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.

scholarly journals Selecting Machine-Learning Scoring Functions for Structure-Based Virtual Screening

2020 ◽  
Author(s):  
Pedro Ballester
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Predictive Accuracy ◽  
Scoring Function ◽  
3D Models ◽  
Large Datasets ◽  
Scoring Functions ◽  
Discovery Process ◽  
Drug Discovery Process

Interest in docking technologies has grown parallel to the ever increasing number and diversity of 3D models for macromolecular therapeutic targets. Structure-Based Virtual Screening (SBVS) aims at leveraging these experimental structures to discover the necessary starting points for the drug discovery process. It is now established that Machine Learning (ML) can strongly enhance the predictive accuracy of scoring functions for SBVS by exploiting large datasets from targets, molecules and their associations. However, with greater choice, the question of which ML-based scoring function is the most suitable for prospective use on a given target has gained importance. Here we analyse two approaches to select an existing scoring function for the target along with a third approach consisting in generating a scoring function tailored to the target. These analyses required discussing the limitations of popular SBVS benchmarks, the alternatives to benchmark scoring functions for SBVS and how to generate them or use them using freely-available software.

scholarly journals Selecting Machine-Learning Scoring Functions for Structure-Based Virtual Screening

2020 ◽  
Author(s):  
Pedro Ballester
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Predictive Accuracy ◽  
Scoring Function ◽  
3D Models ◽  
Large Datasets ◽  
Scoring Functions ◽  
Discovery Process ◽  
Drug Discovery Process

Interest in docking technologies has grown parallel to the ever increasing number and diversity of 3D models for macromolecular therapeutic targets. Structure-Based Virtual Screening (SBVS) aims at leveraging these experimental structures to discover the necessary starting points for the drug discovery process. It is now established that Machine Learning (ML) can strongly enhance the predictive accuracy of scoring functions for SBVS by exploiting large datasets from targets, molecules and their associations. However, with greater choice, the question of which ML-based scoring function is the most suitable for prospective use on a given target has gained importance. Here we analyse two approaches to select an existing scoring function for the target along with a third approach consisting in generating a scoring function tailored to the target. These analyses required discussing the limitations of popular SBVS benchmarks, the alternatives to benchmark scoring functions for SBVS and how to generate them or use them using freely-available software.

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/

Protein-Ligand Docking Methodologies and Its Application in Drug Discovery

2016 ◽  
pp. 196-219
Author(s):  
Sanchaita Rajkhowa ◽  
Ramesh C. Deka
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Drug Design ◽  
High Throughput Screening ◽  
Docking Studies ◽  
Stable Complex ◽  
Scoring Functions ◽  
Binding Modes ◽  
Structure Based Drug Design ◽  
Modern Drug

Molecular docking is a key tool in structural biology and computer-assisted drug design. Molecular docking is a method which predicts the preferred orientation of a ligand when bound in an active site to form a stable complex. It is the most common method used as a structure-based drug design. Here, the authors intend to discuss the various types of docking methods and their development and applications in modern drug discovery. The important basic theories such as sampling algorithm and scoring functions have been discussed briefly. The performances of the different available docking software have also been discussed. This chapter also includes some application examples of docking studies in modern drug discovery such as targeted drug delivery using carbon nanotubes, docking of nucleic acids to find the binding modes and a comparative study between high-throughput screening and structure-based virtual screening.

scholarly journals Drug discovery from Nature: automated high-quality sample preparation

2000 ◽  
Vol 22 (5) ◽  
pp. 149-157 ◽  
Author(s):  
Ralf Thiericke
Keyword(s):  
Drug Discovery ◽  
Sample Preparation ◽  
High Throughput ◽  
High Throughput Screening ◽  
Solid Phase ◽  
Structural Diversity ◽  
Synthetic Compound ◽  
Natural Origin ◽  
Screening Programs ◽  
Compound Libraries

Secondary metabolites from plants, animals and microorganisms have been proven to be an outstanding source for new and innovative drugs and show a striking structural diversity that supplements chemically synthesized compounds or libraries in drug discovery programs. Unfortunately, extracts from natural sources are usually complex mixtures of compounds:: often generated in time consuming and for the most part manual processes. As quality and quantity of the provided samples play a pivotal role in the success of high-throughput screening programs this poses serious problems. In order to make samples of natural origin competitive with synthetic compound libraries, we devised a novel, automated sample preparation procedure based on solid-phase extraction (SPE). By making use of a modified Zymark RapidTrace®SPE workstation an easy-to-handle and effective fractionation method has been developed which allows the generation of highquality samples from natural origin, fulfilling the requirements of an integration into high-throughput screening programs.

High-Throughput siRNA-Based Functional Target Validation

2004 ◽  
Vol 9 (4) ◽  
pp. 286-293 ◽  
Author(s):  
Hong Xin ◽  
Alejandro Bernal ◽  
Frank A. Amato ◽  
Albert Pinhasov ◽  
Jack Kauffman ◽  
...  
Keyword(s):  
Drug Discovery ◽  
High Throughput ◽  
High Throughput Screening ◽  
Target Genes ◽  
Discovery Process ◽  
Target Validation ◽  
Drug Discovery Process ◽  
Functional Identification ◽  
Screening Process ◽  
Lead Compounds

