Showing papers by "Koji Tsuda published in 2020"

Designing metamaterials with quantum annealing and factorization machines

Abstract: The authors provide an algorithm to incorporate quantum annealing into automated materials discovery. Their scheme can be used to design complex structures of wavelength selective radiators showing much better agreement with the thermal atmospheric transparency window.

Application of Bayesian Optimization for Pharmaceutical Product Development

Abstract: Bayesian optimization has been studied in many fields as a technique for global optimization of black-box functions. We applied these techniques for optimizing the formulation and manufacturing methods of pharmaceutical products to eliminate unnecessary experiments and accelerate method development tasks. A simulation dataset was generated by the data augmentation from a design of experiment (DoE) which was executed to optimize the formulation and process parameters of orally disintegrating tablets. We defined a composite score for integrating multiple objective functions, physical properties of tablets, to meet the pharmaceutical criteria simultaneously. Performance measurements were used to compare the influence of the selection of initial training sets, by controlling data size and variation, acquisition functions, and schedules of hyperparameter tuning. Additionally, we investigated performance improvements obtained using Bayesian optimization techniques as opposed to random search strategy. Bayesian optimization efficiently reduces the number of experiments to obtain the optimal formulation and process parameters from about 25 experiments with DoE to 10 experiments. Repeated hyperparameter tuning during the Bayesian optimization process stabilizes variations in performance among different optimization conditions, thus improving average performance. We demonstrated the elimination of unnecessary experiments using Bayesian optimization. Simulations of different conditions depicted their dependencies, which will be useful in many real-world applications. Bayesian optimization is expected to reduce the reliance on individual skills and experiences, increasing the efficiency and efficacy of optimization tasks, expediting formulation and manufacturing research in pharmaceutical development.

Generating Ampicillin-Level Antimicrobial Peptides with Activity-Aware Generative Adversarial Networks

Abstract: Antimicrobial peptides are a potential solution to the threat of multidrug-resistant bacterial pathogens. Recently, deep generative models including generative adversarial networks (GANs) have been shown to be capable of designing new antimicrobial peptides. Intuitively, a GAN controls the probability distribution of generated sequences to cover active peptides as much as possible. This paper presents a peptide-specialized model called PepGAN that takes the balance between covering active peptides and dodging nonactive peptides. As a result, PepGAN has superior statistical fidelity with respect to physicochemical descriptors including charge, hydrophobicity, and weight. Top six peptides were synthesized, and one of them was confirmed to be highly antimicrobial. The minimum inhibitory concentration was 3.1 μg/mL, indicating that the peptide is twice as strong as ampicillin.

Optimization of a Heterogeneous Ternary Li3PO4–Li3BO3–Li2SO4 Mixture for Li-Ion Conductivity by Machine Learning

Abstract: Mixing heterogeneous Li-ion conductive materials is one potential way to enhance Li-ion conductivity more than that of the parent materials. However, the huge number of possible compositions of par...

Artificial Neural Networks Applied as Molecular Wave Function Solvers.

Abstract: We use artificial neural networks (ANNs) based on the Boltzmann machine (BM) architectures as an encoder of ab initio molecular many-electron wave functions represented with the complete active space configuration interaction (CAS-CI) model. As first introduced by the work of Carleo and Troyer for physical systems, the coefficients of the electronic configurations in the CI expansion are parametrized with the BMs as a function of their occupancies that act as descriptors. This ANN-based wave function ansatz is referred to as the neural-network quantum state (NQS). The machine learning is used for training the BMs in terms of finding a variationally optimal form of the ground-state wave function on the basis of the energy minimization. It is relevant to reinforcement learning and does not use any reference data nor prior knowledge of the wave function, while the Hamiltonian is given based on a user-specified chemical structure in the first-principles manner. Carleo and Troyer used the restricted Boltzmann machine (RBM), which has hidden units, for the neural network architecture of NQS, while, in this study, we further introduce its replacement with the BM that has only visible units but with different orders of connectivity. For this hidden-node free BM, the second- and third-order BMs based on quadratic and cubic energy functions, respectively, were implemented. We denote these second- and third-order BMs as BM2 and BM3, respectively. The pilot implementation of the NQS solver into an exact diagonalization module of the quantum chemistry program was made to assess the capability of variants of the BM-based NQS. The test calculations were performed by determining the CAS-CI wave functions of illustrative molecular systems, indocyanine green, and dinitrogen dissociation. The simulated energies have been shown to converge to CAS-CI energy in most cases by improving RBM with an increasing number of hidden nodes. BM3 systematically yields lower energies than BM2, reproducing the CAS-CI energies of dinitrogen across potential energy curves within an error of 50 μEh.

CompRet: a comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration.

Abstract: In computer-assisted synthesis planning (CASP) programs, providing as many chemical synthetic routes as possible is essential for considering optimal and alternative routes in a chemical reaction network. As the majority of CASP programs have been designed to provide one or a few optimal routes, it is likely that the desired one will not be included. To avoid this, an exact algorithm that lists possible synthetic routes within the chemical reaction network is required, alongside a recommendation of synthetic routes that meet specified criteria based on the chemist’s objectives. Herein, we propose a chemical-reaction-network-based synthetic route recommendation framework called “CompRet” with a mathematically guaranteed enumeration algorithm. In a preliminary experiment, CompRet was shown to successfully provide alternative routes for a known antihistaminic drug, cetirizine. CompRet is expected to promote desirable enumeration-based chemical synthesis searches and aid the development of an interactive CASP framework for chemists.

