Fragment molecular orbital (FMO) calculation is a useful ab initio method for analyzing protein–ligand interactions in the current structure-based drug design. When multiple ligands exist for one receptor, a post-FMO calculation tool is required because of large numbers of interaction energy decomposition terms calculated using this method. In this study, a method that combines self-organizing maps (SOM) and hierarchical clustering analysis (HCA) was proposed to analyze the results of the FMO energy components. This method could effectively compress the high-dimensional energy terms and is expected to be useful to analyze the interaction between protein and ligands. A case study of antitype 2 diabetes mellitus target DPP-IV and its inhibitors was analyzed to verify the feasibility of the proposed method. After performing dimensional compression using SOM and further grouping using HCA, we obtained superclasses of the inhibitors based on the dispersion energy (DI), which showed consistency with structural information, indicating that further analyses of detailed energies per superclass can be an effective approach for obtaining important ligand–protein interactions.

We introduce an automated procedure of coarse-grained molecular dynamic simulation for DNA nanostructure that has great potential for realizing molecular robotics. As DNA origami is now a standardized technology to fabricate DNA nanostructures with high precision, various computer-aided design software has been developed. For example, a design tool called caDNAno with a simple and intuitive interface is widely used for designing DNA origami structures. Further, a simulation tool called oxDNA is used to predict the behavior of such nanostructures based on coarse-grained molecular dynamics. These tools, however, are not linked directly; thus, repeating the cycle of design and simulation is cumbersome to the user. Moreover, the computer skills required to setup, launch, and run an oxDNA simulation are a potential barrier for non-experts. In our proposal, oxDNA simulation can be launched on a web server simply by providing a caDNAno file; the web server then analyzes the simulation results and provides a visual response. The validity of the proposal is demonstrated using an example. The advantages of our proposed method compared with other conventional methods are also described. This simple-to-use interface for user-friendly simulation of DNA origami eliminates stress to users and accelerates the design process of complicated DNA nanostructures such as wireframe architecture.

The inhibition of hERG potassium channel is closely related to the prolonged QT interval, and to assess the risk could greatly contribute to the development of safer therapeutic compounds. In the hit-to-lead optimization stage of drug development, quantitative prediction of hERG inhibitory activity is crucial to design drug candidates without cardiotoxicity risk. Here, we developed a hERG regression model combining support vector regression (SVR) and descriptor selection by non-dominated sorting genetic algorithm (NSGA-II) based on AMED cardiotoxicity database consisting of hERG blocking information built by integrating public and commercial databases. To construct a regression model, 6,561 compounds with IC₅₀ and/or Ki values were derived from AMED cardiotoxicity database, and randomly separated into training set (70%) for model building and test set (30%) for performance evaluation. To avoid overfitting by employing many non-relevant explanatory variables, NSGA-II, a variation of genetic algorithm for multiple objective optimization, was used for descriptor selection in order to maximize Q² and minimize RMSE in 5-fold cross validation and minimize the number of used descriptors spontaneously. The prediction performance was then compared to those of ADMET predictor, commercial software providing various ADMET property predictions. The SVR model recorded R² of 0.594 and RMSE of 0.604 for test set, clearly exceeding those of ADMET predictor (0.134 and 0.690, respectively). The regression model is available at our home page (https://drugdesign.riken.jp/hERG).

The viral infection caused by the dengue virus (DENV) is one of the most challenging diseases in the tropical regions of the world. The absence of drugs for dengue to this date calls for intense efforts to discover and develop the much coveted therapeutics for this mosquito-borne disease. One of the most attractive antiviral targets is the DENV RNAdependent RNA polymerase (RdRp), which catalyzes the de novo initiation as well as elongation of the flavivirus RNA genome. In this work, almost 5000 natural products were docked to DENV RdRp. The top 197 molecules with greater binding energies than the known ligand of the target were further clustered down to furnish 35 classes of molecular structures. These compounds with satisfactory predicted drug properties and with known natural origin can be further explored to pave the way for the first anti-dengue drug.

The identification of molecular descriptors that embody the chemical information for druglikeness will be a step forward in data-driven drug discovery and development endeavor. In this study, over 4000 Dragon-type molecular properties were generated for approximately 2000 known drugs and 2000 surrogate nondrugs. Logistic Regression (LogR) and Random Forest (RF) techniques were carried out to unveil the crucial molecular descriptors that can adequately classify a compound as drug or nondrug. Ten one-variable LogR models each demonstrated at least 70% prediction accuracy. A two-variable model consisting of HV cpx and MDDD correctly classified 85% of the test compounds. The best LogR model with 89.0% prediction accuracy identified five most influential descriptors for druglikeness: an information index HV cpx, topological index MDDD, a ring descriptor NNRS, X2A or average connectivity index of order 2, and walk and path count SRW05. The best RF model involving 10 only weakly correlated descriptors was found to be 92.5% accurate and at par with the RF and LogR models that consisted of over 200 variables. The model featured: molecular weight, MW; average molecular weight, AMW; rotatable bond fraction, RBF; percentage carbon, C%; maximal electrotopological negative variation, MAXDN; all-path Wiener index, Wap; structural information content index, neighborhood symmetry of 1 order, SIC1; number of nitrogen atoms, nN; 2D Petitjean shape index, PJI2; and self-returning walk count of order 5, SRW05. Many of these descriptors have straightforward chemical interpretability and future applicability as druglikeness filters in virtual high throughput drug discovery.

In recent years, accelerating the speed of finding seed compounds and reducing the cost of pharmaceutical research has become a necessity. The contribution of in silico drug discovery methods, which predict candidates as new drugs using physicochemical features and substructure fingerprints of compounds, is thus expected. Selecting the seed compounds without conducting experiments could enable us to reduce the time and cost required for drug development. However, estimating the characteristics of compounds in our body using a simple linear model alone is unsatisfactory because effects and distribution of compounds are determined by the environment in our body and their interactions with other molecules. Compared to simple models, more complex models have been prepared to estimate compound characteristics with high predictive accuracy. Thus, it is increasingly important to correctly evaluate the predictive performance when selecting the models appropriate for research purposes. The determinant coefficient, famous as R², is one of the most famous statistical measures for evaluating regression models. However, this measure cannot be used to evaluate nonlinear models. In this paper, the difficulty of using the determinant coefficient is explained and the proper statistical measures were suggested under the following two conditions: mean squared error (MSE) for cross-validation, and MSE along with correlation coefficients for the observed and predicted values of test data. As understanding statistical measures and using them appropriately is necessary, the suggested measures will support the effective selection of promising seed compounds and accelerate drug discovery.