scholarly journals DeepFrag: An Open-Source Browser App for Deep-Learning Lead Optimization

2021 ◽  
Author(s):  
Harrison Green ◽  
Jacob D. Durrant
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Early Stage ◽  
Molecular Structures ◽  
Lead Optimization ◽  
Third Party ◽  
Link Type ◽  
Version 2.0 ◽  
User Friendly ◽  
Deep Learning Model

1AbstractLead optimization, a critical step in early-stage drug discovery, involves making chemical modifications to a small-molecule ligand to improve its drug-like properties (e.g., binding affinity). We recently developed DeepFrag, a deep-learning model capable of recommending such modifications. Though a powerful hypothesis-generating tool, DeepFrag is currently implemented in Python and so requires a certain degree of computational expertise. To encourage broader adoption, we have created the DeepFrag browser app, which provides a user-friendly graphical user interface that runs the DeepFrag model in users’ web browsers. The browser app does not require users to upload their molecular structures to a third-party server, nor does it require the separate installation of any third-party software. We are hopeful that the app will be a useful tool for both researchers and students. It can be accessed free of charge, without requiring registration, at http://durrantlab.com/deepfrag. The source code is also available at http://git.durrantlab.com/jdurrant/deepfrag-app, released under the terms of the open-source Apache License, Version 2.0.

scholarly journals HistoClean: Open-source Software for Histological Image Pre-processing and Augmentation to Improve Development of Robust Convolutional Neural Networks

2021 ◽  
Author(s):  
Kristopher D McCombe ◽  
Stephanie G Craig ◽  
Amélie Viratham Pulsawatdi ◽  
Javier I Quezada-Marín ◽  
Matthew Hagan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Digital Pathology ◽  
Region Of Interest ◽  
Histological Image ◽  
Cancer Prediction ◽  
New Research ◽  
User Friendly ◽  
Deep Learning Model ◽  
Friendly Graphical User Interface

The growth of digital pathology over the past decade has opened new research pathways and insights in cancer prediction and prognosis. In particular, there has been a surge in deep learning and computer vision techniques to analyse digital images. Common practice in this area is to use image pre-processing and augmentation to prevent bias and overfitting, creating a more robust deep learning model. Herein we introduce HistoClean; user-friendly, graphical user interface that brings together multiple image processing modules into one easy to use toolkit. In this study, we utilise HistoClean to pre-process images for a simple convolutional neural network used to detect stromal maturity, improving the accuracy of the model at a tile, region of interest, and patient level. HistoClean is free and open-source and can be downloaded from the Github repository here: https://github.com/HistoCleanQUB/HistoClean.

scholarly journals Deep Learning to Estimate Cardiac Magnetic Resonance-Derived Left Ventricular Mass

2020 ◽  
Author(s):  
Shaan Khurshid ◽  
Samuel Friedman ◽  
James P. Pirruccello ◽  
Paolo Di Achille ◽  
Nathaniel Diamant ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Open Source ◽  
Pearson Correlation ◽  
Absolute Error ◽  
Learning Model ◽  
Left Ventricular ◽  
Lv Mass ◽  
Deep Learning Model

ABSTRACTBackgroundCardiac magnetic resonance (CMR) is the gold standard for left ventricular hypertrophy (LVH) diagnosis. CMR-derived LV mass can be estimated using proprietary algorithms (e.g., inlineVF), but their accuracy and availability may be limited.ObjectiveTo develop an open-source deep learning model to estimate CMR-derived LV mass.MethodsWithin participants of the UK Biobank prospective cohort undergoing CMR, we trained two convolutional neural networks to estimate LV mass. The first (ML4Hreg) performed regression informed by manually labeled LV mass (available in 5,065 individuals), while the second (ML4Hseg) performed LV segmentation informed by inlineVF contours. We compared ML4Hreg, ML4Hseg, and inlineVF against manually labeled LV mass within an independent holdout set using Pearson correlation and mean absolute error (MAE). We assessed associations between CMR-derived LVH and prevalent cardiovascular disease using logistic regression adjusted for age and sex.ResultsWe generated CMR-derived LV mass estimates within 38,574 individuals. Among 891 individuals in the holdout set, ML4Hseg reproduced manually labeled LV mass more accurately (r=0.864, 95% CI 0.847-0.880; MAE 10.41g, 95% CI 9.82-10.99) than ML4Hreg (r=0.843, 95% CI 0.823-0.861; MAE 10.51, 95% CI 9.86-11.15, p=0.01) and inlineVF (r=0.795, 95% CI 0.770-0.818; MAE 14.30, 95% CI 13.46-11.01, p<0.01). LVH defined using ML4Hseg demonstrated the strongest associations with hypertension (odds ratio 2.76, 95% CI 2.51-3.04), atrial fibrillation (1.75, 95% CI 1.37-2.20), and heart failure (4.53, 95% CI 3.16-6.33).ConclusionsML4Hseg is an open-source deep learning model providing automated quantification of CMR-derived LV mass. Deep learning models characterizing cardiac structure may facilitate broad cardiovascular discovery.