Pushing property limits in materials discovery via boundless objective-free exploration

Abstract: Materials chemists develop chemical compounds to meet often conflicting demands of industrial applications. This process may not be properly modeled by black-box optimization because the target property is not well defined in some cases. Herein, we propose a new algorithm for automated materials discovery called BoundLess Objective-free eXploration (BLOX) that uses a novel criterion based on kernel-based Stein discrepancy in the property space. Unlike other objective-free exploration methods, a boundary for the materials properties is not needed; hence, BLOX is suitable for open-ended scientific endeavors. We demonstrate the effectiveness of BLOX by finding light-absorbing molecules from a drug database. Our goal is to minimize the number of density functional theory calculations required to discover out-of-trend compounds in the intensity-wavelength property space. Using absorption spectroscopy, we experimentally verified that eight compounds identified as outstanding exhibit the expected optical properties. Our results show that BLOX is useful for chemical repurposing, and we expect this search method to have numerous applications in various scientific disciplines.

Efficient query autocompletion with edit distance-based error tolerance

Abstract: Query autocompletion is an important feature saving users many keystrokes from typing the entire query. In this paper, we study the problem of query autocompletion that tolerates errors in users’ input using edit distance constraints. Previous approaches index data strings in a trie, and continuously maintain all the prefixes of data strings whose edit distances from the query string are within the given threshold. The major inherent drawback of these approaches is that the number of such prefixes is huge for the first few characters of the query string and is exponential in the alphabet size. This results in slow query response even if the entire query approximately matches only few prefixes. We propose a novel neighborhood generation-based method to process error-tolerant query autocompletion. Our proposed method only maintains a small set of active nodes, thus saving both space and time to process the query. We also study efficient duplicate removal, a core problem in fetching query answers, and extend our method to support top-k queries. Optimization techniques are proposed to reduce the index size. The efficiency of our method is demonstrated through extensive experiments on real datasets.

Bayesian Optimization in Materials Science

Abstract: Bayesian optimization (BO) algorithm is a global optimization approach, and it has been recently gained growing attention in materials science field for the search and design of new functional materials. Herein, we briefly give an overview of recent applications of BO algorithm in the determination of physical parameters of physics model, the design of experimental synthesis conditions, the discovery of functional materials with targeted properties, and the global optimization of atomic structures. The basic methodologies of BO in these applications are also addressed.

Data integration for accelerated materials design via preference learning

Abstract: Machine learning applications in materials science are often hampered by shortage of experimental data. Integration with external datasets from past experiments is a viable way to solve the problem. But complex calibration is often necessary to use the data obtained under different conditions. In this paper, we present a novel calibration-free strategy to enhance the performance of Bayesian optimization with preference learning. The entire learning process is solely based on pairwise comparison of quantities (i.e., higher or lower) in the same dataset, and experimental design can be done without comparing quantities in different datasets. We demonstrate that Bayesian optimization is significantly enhanced via data integration for organic molecules and inorganic solid-state materials. Our method increases the chance that public datasets are reused and may encourage data sharing in various fields of physics.

Machine learning to reveal hidden risk combinations for the trajectory of posttraumatic stress disorder symptoms.

Abstract: The nature of the recovery process of posttraumatic stress disorder (PTSD) symptoms is multifactorial. The Massive Parallel Limitless-Arity Multiple-testing Procedure (MP-LAMP), which was developed to detect significant combinational risk factors comprehensively, was utilized to reveal hidden combinational risk factors to explain the long-term trajectory of the PTSD symptoms. In 624 population-based subjects severely affected by the Great East Japan Earthquake, 61 potential risk factors encompassing sociodemographics, lifestyle, and traumatic experiences were analyzed by MP-LAMP regarding combinational associations with the trajectory of PTSD symptoms, as evaluated by the Impact of Event Scale-Revised score after eight years adjusted by the baseline score. The comprehensive combinational analysis detected 56 significant combinational risk factors, including 15 independent variables, although the conventional bivariate analysis between single risk factors and the trajectory detected no significant risk factors. The strongest association was observed with the combination of short resting time, short walking time, unemployment, and evacuation without preparation (adjusted P value = 2.2 × 10−4, and raw P value = 3.1 × 10−9). Although short resting time had no association with the poor trajectory, it had a significant interaction with short walking time (P value = 1.2 × 10−3), which was further strengthened by the other two components (P value = 9.7 × 10−5). Likewise, components that were not associated with a poor trajectory in bivariate analysis were included in every observed significant risk combination due to their interactions with other components. Comprehensive combination detection by MP-LAMP is essential for explaining multifactorial psychiatric symptoms by revealing the hidden combinations of risk factors.

Computer Vision-Based Approach for Quantifying Occupational Therapists' Qualitative Evaluations of Postural Control.

Abstract: This study aimed to leverage computer vision (CV) technology to develop a technique for quantifying postural control. A conventional quantitative index, occupational therapists' qualitative clinical evaluations, and CV-based quantitative indices using an image analysis algorithm were applied to evaluate the postural control of 34 typically developed preschoolers. The effectiveness of the CV-based indices was investigated relative to current methods to explore the clinical applicability of the proposed method. The capacity of the CV-based indices to reflect therapists' qualitative evaluations was confirmed. Furthermore, compared to the conventional quantitative index, the CV-based indices provided more detailed quantitative information with lower costs. CV-based evaluations enable therapists to quantify details of motor performance that are currently observed qualitatively. The development of such precise quantification methods will improve the science and practice of occupational therapy and allow therapists to perform to their full potential.