On-Line Data Processing, Simulation and Forecasting of the Coronavirus Disease (COVID-19) Propagation in Ukraine Based on Machine Learning Approach

The acid dissociation constant pKa dictates a molecule’s ionic status, and is a critical physicochemical property in rationalizing acid-base chemistry in solution and in many biological contexts. Although numerous theoretic approaches have been developed for predicating aqueous pKa, fast and accurate prediction of non-aqueous pKas has remained a major challenge. On the basis of iBonD experimental pKa database curated across 39 solvents, a holistic pKa prediction model was established by using machine learning approach. Structural and physical organic parameters combined descriptors (SPOC) were introduced to represent the electronic and structural features of molecules. With SPOC and ionic status labelling (ISL), the holistic models trained with neural network or XGBoost algorithm showed the best prediction performance <a>with MAE value as low as 0.87</a> pKa unit. The holistic model showed better performance than all the tested single-solvent models (SSMs), verifying the transfer learning features. The capability of prediction in diverse solvents allows for a comprehensive mapping of all the possible pKa correlations between different solvents. The iBonD holistic model was validated by prediction of aqueous pKa and micro-pKa of pharmaceutical molecules and pKas of organocatalysts in DMSO and MeCN with high accuracy. An on-line prediction platform (<a href="http://pka.luoszgroup.com/">http://pka.luoszgroup.com</a>) was constructed based on the current model.

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Optical system for on line monitoring of welding: a machine learning approach for optimal set up

ACTA IMEKO ◽

10.21014/acta_imeko.v5i4.420 ◽

2016 ◽

Vol 5 (4) ◽

pp. 4 ◽

Cited By ~ 1

Author(s):

David Di Gasbarro ◽

Giulio D'Emilia ◽

Emanuela Natale

Keyword(s):

Machine Learning ◽

Vision System ◽

Low Cost ◽

Laser Sheet ◽

Measuring System ◽

P Value ◽

Learning Approach ◽

Optical Measuring ◽

Machine Learning Approach ◽

On Line

In this paper a methodology is described for continuous checking of the settings of a low cost vision system for automatic geometrical measurement of welding embedded on components of complicated shape. The measurement system is based on a laser sheet. Measuring conditions and the corresponding uncertainty are analyzed by evaluating their p-value and its closeness to an optimal measurement configuration also when working conditions are changed. The method aims to check the holding of optimal measuring conditions by using a machine learning approach for the vision system: based on a such methodology single images can be used to check the settings, therefore allowing a continuous and on line monitoring of the optical measuring system capabilities.According to this procedure, the optical measuring system is able to reach and to hold uncertainty levels adequate for automatic dimensional checking of welding and of defects, taking into account the effects of system hardware/software incorrect settings and environmental effects, like varying lighting conditions. The paper also studies the effects of process variability on the method for quantitative evaluation, in order to propose on line solutions for this system.

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Holistic Prediction of pKa in Diverse Solvents Based on Machine Learning Approach

10.26434/chemrxiv.12421082 ◽

2020 ◽

Author(s):

Qi Yang ◽

Yao Li ◽

Jin-Dong Yang ◽

Yidi Liu ◽

Long Zhang ◽

...

Keyword(s):

Machine Learning ◽

Current Model ◽

Structural Features ◽

Acid Dissociation ◽

Learning Approach ◽

Acid Dissociation Constant ◽

Holistic Model ◽

Solvent Models ◽

Machine Learning Approach ◽

On Line

The acid dissociation constant pKa dictates a molecule’s ionic status, and is a critical physicochemical property in rationalizing acid-base chemistry in solution and in many biological contexts. Although numerous theoretic approaches have been developed for predicating aqueous pKa, fast and accurate prediction of non-aqueous pKas has remained a major challenge. On the basis of iBonD experimental pKa database curated across 39 solvents, a holistic pKa prediction model was established by using machine learning approach. Structural and physical organic parameters combined descriptors (SPOC) were introduced to represent the electronic and structural features of molecules. With SPOC and ionic status labelling (ISL), the holistic models trained with neural network or XGBoost algorithm showed the best prediction performance <a>with MAE value as low as 0.87</a> pKa unit. The holistic model showed better performance than all the tested single-solvent models (SSMs), verifying the transfer learning features. The capability of prediction in diverse solvents allows for a comprehensive mapping of all the possible pKa correlations between different solvents. The iBonD holistic model was validated by prediction of aqueous pKa and micro-pKa of pharmaceutical molecules and pKas of organocatalysts in DMSO and MeCN with high accuracy. An on-line prediction platform (<a href="http://pka.luoszgroup.com/">http://pka.luoszgroup.com</a>) was constructed based on the current model.

Download Full-text