Classification of scanning electron microscope images of pharmaceutical excipients using deep convolutional neural networks with transfer learning

Convolutional Neural Networks (CNNs) are image analysis techniques that have been applied to image classification in various fields. In this study, we applied a CNN to classify scanning electron microscopy (SEM) images of pharmaceutical raw material powders to determine if a CNN can evaluate particle morphology. We tested 10 pharmaceutical excipients with widely different particle morphologies. SEM images for each excipient were acquired and divided into training, validation, and test sets. Classification models were constructed by applying transfer learning to pretrained CNN models such as VGG16 and ResNet50. The results of a 5-fold cross-validation showed that the classification accuracy of the CNN model was sufficiently high using either pretrained model and that the type of excipient could be classified with high accuracy. The results suggest that the CNN model can detect differences in particle morphology, such as particle size, shape, and surface condition. By applying Grad-CAM to the constructed CNN model, we succeeded in finding particularly important regions in the particle image of the excipients. CNNs have been found to have the potential to be applied to the identification and characterization of raw material powders for pharmaceutical development

Improving Compound–Protein Interaction Prediction by Self-Training with Augmenting Negative Samples

Identifying compound-protein interactions (CPIs) is crucial for drug discovery. Since experimentally validating CPIs is often time-consuming and costly, computational approaches are expected to facilitate the process. Rapid growths of available CPI databases have accelerated the development of many machine-learning methods for CPI predictions. However, their performance, particularly their generalizability against external data, often suffers from a data imbalance attributed to the lack of experimentally validated inactive (negative) samples. In this study, we developed a self-training method for augmenting both credible and informative negative samples to improve the performance of models impaired by data imbalances. The constructed model demonstrated higher performance than those constructed with other conventional methods for solving data imbalances, and the improvement was prominent for external datasets. Moreover, examination of the prediction score thresholds for pseudo-labeling during self-training revealed that augmenting the samples with ambiguous prediction scores is beneficial for constructing a model with high generalizability. The present study provides guidelines for improving CPI predictions on real-world data, thus facilitating drug discovery

kGCN: a graph-based deep learning framework for chemical structures

Deep learning is developing as an important technology to perform various tasks in cheminformatics. In particular, graph convolutional neural networks (GCNs) have been reported to perform well in many types of prediction tasks related to molecules. Although GCN exhibits considerable potential in various applications, appropriate utilization of this resource for obtaining reasonable and reliable prediction results requires thorough understanding of GCN and programming. To leverage the power of GCN to benefit various users from chemists to cheminformaticians, an open-source GCN tool, kGCN, is introduced. To support the users with various levels of programming skills, kGCN includes three interfaces: a graphical user interface (GUI) employing KNIME for users with limited programming skills such as chemists, as well as command-line and Python library interfaces for users with advanced programming skills such as cheminformaticians. To support the three steps required for building a prediction model, i.e., pre-processing, model tuning, and interpretation of results, kGCN includes functions of typical pre-processing, Bayesian optimization for automatic model tuning, and visualization of the atomic contribution to prediction for interpretation of results. kGCN supports three types of approaches, single-task, multi-task, and multi-modal predictions. The prediction of compound-protein interaction for four matrixmetalloproteases, MMP-3, -9, -12 and -13, in the inhibition assays is performed as a representative case study using kGCN. Additionally, kGCN provides the visualization of atomic contributions to the prediction. Such visualization is useful for the validation of the prediction models and the design of molecules based on the prediction model, realizing “explainable AI” for understanding the factors affecting AI prediction. kGCN is available at https://github.com/clinfo

Design and optimization of a wideband impact mode piezoelectric power generator

This paper proposes a new design of an impact mode piezoelectric power generator that is able to operate in a wide frequency bandwidth by using a round piezoelectric ceramic as the energy converter. The evaluation results show that the output of the power generator can be optimized by implementing a so-called indirect impact configuration. To realize this type of configuration, a shim plate is placed between the piezoelectric ceramic and the hitting structure. At a certain base excitation frequency, the output efficiency of this configuration increases to about 4.3 times that of the direct impact configuration. Furthermore, it is demonstrated that the designated power generator is able to generate electric energy up to approximately 1.57 mJ within 120 s from the vibration of a moving vehicle

Hydrophobic interactions at subsite S1′ of human dipeptidyl peptidase IV contribute significantly to the inhibitory effect of tripeptides

