scholarly journals Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

Molecules ◽  
2020 ◽  
Vol 25 (20) ◽  
pp. 4723 ◽  
Author(s):  
Javier Vázquez ◽  
Manel López ◽  
Enric Gibert ◽  
Enric Herrero ◽  
F. Javier Luque

Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.

2019 ◽  
Vol 4 (6) ◽  
Author(s):  
Eleni Koulouridi ◽  
Marilia Valli ◽  
Fidele Ntie-Kang ◽  
Vanderlan da Silva Bolzani

Abstract Databases play an important role in various computational techniques, including virtual screening (VS) and molecular modeling in general. These collections of molecules can contain a large amount of information, making them suitable for several drug discovery applications. For example, vendor, bioactivity data or target type can be found when searching a database. The introduction of these data resources and their characteristics is used for the design of an experiment. The description of the construction of a database can also be a good advisor for the creation of a new one. There are free available databases and commercial virtual libraries of molecules. Furthermore, a computational chemist can find databases for a general purpose or a specific subset such as natural products (NPs). In this chapter, NP database resources are presented, along with some guidelines when preparing an NP database for drug discovery purposes.


2019 ◽  
Author(s):  
Chen Farhy ◽  
Luis Orozco ◽  
Fu-Yue Zeng ◽  
Ian Pass ◽  
Jarkko Ylanko ◽  
...  

AbstractWith the advent of automatic cell imaging and machine learning, high-content phenotypic screening has become the approach of choice for drug discovery due to its ability to extract drug specific multilayered data and compare it to known profiles. In the field of epigenetics such screening approaches has suffered from the lack of tools sensitive to selective epigenetic perturbations. Here we describe a novel approach Microscopic Imaging of Epigenetic Landscapes (MIEL) that captures patterns of nuclear staining of epigenetic marks (e.g. acetylated and methylated histones) and employs machine learning to accurately distinguish between such patterns (1). We demonstrated that MIEL has superior resolution compared to conventional intensity thresholding techniques and enables efficient detection of epigenetically active compounds, function-based classification, flagging possible off-target effects and even predict novel drug function. We validated MIEL platform across multiple cells lines and using dose-response curves to insure the robustness of this approach for the high content high throughput drug discovery.


2017 ◽  
Vol 37 (4) ◽  
Author(s):  
Sarah K. Wooller ◽  
Graeme Benstead-Hume ◽  
Xiangrong Chen ◽  
Yusuf Ali ◽  
Frances M.G. Pearl

Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational exploitation of the increasing volumes of data generated during all phases of drug discovery is enabling key challenges of the process to be addressed. Here, we highlight some of the areas in which bioinformatics resources and methods are being developed to support the drug discovery pipeline. These include the creation of large data warehouses, bioinformatics algorithms to analyse ‘big data’ that identify novel drug targets and/or biomarkers, programs to assess the tractability of targets, and prediction of repositioning opportunities that use licensed drugs to treat additional indications.


Author(s):  
Ahmad Abu Turab Naqvi ◽  
Md. Imtaiyaz Hassan

Molecular docking is the prediction of conformational complementarity between ligand and receptor molecule. The process of docking integrates two schematic approaches namely sampling of ligand conformations and ranking of selected conformations based on scoring functions. The authors have discussed established methodologies for molecular docking and well-known tools implementing these methods. A brief account of different classes of scoring functions such as force field based, empirical, knowledge based, and descriptor based scoring functions is given along with the exemplary implementations of these scoring functions. By replacing test and trial based ligand screening with structure based virtual screening, molecular docking has helped in shortening the duration of novel drug discovery up to some extent. However, the developments made in the field of drug discovery are assisted by the advances in the techniques of molecular docking, but there is strong need of enrichment in the techniques, especially in scoring functions, to tackle the inbound problems of de novo drug discovery.


2012 ◽  
Vol 9 (3) ◽  
pp. e219-e225 ◽  
Author(s):  
Manuela S. Murgueitio ◽  
Marcel Bermudez ◽  
Jérémie Mortier ◽  
Gerhard Wolber

2021 ◽  
Author(s):  
David E. Graff ◽  
Eugene I. Shakhnovich ◽  
Connor W Coley

Structure-based virtual screening is an important tool in early stage drug discovery that scores the interactions between a target protein and candidate ligands. As virtual libraries continue to grow (in...


Oncology ◽  
2017 ◽  
pp. 876-890
Author(s):  
Ahmad Abu Turab Naqvi ◽  
Md. Imtaiyaz Hassan

Molecular docking is the prediction of conformational complementarity between ligand and receptor molecule. The process of docking integrates two schematic approaches namely sampling of ligand conformations and ranking of selected conformations based on scoring functions. The authors have discussed established methodologies for molecular docking and well-known tools implementing these methods. A brief account of different classes of scoring functions such as force field based, empirical, knowledge based, and descriptor based scoring functions is given along with the exemplary implementations of these scoring functions. By replacing test and trial based ligand screening with structure based virtual screening, molecular docking has helped in shortening the duration of novel drug discovery up to some extent. However, the developments made in the field of drug discovery are assisted by the advances in the techniques of molecular docking, but there is strong need of enrichment in the techniques, especially in scoring functions, to tackle the inbound problems of de novo drug discovery.


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