DFRscore: Deep Learning-Based Scoring of Synthetic Complexity with Drug-Focused Retrosynthetic Analysis for High-Throughput Virtual Screening

Recently emerging generative AI models enable us to produce a vast number of compounds for potential applications. While they can provide novel molecular structures, the synthetic feasibility of the generated molecules is often questioned. To address this issue, a few recent studies have attempted to use deep learning models to estimate the synthetic accessibility of many molecules rapidly. However, retrosynthetic analysis tools used to train the models rely on reaction templates automatically extracted from a large reaction database that are not domain-specific and may exhibit low chemical correctness. To overcome this limitation, we introduce DFRscore (Drug-Focused Retrosynthetic score), a deep learning-based approach for a more practical assessment of synthetic accessibility in drug discovery. The DFRscore model is trained exclusively on drug-focused reactions, providing a predicted number of minimally required synthetic steps for each compound. This approach enables practitioners to filter out compounds that do not meet their desired level of synthetic accessibility at an early stage of high-throughput virtual screening for accelerated drug discovery. The proposed strategy can be easily adapted to other domains by adjusting the synthesis planning setup of the reaction templates and starting materials.

Deep Learning-Based Chemical Similarity for Accelerated Organic Light-Emitting Diode Materials Discovery

Thermally activated delayed fluorescence (TADF) material has attracted great attention as a promising metal-free organic light-emitting diode material with a high theoretical efficiency. To accelerate the discovery of novel TADF materials, computer-aided material design strategies have been developed. However, they have clear limitations due to the accessibility of only a few computationally tractable properties. Here, we propose TADF-likeness, a quantitative score to evaluate the TADF potential of molecules based on a data-driven concept of chemical similarity to existing TADF molecules. We used a deep autoencoder to characterize the common features of existing TADF molecules with common chemical descriptors. The score was highly correlated with the four essential electronic properties of TADF molecules and had a high success rate in large-scale virtual screening of millions of molecules to identify promising candidates at almost no cost, validating its feasibility for accelerating TADF discovery. The concept of TADF-likeness can be extended to other fields of materials discovery.

Accelerated chemical science with AI

In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to address the time it takes to bring materials from initial discovery to commercialization. Artificial intelligence (AI)-based techniques, in particular, are having a transformative and accelerating impact on many if not most, technological domains. To shed light on these questions, the authors and participants gathered in person for the ASLLA Symposium on the theme of 'Accelerated Chemical Science with AI' at Gangneung, Republic of Korea. We present the findings, ideas, comments, and often contentious opinions expressed during four panel discussions related to the respective general topics: 'Data', 'New applications', 'Machine learning algorithms', and 'Education'. All discussions were recorded, transcribed into text using Open AI's Whisper, and summarized using LG AI Research's EXAONE LLM, followed by revision by all authors. For the broader benefit of current researchers, educators in higher education, and academic bodies such as associations, publishers, librarians, and companies, we provide chemistry-specific recommendations and summarize the resulting conclusions.

Diffusion-based generative AI for exploring transition states from 2D molecular graphs

The exploration of transition state (TS) geometries is crucial for elucidating chemical reaction mechanisms and modeling their kinetics. Recently, machine learning (ML) models have shown remarkable performance for prediction of TS geometries. However, they require 3D conformations of reactants and products often with their appropriate orientations as input, which demands substantial efforts and computational cost. Here, we propose a generative approach based on the stochastic diffusion method, namely TSDiff, for prediction of TS geometries just from 2D molecular graphs. TSDiff outperforms the existing ML models with 3D geometries in terms of both accuracy and efficiency. Moreover, it enables to sample various TS conformations, because it learns the distribution of TS geometries for diverse reactions in training. Thus, TSDiff finds more favorable reaction pathways with lower barrier heights than those in the reference database. These results demonstrate that TSDiff shows promising potential for an efficient and reliable TS exploration.

Critical role of electrons in the short lifetime of blue OLEDs

Designing robust blue organic light-emitting diodes is a long-standing challenge in the display industry. The highly energetic states of blue emitters cause various degradation paths, leading to collective luminance drops in a competitive manner. However, a key mechanism of the operational degradation of organic light-emitting diodes has yet to be elucidated. Here, we show that electron-induced degradation reactions play a critical role in the short lifetime of blue organic light-emitting diodes. Our control experiments demonstrate that the operational lifetime of a whole device can only be explained when excitons and electrons exist together. We examine the atomistic mechanisms of the electron-induced degradation reactions by analyzing their energetic profiles using computational methods. Mass spectrometric analysis of aged devices further confirm the key mechanisms. These results provide new insight into rational design of robust blue organic light-emitting diodes.

Gaussian-Approximated Poisson Preconditioner for Iterative Diagonalization in Real-Space Density Functional Theory

Various real-space methods optimized on massive parallel computers have been developed for efficient large-scale density functional theory (DFT) calculations of materials and biomolecules. The iterative diagonalization of the Hamiltonian matrix is a computational bottleneck in real-space DFT calculations. Despite the development of various iterative eigensolvers, the absence of efficient real-space preconditioners has hindered their overall efficiency. An efficient preconditioner must satisfy two conditions: appropriate acceleration of the convergence of the iterative process and inexpensive computation. This study proposed a Gaussian-approximated Poisson preconditioner (GAPP) that satisfied both conditions and was suitable for real-space methods. A low computational cost was realized through the Gaussian approximation of a Poisson Green's function. Fast convergence was achieved through the proper determination of Gaussian coefficients to fit the Coulomb energies. The performance of GAPP was evaluated for several molecular and extended systems, and it showed the highest efficiency among the existing preconditioners adopted in real-space codes.

