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.

In recent years, competition in organic photovoltaic cells (OPVs) performance improvement and organic semiconductor development has intensified. In response, there has been an upsurge in the development of predictive models for OPV performance utilizing machine learning. Until now, chemistry researchers have used various approaches when creating OPV cells as well as developing new materials to improve power conversion efficiency (PCE). However, not many of those original approaches have been used for performance prediction due to the small sample size. In this study, we conducted Data-science approach where we collected information from 115 scientific literatures and constructed a dataset with the addition of some new proposed variables to describe the structure and material composition of the active layer. This allows us to use 25 variables to describe OPVs in which the active layer forms a 1~3-level structure (1-layer, two- tiered and three-tiered). Proposed work also includes post-processing and measurement data that have not been addressed in existing studies. Several regression models were constructed with coefficients of determination exceeding 0.9 by supervised learning methods (random forest (RF), monmlp, etc.) using this data.

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.

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.

Proteolytic cleavage is influenced by the physicochemical properties of amino acids surrounding the cleavage site. Among these properties are 553 amino acid indices, and we considered that combining these indices with machine learning could create QSAR models for protease activity. In this study, we focused on γ-secretase, an enzyme known to be involved in the pathogenesis of Alzheimer’s disease. We created 10,680 regression models for the protease activity of γ-secretase by using 10 amino acid indices compressed from the 553 amino acid indices through principal component analysis, 12 pocket models of protease binding sites, and 89 machine learning models. We used these regression models to predict cleavage sites for 23 substrates where the cleavage sites were known and examined the amino acid property information used in the model with the highest prediction accuracy (87.0%). We found that the amino acid property information used in this model was related to the secondary structure of proteins, which may imply that it contains important information on the transmembrane cleavage of γ-secretase.

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.