Structure−Activity Landscape Index:  Identifying and Quantifying Activity Cliffs

2008 ◽  
Vol 48 (3) ◽  
pp. 646-658 ◽  
Author(s):  
Rajarshi Guha ◽  
John H. Van Drie
Keyword(s):  
Landscape Index ◽  
Structure Activity ◽  
Activity Cliffs

scholarly journals Activity Landscape and Molecular Modeling to Explore the SAR of Dual Epigenetic Inhibitors: A Focus on G9a and DNMT1

2018 ◽  
Author(s):  
Edgar López-López ◽  
Fernando D. Prieto-Martínez ◽  
José L. Medina-Franco
Keyword(s):  
Data Set ◽  
Structure Activity ◽  
Activity Difference ◽  
Dual Inhibitors ◽  
Structure Similarity ◽  
Dynamics Simulations ◽  
Key Residues ◽  
High Structure ◽  
Molecular Docking And Dynamics ◽  
Activity Cliffs

In this work we discuss the insights from activity landscape, docking and molecular dynamics towards the understanding of the structure-activity relationships of dual inhibitors of major epigenetic targets: lysine metiltransferase (G9a) and DNA metiltranferase 1 (DNMT1). The study was based on a novel data set of 50 published compounds with reported experimental activity for both targets. The activity landscape analysis revealed the presence of activity cliffs, e.g., pairs of compounds with high structure similarity but large activity difference. Activity cliffs were further rationalized at the molecular level by means of molecular docking and dynamics simulations that led to the identification of interactions with key residues involved in the dual activity or selectivity with the epigenetic targets.

scholarly journals Prediction of activity cliffs on the basis of images using convolutional neural networks

2021 ◽  
Author(s):  
Javed Iqbal ◽  
Martin Vogt ◽  
Jürgen Bajorath
Keyword(s):  
Performance Measures ◽  
Image Data ◽  
Structural Features ◽  
High Accuracy ◽  
Black Box ◽  
Structure Activity ◽  
Activity Cliff ◽  
High Level ◽  
Feature Visualization ◽  
Activity Cliffs

AbstractAn activity cliff (AC) is formed by a pair of structurally similar compounds with a large difference in potency. Accordingly, ACs reveal structure–activity relationship (SAR) discontinuity and provide SAR information for compound optimization. Herein, we have investigated the question if ACs could be predicted from image data. Therefore, pairs of structural analogs were extracted from different compound activity classes that formed or did not form ACs. From these compound pairs, consistently formatted images were generated. Image sets were used to train and test convolutional neural network (CNN) models to systematically distinguish between ACs and non-ACs. The CNN models were found to predict ACs with overall high accuracy, as assessed using alternative performance measures, hence establishing proof-of-principle. Moreover, gradient weights from convolutional layers were mapped to test compounds and identified characteristic structural features that contributed to successful predictions. Weight-based feature visualization revealed the ability of CNN models to learn chemistry from images at a high level of resolution and aided in the interpretation of model decisions with intrinsic black box character.

scholarly journals Matched molecular pair-based data sets for computer-aided medicinal chemistry

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 36 ◽  
Author(s):  
Ye Hu ◽  
Antonio de la Vega de León ◽  
Bijun Zhang ◽  
Jürgen Bajorath
Keyword(s):  
Scientific Community ◽  
Medicinal Chemistry ◽  
Data Sets ◽  
Research Projects ◽  
High Confidence ◽  
Activity Data ◽  
Structural Modifications ◽  
Matched Molecular Pairs ◽  
Structure Activity ◽  
Activity Cliffs

Matched molecular pairs (MMPs) are widely used in medicinal chemistry to study changes in compound properties including biological activity, which are associated with well-defined structural modifications. Herein we describe up-to-date versions of three MMP-based data sets that have originated from in-house research projects. These data sets include activity cliffs, structure-activity relationship (SAR) transfer series, and second generation MMPs based upon retrosynthetic rules. The data sets have in common that they have been derived from compounds included in the latest release of the ChEMBL database for which high-confidence activity data are available. Thus, the activity data associated with MMP-based activity cliffs, SAR transfer series, and retrosynthetic MMPs cover the entire spectrum of current pharmaceutical targets. Our data sets are made freely available to the scientific community.

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