Computed Property Data Base: CPDB-Development of an Integrated Software Tool for Molecular Design: MolWorks-

Tajima, Sumie; Yagi, T.; Fukuda, Takayuki; Nagashima, Umpei

doi:10.2477/jccj.5.23

Cited by 2 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Correlation of observed and estimated values for the test data of the neural network is shown in Figure 4. That of Joback's method is shown in Figure 5 [9]. The correlation of observed and predicted molecular boiling points by this work is better than the values by Joback's equation.…”

Section: Resultsmentioning

confidence: 63%

Generation of a Novel Equation for Molecular Boiling Point Estimation Using a Neural Network

Tajima¹,

Nagashima

2010

J. Comput. Chem. Jpn.

Self Cite

View full text Add to dashboard Cite

We have developed a novel property estimation equation with a group contribution scheme for molecular properties (boiling points), in the standard condition using a three layers perceptron type neural network and are equipped MolWorks™ with it. 142 groups are newly defined as a set to reproduce the differences of isomers and to realize more accurate predictions than are available with usual methods. 765 data of molecular boiling points are selected for training of the neural network. 953 data were applied to evaluate the efficiency of the equation. The correlation of observed and predicted molecular boiling points by this work is better than the values obtained by Joback's equation. The equation is applicable to estimate a wide thermal range, including high and low temperature regions. Furthermore, the equation well reproduces the differences of boiling points for not only ortho-, meta-, and para-isomers but also for cis-and trans-isomers.

show abstract

Section: Resultsmentioning

confidence: 63%

Generation of a Novel Equation for Molecular Boiling Point Estimation Using a Neural Network

Tajima¹,

Nagashima

2010

J. Comput. Chem. Jpn.

Self Cite

View full text Add to dashboard Cite

show abstract

“…We developed a new descriptor by modifying the Mol-Works software. 30 Table 1 shows the set of the molecular descriptors used in the current study. Newly added descriptors in the current study are the 2nd-17th descriptors in Table 1.…”

Section: Descriptorsmentioning

confidence: 99%

Quantitative analysis of aggregation-solubility relationship by in-silico solubility prediction

Mäshimo¹,

Fukunishi

Orita³

et al. 2010

IJHTS

View full text Add to dashboard Cite

Aggregator (frequent hitter) compounds show non-selective binding activity against any target protein and must be removed from the compound library to reduce false positives in drug screening. A previous study suggested that aggregators show high hydrophobicity. The LogS values of aggregators and non-aggregators were estimated by the artificial neural network (ANN) model, the multi-linear regression (MLR) model, and the partial least squares regression (PLS) models, with the weighted learning (WL) method, and the results showed the same trend. The WL method is weighted on the data of the learning set molecules that are similar to the test molecule and improves the prediction accuracy. Bayesian analysis was applied, revealing a simple relationship between aggregation and solubility. Namely, the molecules with LogS  −5 were non-aggregators. In contrast, most of the molecules with LogS  −5 were aggregators. We also made a simple look-up table of probability of aggregation depending on the molecular weight and the number of hetero-atoms.

show abstract

Computed Property Data Base: CPDB-Development of an Integrated Software Tool for Molecular Design: MolWorks-

Cited by 2 publications

References 2 publications

Generation of a Novel Equation for Molecular Boiling Point Estimation Using a Neural Network

Generation of a Novel Equation for Molecular Boiling Point Estimation Using a Neural Network

Quantitative analysis of aggregation-solubility relationship by in-silico solubility prediction

Contact Info

Product

Resources

About