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.

エポキシ樹脂は、塗料や接着剤などとして用いられる機能性材料の一つである。樹脂合成に用いられる硬化剤は、反応性や物性特性の面で樹脂に大きな影響を与えるため、硬化剤の選択は非常に重要である。硬化剤を様々に変化させて硬化反応を行う場合、反応温度や硬化物の硬度などを実験的に調査することができるが、反応の基盤となる反応機構を明らかにすることは難しい。本研究では、密度汎関数理論計算を用いてイミダゾールを硬化剤として用いたエポキシ－イミダゾール樹脂の硬化反応の反応機構の解析を行った。計算結果より、エポキシ－イミダゾールの硬化反応は、反応基質および反応途中で形成されるイミダゾール類が求核種となって、五段階の反応経路により進行することがわかった。本反応の活性種はイミダゾールアニオンであり、これを発生させることで、活性化自由エネルギーの低いエポキシドの開環反応が繰り返し進行することが明らかとなった。

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.