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
No of total compounds	General 307 low molecular weight compounds
No of C atoms/compound	1–9
M.W.	32.0–252.7 (ave. 98.6)
Solubility data	HSPs (δD, δH, δP), SP & LogP
Analytical data	H/C-NMR assignments, density & refractive index
Molecular data	Molecular components (7 items) & RDKIT's descriptors (14 items)
Downlaod	dataset
Machine learning
Algorithm	in-phases (3-step) deep neural network
Validation method	5-division cross-validation
Hyper parameter selection	Bayesian optimization method
R2	δD:0.81, δH:0.61, δP:0.61, SP:0.58, LogP:0.69
RMSE	δD:0.51, δH:3.00, δP:2.16, SP:1.85, LogP:0.59

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.

Citation

  Atsushi KUROTANI, Toshifumi KAKIUCHI and Jun KIKUCHI, Solubility prediction from molecular properties and analytical data using an in-phase deep neural network (ip-DNN). ACS Omega, 2021, vol6, 22, 14278-14287

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
  

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