Dynamic Optimization of Neural Network Structures Using Probabilistic Modeling

Deep neural networks (DNNs) are powerful machine learning models and have succeeded in various artificial intelligence tasks. Although various architectures and modules for the DNNs have been proposed, selecting and designing the appropriate network structure for a target problem is a challenging task. In this paper, we propose a method to simultaneously optimize the network structure and weight parameters during neural network training. We consider a probability distribution that generates network structures, and optimize the parameters of the distribution instead of directly optimizing the network structure. The proposed method can apply to the various network structure optimization problems under the same framework. We apply the proposed method to several structure optimization problems such as selection of layers, selection of unit types, and selection of connections using the MNIST, CIFAR-10, and CIFAR-100 datasets. The experimental results show that the proposed method can find the appropriate and competitive network structures.Comment: To appear in the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), 9 page

Novel inhibitor candidates of TRPV2 prevent damage of dystrophic myocytes and ameliorate against dilated cardiomyopathy in a hamster model

Transient receptor potential cation channel, subfamily V, member 2 (TRPV2) is a principal candidate for abnormal Ca²⁺-entry pathways, which is a potential target for therapy of muscular dystrophy and cardiomyopathy. Here, an in silico drug screening and the following cell-based screening to measure the TRPV2 activation were carried out in HEK293 cells expressing TRPV2 using lead compounds (tranilast or SKF96365) and off-patent drug stocks. We identified 4 chemical compounds containing amino-benzoyl groups and 1 compound (lumin) containing an ethylquinolinium group as candidate TRPV2 inhibitors. Three of these compounds inhibited Ca²⁺ entry through both mouse and human TRPV2, with IC₅₀ of less than 10 μM, but had no apparent effect on other members of TRP family such as TRPV1 and TRPC1. Particularly, lumin inhibited agonist-induced TRPV2 channel activity at a low dose. These compounds inhibited abnormally increased Ca²⁺ influx and prevented stretch-induced skeletal muscle damage in cultured myocytes from dystrophic hamsters (J2N-k). Further, they ameliorated cardiac dysfunction, and prevented disease progression in vivo in the same J2N-k hamsters developing dilated cardiomyopathy as well as muscular dystrophy. The identified compounds described here are available as experimental tools and represent potential treatments for patients with cardiomyopathy and muscular dystrophy

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

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

Online multiscale dynamic topic models

We propose an online topic model for sequentially analyzing the time evolution of topics in document collections. Topics naturally evolve with multiple timescales. For example, some words may be used consistently over one hundred years, while other words emerge and disappear over periods of a few days. Thus, in the proposed model, current topicspecific distributions over words are assumed to be generated based on the multiscale word distributions of the previous epoch. Considering both the long-timescale dependency as well as the short-timescale dependency yields a more robust model. We derive efficient online inference procedures based on a stochastic EM algorithm, in which the model is sequentially updated using newly obtained data; this means that past data are not required to make the inference. We demonstrate the effectiveness of the proposed method in terms of predictive performance and computational efficiency by examining collections of real documents with timestamps

Pivotal role of Sirt6 in the crosstalk among ageing, metabolic syndrome and osteoarthritis

AbstractOsteoarthritis (OA) is a chronic degenerative joint disorder commonly associated with metabolic syndrome. As ageing and obesity has a great impact on the initiation/severity of OA, herein we sought to investigate the involvement of Sirt6 in the crosstalk between ageing and metabolic syndrome/OA. Sirt6 haploinsufficiency in mice promoted the expression of inflammatory cytokines in the IPFP. Enhanced inflammation of the IPFP in the aged Sirt6 ± HFD group was paralleled with accelerated OA change, including osteophyte growth and chondrocyte hypertrophy. Conversely, mesenchyme-specific Sirt6-deficient mice revealed both attenuated chondrocyte hypertrophy and proteoglycan synthesis, although chondrocyte senescence was enhanced as shown in the aged WT mice. Thus Sirt6 has key roles in the relationship among ageing, metabolic syndrome, and OA

Enzymatic control of anhydrobiosis-related accumulation of trehalose in the sleeping chironomid, Polypedilum vanderplanki

Larvae of an anhydrobiotic insect, Polypedilum vanderplanki, accumulate very large amounts of trehalose as a compatible solute on desiccation, but the molecular mechanisms underlying this accumulation are unclear. We therefore isolated the genes coding for trehalose metabolism enzymes, i.e. trehalose-6-phosphate synthase (TPS) and trehalose-6-phosphate phosphatase (TPP) for the synthesis step, and trehalase (TREH) for the degradation step. Although computational prediction indicated that the alternative splicing variants (PvTpsα/β) obtained encoded probable functional motifs consisting of a typical consensus domain of TPS and a conserved sequence of TPP, PvTpsα did not exert activity as TPP, but only as TPS. Instead, a distinct gene (PvTpp) obtained expressed TPP activity. Previous reports have suggested that insect TPS is, exceptionally, a bifunctional enzyme governing both TPS and TPP. In this article, we propose that TPS and TPP activities in insects can be attributed to discrete genes. The translated product of the TREH ortholog (PvTreh) certainly degraded trehalose to glucose. Trehalose was synthesized abundantly, consistent with increased activities of TPS and TPP and suppressed TREH activity. These results show that trehalose accumulation observed during anhydrobiosis induction in desiccating larvae can be attributed to the activation of the trehalose synthetic pathway and to the depression of trehalose hydrolysis