scholarly journals Virtual Screening of Potential Anticancer Drugs based on Microbial Products

2021 ◽  
Author(s):  
Gaspar Pinto ◽  
Natalie Hendrikse ◽  
Jan Stourac ◽  
Jiri Damborsky ◽  
David Bednar
Keyword(s):  
Virtual Screening ◽  
Anticancer Drugs ◽  
Screening Tools ◽  
Background Information ◽  
Web Tool ◽  
Expensive Drug ◽  
Screening Experiments ◽  
Web Tools ◽  
First Case ◽  
Microbial Products

The development of microbial products for cancer treatment has been in the spotlight in recent years. In order to accelerate the lengthy and expensive drug development process, in silico screening tools are systematically employed, especially during the initial discovery phase. Moreover, considering the steadily increasing number of molecules approved by authorities for commercial use, there is a demand for faster methods to repurpose such drugs. Here we present a review on virtual screening web tools, publicly available databases of molecular targets and libraries of ligands, with the aim to facilitate the discovery of potential anticancer drugs based on microbial products. We provide an entry-level step-by-step description of the workflow for virtual screening of microbial metabolites with known protein targets, as well as two practical examples using freely available web tools. The first case presents a virtual screening study of drugs developed from microbial products using Caver Web, a web tool that performs docking along a tunnel. The second case comprises a comparative analysis between a healthy isocitrate dehydrogenase 1, a mutant that results in cancer, using the recently developed web tool PredictSNPOnco. In summary, this review provides the basic and essential background information necessary for virtual screening experiments, which may accelerate the discovery of novel anticancer drugs.

Virtual screening of potential anticancer drugs based on microbial products

2021 ◽  
Author(s):  
Gaspar P. Pinto ◽  
Natalie M. Hendrikse ◽  
Jan Stourac ◽  
Jiri Damborsky ◽  
David Bednar
Keyword(s):  
Virtual Screening ◽  
Anticancer Drugs ◽  
Microbial Products

scholarly journals Appying a novel web-tool for performing virtual screening experiments

2015 ◽  
Author(s):  
Vinicius Seus ◽  
Karina Machado ◽  
Jorge Gomes
Keyword(s):  
Virtual Screening ◽  
Web Tool ◽  
Screening Experiments

Positive Predictive Value Surfaces as a Complementary Tool to Assess the Performance of Virtual Screening Methods

2020 ◽  
Vol 20 (14) ◽  
pp. 1447-1460
Author(s):  
Juan F. Morales ◽  
Sara Chuguransky ◽  
Lucas N. Alberca ◽  
Juan I. Alice ◽  
Sofía Goicoechea ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Positive Predictive Value ◽  
Ensemble Learning ◽  
Predictive Value ◽  
Experimental Testing ◽  
Screening Tools ◽  
Screening Methods ◽  
Learning Approaches ◽  
Classification Problems ◽  
Screening Experiments

Background: Since their introduction in the virtual screening field, Receiver Operating Characteristic (ROC) curve-derived metrics have been widely used for benchmarking of computational methods and algorithms intended for virtual screening applications. Whereas in classification problems, the ratio between sensitivity and specificity for a given score value is very informative, a practical concern in virtual screening campaigns is to predict the actual probability that a predicted hit will prove truly active when submitted to experimental testing (in other words, the Positive Predictive Value - PPV). Estimation of such probability is however, obstructed due to its dependency on the yield of actives of the screened library, which cannot be known a priori. Objective: To explore the use of PPV surfaces derived from simulated ranking experiments (retrospective virtual screening) as a complementary tool to ROC curves, for both benchmarking and optimization of score cutoff values. Methods: The utility of the proposed approach is assessed in retrospective virtual screening experiments with four datasets used to infer QSAR classifiers: inhibitors of Trypanosoma cruzi trypanothione synthetase; inhibitors of Trypanosoma brucei N-myristoyltransferase; inhibitors of GABA transaminase and anticonvulsant activity in the 6 Hz seizure model. Results: Besides illustrating the utility of PPV surfaces to compare the performance of machine learning models for virtual screening applications and to select an adequate score threshold, our results also suggest that ensemble learning provides models with better predictivity and more robust behavior. Conclusion: PPV surfaces are valuable tools to assess virtual screening tools and choose score thresholds to be applied in prospective in silico screens. Ensemble learning approaches seem to consistently lead to improved predictivity and robustness.

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

2010 ◽  
Vol 50 (12) ◽  
pp. 2079-2093 ◽  
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

scholarly journals AI-Empowered Computational Examination of Chest Imaging for COVID-19 Treatment: A Review

2021 ◽  
Vol 4 ◽  
Author(s):  
Hanqiu Deng ◽  
Xingyu Li
Keyword(s):  
False Negative ◽  
Screening Tools ◽  
Polymerase Chain Reaction Test ◽  
Test Results ◽  
First Case ◽  
Lung Scan ◽  
Screening Efficiency ◽  
Polymerase Chain ◽  
Chain Reaction Test ◽  
Full Swing

