フラグメント分子軌道(fragment molecular orbital; FMO)法は、近年巨大分子系の電子状態計算手法として、注目を集めている。FMO法の特徴として、系を分割したフラグメント間の相互作用エネルギーinter fragment interaction energy (IFIE)が計算でき、応用上IFIEが極めて有用であることが示されている。しかしながら、共有結合しているフラグメント間のIFIEが非常に大きな値（約-15.2 hartree）取るため、共有結合しているフラグメント間（1-2）のIFIE解析を妨げる大きな原因となっている。そこで、この問題を解決するための補正方法として仮想的なフラグメントの解離過程を想定したヘテロ解離補正(J. Comput. Aided Chem. 18, 143 (2017))を提案した。しかしながら、C-C共有結合は、ラジカル開裂することが知られており、さらなる補正方法の改良が必要とされていた。そこで本研究では、前回提案したヘテロ開裂補正法に追加する形で、ラジカル開裂補正法を提案し、検討を行った。

抄録全体を表示

Fluorescent substances are used in a wide range of applications, and the method that effectively design molecules having desirable absorption and emission wavelength is required. In this study, we used boron-dipyrromethene (BODIPY) compounds as a case study, and constructed high precision wavelength prediction model using ensemble learning. Prediction accuracy improved in stacking model using RDKit descriptors and Morgan fingerprint. The variables related to the molecular skeleton and the conjugation length were shown to be important. We also proposed an applicability domain (AD) estimation model that directly use the descriptors based on Tanimoto distance. The performance of the AD models was shown better than the OCSVM-based model. Using our proposed stacking model and AD model, newly generated compounds were screened and we obtained 602 compounds which were estimated inside the AD in both absorption wavelength and emission wavelength.

抄録全体を表示

半経験的分子軌道法を用いて、n-トリデンテンにおけるY字芳香族性の発現を予言する魔法数の有効性を検討した。魔法数と同数のπ電子を含むn-トリデンテンのイオンにおいて、反応論的な安定化と関連するHOMO-LUMOギャップの値が増加する傾向を示唆する計算結果が得られた。また、エネルギー論的観点と反応論的観点の両方から、6-トリデンテンの陰イオンにおいてY字芳香族性が発現する可能性が示唆された。

抄録全体を表示

Prof. Kimito Funatsu received the Honor Award in Division of Chemoinformatics, the Chemical Society of Japan in the 42th Annual Meeting of Chemoinformatics held on Nov. 28th 2019. The awarding recognizes his significant contributions in the development of the cheminformatics discipline in the world as well as in Japan. His research efforts extend over multiple domains such as (i) system development including elucidation of chemical structures and prediction of organic reactions, (ii) quantitative structure activity relationship (QSAR), (iii) quantitative structure property relationship (QSPR), and (iv) international collaborations in chemoinformatics. In the present review, we focus on chemoinformatics in the world as well as in Japan based on “Special issue dedicating to Honor Award: Prof. Kimito Funatsu”, which consists of five invited papers by the world-famous distinguished foreign researchers, and six papers from domestic researchers. Taking these papers into consideration, we try to discuss the meanings of the Honor Award dedicating to Prof. Kimito Funatsu.

抄録全体を表示

On the occasion of honoring Kimito Funatsu with the 2019 Herman Skolnik Award, aspects of similarity, diversity and complexity are mentioned in relation to chemoinformatics, chemometrics, Japan, and personal encounters with the awardee.

抄録全体を表示

The achievements of Professor Kimito Funatsu for the development of chemoinformatics in Japan are briefly summarized. Furthermore, some aspects of the collaboration of this author with Kimito Funatsu are discussed.

抄録全体を表示

Computer-assisted de novo drug design has been a central research topic in the field of chemoinformatics for approximately 30 years. Professor Kimito Funatsu’s research has been a formative component in these developments. His seminal work has contributed inverse quantitative-structure-activity relationship (QSAR) models for small molecule and peptide design. This article highlights a class of recurrent neural networks, so-called long short-term memory (LSTM) networks for generative molecular design, which further the conceptual approach of inverse QSAR. We review the LSTM method for molecular design along with selected practical applications.

