About
MatSolCa (MSC) is a free access and usefull Materials informatics tool as Solubility Calculater with in-phases deep neural network which is our special method of 3-step DNN.
Prediction Information
Overview of dataset
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No of total compounds
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General 307 low molecular weight compounds
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No of C atoms/compound
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1–9
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M.W.
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32.0–252.7 (ave. 98.6)
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Solubility data
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HSPs (δD, δH, δP), SP & LogP
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Analytical data
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H/C-NMR assignments, density & refractive index
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Molecular data
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Molecular components (7 items) & RDKIT's descriptors (14 items)
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Downlaod
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dataset
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Machine learning
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Algorithm
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in-phases (3-step) deep neural network
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Validation method
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5-division cross-validation
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Hyper parameter selection
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Bayesian optimization method
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R2
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δD:0.81, δH:0.61, δP:0.61, SP:0.58, LogP:0.69
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RMSE
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δD:0.51, δH:3.00, δP:2.16, SP:1.85, LogP:0.59
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MSC in Brief
The solubility, such as HSPs composed three elements (δD, δH, δP), SP, and LogP, is one of important physical parameter for understanding the characteristics of a substance. These solubility parameters have been used in several situations (e.g., materials science, biorefinery, cosmetic chemistry, and drug discovery). Here we've developed a useful and agile materials informatics tool, named MatSolCa (MSC), as solubility calculator created with special DNN model. The tool can calculate solubility parameters of HSPs, SP and LogP from only analytical data, such as 1D-H/C-NMR data, density and refractive index. We have expectations for effective R & D with MSC.
Policy
We just use your input data for solubility calculation.
Copyright license agreement on this webpage must be in accordance with
CC BY-NC.
Contact
Environmental metabolic analysis research team of RIKEN CSRS