Identifying a novel anticancer agent with microtubule-stabilizing effects through computational cell-based bioactivity prediction models and bioassays

2019 ◽  
Vol 17 (6) ◽  
pp. 1519-1530 ◽  
Author(s):  
Yao Luo ◽  
Ranran Zeng ◽  
Qingqing Guo ◽  
Jianrong Xu ◽  
Xiaoou Sun ◽  
...  
Keyword(s):  
Anticancer Agent ◽  
Prediction Models ◽  
Computational Cell ◽  
Bioactivity Prediction

G03 is a novel anticancer agent with unusual microtubule-stabilizing effects.

Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods

2020 ◽  
Vol 26 (33) ◽  
pp. 4195-4205
Author(s):  
Xiaoyu Ding ◽  
Chen Cui ◽  
Dingyan Wang ◽  
Jihui Zhao ◽  
Mingyue Zheng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Lead Optimization ◽  
Consensus Method ◽  
Molecular Pair ◽  
Bioactivity Prediction ◽  
Compound Synthesis ◽  
Consensus Modeling

Background: Enhancing a compound’s biological activity is the central task for lead optimization in small molecules drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity tests. To address the issue, it is highly demanding to develop high quality in silico bioactivity prediction approaches, to prioritize such more active compound derivatives and reduce the trial-and-error process. Methods: Two kinds of bioactivity prediction models based on a large-scale structure-activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA_BR, SR, and SR_BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold. Results: Among seven individual models, Multi single MMS pairs bioactivity prediction model showed the best performance (R2 = 0.828, MAE = 0.406, RMSE = 0.591), and the baseline model (SA) produced the most lower prediction accuracy (R2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R2 = 0.842, MAE = 0.397 and RMSE = 0.563). Conclusion: An accurate prediction model for bioactivity was built with a consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization.

The Therapeutic and Diagnostic Value of Fluorine

2020 ◽  
Vol 4 (1) ◽  
pp. 17-29
Author(s):  
Isma Attique ◽  
Shabbir Hussain ◽  
Muhammad Amjad ◽  
Khalida Nazir ◽  
Muhammad Shahid Nazir
Keyword(s):  
Anticancer Agent ◽  
Chemotherapeutic Agent ◽  
Functional Materials ◽  
Diagnostic Value ◽  
Vital Role ◽  
Malignant Growth ◽  
Medical Field ◽  
Active Nucleus ◽  
Broad Application ◽  
Tracer Atom

Fluorine has a useful positron transmitting isotope and it enjoys broad application in the medical field. It is utilized in fluorinated agents,therapeutic sciences and steroid field. Fluorine incorporation viafluoroalkylation is a useful approach in the development of new functional materials and in drug design. Fluorine also plays its role as an anticancer agent and is a successful chemotherapeutic agent for certain sorts of malignant growth. 5-fluorouracil plays a vital role in the treatment of cancer. 18 Facts as a radio label tracer atom in PET imaging. 19 F has the second most sensitive and stable NMR-active nucleus.

4 Pattern Recognition for Healing Burns to Compute Evidence: Space Syntax and Machine Learning Analysis of Burns Center Karachi

2013 ◽  
Vol 1 (1) ◽  
pp. 13
Author(s):  
Javaria Manzoor Shaikh ◽  
JaeSeung Park
Keyword(s):  
Pattern Recognition ◽  
Prediction Models ◽  
Uv Light ◽  
Healing Process ◽  
Space Syntax ◽  
Depth Map ◽  
Routine Tasks ◽  
Hyper Plane ◽  
First Time ◽  
Learning Analysis

Usually elongated hospitalization is experienced byBurn patients, and the precise forecast of the placement of patientaccording to the healing acceleration has significant consequenceon healthcare supply administration. Substantial amount ofevidence suggest that sun light is essential to burns healing andcould be exceptionally beneficial for burned patients andworkforce in healthcare building. Satisfactory UV sunlight isfundamental for a calculated amount of burn to heal; this delicaterather complex matrix is achieved by applying patternclassification for the first time on the space syntax map of the floorplan and Browder chart of the burned patient. On the basis of thedata determined from this specific healthcare learning technique,nurse can decide the location of the patient on the floor plan, hencepatient safety first is the priority in the routine tasks by staff inhealthcare settings. Whereas insufficient UV light and vitamin Dcan retard healing process, hence this experiment focuses onmachine learning design in which pattern recognition andtechnology supports patient safety as our primary goal. In thisexperiment we lowered the adverse events from 2012- 2013, andnearly missed errors and prevented medical deaths up to 50%lower, as compared to the data of 2005- 2012 before this techniquewas incorporated.In this research paper, three distinctive phases of clinicalsituations are considered—primarily: admission, secondly: acute,and tertiary: post-treatment according to the burn pattern andhealing rate—and be validated by capable AI- origin forecastingtechniques to hypothesis placement prediction models for eachclinical stage with varying percentage of burn i.e. superficialwound, partial thickness or full thickness deep burn. Conclusivelywe proved that the depth of burn is directly proportionate to thedepth of patient’s placement in terms of window distance. Ourfindings support the hypothesis that the windowed wall is mosthealing wall, here fundamental suggestion is support vectormachines: which is most advantageous hyper plane for linearlydivisible patterns for the burns depth as well as the depth map isused.

