Weighted Tanimoto Extreme Learning Machine with Case Study in Drug Discovery

Czarnecki, Wojciech Marian

doi:10.1109/mci.2015.2437312

Cited by 28 publications

(12 citation statements)

References 37 publications

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“…This new methodology is based on connections between SMARTS (SMiles ARbitrary Target Specification) patterns for finding doublets or triplets of small substructures that constitute a larger fragment. Analyzing these structural moieties, machine learning methods [ 30 , 31 ] are applied to recognize non-typical, activity-specific fragments for a particular target or a group of targets. Key-based substructural fingerprints depict the occurrences of a predefined set of chemical subgroups (keys) [ 32 ] within the target molecule.…”

Section: Introductionmentioning

confidence: 99%

Fingerprint-Based Machine Learning Approach to Identify Potent and Selective 5-HT2BR Ligands

et al. 2018

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The identification of subtype-selective GPCR (G-protein coupled receptor) ligands is a challenging task. In this study, we developed a computational protocol to find compounds with 5-HT2BR versus 5-HT1BR selectivity. Our approach employs the hierarchical combination of machine learning methods, docking, and multiple scoring methods. First, we applied machine learning tools to filter a large database of druglike compounds by the new Neighbouring Substructures Fingerprint (NSFP). This two-dimensional fingerprint contains information on the connectivity of the substructural features of a compound. Preselected subsets of the database were then subjected to docking calculations. The main indicators of compounds’ selectivity were their different interactions with the secondary binding pockets of both target proteins, while binding modes within the orthosteric binding pocket were preserved. The combined methodology of ligand-based and structure-based methods was validated prospectively, resulting in the identification of hits with nanomolar affinity and ten-fold to ten thousand-fold selectivities.

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Section: Introductionmentioning

confidence: 99%

Fingerprint-Based Machine Learning Approach to Identify Potent and Selective 5-HT2BR Ligands

et al. 2018

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“…Thus, the likelihood function becomes The above likelihood function can be minimised using standard ℓ 2 -regularised weighted least squares which gives the following solution Instead of calculating H T ΓH, we can calculate ( γ ⋅ H) T ( γ ⋅ H), where . This technique can speed up the computational time [ 17 ]. This leads to the solution in Eq 12 .…”

Section: Methodsmentioning

confidence: 99%

“…ELM has been applied to protein sequence classification [ 14 – 16 ]. To the best of our knowledge, ELM was first applied to the virtual screening task by [ 17 ] as Weighted Tanimoto ELM (WELM JT ). The algorithm is customised for 2D binary fingerprint descriptor.…”

Section: Introductionmentioning

confidence: 99%

Virtual screening by a new Clustering-based Weighted Similarity Extreme Learning Machine approach

Pasupa

Kudisthalert

2018

PLoS ONE

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Machine learning techniques are becoming popular in virtual screening tasks. One of the powerful machine learning algorithms is Extreme Learning Machine (ELM) which has been applied to many applications and has recently been applied to virtual screening. We propose the Weighted Similarity ELM (WS-ELM) which is based on a single layer feed-forward neural network in a conjunction of 16 different similarity coefficients as activation function in the hidden layer. It is known that the performance of conventional ELM is not robust due to random weight selection in the hidden layer. Thus, we propose a Clustering-based WS-ELM (CWS-ELM) that deterministically assigns weights by utilising clustering algorithms i.e. k-means clustering and support vector clustering. The experiments were conducted on one of the most challenging datasets–Maximum Unbiased Validation Dataset–which contains 17 activity classes carefully selected from PubChem. The proposed algorithms were then compared with other machine learning techniques such as support vector machine, random forest, and similarity searching. The results show that CWS-ELM in conjunction with support vector clustering yields the best performance when utilised together with Sokal/Sneath(1) coefficient. Furthermore, ECFP_6 fingerprint presents the best results in our framework compared to the other types of fingerprints, namely ECFP_4, FCFP_4, and FCFP_6.

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“…The revision of multiple approaches [13,[21][22][23] are considered to establish an innovative method and solution for this research, a modification of approach needs to take place to conform this proposal and solution. The approach is some kind of generalization of the Learning Management System (LMS) approach using the same performance index, which is represented by the mean square error, calculated by comparing the approach output with the target output.…”

Section: Advised Approachmentioning

confidence: 99%

Classification via an Embedded Approach

Rubio

Avila

Melendez

et al. 2017

Designs

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This paper presents the results of an automated volatile organic compound (VOC) classification process implemented by embedding a machine learning algorithm into an Arduino Uno board. An electronic nose prototype is constructed to detect VOCs from three different fruits. The electronic nose is constructed using an array of five tin dioxide (SnO 2 ) gas sensors, an Arduino Uno board used as a data acquisition section, as well as an intelligent classification module by embedding an approach function which receives data signals from the electronic nose. For the intelligent classification module, a training algorithm is also implemented to create the base of a portable, automated, fast-response, and economical electronic nose device. This solution proposes a portable system to identify and classify VOCs without using a personal computer (PC). Results show an acceptable precision for the embedded approach in comparison with the performance of a toolbox used in a PC. This constitutes an embedded solution able to recognize VOCs in a reliable way to create application products for a wide variety of industries, which are able to classify data acquired by an electronic nose, as VOCs. With this proposed and implemented algorithm, a precision of 99% for classification was achieved into the embedded solution.

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Weighted Tanimoto Extreme Learning Machine with Case Study in Drug Discovery

Cited by 28 publications

References 37 publications

Fingerprint-Based Machine Learning Approach to Identify Potent and Selective 5-HT2BR Ligands

Fingerprint-Based Machine Learning Approach to Identify Potent and Selective 5-HT2BR Ligands

Virtual screening by a new Clustering-based Weighted Similarity Extreme Learning Machine approach

Classification via an Embedded Approach

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