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 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.

scholarly journals Advancing the activity cliff concept

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 199 ◽  
Author(s):  
Ye Hu ◽  
Dagmar Stumpfe ◽  
Jürgen Bajorath
Keyword(s):  
Chemical Modifications ◽  
Large Magnitude ◽  
Activity Data ◽  
General Audience ◽  
Structure Activity ◽  
Activity Cliff ◽  
Sar Analysis ◽  
Small Chemical ◽  
Detailed Picture ◽  
Activity Cliffs

The activity cliff concept has experienced increasing interest in medicinal chemistry and chemoinformatics. Activity cliffs have originally been defined as pairs of structurally similar compounds that are active against the same target but have a large difference in potency. Activity cliffs are relevant for structure-activity relationship (SAR) analysis and compound optimization because small chemical modifications can be deduced from cliffs that result in large-magnitude changes in potency. In addition to studying activity cliffs on the basis of individual compounds series, they can be systematically identified through mining of compound activity data. This commentary aims to provide a concise yet detailed picture of our current understanding of activity cliffs. It is also meant to introduce the further refined activity cliff concept to a general audience in drug development.

HAZARD: An Expert System for Risk Assessment of Environmental Chemicals

1987 ◽  
Vol 26 (01) ◽  
pp. 13-23 ◽  
Author(s):  
H. W. Gottinger
Keyword(s):  
Expert System ◽  
Structural Information ◽  
Chemical Carcinogenesis ◽  
Structural Features ◽  
Environmental Chemicals ◽  
Chemical Carcinogens ◽  
Carcinogenic Activity ◽  
Current State ◽  
Structure Activity ◽  
Carcinogenic Potential

AbstractThe purpose of this paper is to report on an expert system in design that screens for potential hazards from environmental chemicals on the basis of structure-activity relationships in the study of chemical carcinogenesis, particularly with respect to analyzing the current state of known structural information about chemical carcinogens and predicting the possible carcinogenicity of untested chemicals. The structure-activity tree serves as an index of known chemical structure features associated with carcinogenic activity. The basic units of the tree are the principal recognized classes of chemical carcinogens that are subdivided into subclasses known as nodes according to specific structural features that may reflect differences in carcinogenic potential among chemicals in the class. An analysis of a computerized data base of known carcinogens (knowledge base) is proposed using the structure-activity tree in order to test the validity of the tree as a classification scheme (inference engine).

scholarly journals Symmetry perception with spiking neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jonathan K. George ◽  
Cesare Soci ◽  
Mario Miscuglio ◽  
Volker J. Sorger
Keyword(s):  
Neural Networks ◽  
Mirror Symmetry ◽  
Satellite Image ◽  
Image Data ◽  
Coincidence Detection ◽  
Spiking Neural Networks ◽  
Feed Forward Neural Network ◽  
Neuromorphic Computing ◽  
Symmetry Perception ◽  
High Level

AbstractMirror symmetry is an abundant feature in both nature and technology. Its successful detection is critical for perception procedures based on visual stimuli and requires organizational processes. Neuromorphic computing, utilizing brain-mimicked networks, could be a technology-solution providing such perceptual organization functionality, and furthermore has made tremendous advances in computing efficiency by applying a spiking model of information. Spiking models inherently maximize efficiency in noisy environments by placing the energy of the signal in a minimal time. However, many neuromorphic computing models ignore time delay between nodes, choosing instead to approximate connections between neurons as instantaneous weighting. With this assumption, many complex time interactions of spiking neurons are lost. Here, we show that the coincidence detection property of a spiking-based feed-forward neural network enables mirror symmetry. Testing this algorithm exemplary on geospatial satellite image data sets reveals how symmetry density enables automated recognition of man-made structures over vegetation. We further demonstrate that the addition of noise improves feature detectability of an image through coincidence point generation. The ability to obtain mirror symmetry from spiking neural networks can be a powerful tool for applications in image-based rendering, computer graphics, robotics, photo interpretation, image retrieval, video analysis and annotation, multi-media and may help accelerating the brain-machine interconnection. More importantly it enables a technology pathway in bridging the gap between the low-level incoming sensor stimuli and high-level interpretation of these inputs as recognized objects and scenes in the world.

scholarly journals Cytotoxic Agents in the Minor Alkaloid Groups of the Amaryllidaceae

Planta Medica ◽  
2021 ◽  
Author(s):  
Jerald J. Nair ◽  
Johannes van Staden
Keyword(s):  
Cancer Cells ◽  
Cancer Cell Line ◽  
Structural Features ◽  
Biological Properties ◽  
Cytotoxic Agents ◽  
Plant Family ◽  
Structure Activity ◽  
Significant Interest ◽  
Different Types ◽  
Minor Alkaloid

AbstractOver 600 alkaloids have to date been identified in the plant family Amaryllidaceae. These have been arranged into as many as 15 different groups based on their characteristic structural features. The vast majority of studies on the biological properties of Amaryllidaceae alkaloids have probed their anticancer potential. While most efforts have focused on the major alkaloid groups, the volume and diversity afforded by the minor alkaloid groups have promoted their usefulness as targets for cancer cell line screening purposes. This survey is an in-depth review of such activities described for around 90 representatives from 10 minor alkaloid groups of the Amaryllidaceae. These have been evaluated against over 60 cell lines categorized into 18 different types of cancer. The montanine and cripowellin groups were identified as the most potent, with some in the latter demonstrating low nanomolar level antiproliferative activities. Despite their challenging molecular architectures, the minor alkaloid groups have allowed for facile adjustments to be made to their structures, thereby altering the size, geometry, and electronics of the targets available for structure-activity relationship studies. Nevertheless, it was seen with a regular frequency that the parent alkaloids were better cytotoxic agents than the corresponding semisynthetic derivatives. There has also been significant interest in how the minor alkaloid groups manifest their effects in cancer cells. Among the various targets and pathways in which they were seen to mediate, their ability to induce apoptosis in cancer cells is most appealing.