抄録全体を表示

Multi-target activity (promiscuity) of small molecules provides the basis of drug polypharmacology. Computationally, promiscuity can be explored through systematic analysis of compound activity data. Inhibitors of the human kinome represent an instructive example.

抄録全体を表示

A history of collaboration between French and Japanese chemoinformatics groups, and Professor Funatsu’s establishment of a Japanese chemoinformatics school, is presented.

抄録全体を表示

Information of transition states of similar reactions is the key to locating those of unknown reactions. In order to utilize this feature, we are constructing a database, called QMRDB, which gathers results of quantum mechanical calculations for elementary reactions as well as those for related molecules. Another database (TSDB) stores information of name reactions in organic synthesis. Retrieval results from these databases are used for analyzing reaction mechanisms which have not been experimentally examined. We developed a cloud system managing both the two databases and theoretical calculations. The present paper describes the summary of the TSDB cloud system and how to use it to perform in silico screenings for synthesizing drug candidates.

抄録全体を表示

Finding direct correlations between electronic structures of molecules and their properties, which we call “electronic-structure informatics”, is one of the challenging issues in chemoinformatics because the electronic degree of freedom is an essential factor determining the chemical characteristics. Herein we develop computational methods to automatically draw two types of orbital correlation diagrams. They are expected useful to perform machine learning including electronic degrees of freedom. In the present approach, we focus on electronic similarity called orbital similarity whose score is defined as spatial overlap between two molecular orbitals (MOs) enclosed with their iso-value surfaces. The similarity scores are also used to derive another orbital correlation diagram called “orbital interaction diagram”. This diagram is to relate MOs of a target molecule with those of its fragments. Through applications to benzene derivatives, these diagrams are shown to be reasonable, indicating potential usefulness of the present method in machine learning for quantitative predictions of molecular properties and chemical reactivities.

抄録全体を表示

Fatty acid synthase (FASN) inhibitors are known to work as anti-cancer drugs. In order to find important factors in their structure-activity relationships and to derive a predictive model for the activity, we herein tried to develop regression models by using descriptors representing chemical reactivities and intermolecular interactions. By employing the descriptors calculated with the electronic-structure theory, regression models for the experimental IC50 values were derived. Good correlations between the predicted and experimental values were obtained for the natural products having inhibitor activity to FASN. The obtained models are expected useful for systematic search for more efficient inhibitors. At the same time, the present results justify the use of the newly suggested descriptors evaluated in electronic-structure calculations.

抄録全体を表示

A number of studies have investigated the relations between structures and activities of metabolites. It has been proposed that structural similarity between metabolites implies activity similarity between them. In light of this fact we propose a method for activity prediction of secondary metabolites based on association philosophy. First we determined the structural similarity scores between targeted metabolite pairs using COMPLIG algorithm. To increase the possibility of clusters rich with known metabolites we calculated structural similarity between metabolite pairs for which activities of both or at least one metabolite is known and then selected the metabolite pairs for which the similarity score is higher than a threshold (s > 0.95). The network of such metabolite pairs was then clustered using the DPClusO algorithm. Statistically significant cluster-activity pairs were then selected using the hypergeometric test. Then biological activities of unannotated metabolites were predicted from the activity of metabolites included in the statistically overrepresented clusters.

抄録全体を表示

In polymer material development, we often need to optimize some physical and chemical properties simultaneously. On the other hand, there is no established method to predict some different properties of polymers by the same approach. In this study, property values of various polymers were collected from the literature. Their relevance was considered by hierarchical clustering. PLSR models were constructed which predicted density, glass transition temperature, and dissolution parameter using descriptors obtained from the monomer unit structure information. R2 of the models were 0.88 ~ 0.97. The concept of informatics has shown the possibility to predict different polymer properties in a similar way.

