We studied the machine learning models for high-refractive organic compounds, based on literature data. Although, the prediction data by the model were well toward common organic compounds, that of the high-refractive compounds were not accord with experimental data, due to the few literature data of high-refractive compounds. Therefore, we created data for 62 high-refractive compounds using high-throughput quantum chemical calculations and performed the transfer learning based on the literature data model. The obtained transfer learning model showed well results for selected high-refractive organic compounds.
Epoxy resins are one of the functional materials used as paints, adhesives, and the like. The hardeners used in resin synthesis have a great impact on resins in terms of reactivity and physical properties, so the selection of hardener is very important. However, once curing reaction starts, it is difficult to experimentally analyze that reactivity and the like. In this study, we investigated the reaction mechanism of the curing reaction of epoxy-imidazole resin with imidazole as a hardener using density functional theory calculations. From the calculation results, it was clarified that the epoxy-imidazole curing reaction proceeds through a five-step reaction pathway with the reaction substrate and the imidazole formed during reaction the reaction serving as nucleophilic species. The active species in this reaction is the imidazole anion, and by generating that, the ring-opening reaction of the epoxide with low activation free energy proceeds repeatedly
Chemical graphs are utilized to predict various physical properties of molecules. A molecule can be represented as an undirected, labeled graph in which atoms are nodes and bonds are the edges of the graph. In this paper, we defined two indexes for a molecule called HG1 and HG2 based on the variance of elements in the eigenvector corresponding to the highest eigenvalue of the adjacency matrix of the molecular graph. We calculated and examined HG1 and HG2 of a huge number of natural products listed in KNApSAcK database. Heterogeneities of molecules can be assessed based on HG1 and HG2 but HG1 is more suitable for this purpose.
Terpenoids, phenylpropanoids, and polyketides are the majority of the secondary metabolites containing carbon, hydrogen, and oxygen. In this work, 19,769 metabolites accumulated in KNApSAcK Core DB were classified into 71 subgroups comprising three major groups (terpenoids, phenylpropanoids, and polyketides) according to scientific literatures. We represented the metabolites as molecular fingerprint including chemical properties, and used those descriptors for classification by random forest model. We found that both training and test metabolites were well classified into the subgroups, with 94.06 %, and 94.23 % accuracy, respectively. Though classification of metabolites based on metabolic pathways is very time-consuming works, machine learnings with molecular fingerprint made it possible to attain the classification. This work will lead a light for systematical and evolutional understanding of diverged secondary metabolites based on secondary metabolic pathways. Data science is an interdisciplinary and applied field that uses techniques and theories drawn from statistics, mathematics, computer science, and information science. Combining these resources data science enables extracting meaningful and practical insights for secondary metabolites.
Deep generative models can virtually generate chemical structures with desired properties. These models are widely used in de -novo molecular design projects, and are becoming an alternative to conventional approaches to chemical structure generation. Although the usefulness of the generative models has already been proven in retrospective validations: using an already known data set, deployment of the generative models in applications has not been frequently reported. Herein, several research articles are surveyed where deep generative models are employed in de novo molecular design projects to clarify the usage of the generative models for successful de novo design.