scholarly journals The Diagnosis of Alzheimer’s Disease: An Ensemble Approach

2020 ◽  
Author(s):  
Jingyan Qiu ◽  
Linjian Li ◽  
Yida Liu ◽  
Yingjun Ou ◽  
Yubei Lin
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Large Scale ◽  
Early Stage ◽  
Training Transfer ◽  
Detection And Diagnosis ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Learning Model ◽  
The Brain

Alzheimer’s disease (AD) is one of the most common forms of dementia. The early stage of the disease is defined as Mild Cognitive Impairment (MCI). Recent research results have shown the prospect of combining Magnetic Resonance Imaging (MRI) scanning of the brain and deep learning to diagnose AD. However, the CNN deep learning model requires a large scale of samples for training. Transfer learning is the key to enable a model with high accuracy by using limited data for training. In this paper, DenseNet and Inception V4, which were pre-trained on the ImageNet dataset to obtain initialization values of weights, are, respectively, used for the graphic classification task. The ensemble method is employed to enhance the effectiveness and efficiency of the classification models and the result of different models are eventually processed through probability-based fusion. Our experiments were completely conducted on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) public dataset. Only the ternary classification is made due to a higher demand for medical detection and diagnosis. The accuracies of AD/MCI/Normal Control (NC) of different models are estimated in this paper. The results of the experiments showed that the accuracies of the method achieved a maximum of 92.65%, which is a remarkable outcome compared with the accuracies of the state-of-the-art methods.

scholarly journals SpacePHARER: Sensitive identification of phages from CRISPR spacers in prokaryotic hosts

2020 ◽  
Author(s):  
R. Zhang ◽  
M. Mirdita ◽  
E. Levy Karin ◽  
C. Norroy ◽  
C. Galiez ◽  
...  
Keyword(s):  
Open Source ◽  
Protein Level ◽  
De Novo ◽  
False Positives ◽  
Metagenomic Data ◽  
Command Line ◽  
Link Type ◽  
Host Relationships ◽  
User Friendly

SummarySpacePHARER (CRISPR Spacer Phage-Host Pair Finder) is a sensitive and fast tool for de novo prediction of phage-host relationships via identifying phage genomes that match CRISPR spacers in genomic or metagenomic data. SpacePHARER gains sensitivity by comparing spacers and phages at the protein-level, optimizing its scores for matching very short sequences, and combining evidences from multiple matches, while controlling for false positives. We demonstrate SpacePHARER by searching a comprehensive spacer list against all complete phage genomes.Availability and implementationSpacePHARER is available as an open-source (GPLv3), user-friendly command-line software for Linux and macOS at spacepharer.soedinglab.org.

scholarly journals A deep learning framework to detect Covid-19 disease via chest X-ray and CT scan images

2021 ◽  
Vol 11 (1) ◽  
pp. 844
Author(s):  
Mohammed Y. Kamil
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Early Stage ◽  
Diagnostic Technique ◽  
Disease Diagnosis ◽  
X Ray ◽  
Learning Framework ◽  
Chest X Ray ◽  
Low Sensitivity ◽  
Deep Learning Model

COVID-19 disease has rapidly spread all over the world at the beginning of this year. The hospitals' reports have told that low sensitivity of RT-PCR tests in the infection early stage. At which point, a rapid and accurate diagnostic technique, is needed to detect the Covid-19. CT has been demonstrated to be a successful tool in the diagnosis of disease. A deep learning framework can be developed to aid in evaluating CT exams to provide diagnosis, thus saving time for disease control. In this work, a deep learning model was modified to Covid-19 detection via features extraction from chest X-ray and CT images. Initially, many transfer-learning models have applied and comparison it, then a VGG-19 model was tuned to get the best results that can be adopted in the disease diagnosis. Diagnostic performance was assessed for all models used via the dataset that included 1000 images. The VGG-19 model achieved the highest accuracy of 99%, sensitivity of 97.4%, and specificity of 99.4%. The deep learning and image processing demonstrated high performance in early Covid-19 detection. It shows to be an auxiliary detection way for clinical doctors and thus contribute to the control of the pandemic.

