Extending the Activity Cliff Concept: Structural Categorization of Activity Cliffs and Systematic Identification of Different Types of Cliffs in the ChEMBL Database

2012 ◽  
Vol 52 (7) ◽  
pp. 1806-1811 ◽  
Author(s):  
Ye Hu ◽  
Jürgen Bajorath
Keyword(s):  
Different Types ◽  
Activity Cliff ◽  
Systematic Identification ◽  
Activity Cliffs

scholarly journals Increasing the public activity cliff knowledge base with new categories of activity cliffs

2020 ◽  
Vol 6 (5) ◽  
pp. FSO472
Author(s):  
Huabin Hu ◽  
Jürgen Bajorath
Keyword(s):  
Knowledge Base ◽  
Structural Characteristics ◽  
Public Knowledge ◽  
Target Distribution ◽  
The Public ◽  
Public Activity ◽  
Activity Cliff ◽  
Systematic Identification ◽  
Dual Site ◽  
Activity Cliffs

Aim: Extending the public knowledge base of activity cliffs (ACs) with new categories of ACs having special structural characteristics. Methodology: Dual-site ACs, isomer ACs and ACs with privileged substructures are described and their systematic identification is detailed. Exemplary results & data: More than 7400 new ACs belonging to different categories with activity against more than 200 targets were identified and are made publicly available. Limitations & next steps: For dual-site ACs, limited numbers of isomers are available as structural analogs for rationalizing contributions to AC formation. The search for such analogs will continue. In addition, the target distribution of ACs containing privileged substructures will be further analyzed.

scholarly journals Systematic identification of target set-dependent activity cliffs

2019 ◽  
Vol 5 (2) ◽  
pp. FSO363 ◽  
Author(s):  
Huabin Hu ◽  
Dagmar Stumpfe ◽  
Jürgen Bajorath
Keyword(s):  
Dependent Activity ◽  
Target Set ◽  
Systematic Identification ◽  
Activity Cliffs

Activity cliffs and activity cliff generators based on chemotype-related activity landscapes

2015 ◽  
Vol 19 (4) ◽  
pp. 1021-1035 ◽  
Author(s):  
Jaime Pérez-Villanueva ◽  
Oscar Méndez-Lucio ◽  
Olivia Soria-Arteche ◽  
José L. Medina-Franco
Keyword(s):  
Related Activity ◽  
Activity Cliff ◽  
Activity Cliff Generators ◽  
Activity Cliffs

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 Identification and Analysis of Activity Cliffs Using 3D Similarity Techniques

2020 ◽  
Author(s):  
Mark Mackey ◽  
Timothy J. Cheeseright ◽  
Paolo Tosco
Keyword(s):  
Structural Change ◽  
Dipeptidyl Peptidase ◽  
The Other ◽  
Alignment Method ◽  
Molecular Features ◽  
Activity Cliff ◽  
Electrostatic Similarity ◽  
Critical Regions ◽  
3D Similarity ◽  
Activity Cliffs

<p>The analysis of activity landscapes and activity cliffs is a widely used method to locate critical regions of SAR. Knowledge of what changes in a series of molecules caused unexpectedly large changes in affinity allows the chemist to focus on the molecular features which are crucial for activity. We examine the usefulness of activity cliff analysis with a metric based on 3D shape and electrostatic similarity, utilizing a ligand-based alignment method. We demonstrate that 3D activity cliff analysis is complementary to the more usual 2D fingerprint-based methods, in that each finds cliffs that the other misses. Moreover, we show that analysis of the activity landscape in the context of a consensus 3D alignment allows the source of the activity cliff to be investigated in terms of the effect that a structural change has on the steric and electrostatic properties of a molecule. The technique is illustrated with two set of compounds with activity against acetylcholinesterase and dipeptidyl peptidase.</p>

Chemical Landscape Analysis with the Opentox Framework

2013 ◽  
Vol 12 (18) ◽  
pp. 1987-2001
Author(s):  
Nina Jeliazkova ◽  
Vedrin Jeliazkov
Keyword(s):  
Biological Activities ◽  
Topographic Map ◽  
Web Browser ◽  
Probabilistic Measure ◽  
Multiple Thresholds ◽  
Ligand Interactions ◽  
Activity Difference ◽  
Activity Cliff ◽  
Definition Of ◽  
Activity Cliffs