Bankruptcy Prediction Models: A Bet on Artificial Neural Networks

2012 ◽  
Vol 3 (2) ◽  
pp. 48-50
Author(s):  
Ana Isabel Velasco Fernández ◽  
◽  
Ricardo José Rejas Muslera ◽  
Juan Padilla Fernández-Vega ◽  
María Isabel Cepeda González
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Artificial Neural

The Role of Revascularisation in Patients Undergoing Non-cardiac Surgery

2010 ◽  
Vol 5 (1) ◽  
pp. 104
Author(s):  
Daniel S Menees ◽  
Eric R Bates ◽  
◽  
Keyword(s):  
Cardiac Surgery ◽  
Prediction Models ◽  
Cardiovascular Complications ◽  
Weight Of Evidence ◽  
Beta Blockade ◽  
Cardiac Morbidity ◽  
Increased Risk ◽  
Reduction Strategies ◽  
Artery Disease ◽  
Risk Reduction Strategies

Coronary artery disease (CAD) affects millions of US citizens. As the population ages, an increasing number of people with CAD are undergoing non-cardiac surgery and face significant peri-operative cardiac morbidity and mortality. Risk-prediction models can be used to help identify those patients at increased risk of peri-operative cardiovascular complications. Risk-reduction strategies utilising pharmacotherapy with beta blockade and statins have shown the most promise. Importantly, the benefit of prophylactic coronary revascularisation has not been demonstrated. The weight of evidence suggests reserving either percutaneous or surgical revascularisation in the pre-operative setting for those patients who would otherwise meet independent revascularisation criteria.

A Comparison of Scaling Methods to Obtain Calibrated Probabilities of Activity for Ligand-Target Predictions

2020 ◽  
Author(s):  
Lewis Mervin ◽  
Avid M. Afzal ◽  
Ola Engkvist ◽  
Andreas Bender
Keyword(s):  
Target Prediction ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Protein Target ◽  
Bioactivity Prediction ◽  
Vector Machines ◽  
Scaling Methods ◽  
Data Points ◽  
Compound Target

In the context of bioactivity prediction, the question of how to calibrate a score produced by a machine learning method into reliable probability of binding to a protein target is not yet satisfactorily addressed. In this study, we compared the performance of three such methods, namely Platt Scaling, Isotonic Regression and Venn-ABERS in calibrating prediction scores for ligand-target prediction comprising the Naïve Bayes, Support Vector Machines and Random Forest algorithms with bioactivity data available at AstraZeneca (40 million data points (compound-target pairs) across 2112 targets). Performance was assessed using Stratified Shuffle Split (SSS) and Leave 20% of Scaffolds Out (L20SO) validation.

Enterprise Performance Analysis of the Selected Service Sector by Applying Modern Methods with an Emphasis on the Creation and Application of the Modified Creditworthy Model (MCWM)

2018 ◽  
Vol 9 (17) ◽  
Author(s):  
Erika Onuferová ◽  
Veronika Čabinová
Keyword(s):  
Service Sector ◽  
Prediction Models ◽  
Economic Value ◽  
Value Added ◽  
Financial Indicators ◽  
Tourism Sector ◽  
Future Success ◽  
Financial Growth ◽  
Net Assets ◽  
Third Dimension

The aim of presented paper was to create and subsequently apply the Modified 3D Creditworthy Model (MCWM) of performance reflecting sectoral characteristics and financial specificities of the selected sample of Slovak tour operators over the years 2013 – 2017. The intention of this research study was to implement the key financial indicators and appropriate prediction models into both dimensions of the traditional 2D Creditworthy Model of performance and to supplement its third dimension applying the selected modern assessment methods – the Economic Value Added and the Return On Net Assets as we consider them to be one of the most important indicators of future success and company's financial growth. This modification will help to better identify the current financial position of tour operators and more accurately identify causes that hinder the development of financial performance of the selected sample of enterprises. However, after adjusting the upper and lower quartile averages of a particular industry, this methodology is applicable in the wider context of enterprises, not only those operating in the tourism sector.

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