Functional inhibitory peptides of human dipeptidyl peptidase 4 (hDPP4) have been highly anticipated as the active ingredient of functional food for type II diabetes; however, the molecular mechanism of hDPP4 inhibition remains unclear. In this study, we focused on dipeptides and tripeptides, which display structure-function correlations that are relatively easy to analyze, and examined their interactions with hDPP4 on an atomic level using a combination of docking studies and an hDPP4 inhibition assay. First, we performed comprehensive binding mode analysis of the dipeptide library and demonstrated that the formation of a tight interaction with the S1 subsite composing part of the substrate pocket is essential for dipeptides to compete with the substrate and strongly inhibit hDPP4. Next, we synthesized tripeptides by adding various amino acids to the C-terminus of Ile-Pro and Val-Pro, which have especially high inhibitory activity among compounds in the dipeptide library, and measured the hDPP4 inhibitory activity of the tripeptides. When hydrophobic amino acids (Ile, Met, Val, Trp) were added, the inhibitory activity increased several-fold. This phenomenon could be explained as follows: the C-terminal amino acid of the tripeptide formed hydrophobic interactions with Tyr547 and Trp629, which compose the S1′ subsite located relatively outside the substrate pocket, thereby stabilizing the hDPP4-peptide binding. The structural information on the interaction between hDPP4 and peptide inhibitors attained in this study is anticipated to be useful in the development of a more potent hDPP4 competitive inhibitor

Gastric Carcinoid with Hypergastrinemia: Report of Three Cases

We report 3 cases of gastric carcinoids with hypergastrinemia. Case 1: A 60-year-old man had a 2 cm carcinoid of the stomach and underwent partial resection. Involvement of the muscularis propria and lymph nodes metastasis were observed microscopically. Follow-up gastroscopy revealed another carcinoid lesion and total gastrectomy was performed. Case 2: A 67-year-old woman with multiple carcinoids of the entire stomach underwent antrectomy. No growth of residual tumors has been detected so far. Case 3: A 61-year-old man had a tumor near the esophagogastric junction and underwent total gastrectomy. Carcinoid component was diffusely intermingled with adenocarcinoma in the tumor and invaded into the subserosa. In all 3 cases, the serum gastrin level was high and atrophic gastritis was microscopically observed. Carcinoid tumor in Case 3 was different from those in Cases 1 and 2 and interestingly, gastric carcinoid with hypergastrinemia showed various types of appearance

Juvenile Granulosa Cell Tumor with Elevated Peripheral Interleukin-6 Level Shows Prolonged Fever and Delayed Puberty

Juvenile granulosa cell tumor (JGCT), classified as a sex cord-stromal tumor, is a rare neoplasm. This is an instructive case report of JGCT accompanied by augmented interleukin (IL)-6 secretion. A 13-year-old girl with prolonged fever and delayed puberty was diagnosed with JGCT of the left ovary based on an imaging study and pathological investigation. Although it was not clear whether IL-6 was secreted from the tumor cells, her serum level of IL-6 was very high. After tumorectomy, the patient’s symptoms immediately disappeared, her IL-6 level decreased, and she entered puberty. Therefore, augmented IL-6 secretion production induced by tumors should be considered a potential cause of prolonged fever and/or delayed puberty

Study of the effect of mechanical impact parameters on an impact-mode piezoelectric ceramic power generator

This paper presents an analytical and experimental study on the effect of mechanical impact parameters on impact-mode piezoelectric ceramic power generators. The parameters are the velocity and mass. The method of analysis is based on a weight drop experiment. The results show that the peak of the instantaneous output voltage is proportional to the impact velocity, and for the output power, it is in a straight line relationship with the same parameter. For the same velocity of impact, the advantage of using heavy objects is clear because its momentum and the impact force are higher. However, an adjustment in the velocity of impact is found to be more effective for higher instantaneous output power than the mass. This finding is supported by the output power that is generated by a 4-g steel ball with a momentum of 4.34 gm/s, which is almost 300% higher than that of an 8-g steel ball for the same momentum. The frequency responses of a vibration-based impact-mode piezoelectric ceramic power generator also support the same conclusion

The Effect of the Parameters of a Vibration-Based Impact Mode Piezoelectric Power Generator

This study reports the effects of the parameters of a vibration-based impactmode piezoelectric power generator. First, an evaluation of the effects of the impact parameters, the mass, and the impact velocity is presented. It is found that the output voltage of the piezoelectric device in impactmode is directly proportional to the velocity,whereas the output power is equal to a quadratic function of the same variable. For the same impact momentum, the effect of the velocity in generating a higher peak output is dominant compared with the mass. Second, the vibration-based impact mode piezoelectric power generator is discussed. The experimental results show that a wider operating frequency bandwidth of the output power can be achieved with the preloading configuration. However, regardingmagnitude, due to the high velocity of impact, the configurationwith a gap between the tip and the piezoelectric device produces a higher output

Analysis of multiple compound–protein interactions reveals novel bioactive molecules

The authors use machine learning of compound-protein interactions to explore drug polypharmacology and to efficiently identify bioactive ligands, including novel scaffold-hopping compounds for two pharmaceutically important protein families: G-protein coupled receptors and protein kinases