scholarly journals Synthesis, Antiprotozoal Activity, and Cheminformatic Analysis of 2-Phenyl-2H-Indazole Derivatives

Molecules ◽  
2021 ◽  
Vol 26 (8) ◽  
pp. 2145
Author(s):  
Karen Rodríguez-Villar ◽  
Lilián Yépez-Mulia ◽  
Miguel Cortés-Gines ◽  
Jacobo David Aguilera-Perdomo ◽  
Edgar A. Quintana-Salazar ◽  
...  
Keyword(s):  
Phenyl Ring ◽  
Medicinal Chemistry ◽  
Structural Features ◽  
Pharmacological Properties ◽  
Antiprotozoal Activity ◽  
One Pot ◽  
Biological Assays ◽  
Structure Activity Relationships ◽  
Structure Activity ◽  
Synthetic Methodologies

Indazole is an important scaffold in medicinal chemistry. At present, the progress on synthetic methodologies has allowed the preparation of several new indazole derivatives with interesting pharmacological properties. Particularly, the antiprotozoal activity of indazole derivatives have been recently reported. Herein, a series of 22 indazole derivatives was synthesized and studied as antiprotozoals. The 2-phenyl-2H-indazole scaffold was accessed by a one-pot procedure, which includes a combination of ultrasound synthesis under neat conditions as well as Cadogan’s cyclization. Moreover, some compounds were derivatized to have an appropriate set to provide structure-activity relationships (SAR) information. Whereas the antiprotozoal activity of six of these compounds against E. histolytica, G. intestinalis, and T. vaginalis had been previously reported, the activity of the additional 16 compounds was evaluated against these same protozoa. The biological assays revealed structural features that favor the antiprotozoal activity against the three protozoans tested, e.g., electron withdrawing groups at the 2-phenyl ring. It is important to mention that the indazole derivatives possess strong antiprotozoal activity and are also characterized by a continuous SAR.

scholarly journals An Automatic Feature Extraction Approach to Image Classification Using Genetic Programming

2020 ◽  
Author(s):  
Ying Bi ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Feature Extraction ◽  
Genetic Programming ◽  
Image Classification ◽  
Image Data ◽  
Data Sets ◽  
Automatic Feature Extraction ◽  
Terminal Set ◽  
Comparable Performance ◽  
International Publishing ◽  
High Level

© Springer International Publishing AG, part of Springer Nature 2018. Feature extraction is an essential process for image data dimensionality reduction and classification. However, feature extraction is very difficult and often requires human intervention. Genetic Programming (GP) can achieve automatic feature extraction and image classification but the majority of existing methods extract low-level features from raw images without any image-related operations. Furthermore, the work on the combination of image-related operators/descriptors in GP for feature extraction and image classification is limited. This paper proposes a multi-layer GP approach (MLGP) to performing automatic high-level feature extraction and classification. A new program structure, a new function set including a number of image operators/descriptors and two region detectors, and a new terminal set are designed in this approach. The performance of the proposed method is examined on six different data sets of varying difficulty and compared with five GP based methods and 42 traditional image classification methods. Experimental results show that the proposed method achieves better or comparable performance than these baseline methods. Further analysis on the example programs evolved by the proposed MLGP method reveals the good interpretability of MLGP and gives insight into how this method can effectively extract high-level features for image classification.

scholarly journals MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation

2019 ◽  
Author(s):  
J-Donald Tournier ◽  
Robert Smith ◽  
David Raffelt ◽  
Rami Tabbara ◽  
Thijs Dhollander ◽  
...  
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Image Data ◽  
Data Access ◽  
General Purpose ◽  
Computational Performance ◽  
Cross Platform ◽  
High Level ◽  
New Applications

AbstractMRtrix3 is an open-source, cross-platform software package for medical image processing, analysis and visualization, with a particular emphasis on the investigation of the brain using diffusion MRI. It is implemented using a fast, modular and flexible general-purpose code framework for image data access and manipulation, enabling efficient development of new applications, whilst retaining high computational performance and a consistent command-line interface between applications. In this article, we provide a high-level overview of the features of the MRtrix3 framework and general-purpose image processing applications provided with the software.

scholarly journals MULTI-CHANNEL FRY COUNTER DESIGN USING OPTOCOUPLER SENSOR

2019 ◽  
Vol 4 (2) ◽  
pp. 97-104
Author(s):  
Lazuardi Umar ◽  
Yanuar Hamzah ◽  
Rahmondia N. Setiadi
Keyword(s):  
Counting Process ◽  
Production Capacity ◽  
High Accuracy ◽  
Limited Capacity ◽  
High Concentration ◽  
Counting System ◽  
Channel Detection ◽  
Accuracy Level ◽  
High Level ◽  
Almost All

This paper describes a design of a fry counter intended to be used by consuming fish farmer. Along this time, almost all the fry counting process is counted by manual, which is done by a human. It is requiring much energy and needs high concentration; thus, can cause a high level of exhaustion for the fry counting worker. Besides that, the human capability and capacity of counting are limited to a low level. A fry counter design in this study utilizes a multi-channel optocoupler sensor to increase the counting capacity. The multi-channel fry counter counting system is developed as a solution to a limited capacity of available fry counter. This design uses an input signal extender system on controller including the interrupt system. From the experiment, high accuracy level is obtained on the counting and channel detection, therefore, this design can be implemented and could help farmers to increase the production capacity of consuming fish.

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