The Structure-Activity Relationships (SAR) landscape and activity cliffs concepts have their origins in medicinal chemistry and receptor-ligand interactions modelling. While intuitive, the definition of an activity cliff as a “pair of structurally similar compounds with large differences in potency” is commonly recognized as ambiguous. This paper proposes a new and efficient method for identifying activity cliffs and visualization of activity landscapes. The activity cliffs definition could be improved to reflect not the cliff steepness alone, but also the rate of the change of the steepness. The method requires explicitly setting similarity and activity difference thresholds, but provides means to explore multiple thresholds and to visualize in a single map how the thresholds affect the activity cliff identification. The identification of the activity cliffs is addressed by reformulating the problem as a statistical one, by introducing a probabilistic measure, namely, calculating the likelihood of a compound having large activity difference compared to other compounds, while being highly similar to them. The likelihood is effectively a quantification of a SAS Map with defined thresholds. Calculating the likelihood relies on four counts only, and does not require the pairwise matrix storage. This is a significant advantage, especially when processing large datasets. The method generates a list of individual compounds, ranked according to the likelihood of their involvement in the formation of activity cliffs, and goes beyond characterizing cliffs by structure pairs only. The visualisation is implemented by considering the activity plane fixed and analysing the irregularities of the similarity itself. It provides a convenient analogy to a topographic map and may help identifying the most appropriate similarity representation for each specific SAR space. The proposed method has been applied to several datasets, representing different biological activities. Finally, the method is implemented as part of an existing open source Ambit package and could be accessed via an OpenTox API compliant web service and via an interactive application, running within a modern, JavaScript enabled web browser. Combined with the functionalities already offered by the OpenTox framework, like data sharing and remote calculations, it could be a useful tool for exploring chemical landscapes online.

scholarly journals Advancing the activity cliff concept, part II

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 75 ◽  
Author(s):  
Dagmar Stumpfe ◽  
Antonio de la Vega de León ◽  
Dilyana Dimova ◽  
Jürgen Bajorath
Keyword(s):  
Medicinal Chemistry ◽  
Current Understanding ◽  
Activity Relationship ◽  
Ligand Efficiency ◽  
Research Activity ◽  
High Interest ◽  
Structure Activity ◽  
Activity Cliff ◽  
Activity Cliffs

We present a follow up contribution to further complement a previous commentary on the activity cliff concept and recent advances in activity cliff research. Activity cliffs have originally been defined as pairs of structurally similar compounds that display a large difference in potency against a given target. For medicinal chemistry, activity cliffs are of high interest because structure-activity relationship (SAR) determinants can often be deduced from them. Herein, we present up-to-date results of systematic analyses of the ligand efficiency and lipophilic efficiency relationships between activity cliff-forming compounds, which further increase their attractiveness for the practice of medicinal chemistry. In addition, we summarize the results of a new analysis of coordinated activity cliffs and clusters they form. Taken together, these findings considerably add to our evaluation and current understanding of the activity cliff concept. The results should be viewed in light of the previous commentary article.

MMP-Cliffs: Systematic Identification of Activity Cliffs on the Basis of Matched Molecular Pairs

2012 ◽  
Vol 52 (5) ◽  
pp. 1138-1145 ◽  
Author(s):  
Xiaoying Hu ◽  
Ye Hu ◽  
Martin Vogt ◽  
Dagmar Stumpfe ◽  
Jürgen Bajorath
Keyword(s):  
Matched Molecular Pairs ◽  
Systematic Identification ◽  
Activity Cliffs

Systematic Identification and Classification of Three-Dimensional Activity Cliffs

2012 ◽  
Vol 52 (6) ◽  
pp. 1490-1498 ◽  
Author(s):  
Ye Hu ◽  
Norbert Furtmann ◽  
Michael Gütschow ◽  
Jürgen Bajorath
Keyword(s):  
Three Dimensional ◽  
Systematic Identification ◽  
Activity Cliffs
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