The drug discovery process pursued by major pharmaceutical companies for many years starts with target identification followed by high-throughput screening (HTS) with the goal of identifying lead compounds. To accomplish this goal, significant resources are invested into automation of the screening process or HTS. Robotic systems capable of handling thousands of data points per day are implemented across the pharmaceutical sector. Many of these systems are amenable to handling cell-based screening protocols as well. On the other hand, as companies strive to develop innovative products based on novel mechanisms of action(s), one of the current bottlenecks of the industry is the target validation process. Traditionally, bioinformatics and HTS groups operate separately at different stages of the drug discovery process. The authors describe the convergence and integration of HTS and bioinformatics to perform high-throughput target functional identification and validation. As an example of this approach, they initiated a project with a functional cell-based screen for a biological process of interest using libraries of small interfering RNA (siRNA) molecules. In this protocol, siRNAs function as potent gene-specific inhibitors. siRNA-mediated knockdown of the target genes is confirmed by TaqMan analysis, and genes with impacts on biological functions of interest are selected for further analysis. Once the genes are confirmed and further validated, they may be used for HTS to yield lead compounds.

scholarly journals Dealing with the data deluge in high throughput screening

2000 ◽  
Vol 22 (5) ◽  
pp. 145-148 ◽  
Author(s):  
Philip Skehan
Keyword(s):  
High Throughput Screening ◽  
Response Patterns ◽  
Screening Programs ◽  
Lead Compounds ◽  
Stage Of Development ◽  
Similar Chemical ◽  
Drug Selectivity ◽  
Reporter Assays ◽  
Pattern Recognition Analysis ◽  
Chemical Response

Numerical taxonomy and pattern recognition analysis offer powerful tools that can greatly reduce the information burden of multiple-assay screening programs. These methods can be used to rationally design prescreens, identify assays that have similar chemical response patterns, select reporter assays for chemical response groups, evaluate drug selectivity, and predict a drug's likely mechanism of action. When combined with assays designed to identify lead compounds that have characteristics likely to cause failure at a later and more expensive stage of development, a simple three-stage primary discovery process consisting of a rational prescreen, reporters, and clinical failure assay can reduce the number of required culture wells by more than 20-fold and can eliminate all but 1–2 drugs per 1000 tested as leads for further evaluation and development.

scholarly journals Drug discovery by integration of pharmacophore modeling, virtual screening and biological evaluation by means of bioinformatics resources

2016 ◽  
Author(s):  
Serena Dotolo ◽  
Angelo Facchiano
Keyword(s):  
Molecular Dynamics ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Development Process ◽  
Chemical Space ◽  
Pharmacophore Modeling ◽  
Biological Evaluation ◽  
Lead Compounds ◽  
Computational Screening ◽  
Computational Strategy

Drug discovery is a step-by-step process very important in biopharmaceutical field. We are interested in identifying new investigational drug-likes as potential inhibitors of determinate biological-therapeutic targets, trying to decrease the side effects and to safeguard the human health. However, it is a long and very expensive process. Therefore, we are using a new computational strategy, based on Pharmacophore modeling, to select bioactive substances (natural or synthetic), through the integration of bioinformatics online tools and local resource and platforms, in order to include into the strategy also knowledge from high-throughput studies, for new potential lead compounds generation-optimization, trying to accelerate the early phase of the drug development process. The protocol of this new computational strategy is characterized by a multi-step design focused on: 1) screening in RCSB-PDB for a crystal structure of a specific biological target, suitable for the following steps; 2) pharmacophore modeling and virtual computational screening, by using public domain databases of bioactive compounds, as the ZINC12 database [5], in order to find a promising molecule that could become a new potential medicine. 3) molecular and biological evaluation, to check the compounds selected by virtual screening, for their biological properties through public databases, as PubChem Compound, SciFinder, and Chemicalize to trace their origin and underline their most important physical-chemical features, PathPred (an enzyme-catalyzed metabolic pathway predictor server) to highlight and identify their biosynthetic-metabolic pathways and investigating the biotransformation of best candidates, analyzing their metabolites and their potential biological activity. Moreover, ADMET/toxicity predictor server applying the Lipinski-Veber filter are used to calculate the bioavailability the ADMET/toxicity properties. After this check, only molecules with good bioavailability, good predicted activity and good ADMET properties are considered as hits compounds or drug-likes to direct the design of next experimental assays [6]. Finally, the lead compounds selected are analyzed through molecular dynamics simulations. 4) simulations of molecular dynamics on the best lead compounds, to investigate atomic details of protein-compound molecular interactions in different conditions (different organic solutions, organisms and systems). REFERENCES [1] Dubey A, Facchiano A, Ramteke PW, Marabotti A. “In silico approach to find chymase inhibitors among biogenic compounds.” Future Med Chem. 2016; 8(8):841-51 [2] Dubey A, Marabotti A, Ramteke PW, Facchiano A. "Interaction of human chymase with ginkgolides, terpene trilactones of Ginkgo biloba investigated by molecular docking simulations.” Biochem Biophys Res Commun. 2016; 473(2):449-54. [3] Katara P. “Role of bioinformatics and pharmacogenomics in drug discovery and development process”. Netw Model Anal Health Inform Bioinforma 2013; 2: 225-230. [4] Sunseri J. and Koes D. R. “Pharmit: Interactive Exploration of Chemical Space”.Nucl. Acids Res. 2016; 44(W1): W442-448. [5] Irwin J.J. and Shoichet B.K. “ZINC- A free database of Commercially Available Compounds for Virtual Screening”. J.Chem.Inf.Model. 2005; 45: 177-182. [6] Kaserer T., Beck K. R., Akram M., Odermatt A., Schuster D. “Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Application Exemplified on Hydroxysteroid Dehydrogenases”.Molecules 2015; 20: 22799–22832.

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