scholarly journals The iterative application of a large chemical space in the drug discovery process

2021 ◽  
Vol 19 (4(76)) ◽  
pp. 3-11
Author(s):  
Olena V. Savych ◽  
Anastasia V. Gryniukova ◽  
Diana O. Alieksieieva ◽  
Igor M. Dziuba ◽  
Petro O. Borysko ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Large Scale ◽  
Chemical Space ◽  
Building Blocks ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Thermal Shift Assay ◽  
Virtual Libraries ◽  
Thermal Shift

Aim. To demonstrate the advantages of large-scale virtual libraries generated using chemical protocols previously validated in primary steps of the drug discovery process.Results and discussion. Two validated parallel chemistry protocols reported earlier were used to create the chemical space. It was then sampled based on diversity metric, and the sample was subjected to the virtual screening on BRD4 target. Hits of virtual screening were synthesized and tested in the thermal shift assay.Experimental part. The chemical space was generated using commercially available building blocks and synthetic protocols suitable for parallel chemistry and previously reported. After narrowing it down, using MedChem filters, the resulting sub-space was clustered based on diversity metrics. Centroids of the clusters were put to the virtual screening against the BRD4 active center. 29 Hits from the docking were synthesized and subjected to the thermal shift assay with BRD4, and 2 compounds showed noticeable dTm.Conclusions. A combination of cheminformatics and molecular docking was applied to find novel potential binders for BRD4 from a large chemical space. The selected set of predicted molecules was synthesized with a 72 % success rate and tested in a thermal shift assay to reveal a 6 % hit rate. The selection can be performed iteratively to fast support of the drug discovery.

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 Taking chance out of drug discovery

2009 ◽  
Vol 31 (6) ◽  
pp. 40-42
Author(s):  
Simon Pearce
Keyword(s):  
Drug Discovery ◽  
Research And Development ◽  
Phenyl Ring ◽  
Reactive Intermediates ◽  
Building Blocks ◽  
Bioactive Molecules ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Drug Discovery And Development ◽  
Compound Screening

Searching for bioactive molecules, using rapid compound screening, fragment-based design and unique building blocks, should not be like looking for a needle in a haystack. This article describes a range of innovative and diverse screening compounds for drug discovery and development. Available at both research and development scales, the line includes special products associated with new heterocyclic and phenyl ring-based chemical building blocks, including an exclusive and expanding range of reactive intermediates specifically designed for lead optimization, as well as a growing fragment collection. I explain how these products and services are helping to accelerate the search for bioactive molecules and are shortening the drug discovery process by reducing the element of chance.

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 Virtual Screening of Drug Likeness using Tree Based Ensemble Classifier

2018 ◽  
Vol 11 (3) ◽  
pp. 1513-1519 ◽  
Author(s):  
R. Ani ◽  
Roshini Manohar ◽  
Gayathri Anil ◽  
O.S. Deepa
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Molecular Descriptor ◽  
Computational Procedure ◽  
Ensemble Classifier ◽  
Support Vector ◽  
Classification Algorithms ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Rotation Forest

In earlier years, the Drug discovery process took years to identify and process a Drug. It takes a normal of 12 years for a Drug to travel from the research lab to the patient. With the introduction of Machine Learning in Drug discovery, the whole process turned out to be simple. The utilization of computational tools in the early stages of Drug development has expanded in recent decades. A computational procedure carried out in Drug discovery process is Virtual Screening (VS). VS are used to identify the compounds which can bind to a Drug target. The preliminary process before analyzing the bonding of ligand and drug protein target is the prediction of drug likeness of compounds. The main objective of this study is to predict Drug likeness properties of Drug compounds based on molecular descriptor information using Tree based ensembles. In this study, many classification algorithms are analyzed and the accuracy for the prediction of drug likeness is calculated. The study shows that accuracy of rotation forest outperforms the accuracy of other classification algorithms in the prediction of drug likeness of chemical compounds. The measured accuracies of the Rotation Forest, Random Forest, Support Vector Machines, KNN, Decision Tree and Naïve Bayes are 98%, 97%, 94.8%, 92.8%, 91.4%, 89.5% respectively.

