs available.

scholarly journals Fusing Docking Scoring Functions Improves the Virtual Screening Performance for Discovering Parkinson's Disease Dual Target Ligands

2017 ◽  
Vol 15 (8) ◽  
Author(s):  
Yunierkis Perez-Castillo ◽  
Aliuska Morales Helguera ◽  
M.Natalia D. S. Cordeiro ◽  
Eduardo Tejera ◽  
Cesar Paz-y-Mino ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Virtual Screening ◽  
Scoring Functions ◽  
Screening Performance ◽  
Virtual Screening Performance ◽  
Dual Target

scholarly journals Drug Repurposing for Covid-19: Discovery of Potential Small-Molecule Inhibitors of Spike Protein-ACE2 Receptor Interaction Through Virtual Screening and Consensus Scoring

2020 ◽  
Author(s):  
Sachin Patil ◽  
Jeremy Hofer ◽  
Pedro J. Ballester ◽  
Elena Fattakhova ◽  
Juliette DiFlumeri ◽  
...  
Keyword(s):  
Virtual Screening ◽  
High Performance ◽  
Drug Repurposing ◽  
Spike Protein ◽  
Binding Potential ◽  
Receptor Interaction ◽  
Scoring Functions ◽  
Host Interaction ◽  
Binding Interface ◽  
Consensus Scoring

<p><b>Objective</b></p><p> </p><p>There is an increased interest in drug repurposing against Covid-19 (SARS-CoV-2) as its spread has significantly outpaced development of effective therapeutics. Our aim is to identify approved drugs that can inhibit the interaction of SARS-CoV-2 spike protein with human angiotensin-converting enzyme 2 (ACE2) that is critical for coronavirus infection. </p><p> </p><p><b>Methods</b></p><p> </p><p>The published crystal structure of SARS-CoV-2 spike protein-ACE2 receptor interaction was first analyzed for druggable binding pockets. The binding interface was then probed by an integrated virtual screening protocol executed by a high-performance computer cluster, involving docking and consensus scoring using various machine-learning, empirical and knowledge-based scoring functions. The consensus-ranked lists of screened drugs were generated via ‘rank-by-rank’ and ‘rank-by-number’ schemes.</p><p> </p><p><b>Findings</b></p><p> </p><p>Although spike protein and ACE2 lacked druggable pockets in their unbound forms, they presented a well-defined pocket when bound together. Accordingly, we identified many drugs with high binding potential against this protein-protein interaction pocket. Importantly, several antivirals against two major (+)ssRNA viruses (HCV and HIV) constituted major group of our top hits, of which Atazanavir, Grazoprevir, Saquinavir, Simeprevir, Telaprevir and Tipranavir could be of most importance for immediate experimental/clinical investigations. Additional notable hits included many anti-inflammatory/antioxidant, antibiotic/antifungal, and other relevant compounds with proven activity against respiratory diseases, further emphasizing robustness of our current study. Notably, we also discovered Maraviroc, the only FDA-approved drug capable of targeting virus-host interaction and blocking HIV entry. </p><p> </p><p><b>Conclusion</b></p><p> </p>Our newly identified compounds warrant further experimental investigation against SARS-CoV-2 spike-ACE2 interaction, which if proven effective may present much-needed immediate clinical potential against Covid-19.

scholarly journals Virtual Screening with Gnina 1.0

Molecules ◽  
2021 ◽  
Vol 26 (23) ◽  
pp. 7369
Author(s):  
Jocelyn Sunseri ◽  
David Ryan Koes
Keyword(s):  
Virtual Screening ◽  
Scoring Function ◽  
Compound Library ◽  
Autodock Vina ◽  
Convolutional Networks ◽  
Development Costs ◽  
Screening Performance ◽  
Computationally Intensive ◽  
Speed And Accuracy ◽  
Virtual Screening Performance

Virtual screening—predicting which compounds within a specified compound library bind to a target molecule, typically a protein—is a fundamental task in the field of drug discovery. Doing virtual screening well provides tangible practical benefits, including reduced drug development costs, faster time to therapeutic viability, and fewer unforeseen side effects. As with most applied computational tasks, the algorithms currently used to perform virtual screening feature inherent tradeoffs between speed and accuracy. Furthermore, even theoretically rigorous, computationally intensive methods may fail to account for important effects relevant to whether a given compound will ultimately be usable as a drug. 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. We find, on average, that Gnina outperforms conventional empirical scoring. The default scoring in Gnina outperforms the empirical AutoDock Vina scoring function on 89 of the 117 targets of the DUD-E and LIT-PCBA virtual screening benchmarks with a median 1% early enrichment factor that is more than twice that of Vina. 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 through a sophisticated interpretation of molecular interactions versus fitting to non-informative simplistic property distributions.

scholarly journals Drug Repurposing for Covid-19: Discovery of Potential Small-Molecule Inhibitors of Spike Protein-ACE2 Receptor Interaction Through Virtual Screening and Consensus Scoring

2020 ◽  
Author(s):  
Sachin Patil ◽  
Jeremy Hofer ◽  
Pedro J. Ballester ◽  
Elena Fattakhova ◽  
Juliette DiFlumeri ◽  
...  
Keyword(s):  
Virtual Screening ◽  
High Performance ◽  
Drug Repurposing ◽  
Spike Protein ◽  
Binding Potential ◽  
Receptor Interaction ◽  
Scoring Functions ◽  
Host Interaction ◽  
Binding Interface ◽  
Consensus Scoring

<p><b>Objective</b></p><p> </p><p>There is an increased interest in drug repurposing against Covid-19 (SARS-CoV-2) as its spread has significantly outpaced development of effective therapeutics. Our aim is to identify approved drugs that can inhibit the interaction of SARS-CoV-2 spike protein with human angiotensin-converting enzyme 2 (ACE2) that is critical for coronavirus infection. </p><p> </p><p><b>Methods</b></p><p> </p><p>The published crystal structure of SARS-CoV-2 spike protein-ACE2 receptor interaction was first analyzed for druggable binding pockets. The binding interface was then probed by an integrated virtual screening protocol executed by a high-performance computer cluster, involving docking and consensus scoring using various machine-learning, empirical and knowledge-based scoring functions. The consensus-ranked lists of screened drugs were generated via ‘rank-by-rank’ and ‘rank-by-number’ schemes.</p><p> </p><p><b>Findings</b></p><p> </p><p>Although spike protein and ACE2 lacked druggable pockets in their unbound forms, they presented a well-defined pocket when bound together. Accordingly, we identified many drugs with high binding potential against this protein-protein interaction pocket. Importantly, several antivirals against two major (+)ssRNA viruses (HCV and HIV) constituted major group of our top hits, of which Atazanavir, Grazoprevir, Saquinavir, Simeprevir, Telaprevir and Tipranavir could be of most importance for immediate experimental/clinical investigations. Additional notable hits included many anti-inflammatory/antioxidant, antibiotic/antifungal, and other relevant compounds with proven activity against respiratory diseases, further emphasizing robustness of our current study. Notably, we also discovered Maraviroc, the only FDA-approved drug capable of targeting virus-host interaction and blocking HIV entry. </p><p> </p><p><b>Conclusion</b></p><p> </p>Our newly identified compounds warrant further experimental investigation against SARS-CoV-2 spike-ACE2 interaction, which if proven effective may present much-needed immediate clinical potential against Covid-19.

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