scholarly journals Multi-Level Deep Learning Model for Potato Leaf Disease Recognition

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2064
Author(s):  
Javed Rashid ◽  
Imran Khan ◽  
Ghulam Ali ◽  
Sultan H. Almotiri ◽  
Mohammed A. AlGhamdi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Stage ◽  
Disease Detection ◽  
Potato Leaf ◽  
Crop Species ◽  
Leaf Disease ◽  
Crop Diseases ◽  
Learning Technique ◽  
Multi Level ◽  
Deep Learning Model

Potato leaf disease detection in an early stage is challenging because of variations in crop species, crop diseases symptoms and environmental factors. These factors make it difficult to detect potato leaf diseases in the early stage. Various machine learning techniques have been developed to detect potato leaf diseases. However, the existing methods cannot detect crop species and crop diseases in general because these models are trained and tested on images of plant leaves of a specific region. In this research, a multi-level deep learning model for potato leaf disease recognition has developed. At the first level, it extracts the potato leaves from the potato plant image using the YOLOv5 image segmentation technique. At the second level, a novel deep learning technique has been developed using a convolutional neural network to detect the early blight and late blight potato diseases from potato leaf images. The proposed potato leaf disease detection model was trained and tested on a potato leaf disease dataset. The potato leaf disease dataset contains 4062 images collected from the Central Punjab region of Pakistan. The proposed deep learning technique achieved 99.75% accuracy on the potato leaf disease dataset. The performance of the proposed techniques was also evaluated on the PlantVillage dataset. The proposed technique is also compared with the state-of-the-art models and achieved significantly concerning the accuracy and computational cost.

scholarly journals pdb-tools: a swiss army knife for molecular structures

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1961 ◽  
Author(s):  
João P. G. L. M. Rodrigues ◽  
João M.C. Teixeira ◽  
Mikaël Trellet ◽  
Alexandre M. J. J. Bonvin
Keyword(s):  
Molecular Structure ◽  
Open Source ◽  
Protein Data Bank ◽  
Structure Data ◽  
Data Bank ◽  
Molecular Structures ◽  
Command Line ◽  
Efficient Manner ◽  
Link Type

The pdb-tools are a collection of Python scripts for working with molecular structure data in the Protein Data Bank (PDB) format. They allow users to edit, convert, and validate PDB files, from the command-line, in a simple but efficient manner. The pdb-tools are implemented in Python, without any external dependencies, and are freely available under the open-source Apache License at https://github.com/haddocking/pdb-tools/ and on PyPI.

scholarly journals SurfStamp: 3D Printer Compatible Molecular Surface Representation

2020 ◽  
Author(s):  
Toshiyuki Oda
Keyword(s):  
Open Source ◽  
Source Code ◽  
3D Structure ◽  
Three Dimensional ◽  
Molecular Surface ◽  
3D Printer ◽  
3D Object ◽  
Link Type ◽  
Surface Models ◽  
Version 2.0

AbstractSurfStamp is an application that is used to generate textures for surface models of proteins. The textures contain information about surface residues and the information is drawn directly on the 3D object of the models. This approach is more intuitive than the labeling functions that most three-dimensional (3D) structure viewers use to show residue information. Therefore, the use of this application enables researchers, readers, or audiences to easily determine which residues are contributing the surface they are focusing on.AvailabilityThe application is provided under the open-source Apache License Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0). The application and source code are available from https://github.com/yamule/SurfStamp-public/releases.

A multi-task deep learning neural network for predicting flammability-related properties from molecular structures

2021 ◽  
Author(s):  
Ao Yang ◽  
Yang Su ◽  
Zihao Wang ◽  
Saimeng Jin ◽  
Jingzheng Ren ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Process Design ◽  
Early Stage ◽  
Molecular Structures ◽  
Product And Process ◽  
Deep Learning Neural Network

It is significant that the hazardous properties of chemicals including replacements for banned or restricted products are assessed at an early stage of product and process design. This work proposes...

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