scholarly journals Natural Product Scores and Fingerprints Extracted from Artificial Neural Networks.

2021 ◽  
Author(s):  
Janosch Menke ◽  
Joana Massa ◽  
Oliver Koch
Keyword(s):  
Neural Networks ◽  
Natural Products ◽  
Artificial Neural Networks ◽  
Virtual Screening ◽  
Natural Product ◽  
Recent Work ◽  
The Novel ◽  
Multi Layer Perceptron ◽  
Output Layer ◽  
Depth Analysis

<div>Due to its desirable properties, natural products are an important ligand class for medicinal chemists. However, due to their structural distinctiveness, traditional cheminformatic approaches, like ligand-based virtual screening, often perform worse for natural products. Based on our recent work, we evaluated the ability of neural networks to generate fingerprints more appropriate for the use with natural products. A manually curated dataset of natural products and synthetic decoys was used to train a multi-layer perceptron network and an autoencoder-like network. An in-depth analysis showed that the extracted natural product specific neural fingerprints outperforms traditional as well as natural product specific fingerprints on three datasets. Further, we explore how the activation from the output layer of a network can work as a novel natural product likeness score. Overall two natural product specific datasets were generated, which are publicly available together with the code to create the fingerprints and the novel natural product likeness score.<br></div>

scholarly journals Natural Product Scores and Fingerprints Extracted from Artificial Neural Networks.

2021 ◽  
Author(s):  
Janosch Menke ◽  
Joana Massa ◽  
Oliver Koch
Keyword(s):  
Neural Networks ◽  
Natural Products ◽  
Artificial Neural Networks ◽  
Virtual Screening ◽  
Natural Product ◽  
Recent Work ◽  
The Novel ◽  
Multi Layer Perceptron ◽  
Output Layer ◽  
Depth Analysis

<div>Due to its desirable properties, natural products are an important ligand class for medicinal chemists. However, due to their structural distinctiveness, traditional cheminformatic approaches, like ligand-based virtual screening, often perform worse for natural products. Based on our recent work, we evaluated the ability of neural networks to generate fingerprints more appropriate for the use with natural products. A manually curated dataset of natural products and synthetic decoys was used to train a multi-layer perceptron network and an autoencoder-like network. An in-depth analysis showed that the extracted natural product specific neural fingerprints outperforms traditional as well as natural product specific fingerprints on three datasets. Further, we explore how the activation from the output layer of a network can work as a novel natural product likeness score. Overall two natural product specific datasets were generated, which are publicly available together with the code to create the fingerprints and the novel natural product likeness score.<br></div>

A primer on natural product-based virtual screening

2019 ◽  
Vol 4 (6) ◽  
Author(s):  
Eleni Koulouridi ◽  
Marilia Valli ◽  
Fidele Ntie-Kang ◽  
Vanderlan da Silva Bolzani
Keyword(s):  
Natural Products ◽  
Drug Discovery ◽  
Molecular Modeling ◽  
Virtual Screening ◽  
Natural Product ◽  
General Purpose ◽  
Target Type ◽  
Computational Techniques ◽  
Specific Subset ◽  
Virtual Libraries

Abstract Databases play an important role in various computational techniques, including virtual screening (VS) and molecular modeling in general. These collections of molecules can contain a large amount of information, making them suitable for several drug discovery applications. For example, vendor, bioactivity data or target type can be found when searching a database. The introduction of these data resources and their characteristics is used for the design of an experiment. The description of the construction of a database can also be a good advisor for the creation of a new one. There are free available databases and commercial virtual libraries of molecules. Furthermore, a computational chemist can find databases for a general purpose or a specific subset such as natural products (NPs). In this chapter, NP database resources are presented, along with some guidelines when preparing an NP database for drug discovery purposes.

Learning Fuzzy Network Using Sequence Bound Global Particle Swarm Optimizer

2012 ◽  
Vol 2 (1) ◽  
pp. 54-70 ◽  
Author(s):  
Satchidananda Dehuri ◽  
Sung-Bae Cho
Keyword(s):  
Radial Basis Function Network ◽  
Experimental Studies ◽  
Particle Swarm ◽  
The Novel ◽  
Multi Layer Perceptron ◽  
Particle Swarm Optimizer ◽  
Output Layer ◽  
Hidden Layer ◽  
Original Domain ◽  
Fuzzy Network

This paper proposes an algorithm for classification by learning fuzzy network with a sequence bound global particle swarm optimizer. The aim of this work can be achieved in two folded. Fold one provides an explicit mapping of an input features from original domain to fuzzy domain with a multiple fuzzy sets and the second fold discusses the novel sequence bound global particle swarm optimizer for evolution of optimal set of connection weights between hidden layer and output layer of the fuzzy network. The novel sequence bound global particle swarm optimizer can solve the problem of premature convergence when learning the fuzzy network plagued with many local optimal solutions. Unlike multi-layer perceptron with many hidden layers it has only single hidden layer. The output layer of this network contains one neuron. This network advocates a simple and understandable architecture for classification. The experimental studies show that the classification accuracy of the proposed algorithm is promising and superior to other alternatives such as multi-layer perceptron and radial basis function network.

