scholarly journals Greedy 3-Point Search (G3PS)—A Novel Algorithm for Pharmacophore Alignment

Molecules ◽  
2021 ◽  
Vol 26 (23) ◽  
pp. 7201
Author(s):  
Christian Permann ◽  
Thomas Seidel ◽  
Thierry Langer
Keyword(s):  
Virtual Screening ◽  
False Negative ◽  
Three Dimensional ◽  
False Negative Rate ◽  
Alignment Algorithm ◽  
Screening Experiments ◽  
Point Search ◽  
Optimal Alignments ◽  
Pharmacophore Models ◽  
Feature Pattern

Chemical features of small molecules can be abstracted to 3D pharmacophore models, which are easy to generate, interpret, and adapt by medicinal chemists. Three-dimensional pharmacophores can be used to efficiently match and align molecules according to their chemical feature pattern, which facilitates the virtual screening of even large compound databases. Existing alignment methods, used in computational drug discovery and bio-activity prediction, are often not suitable for finding matches between pharmacophores accurately as they purely aim to minimize RMSD or maximize volume overlap, when the actual goal is to match as many features as possible within the positional tolerances of the pharmacophore features. As a consequence, the obtained alignment results are often suboptimal in terms of the number of geometrically matched feature pairs, which increases the false-negative rate, thus negatively affecting the outcome of virtual screening experiments. We addressed this issue by introducing a new alignment algorithm, Greedy 3-Point Search (G3PS), which aims at finding optimal alignments by using a matching-feature-pair maximizing search strategy while at the same time being faster than competing methods.

scholarly journals Three-dimensional camera anthropometry to assess risk of cephalopelvic disproportion-related obstructed labour in Ethiopia

2019 ◽  
Vol 9 (5) ◽  
pp. 20190036 ◽  
Author(s):  
Lorenzo Tolentino ◽  
Mahlet Yigeremu ◽  
Sisay Teklu ◽  
Shehab Attia ◽  
Michael Weiler ◽  
...  
Keyword(s):  
False Negative ◽  
Three Dimensional ◽  
False Negative Rate ◽  
Detection Rates ◽  
Cephalopelvic Disproportion ◽  
Obstructed Labour ◽  
Tape Measure ◽  
Negative Rate ◽  
Positive Rate ◽  
Tape Measurement

Cephalopelvic disproportion (CPD)-related obstructed labour requires delivery via Caesarean section (C/S); however, in low-resource settings around the world, facilities with C/S capabilities are often far away. This paper reports three low-cost tools to assess the risk of CPD, well before labour, to provide adequate time for referral and planning for delivery. We performed tape measurement- and three-dimensional (3D) camera-based anthropometry, using two 3D cameras (Kinect and Structure) on primigravida, gestational age ≥ 36 weeks, from Addis Ababa, Ethiopia. Novel risk scores were developed and tested to identify models with the highest predicted area under the receiver-operator characteristic curve (AUC), detection rate (true positive rate at a 5% false-positive rate, FPR) and triage rate (true negative rate at a 0% false-negative rate). For tape measure, Kinect and Structure, the detection rates were 53%, 61% and 64% (at 5% FPR), the triage rates were 30%, 56% and 63%, and the AUCs were 0.871, 0.908 and 0.918, respectively. Detection rates were 77%, 80% and 84% at the maximum J -statistic, which corresponded to FPRs of 10%, 15% and 11%, respectively, for tape measure, Kinect and Structure. Thus, tape measurement anthropometry was a very good predictor and Kinect and Structure anthropometry were excellent predictors of CPD risk.

scholarly journals Virtual Screening Using Pharmacophore Models Retrieved from Molecular Dynamic Simulations

2019 ◽  
Vol 20 (23) ◽  
pp. 5834 ◽  
Author(s):  
Pavel Polishchuk ◽  
Alina Kutlushina ◽  
Dayana Bashirova ◽  
Olena Mokshyna ◽  
Timur Madzhidov
Keyword(s):  
Virtual Screening ◽  
Molecular Dynamic ◽  
Complex Structure ◽  
Three Dimensional ◽  
Cyclin Dependent Kinase ◽  
Dynamic Simulations ◽  
Crystal Complex ◽  
Pharmacophore Models ◽  
Selection And Ranking ◽  
Selection Of