Since the first case of coronavirus disease 2019 (COVID-19) was discovered in December 2019, COVID-19 swiftly spread over the world. By the end of March 2021, more than 136 million patients have been infected. Since the second and third waves of the COVID-19 outbreak are in full swing, investigating effective and timely solutions for patients’ check-ups and treatment is important. Although the SARS-CoV-2 virus-specific reverse transcription polymerase chain reaction test is recommended for the diagnosis of COVID-19, the test results are prone to be false negative in the early course of COVID-19 infection. To enhance the screening efficiency and accessibility, chest images captured via X-ray or computed tomography (CT) provide valuable information when evaluating patients with suspected COVID-19 infection. With advanced artificial intelligence (AI) techniques, AI-driven models training with lung scans emerge as quick diagnostic and screening tools for detecting COVID-19 infection in patients. In this article, we provide a comprehensive review of state-of-the-art AI-empowered methods for computational examination of COVID-19 patients with lung scans. In this regard, we searched for papers and preprints on bioRxiv, medRxiv, and arXiv published for the period from January 1, 2020, to March 31, 2021, using the keywords of COVID, lung scans, and AI. After the quality screening, 96 studies are included in this review. The reviewed studies were grouped into three categories based on their target application scenarios: automatic detection of coronavirus disease, infection segmentation, and severity assessment and prognosis prediction. The latest AI solutions to process and analyze chest images for COVID-19 treatment and their advantages and limitations are presented. In addition to reviewing the rapidly developing techniques, we also summarize publicly accessible lung scan image sets. The article ends with discussions of the challenges in current research and potential directions in designing effective computational solutions to fight against the COVID-19 pandemic in the future.

scholarly journals A Free Web-Based Protocol to Assist Structure-Based Virtual Screening Experiments

2019 ◽  
Vol 20 (18) ◽  
pp. 4648 ◽  
Author(s):  
Nathalie Lagarde ◽  
Elodie Goldwaser ◽  
Tania Pencheva ◽  
Dessislava Jereva ◽  
Ilza Pajeva ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Web Service ◽  
Molecular Mechanics ◽  
In Silico ◽  
Chemical Biology ◽  
In Silico Screening ◽  
Web Based ◽  
Screening Experiments ◽  
The Web

Chemical biology and drug discovery are complex and costly processes. In silico screening approaches play a key role in the identification and optimization of original bioactive molecules and increase the performance of modern chemical biology and drug discovery endeavors. Here, we describe a free web-based protocol dedicated to small-molecule virtual screening that includes three major steps: ADME-Tox filtering (via the web service FAF-Drugs4), docking-based virtual screening (via the web service MTiOpenScreen), and molecular mechanics optimization (via the web service AMMOS2 [Automatic Molecular Mechanics Optimization for in silico Screening]). The online tools FAF-Drugs4, MTiOpenScreen, and AMMOS2 are implemented in the freely accessible RPBS (Ressource Parisienne en Bioinformatique Structurale) platform. The proposed protocol allows users to screen thousands of small molecules and to download the top 1500 docked molecules that can be further processed online. Users can then decide to purchase a small list of compounds for in vitro validation. To demonstrate the potential of this online-based protocol, we performed virtual screening experiments of 4574 approved drugs against three cancer targets. The results were analyzed in the light of published drugs that have already been repositioned on these targets. We show that our protocol is able to identify active drugs within the top-ranked compounds. The web-based protocol is user-friendly and can successfully guide the identification of new promising molecules for chemical biology and drug discovery purposes.

scholarly journals PSOVina: The hybrid particle swarm optimization algorithm for protein–ligand docking

2015 ◽  
Vol 13 (03) ◽  
pp. 1541007 ◽  
Author(s):  
Marcus C. K. Ng ◽  
Simon Fong ◽  
Shirley W. I. Siu
Keyword(s):  
Particle Swarm Optimization ◽  
Virtual Screening ◽  
Particle Swarm ◽  
Ligand Docking ◽  
Conformational Search ◽  
Search Problem ◽  
Data Set ◽  
Swarm Optimization ◽  
Screening Experiments ◽  
Hybrid Particle Swarm Optimization

Protein–ligand docking is an essential step in modern drug discovery process. The challenge here is to accurately predict and efficiently optimize the position and orientation of ligands in the binding pocket of a target protein. In this paper, we present a new method called PSOVina which combined the particle swarm optimization (PSO) algorithm with the efficient Broyden–Fletcher–Goldfarb–Shannon (BFGS) local search method adopted in AutoDock Vina to tackle the conformational search problem in docking. Using a diverse data set of 201 protein–ligand complexes from the PDBbind database and a full set of ligands and decoys for four representative targets from the directory of useful decoys (DUD) virtual screening data set, we assessed the docking performance of PSOVina in comparison to the original Vina program. Our results showed that PSOVina achieves a remarkable execution time reduction of 51–60% without compromising the prediction accuracies in the docking and virtual screening experiments. This improvement in time efficiency makes PSOVina a better choice of a docking tool in large-scale protein–ligand docking applications. Our work lays the foundation for the future development of swarm-based algorithms in molecular docking programs. PSOVina is freely available to non-commercial users at http://cbbio.cis.umac.mo .

scholarly journals Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace

2020 ◽  
Author(s):  
Natesh Singh ◽  
Ludovic Chaput ◽  
Bruno O Villoutreix
Keyword(s):  
Virtual Screening ◽  
Drug Repositioning ◽  
Pharmaceutical Research ◽  
Bioactive Molecules ◽  
Screening Methods ◽  
Web Servers ◽  
Web Based ◽  
Drug Candidates ◽  
Screening Experiments ◽  
Molecule Docking

Abstract The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.

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