抄録全体を表示

Pesticides are considered a vital component of modern farming, playing major roles in maintaining high agricultural productivity. Pesticide recovery rates in vegetables and fruits determined using GC/MS depends on various factors including the matrix effect and chemical interactions between pesticides and mixing compounds in crops. In this study, the recovery rate of a pesticide is defined by a ratio of peak area of 50 ppb spiked in a crop sample to that in the solvent standard calibration curve. The estimation of recovery rates of pesticides in crops leads to evaluation of precise contents of them in the crops. In the present study, we performed regression models of the recovery rates based on molecular descriptors using R-packages rcdk and caret. Each of the chemical structures of 248 pesticides was converted to 174 molecular descriptors, then, for 7 crops, we created 69 ordinary and 20 ensemble learning regression models for estimating the recovery rates from the molecular descriptors using R-package caret. In the present study, two machine learning regression methods called mSBC and xgbLinear performed the best in view of prediction rates and execution times. In those two regression models predictions of recovery rates of pesticides are carried out in local distribution of chemical properties out of the 174 molecular descriptors. This concludes that closely related pesticides in the chemical space have also very similar recovery rates.

抄録全体を表示

動物実験の削減を目的に、化学物質の毒性予測手法の開発が行われている。化学物質の構造情報を基に経験的にグループ化し、線形回帰で予測する方法が本分野では一般的である(従来法)。一方で、従来法は金属化合物の予測が困難という課題がある。著者らは化学物質をクラスタリングし、生じたクラスタと化学物質の類似度を用いて、生態毒性値を予測する手法を開発した。これにより金属化合物の予測が可能となったが、訓練後の機械学習モデルに対する解釈が難しいという課題が残った。この対策として、本研究では、説明変数にフィンガープリントを用いて構造情報を2値化して集計を行うと共に、目的変数を変更した2種類の機械学習モデルを作成して魚類急性生態毒性値の予測に対する構造的特徴の寄与状況を比較した。この解析により、訓練後の機械学習モデルに関し、酸素、ベンゼン環、メチル基の数など、極性に基づいて生態毒性値を予測していると解釈する事が可能であった。さらに、開発手法の予測精度は以前の方法よりも優れることも確認できた。

抄録全体を表示

動物試験の削減を目的とした化学物質における生態毒性値の予測手法として、官能基で化学物質を分類後、線形回帰で毒性値を予測する方法が一般的である(従来法)。従来法は官能基を複数持つ化学物質に対し複数の結果を出力し、金属化合物や電解質は扱えない。著者らは化学物質をクラスタリングし、生じたクラスタとの類似度から毒性を予測する手法の開発を行ってきたが、慢性生態毒性に関しては精度が従来法に比べて劣っていた。これは、急性に比べ慢性毒性試験のデータ数が少ない事に加え、作用機序が多様であることが原因と推定された。本研究では多様性への対策として、生態毒性用のフィンガープリントの開発を行った。更にこの新しいフィンガープリントと既存の特徴量を併用し、機械学習モデルの結果を特徴量として利用する方法を用いた生態慢性毒性の予測手法を開発した。従来法の課題を克服し、同等以上の予測精度を得た。

抄録全体を表示

Batch or semi-batch processes have been of great use in various industrial chemical plants. For efficiently monitoring such processes, soft-sensor models can be employed. Many of previously proposed soft-sensor models assumed that objective variable values for model construction can be available at any time during process operation. However, in many chemical plants, it is difficult to sample product from the ongoing process due to such extreme reaction conditions as high pressure and temperature. Therefore, understanding the relationship between time-series soft-sensor model’s predictability and the number of sampling points is important. In the present work, we clarified this relationship using simulation datasets, which can be easily reproduced. When sampling points were scarce, data augmentation strategy was also found to be effective. Soft-sensor models can be effectively built using sampling points in the early phase of the process. These findings were applied to build a soft-sensor model of an industrial semi-batch process.

抄録全体を表示