Pharmacophore models are widely used for the identification of promising primary hits in compound large libraries. Recent studies have demonstrated that pharmacophores retrieved from protein-ligand molecular dynamic trajectories outperform pharmacophores retrieved from a single crystal complex structure. However, the number of retrieved pharmacophores can be enormous, thus, making it computationally inefficient to use all of them for virtual screening. In this study, we proposed selection of distinct representative pharmacophores by the removal of pharmacophores with identical three-dimensional (3D) pharmacophore hashes. We also proposed a new conformer coverage approach in order to rank compounds using all representative pharmacophores. Our results for four cyclin-dependent kinase 2 (CDK2) complexes with different ligands demonstrated that the proposed selection and ranking approaches outperformed the previously described common hits approach. We also demonstrated that ranking, based on averaged predicted scores obtained from different complexes, can outperform ranking based on scores from an individual complex. All developments were implemented in open-source software pharmd.

scholarly journals D3Similarity: A Ligand-Based Approach for Predicting Drug Targets and for Virtual Screening of Active Compounds Against COVID-19

2020 ◽  
Author(s):  
Zhengdan Zhu ◽  
Xiaoyu Wang ◽  
Yanqing Yang ◽  
Xinben Zhang ◽  
Kaijie Mu ◽  
...  
Keyword(s):  
Protein Structure ◽  
Virtual Screening ◽  
Drug Targets ◽  
False Negative ◽  
Three Dimensional ◽  
Target Prediction ◽  
Molecular Structures ◽  
Bioactive Molecules ◽  
Active Compounds ◽  
Target Proteins

<p>Discovering efficient drugs and identifying target proteins are still an unmet but urgent need for curing COVID-19. Protein structure based docking is a widely applied approach for discovering active compounds against drug targets and for predicting potential targets of active compounds. However, this approach has its inherent deficiency caused by, e.g., various different conformations with largely varied binding pockets adopted by proteins, or the lack of true target proteins in the database. This deficiency may result in false negative results. As a complementary approach to the protein structure based platform for COVID-19, termed as D3Docking in our recent work, we developed the ligand-based method, named D3Similarity, which is based on the molecular similarity evaluation between the submitted molecule(s) and those in an active compound database. The database is constituted by all the reported bioactive molecules against the coronaviruses SARS, MERS and SARS-CoV-2, some of which have target or mechanism information but some don’t. Based on the two-dimensional and three-dimensional similarity evaluation of molecular structures, virtual screening and target prediction could be performed according to similarity ranking results. With two examples, we demonstrated the reliability and efficiency of D3Similarity for drug discovery and target prediction against COVID-19. D3Similarity is available free of charge at <a href="https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php">https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php</a>.</p>

scholarly journals Accuracy of Healthcare Professionals’ Nasopharyngeal Swab Technique in SARS-CoV-2 Specimen Collection

2020 ◽  
Author(s):  
Robbie S. R. Woods ◽  
Kellie Nwaokorie ◽  
Jana Crowley ◽  
Michael Walsh ◽  
Eoghan de Barra ◽  
...  
Keyword(s):  
Healthcare Professionals ◽  
False Negative ◽  
Three Dimensional ◽  
False Negative Rate ◽  
Nasopharyngeal Swab ◽  
Three Dimensional Model ◽  
Tertiary Referral Centre ◽  
Specimen Collection ◽  
A Prospective Study ◽  
Novel Design

AbstractBackgroundThe COVID-19 pandemic has caused huge pressure on healthcare systems worldwide. Public health measures to control the virus are reliant on testing, including appropriate collection of specimens for analysis.MethodsA prospective study of nasopharyngeal swab technique by staff in an academic tertiary referral centre was carried out. Nasopharyngeal swab technique was evaluated by a novel design of a navigated swab on a three-dimensional model head.ResultsSwab technique of 228 participants was assessed. Technique was poor, with a success rate of nasopharyngeal swabbing at 38.6%. Angle and length of insertion were significantly different between those with successful and unsuccessful technique. Doctors were significantly more accurate than nurses and non-healthcare professionals (p<0.01).ConclusionInaccurate specimen collection from poor swab technique could contribute to a false negative rate of testing for SARS-CoV-2. Specific training in nasopharyngeal anatomy and swab technique may improve the accuracy of nasopharyngeal swabbing.