Virtual Screening and Its Applications in Drug Discovery Process

2019 ◽  
pp. 101-126
Author(s):  
Gurusamy Mariappan ◽  
Anju Kumari
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Drug Design ◽  
Screening Tool ◽  
Log P ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Financial Loss ◽  
Structure Based Drug Design ◽  
Modern Drug

Virtual screening plays an important role in the modern drug discovery process. The pharma companies invest huge amounts of money and time in drug discovery and screening. However, at the final stage of clinical trials, several molecules fail, which results in a large financial loss. To overcome this, a virtual screening tool was developed with super predictive power. The virtual screening tool is not only restricted tool small molecules but also to macromolecules such as protein, enzyme, receptors, etc. This gives an insight into structure-based and Ligand-based drug design. VS gives reliable information to direct the process of drug discovery (e.g., when the 3D image of the receptor is known, structure-based drug design is recommended). The pharmacophore-based model is advisable when the information about the receptor or any macromolecule is unknown. In this ADME, parameters such as Log P, bioavailability, and QSAR can be used as filters. This chapter shows both models with various representative examples that facilitate the scientist to use computational screening tools in modern drug discovery processes.

Forging New Scaffolds from Old: Combining Scaffold Hopping and Hierarchical Virtual Screening for Identifying Novel Bcl-2 Inhibitors

2019 ◽  
Vol 19 (13) ◽  
pp. 1162-1172 ◽  
Author(s):  
Vishnupriya Kanakaveti ◽  
Sakthivel Rathinasamy ◽  
Suresh K. Rayala ◽  
Michael Gromiha
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Physicochemical Characterization ◽  
B Cell Lymphoma ◽  
Rational Approach ◽  
Screening Methods ◽  
Scaffold Hopping ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Lead Compounds

Background: Though virtual screening methods have proven to be potent in various instances, the technique is practically incomplete to quench the need of drug discovery process. Thus, the quest for novel designing approaches and chemotypes for improved efficacy of lead compounds has been intensified and logistic approaches such as scaffold hopping and hierarchical virtual screening methods were evolved. Till now, in all the previous attempts these two approaches were applied separately. Objective: In the current work, we made a novel attempt in terms of blending scaffold hopping and hierarchical virtual screening. The prime objective is to assess the hybrid method for its efficacy in identifying active lead molecules for emerging PPI target Bcl-2 (B-cell Lymphoma 2). Method: We designed novel scaffolds from the reported cores and screened a set of 8270 compounds using both scaffold hopping and hierarchical virtual screening for Bcl-2 protein. Also, we enumerated the libraries using clustering, PAINS filtering, physicochemical characterization and SAR matching. Results: We generated a focused library of compounds towards Bcl-2 interface, screened the 8270 compounds and identified top hits for seven families upon fine filtering with PAINS algorithm, features, SAR mapping, synthetic accessibility and similarity search. Our approach retrieved a set of 50 lead compounds. Conclusions: Finding rational approach meeting the needs of drug discovery process for PPI targets is the need of the hour which can be fulfilled by an extended scaffold hopping approach resulting in focused PPI targeting by providing novel leads with better potency.

scholarly journals Ligand and structure-based virtual screening applied to the SARS-CoV-2 main protease: an in silico repurposing study

2020 ◽  
Vol 12 (20) ◽  
pp. 1815-1828 ◽  
Author(s):  
Witor Ribeiro Ferraz ◽  
Renan Augusto Gomes ◽  
Andre Luis S Novaes ◽  
Gustavo Henrique Goulart Trossini
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Drug Repurposing ◽  
Binding Modes ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Future Studies ◽  
Main Protease ◽  
Promising Target ◽  
Approved Drugs

Aim: The identification of drugs for the coronavirus disease-19 pandemic remains urgent. In this manner, drug repurposing is a suitable strategy, saving resources and time normally spent during regular drug discovery frameworks. Essential for viral replication, the main protease has been explored as a promising target for the drug discovery process. Materials & methods: Our virtual screening pipeline relies on the known 3D properties of noncovalent ligands and features of crystalized complexes, applying consensus analyses in each step. Results: Two oral (bedaquiline and glibenclamide) and one buccal drug (miconazole) presented 3D similarity to known ligands, reasonable predicted binding modes and micromolar predicted binding affinity values. Conclusion: We identified three approved drugs as promising inhibitors of the main viral protease and suggested design insights for future studies for development of novel selective inhibitors.

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