Molecular Similarity Methods and QSAR Models as Tools for Virtual Screening

2005 ◽  
pp. 87-122
Author(s):  
Jürgen Bajorath
Keyword(s):  
Virtual Screening ◽  
Molecular Similarity ◽  
Qsar Models

scholarly journals Multi-Target Chemometric Modelling, Fragment Analysis and Virtual Screening with ERK Inhibitors as Potential Anticancer Agents

Molecules ◽  
2019 ◽  
Vol 24 (21) ◽  
pp. 3909 ◽  
Author(s):  
Amit Kumar Halder ◽  
Amal Kanta Giri ◽  
Maria Natália Dias Soeiro Cordeiro
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
Linear Model ◽  
Anticancer Agents ◽  
Quantitative Structure Activity Relationship ◽  
Experimental Conditions ◽  
Fragment Analysis ◽  
Linear Discriminant ◽  
Qsar Models ◽  
Dynamics Simulations

Two isoforms of extracellular regulated kinase (ERK), namely ERK-1 and ERK-2, are associated with several cellular processes, the aberration of which leads to cancer. The ERK-1/2 inhibitors are thus considered as potential agents for cancer therapy. Multitarget quantitative structure–activity relationship (mt-QSAR) models based on the Box–Jenkins approach were developed with a dataset containing 6400 ERK inhibitors assayed under different experimental conditions. The first mt-QSAR linear model was built with linear discriminant analysis (LDA) and provided information regarding the structural requirements for better activity. This linear model was also utilised for a fragment analysis to estimate the contributions of ring fragments towards ERK inhibition. Then, the random forest (RF) technique was employed to produce highly predictive non-linear mt-QSAR models, which were used for screening the Asinex kinase library and identify the most potential virtual hits. The fragment analysis results justified the selection of the hits retrieved through such virtual screening. The latter were subsequently subjected to molecular docking and molecular dynamics simulations to understand their possible interactions with ERK enzymes. The present work, which utilises in-silico techniques such as multitarget chemometric modelling, fragment analysis, virtual screening, molecular docking and dynamics, may provide important guidelines to facilitate the discovery of novel ERK inhibitors.

scholarly journals Network-based piecewise linear regression for QSAR modelling

2019 ◽  
Vol 33 (9) ◽  
pp. 831-844
Author(s):  
Jonathan Cardoso-Silva ◽  
Lazaros G. Papageorgiou ◽  
Sophia Tsoka
Keyword(s):  
Virtual Screening ◽  
Linear Regression ◽  
Predictive Accuracy ◽  
Piecewise Linear ◽  
Chemical Properties ◽  
Structural Similarity ◽  
Quantitative Structure Activity Relationship ◽  
Support Vector ◽  
Piecewise Linear Regression ◽  
Qsar Models

Abstract Quantitative Structure-Activity Relationship (QSAR) models are critical in various areas of drug discovery, for example in lead optimisation and virtual screening. Recently, the need for models that are not only predictive but also interpretable has been highlighted. In this paper, a new methodology is proposed to build interpretable QSAR models by combining elements of network analysis and piecewise linear regression. The algorithm presented, modSAR, splits data using a two-step procedure. First, compounds associated with a common target are represented as a network in terms of their structural similarity, revealing modules of similar chemical properties. Second, each module is subdivided into subsets (regions), each of which is modelled by an independent linear equation. Comparative analysis of QSAR models across five data sets of protein inhibitors obtained from ChEMBL is reported and it is shown that modSAR offers similar predictive accuracy to popular algorithms, such as Random Forest and Support Vector Machine. Moreover, we show that models built by modSAR are interpretatable, capable of evaluating the applicability domain of the compounds and serve well tasks such as virtual screening and the development of new drug leads.

Identifying the structural features and diversifying the chemical domain of peripherally acting CB1 receptor antagonists using molecular modeling techniques

RSC Advances ◽  
2016 ◽  
Vol 6 (2) ◽  
pp. 1466-1483 ◽  
Author(s):  
Mayank Kumar Sharma ◽  
Prashant R. Murumkar ◽  
Guanglin Kuang ◽  
Yun Tang ◽  
Mange Ram Yadav
Keyword(s):  
Molecular Modeling ◽  
Virtual Screening ◽  
3D Qsar ◽  
Structural Features ◽  
Cb1 Receptor ◽  
Receptor Antagonists ◽  
Modeling Techniques ◽  
Qsar Models ◽  
Cb1 Receptor Antagonists

A four featured pharmacophore and predictive 3D-QSAR models were developed which were used for virtual screening of the Asinex database to get chemically diverse hits of peripherally active CB1 receptor antagonists.

scholarly journals Automated Framework for Developing Predictive Machine Learning Models for Data-Driven Drug Discovery

2020 ◽  
Author(s):  
Bruno J. Neves ◽  
José T. Moreira-Filho ◽  
Arthur C. Silva ◽  
Joyce V. V. B. Borba ◽  
Melina Mottin ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Best Practices ◽  
Chemical Space ◽  
Biological Data ◽  
Data Driven ◽  
Learning Models ◽  
Structure Activity ◽  
Research Problems ◽  
Qsar Models ◽  
Machine Learning Models

In this manuscript we describe the development of an automated framework for the curation of chemogenomics data and to develop QSAR models for virtual screening using the open-source KNIME software. The workflow includes four modules: (i) dataset preparation and curation; (ii) chemical space analysis and structure-activity relationships (SAR) rules; (iii) modeling; and (iv) virtual screening (VS). As case studies, we applied these workflows to four datasets associated with different endpoints. The implemented protocol can efficiently curate chemical and biological data in public databases and generates robust QSAR models. We provide scientists a simple and guided cheminformatics workbench following the best practices widely accepted by the community, in which scientists can adapt to solve their research problems. The workflows are freely available for download in GitHub.

scholarly journals Virtual screening tailored ensembles of QSAR models for the discovery of dual A2A Adenosine Receptor Antagonists / Monoamine Oxidase B Inhibitors

2015 ◽  
Author(s):  
Fernanda Borges ◽  
Maykel Cruz-Monteagudo ◽  
Aliuska Morales-Helguera ◽  
Yunierkis Pérez-Castillo ◽  
M. Natália D. S. Cordeiro ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Monoamine Oxidase ◽  
Adenosine Receptor ◽  
Monoamine Oxidase B ◽  
Receptor Antagonists ◽  
Qsar Models

scholarly journals QSAR Models and Virtual Screening for Discovery of New Analgesic Leads

2017 ◽  
Author(s):  
Juan Castillo-Garit ◽  
Arelys López ◽  
Yovani Marrero-Ponce ◽  
Gerardo Casañola-Martín ◽  
Vicente Arán
Keyword(s):  
Virtual Screening ◽  
Qsar Models
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