scholarly journals Optimized Double-Regional Filtering Algorithm on MRI Three-Dimensional Reconstructed Images for the Evaluation of Effects of Delivery on the Pelvis of Primiparas

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Qiong Jin ◽  
Mian Huang ◽  
Jun Lin ◽  
Shansan Wu ◽  
Zhang Shen ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
False Positive Rate ◽  
False Negative ◽  
Three Dimensional ◽  
False Negative Rate ◽  
Galois Field ◽  
Pelvic Organ ◽  
Control Group ◽  
Nonlocal Means ◽  
Positive Rate

This study was to explore the denoising and segmentation effect of dual-domain image denoising (DDID) algorithm, and the Galois field (GF) and nonlocal means (NLM) algorithms were introduced for comparative analysis. 40 primiparas in the hospital from January 2018 to January 2020 were divided into an experimental group (caesarean section (CS), group E) and a control group (vaginal delivery (VD), group C). The peak signal-to-noise ratio (PSNR) and segmentation parameters of DDID algorithm were compared with GF algorithm and NLM algorithm. It was found that the DDID showed higher overall accuracy (OA) and lower false positive rate (FPR) and false negative rate (FNR). The PSNR of DDID was higher than the other two algorithms. GF algorithm showed the highest edge retention index (ERI). The incidence of pelvic organ prolapse (POP) in group E and group C was 9/20 (45%) and 5/20 (25%), respectively, with extreme difference ( P < 0.05 ). Evaluation of the effects of delivery on the pelvis of primiparas with MRI three-dimensional (3D) reconstructed images based on the optimized DDID showed a superior and stable denoising effect and good segmentation, so it was worthy of clinical promotion and application.

scholarly journals Error Rates and Powers in Genome-Scale RNAi Screens

2009 ◽  
Vol 14 (3) ◽  
pp. 230-238 ◽  
Author(s):  
Xiaohua Douglas Zhang ◽  
Shane D. Marine ◽  
Marc Ferrer
Keyword(s):  
High Throughput Screening ◽  
Control Method ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Error Rates ◽  
Type I ◽  
Small Interfering Rnas ◽  
Screening Experiments ◽  
Genome Scale

For hit selection in genome-scale RNAi research, we do not want to miss small interfering RNAs (siRNAs) with large effects; meanwhile, we do not want to include siRNAs with small or no effects in the list of selected hits. There is a strong need to control both the false-negative rate (FNR), in which the siRNAs with large effects are not selected as hits, and the restricted false-positive rate (RFPR), in which the siRNAs with no or small effects are selected as hits. An error control method based on strictly standardized mean difference (SSMD) has been proposed to maintain a flexible and balanced control of FNR and RFPR. In this article, the authors illustrate how to maintain a balanced control of both FNR and RFPR using the plot of error rate versus SSMD as well as how to keep high powers using the plot of power versus SSMD in RNAi high-throughput screening experiments. There are relationships among FNR, RFPR, Type I and II errors, and power. Understanding the differences and links among these concepts is essential for people to use statistical terminology correctly and effectively for data analysis in genome-scale RNAi screens. Here the authors explore these differences and links. (Journal of Biomolecular Screening 2009:230-238)

scholarly journals D3Similarity: A Ligand-Based Approach for Predicting Drug Targets and for Virtual Screening of Active Compounds Against COVID-19

2020 ◽  
Author(s):  
Zhengdan Zhu ◽  
Xiaoyu Wang ◽  
Yanqing Yang ◽  
Xinben Zhang ◽  
Kaijie Mu ◽  
...  
Keyword(s):  
Protein Structure ◽  
Virtual Screening ◽  
Drug Targets ◽  
False Negative ◽  
Three Dimensional ◽  
Target Prediction ◽  
Molecular Structures ◽  
Bioactive Molecules ◽  
Active Compounds ◽  
Target Proteins

