Theoretical and data-driven approaches to semiconductors and dielectrics: from prediction to experiment.

Computational approaches using theoretical calculations and data scientific methods have become increasingly important in materials science and technology, with the development of relevant methodologies and algorithms, the availability of large materials data, and the enhancement of computer performance. As reviewed herein, we have developed computational methods for the design and prediction of inorganic materials with a particular focus on the exploration of semiconductors and dielectrics. High-throughput first-principles calculations are used to systematically and accurately predict the local atomic and electronic structures of polarons, point defects, surfaces, and interfaces, as well as bulk fundamental properties.

Performance of uncertainty-based active learning for efficient approximation of black-box functions in materials science.

Obtaining a fine approximation of a black-box function is important for understanding and evaluating innovative materials. Active learning aims to improve the approximation of black-box functions with fewer training data. In this study, we investigate whether active learning based on uncertainty sampling enables the efficient approximation of black-box functions in regression tasks using various material databases.

Combining three-dimensional acoustic coring and a convolutional neural network to quantify species contributions to benthic ecosystems.

The seafloor is inhabited by a large number of benthic invertebrates, and their importance in mediating carbon mineralization and biogeochemical cycles is recognized. However, the majority of fauna live below the sediment surface, so most means of survey rely on destructive sampling methods that are limited to documenting species presence rather than event driven activity and functionally important aspects of species behaviour. We have developed and tested a laboratory-based three-dimensional acoustic coring system that is capable of non-invasively visualizing the presence and activity of invertebrates within the sediment matrix.

Predicting condensate formation of protein and RNA under various environmental conditions.

Background: Liquid-liquid phase separation (LLPS) by biomolecules plays a central role in various biological phenomena and has garnered significant attention. The behavior of LLPS is strongly influenced by the characteristics of RNAs and environmental factors such as pH and temperature, as well as the properties of proteins. Recently, several databases recording LLPS-related biomolecules have been established, and prediction models of LLPS-related phenomena have been explored using these databases.

Discovery of Novel 11-Membered Templates as Squalene Synthase Inhibitors.

Squalene synthase is one of the most promising pharmaceutical targets to treat hyperlipidemia. Inhibition of the squalene synthase causes a decrease in the hepatic cholesterol concentration. We have already reported the design and synthesis of highly potent benzhydrol-type squalene inhibitors.

Quantitative analysis of protein dynamics using a deep learning technique combined with experimental cryo-EM density data and MD simulations.

Protein functions associated with biological activity are precisely regulated by both tertiary structure and dynamic behavior. Thus, elucidating the high-resolution structures and quantitative information on in-solution dynamics is essential for understanding the molecular mechanisms. The main experimental approaches for determining tertiary structures include nuclear magnetic resonance (NMR), X-ray crystallography, and cryogenic electron microscopy (cryo-EM).

AI-driven molecular generation of not-patented pharmaceutical compounds using world open patent data.

Developing compounds with novel structures is important for the production of new drugs. From an intellectual perspective, confirming the patent status of newly developed compounds is essential, particularly for pharmaceutical companies. The generation of a large number of compounds has been made possible because of the recent advances in artificial intelligence (AI).

Koopmans' Theorem-Compliant Long-Range Corrected (KTLC) Density Functional Mediated by Black-Box Optimization and Data-Driven Prediction for Organic Molecules.

Density functional theory (DFT) is a significant computational tool that has substantially influenced chemistry, physics, and materials science. DFT necessitates parametrized approximation for determining an expected value. Hence, to predict the properties of a given molecule using DFT, appropriate parameters of the functional should be set for each molecule.

Individual health-disease phase diagrams for disease prevention based on machine learning.

Early disease detection and prevention methods based on effective interventions are gaining attention worldwide. Progress in precision medicine has revealed that substantial heterogeneity exists in health data at the individual level and that complex health factors are involved in chronic disease development. Machine-learning techniques have enabled precise personal-level disease prediction by capturing individual differences in multivariate data.

Cardio-Ankle Vascular Index Reflects the Efficacy of Waon Therapy in Heart Failure Patients.

Waon therapy is a form of thermal treatment in a dry sauna developed by Tei. Although Waon therapy is reportedly effective for chronic heart failure (CHF) patients, not all patients respond to the therapy. The reason for this ineffectiveness has not been fully clarified.

Selective Inhibitor Design for Kinase Homologs Using Multiobjective Monte Carlo Tree Search.

Designing highly selective molecules for a drug target protein is a challenging task in drug discovery. This task can be regarded as a multiobjective problem that simultaneously satisfies criteria for various objectives, such as selectivity for a target protein, pharmacokinetic endpoints, and drug-like indices. Recent breakthroughs in artificial intelligence have accelerated the development of molecular structure generation methods, and various researchers have applied them to computational drug designs and successfully proposed promising drug candidates.

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.

QCforever: A Quantum Chemistry Wrapper for Everyone to Use in Black-Box Optimization.

