Molecular Bioactivity Prediction of HDAC1: Based on Deep Neural Nets

2022 ◽  
pp. 229-240
Author(s):  
Miaomiao Chen ◽  
Shan Li ◽  
Yu Ding ◽  
Hongwei Jin ◽  
Jie Xia
Keyword(s):  
Neural Nets ◽  
Bioactivity Prediction

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.

Advanced Statistical Tools for Improving Yield and Reliability

1999 ◽  
Author(s):  
Richard C. Kittler
Keyword(s):  
Data Mining ◽  
Analysis Data ◽  
Neural Nets ◽  
Future Trends ◽  
Statistical Tools ◽  
Mining Technology ◽  
Explanatory Variables ◽  
Data Coverage ◽  
Statistical Approaches ◽  
Adequate Data

Abstract Analysis of manufacturing data as a tool for failure analysts often meets with roadblocks due to the complex non-linear behaviors of the relationships between failure rates and explanatory variables drawn from process history. The current work describes how the use of a comprehensive engineering database and data mining technology over-comes some of these difficulties and enables new classes of problems to be solved. The characteristics of the database design necessary for adequate data coverage and unit traceability are discussed. Data mining technology is explained and contrasted with traditional statistical approaches as well as those of expert systems, neural nets, and signature analysis. Data mining is applied to a number of common problem scenarios. Finally, future trends in data mining technology relevant to failure analysis are discussed.

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.

Discovery of Some Antiviral Natural products to fight against Novel Corona Virus (SARS-CoV-2) using Insilico approach

2020 ◽  
Vol 23 ◽  
Author(s):  
Ashish Shah ◽  
Vaishali Patel ◽  
Bhumika Parmar
Keyword(s):  
Binding Affinity ◽  
Protein S ◽  
Docking Studies ◽  
Spike Protein ◽  
Enveloped Viruses ◽  
Bioactivity Prediction ◽  
Corona Virus ◽  
Main Protease ◽  
Antiviral Activities ◽  
Deadly Disease

Background: Novel Corona virus is a type of enveloped viruses with a single stranded RNA enclosing helical nucleocapsid. The envelope consists of spikes on the surface which are made up of proteins through which virus enters into human cells. Until now there is no specific drug or vaccine available to treat COVID-19 infection. In this scenario, reposting of drug or active molecules may provide rapid solution to fight against this deadly disease. Objective: We had selected 30 phytoconstituents from the different plants which are reported for antiviral activities against corona virus (CoVs) and performed insilico screening to find out phytoconstituents which have potency to inhibit specific target of novel corona virus. Methods: We had perform molecular docking studies on three different proteins of novel corona virus namely COVID-19 main protease (3CL pro), papain-like protease (PL pro) and spike protein (S) attached to ACE2 binding domain. The screening of the phytoconstituents on the basis of binding affinity compared to standard drugs. The validations of screened compounds were done using ADMET and bioactivity prediction. Results: We had screened five compounds biscoclaurine, norreticuline, amentoflavone, licoricidin and myricetin using insilico approach. All compounds found safe in insilico toxicity studies. Bioactivity prediction reviles that these all compounds may act through protease or enzyme inhibition. Results of compound biscoclaurine norreticuline were more interesting as this biscoclaurine had higher binding affinity for the target 3CLpro and PLpro targets and norreticuline had higher binding affinity for the target PLpro and Spike protein. Conclusion: Our study concludes that these compounds could be further explored rapidly as it may have potential to fight against COVID-19.

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