<p>Discovering efficient drugs and identifying target proteins are still an unmet but urgent need for curing COVID-19. Protein structure based docking is a widely applied approach for discovering active compounds against drug targets and for predicting potential targets of active compounds. However, this approach has its inherent deficiency caused by, e.g., various different conformations with largely varied binding pockets adopted by proteins, or the lack of true target proteins in the database. This deficiency may result in false negative results. As a complementary approach to the protein structure based platform for COVID-19, termed as D3Docking in our recent work, we developed the ligand-based method, named D3Similarity, which is based on the molecular similarity evaluation between the submitted molecule(s) and those in an active compound database. The database is constituted by all the reported bioactive molecules against the coronaviruses SARS, MERS and SARS-CoV-2, some of which have target or mechanism information but some don’t. Based on the two-dimensional and three-dimensional similarity evaluation of molecular structures, virtual screening and target prediction could be performed according to similarity ranking results. With two examples, we demonstrated the reliability and efficiency of D3Similarity for drug discovery and target prediction against COVID-19. D3Similarity is available free of charge at <a href="https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php">https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php</a>.</p>

scholarly journals Machine learning classifies predictive kinematic features in a mouse model of neurodegeneration

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruyi Huang ◽  
Ali A. Nikooyan ◽  
Bo Xu ◽  
M. Selvan Joseph ◽  
Hamidreza Ghasemi Damavandi ◽  
...  
Keyword(s):  
False Negative ◽  
Three Dimensional ◽  
False Negative Rate ◽  
Motor Deficits ◽  
Wild Type ◽  
Ranking Algorithms ◽  
Trunk Tilt ◽  
Leave One Out ◽  
Gait Disturbances ◽  
The Impact

AbstractMotor deficits are observed in Alzheimer’s disease (AD) prior to the appearance of cognitive symptoms. To investigate the role of amyloid proteins in gait disturbances, we characterized locomotion in APP-overexpressing transgenic J20 mice. We used three-dimensional motion capture to characterize quadrupedal locomotion on a treadmill in J20 and wild-type mice. Sixteen J20 mice and fifteen wild-type mice were studied at two ages (4- and 13-month). A random forest (RF) classification algorithm discriminated between the genotypes within each age group using a leave-one-out cross-validation. The balanced accuracy of the RF classification was 92.3 ± 5.2% and 93.3 ± 4.5% as well as False Negative Rate (FNR) of 0.0 ± 0.0% and 0.0 ± 0.0% for the 4-month and 13-month groups, respectively. Feature ranking algorithms identified kinematic features that when considered simultaneously, achieved high genotype classification accuracy. The identified features demonstrated an age-specific kinematic profile of the impact of APP-overexpression. Trunk tilt and unstable hip movement patterns were important in classifying the 4-month J20 mice, whereas patterns of shoulder and iliac crest movement were critical for classifying 13-month J20 mice. Examining multiple kinematic features of gait simultaneously could also be developed to classify motor disorders in humans.

Clinically Meaningful Change

Methodology ◽  
2019 ◽  
Vol 15 (3) ◽  
pp. 97-105
Author(s):  
Rodrigo Ferrer ◽  
Antonio Pardo
Keyword(s):  
Effect Size ◽  
False Negative ◽  
False Negative Rate ◽  
Point Of View ◽  
Skewed Distribution ◽  
Effect Sizes ◽  
False Negatives ◽  
Large Size ◽  
Before And After ◽  
Post Test

Abstract. In a recent paper, Ferrer and Pardo (2014) tested several distribution-based methods designed to assess when test scores obtained before and after an intervention reflect a statistically reliable change. However, we still do not know how these methods perform from the point of view of false negatives. For this purpose, we have simulated change scenarios (different effect sizes in a pre-post-test design) with distributions of different shapes and with different sample sizes. For each simulated scenario, we generated 1,000 samples. In each sample, we recorded the false-negative rate of the five distribution-based methods with the best performance from the point of view of the false positives. Our results have revealed unacceptable rates of false negatives even with effects of very large size, starting from 31.8% in an optimistic scenario (effect size of 2.0 and a normal distribution) to 99.9% in the worst scenario (effect size of 0.2 and a highly skewed distribution). Therefore, our results suggest that the widely used distribution-based methods must be applied with caution in a clinical context, because they need huge effect sizes to detect a true change. However, we made some considerations regarding the effect size and the cut-off points commonly used which allow us to be more precise in our estimates.

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