Applying of Neural Networks for Testing of Tracers with using of Empirical Data

2019 ◽  
Vol 218 (3) ◽  
pp. 5-23
Author(s):  
Dariusz Ampuła
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Test Results ◽  
Test Methodology ◽  
Laboratory Test Results ◽  
Depth Analysis ◽  
Evaluation Module ◽  
Artificial Neural ◽  
Marine Climate ◽  
Over 40 Years

Abstract An attempt of designing artificial neural networks for empirical laboratory test results tracers No. 5, No. 7 and No. 8 was introduced in the article. These tracers are applied in cartridges with calibres from 37 mm to 122 mm which are still used and stored both in the marine climate and land. The results of laboratory tests of tracers in the field of over 40 years of tests have been analysed. They have been properly prepared in accordance with the requirements that are necessary to design of neural networks. Only the evaluation module of these tracers was evaluated, because this element of tests, fulfilled the necessary assumptions needed to build artificial neural networks. Several hundred artificial neural networks have been built for each type of analysed tracers. After an in-depth analysis of received results, it was chosen one the best neural network, the main parameters of which were described and discussed in the article. Received results of working built of neural networks were compared with previously functioning manual evaluation module of these tracers. On the basis conducted analyses, proposed the modification of functioning test methodology by replacing the previous manual evaluation modules through elaborated automatic models of artificial neural networks. Artificial neural networks have a very important feature, namely they are used in the prediction of specific output data. This feature successfully used in diagnostic tests of other elements of ammunition.

Image analysis and multi-layer perceptron artificial neural networks for the discrimination between benign and malignant endometrial lesions

2017 ◽  
Vol 45 (3) ◽  
pp. 202-211 ◽  
Author(s):  
Georgios-Marios Makris ◽  
Abraham Pouliakis ◽  
Charalampos Siristatidis ◽  
Niki Margari ◽  
Emmanouil Terzakis ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Artificial Neural Networks ◽  
Multi Layer Perceptron ◽  
Artificial Neural

Application of self-organising maps and multi-layer perceptron-artificial neural networks for streamflow and water level forecasting in data-poor catchments: the case of the Lower Shire floodplain, Malawi

2014 ◽  
Vol 45 (6) ◽  
pp. 838-854 ◽  
Author(s):  
F. D. Mwale ◽  
A. J. Adeloye ◽  
R. Rustum
Keyword(s):  
Neural Networks ◽  
Developing Countries ◽  
Artificial Neural Networks ◽  
Water Level ◽  
Lead Time ◽  
Flood Forecasting ◽  
Multi Layer Perceptron ◽  
Raw Data ◽  
Artificial Neural ◽  
Forecasting And Warning

With a paradigm shift from flood protection to flood risk management that emphasises learning to live with the floods, flood forecasting and warning have received more attention in recent times. However, for developing countries, the lack of adequate and good quality data to support traditional hydrological modelling for flood forecasting and warning poses a big challenge. While there has been increasing attention worldwide towards data-driven models, their application in developing countries has been limited. A combination of self-organising maps (SOM) and multi-layer perceptron artificial neural networks (MLP-ANN) is applied to the Lower Shire floodplain of Malawi for flow and water level forecasting. The SOM was used to extract features from the raw data, which then formed the basis of infilling the gap-riddled data to provide more complete and much longer records that enhanced predictions. The MLP-ANN was used for the forecasting, using alternately the SOM features and the infilled raw data. Very satisfactory forecasts were obtained with the latter for up to 2-day lead time, with both the Nash–Sutcliffe index and coefficient of correlation being in excess of 0.9. When SOM features were used, however, the lead time for very satisfactory forecasts increased to 5 days.

scholarly journals High-Performance Tracking for Piezoelectric Actuators Using Super-Twisting Algorithm Based on Artificial Neural Networks

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 244
Author(s):  
Cristian Napole ◽  
Oscar Barambones ◽  
Mohamed Derbeli ◽  
Isidro Calvo ◽  
Mohammed Yousri Silaa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
High Performance ◽  
Piezoelectric Actuators ◽  
The Novel ◽  
Tracking Accuracy ◽  
Novel Structure ◽  
Novel Approach ◽  
Artificial Neural ◽  
Twisting Algorithm

Piezoelectric actuators (PEA) are frequently employed in applications where nano-Micr-odisplacement is required because of their high-precision performance. However, the positioning is affected substantially by the hysteresis which resembles in an nonlinear effect. In addition, hysteresis mathematical models own deficiencies that can influence on the reference following performance. The objective of this study was to enhance the tracking accuracy of a commercial PEA stack actuator with the implementation of a novel approach which consists in the use of a Super-Twisting Algorithm (STA) combined with artificial neural networks (ANN). A Lyapunov stability proof is bestowed to explain the theoretical solution. Experimental results of the proposed method were compared with a proportional-integral-derivative (PID) controller. The outcomes in a real PEA reported that the novel structure is stable as it was proved theoretically, and the experiments provided a significant error reduction in contrast with the PID.

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