ChemTS: An Efficient Python Library for de novo Molecular Generation

Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of predetermined fragments. Recently, deep neural network models such as variational auto encoders (VAEs) and recurrent neural networks (RNNs) are shown to be effective in de novo design of molecules without any predetermined fragments. This paper presents a novel python library ChemTS that explores the chemical space by combining Monte Carlo tree search (MCTS) and an RNN. In a benchmarking problem of optimizing the octanol-water partition coefficient and synthesizability, our algorithm showed superior efficiency in finding high-scoring molecules. ChemTS is available at https://github.com/tsudalab/ChemTS

A Stream Calculus of Bottomed Sequences for Real Number Computation

AbstractA calculus XPCF of 1⊥-sequences, which are infinite sequences of {0,1,⊥} with at most one copy of bottom, is proposed and investigated. It has applications in real number computation in that the unit interval I is topologically embedded in the set Σ⊥,1ω of 1⊥-sequences and a real function on I can be written as a program which inputs and outputs 1⊥-sequences. In XPCF, one defines a function on Σ⊥,1ω only by specifying its behaviors for the cases that the first digit is 0 and 1. Then, its value for a sequence starting with a bottom is calculated by taking the meet of the values for the sequences obtained by filling the bottom with 0 and 1. The validity of the reduction rule of this calculus is justified by the adequacy theorem to a domain-theoretic semantics. Some example programs including addition and multiplication are shown. Expressive powers of XPCF and related languages are also investigated

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Cryptic Lineages in the Cardiocondyla sl. kagutsuchi Terayama (Hymenoptera: Formicidae) Discovered by Phylogenetic and Morphological Approaches

The taxonomy of ant species in the genus Cardiocondyla is very confused due to the extreme difficulty in separating many species based on morphology alone. In Japan, one group of the species complex Cardiocondyla sl. kagutsuchi has both winged and wingless worker-like (ergatoid) males (dimorphic) whereas others have only ergatoid males (monomorphic). The presence of both groups prompted us to hypothesize that C. sl. kagutsuchi presumably includes several independent species with differences in their male wing morphologies. However, whether any species boundary actually exists between the male groups has remained unsolved over the 10+ years since the previous revision of this genus. In this study, using discriminant and phylogenetic analyses, we compared the worker caste morphology of this species complex among lineages detected by phylogenetic analyses. In addition, we examined the number of sexuals present in field colonies. Our results revealed the existence of at least three morphological and phylogenetic groups within this species complex

The Manchurian Incident and the Soviet Reserves Policies

Russia and Japan : a historical survey : joint symposium of the SB RAS and the CNEAS TU / edited by Kyosuke Terayam

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. Owing to the multiconformational states of biomolecules and noisy character of diffraction images, diffraction image improvement by multi-image processing is often ineffective for such molecules. Herein, a single-image super-resolution (SR) model was constructed using an SR convolutional neural network (SRCNN). Data preparation was performed in silico to consider the actual observation situation with unknown molecular orientations and the fluctuation of molecular structure and incident X-ray intensity. It was demonstrated that the trained SRCNN model improved the single-particle diffraction image quality, corresponding to an observed image with an incident X-ray intensity (approximately three to seven times higher than the original X-ray intensity), while retaining the individuality of the diffraction images. The feasibility of SPA for flexible biomolecules with sizes ≤100 nm was dramatically increased by introducing the SRCNN improvement at the beginning of the various structural analysis schemes

A Surprisingly Non-attractiveness of Commercial Poison Baits to Newly Established Population of White-Footed Ant, Technomyrmex brunneus (Hymenoptera: Formicidae), in a Remote Island of Japan

The white-footed ant, Technomyrmex brunneus, was newly introduced and established in a remote island of Japan and has caused unacceptable damage to the daily life of residents. To establish proper control measures, the present study investigated whether T. brunneus is effectively attracted to commercially available poison baits used to exterminate common household pest ants and the Argentine ant in Japan. Cafeteria experiments using three types of nontoxic baits and eight types of commercial poison baits for ants were conducted in the field, and the attractiveness was compared among the baits. The liquid poison bait “Arimetsu,” which consists of 42.6% water, 55.4% sugar, and 2.0% borate, and nontoxic 10% (w/v) sucrose water showed the highest attractiveness. On the other hand, other commercial poison baits were not as attractive. Therefore, sucrose liquid is the most effective attractive component to use in poison baits for T. brunneus

Efficient Search for Energetically Favorable Molecular Conformations against Metastable States via Gray-Box Optimization

In order to accurately understand and estimate molecular properties, finding energetically favorable molecular conformations is the most fundamental task for atomistic computational research on molecules and materials. Geometry optimization based on quantum chemical calculations has enabled the conformation prediction of arbitrary molecules, including de novo ones. However, it is computationally expensive to perform geometry optimizations for enormous conformers. In this study, we introduce the gray-box optimization (GBO) framework, which enables optimal control over the entire geometry optimization process, among multiple conformers. Algorithms designed for GBO roughly estimate energetically preferable conformers during their geometry optimization iterations. They then preferentially compute promising conformers. To evaluate the performance of the GBO framework, we applied it to a test set consisting of seven dipeptides and mycophenolic acid to determine their stable conformations at the density functional theory level. We thus preferentially obtained energetically favorable conformations. Furthermore, the computational costs required to find the most stable conformation were significantly reduced (approximately 1% on average, compared to the naive approach for the dipeptides)