To obtain observable physical or molecular properties such as ionization potential and fluorescent wavelength with quantum chemical (QC) computation, multi-step computation manipulated by a human is required. Hence, automating the multi-step computational process and making it a black box that can be handled by anybody are important for effective database construction and fast realistic material design through the framework of black-box optimization where machine learning algorithms are introduced as a predictor. Here, we propose a Python library, QCforever, to automate the computation of some molecular properties and chemical phenomena induced by molecules.

Second-Generation Antipsychotic Long-Acting Injection Reduced the Time of Restrictive Interventions in Patients With Schizophrenia: Retrospective, A 4-Year Mirror-Image Study.

Background: Whether second-generation antipsychotic long-acting injection (SGA-LAI) reduces psychotic symptoms at relapse compared with oral antipsychotics remains unclear. The present study investigated the effects of SGA-LAI on the time (in hours) of restrictive interventions in hospitalization by conducting a retrospective observational 4-year mirror-image study at a single medical center in Japan.

Method: We performed a retrospective observational mirror-image study conducted between November 2013 and January 2018.

A Novel Three-Dimensional Imaging System Based on Polysaccharide Staining for Accurate Histopathological Diagnosis of Inflammatory Bowel Diseases.

Background & Aims: Tissue-clearing and three-dimensional (3D) imaging techniques aid clinical histopathological evaluation; however, further methodological developments are required before use in clinical practice.

Methods: We sought to develop a novel fluorescence staining method based on the classical periodic acid-Schiff stain. We further attempted to develop a 3D imaging system based on this staining method and evaluated whether the system can be used for quantitative 3D pathological evaluation and deep learning-based automatic diagnosis of inflammatory bowel diseases.

Single-Image Super-Resolution Improvement of X-ray Single-Particle Diffraction Images Using a Convolutional Neural Network.

Femtosecond X-ray pulse lasers are promising probes for the elucidation of the multiconformational states of biomolecules because they enable snapshots of single biomolecules to be observed as coherent diffraction images. Multi-image processing using an X-ray free-electron laser has proven to be a successful structural analysis method for viruses. However, the performance of single-particle analysis (SPA) for flexible biomolecules with sizes ≤100 nm remains difficult.

Understanding the evolution of a de novo molecule generator via characteristic functional group monitoring.

Recently, artificial intelligence (AI)-enabled de novo molecular generators (DNMGs) have automated molecular design based on data-driven or simulation-based property estimates. In some domains like the game of Go where AI surpassed human intelligence, humans are trying to learn from AI about the best strategy of the game. To understand DNMG's strategy of molecule optimization, we propose an algorithm called characteristic functional group monitoring (CFGM).

Estimation of the maximum utilization area including home range and peripheral sites.

There is increasing evidence that occasional utilization area (peripheral sites), in addition to typical utilization area (home range), is important for wildlife conservation and management. Here we estimated the maximum utilization area (MUA), including both typical and occasional utilization areas, based on asymptotic curves of utilization area plotted against sample size. In previous studies, these curves have conventionally been plots of cumulative utilization area versus sample size, but this cumulative method is sensitive to stochastic effects.

Enhanced Conformational Sampling with an Adaptive Coarse-Grained Elastic Network Model Using Short-Time All-Atom Molecular Dynamics.

Compared to all-atom molecular dynamics (AA-MD) simulations, coarse-grained (CG) MD simulations can significantly reduce calculation costs. However, existing CG-MD methods are unsuitable for sampling structures that depart significantly from the initial structure without any biased force. In this study, we developed a new adaptive CG elastic network model (ENM), in which the dynamic cross-correlation coefficient based on short-time AA-MD of at most ns order is considered.

De novo creation of a naked eye-detectable fluorescent molecule based on quantum chemical computation and machine learning.

Designing fluorescent molecules requires considering multiple interrelated molecular properties, as opposed to properties that straightforwardly correlated with molecular structure, such as light absorption of molecules. In this study, we have used a de novo molecule generator (DNMG) coupled with quantum chemical computation (QC) to develop fluorescent molecules, which are garnering significant attention in various disciplines. Using massive parallel computation (1024 cores, 5 days), the DNMG has produced 3643 candidate molecules.

AI-Driven Synthetic Route Design Incorporated with Retrosynthesis Knowledge.

Computer-aided synthesis planning (CASP) aims to assist chemists in performing retrosynthetic analysis for which they utilize their experiments, intuition, and knowledge. Recent breakthroughs in machine learning (ML) techniques, including deep neural networks, have significantly improved data-driven synthetic route designs without human intervention. However, learning chemical knowledge by ML for practical synthesis planning has not yet been adequately achieved and remains a challenging problem.

Brain Diseases and Fall-Related Surgery in Older Persons.

Background: It is known that age-related brain symptoms (gait difficulty and dementia) increase the likelihood of fall-related surgery. In contrast, it is not known which types of brain disease underlie such symptoms most.

Objective: The aim of this study was to correlate brain diseases with the types of surgeries performed at our hospital for patients who had fallen.