Dynamic Precision Approach for Accelerating Large-Scale Eigenvalue Solvers in Electronic Structure Calculations on Graphics Processing Units

Single precision (SP) arithmetic can be greatly accelerated as compared to double precision (DP) arithmetic on graphics processing units (GPUs). However, the use of SP in the whole process of electronic structure calculations is inappropriate for the required accuracy. We propose a 3-fold dynamic precision approach for accelerated calculations but still with the accuracy of DP. Here, SP, DP, and mixed precision are dynamically switched during an iterative diagonalization process. We applied this approach to the locally optimal block preconditioned conjugate gradient method to accelerate a large-scale eigenvalue solver for the Kohn-Sham equation. We determined a proper threshold for switching each precision scheme by examining the convergence pattern on the eigenvalue solver only with the kinetic energy operator of the Kohn-Sham Hamiltonian. As a result, we achieved up to 8.53× and 6.60× speedups for band structure and self-consistent field calculations, respectively, for test systems under various boundary conditions on NVIDIA GPUs.

Molecular Generative Model via Retrosynthetically Prepared Chemical Building Block Assembly

Deep generative models are attracting attention as a smart molecular design strategy. However, previous models often render molecules with low synthesizability, hindering their real-world applications. Here, a novel graph-based conditional generative model which makes molecules by tailoring retrosynthetically prepared chemical building blocks until achieving target properties in an auto-regressive fashion is proposed. This strategy improves the synthesizability and property control of the resulting molecules and also helps learn how to select appropriate building blocks and bind them together to achieve target properties. By applying a negative sampling method to the selection process of building blocks, this model overcame a critical limitation of previous fragment-based models, which can only use molecules from the training set during generation. As a result, the model works equally well with unseen building blocks without sacrificing computational efficiency. It is demonstrated that the model can generate potential inhibitors with high docking scores against the 3CL protease of SARS-COV-2.

Neural network-based pseudopotential: development of a transferable local pseudopotential

Transferable local pseudopotentials (LPPs) are essential for fast quantum simulations of materials. However, various types of LPPs suffer from low transferability, especially since they do not consider the norm-conserving condition. Here we propose a novel approach based on a deep neural network to produce transferable LPPs. We introduced a generalized Kerker method expressed with the deep neural network to represent the norm-conserving pseudo-wavefunctions. Its unique feature is that all necessary conditions of pseudopotentials can be explicitly considered in terms of a loss function. Then, it can be minimized using the back-propagation technique just with single point all-electron atom data. To assess the transferability and accuracy of the neural network-based LPPs (NNLPs), we carried out density functional theory calculations for the s- and p-block elements of the second to the fourth periods. The NNLPs outperformed other types of LPPs in both atomic and bulk calculations for most elements. In particular, they showed good transferability by predicting various properties of bulk systems including binary alloys with higher accuracy than LPPs tailored to bulk data.

System-Specific Separable Basis Based on Tucker Decomposition: Application to Density Functional Calculations

For fast density functional calculations, a suitable basis that can accurately represent the orbitals within a reasonable number of dimensions is essential. Here, we propose a new type of basis constructed from Tucker decomposition of a finite-difference (FD) Hamiltonian matrix, which is intended to reflect the system information implied in the Hamiltonian matrix and satisfies orthonormality and separability conditions. By introducing the system-specific separable basis, the computation time for FD density functional calculations for seven two- and three-dimensional periodic systems was reduced by a factor of 2–71 times, while the errors in both the atomization energy per atom and the band gap were limited to less than 0.1 eV. The accuracy and speed of the density functional calculations with the proposed basis can be systematically controlled by adjusting the rank size of Tucker decomposition.

PIGNet: a physics-informed deep learning model toward generalized drug-target interaction predictions

Recently, deep neural network (DNN)-based drug–target interaction (DTI) models were highlighted for their high accuracy with affordable computational costs. Yet, the models' insufficient generalization remains a challenging problem in the practice of in silico drug discovery. We propose two key strategies to enhance generalization in the DTI model. The first is to predict the atom–atom pairwise interactions via physics-informed equations parameterized with neural networks and provides the total binding affinity of a protein–ligand complex as their sum. We further improved the model generalization by augmenting a broader range of binding poses and ligands to training data. We validated our model, PIGNet, in the comparative assessment of scoring functions (CASF) 2016, demonstrating the outperforming docking and screening powers than previous methods. Our physics-informing strategy also enables the interpretation of predicted affinities by visualizing the contribution of ligand substructures, providing insights for further ligand optimization.

PIGNet, a deep neural network-based drug–target interaction model guided by physics and extensive data augmentation, shows significantly improved generalization ability and model performance.

Response to Comment on "Reversible disorder-order transitions in atomic crystal nucleation"

Yu et al. suggested calculating precisely the size ranges of the three parts of our figure 3A, adjusting the free-energy levels in figure 3B, and considering the shape effect in the first-principles calculation. The first and second suggestions raise strong concerns for misinterpretation and overinterpretation of our experiments. The original calculation is sufficient to support our claim about crystalline-to-disordered transformations.

Circularly Polarized Light Can Override and Amplify Asymmetry in Supramolecular Helices

Circularly polarized light (CPL) is an inherently chiral entity and is considered one of the possible deterministic signals that led to the evolution of homochirality. While accumulating examples indicate that chirality beyond the molecular level can be induced by CPL, not much is yet known about circumstances where the spin angular momentum of light competes with existing molecular chiral information during the chirality induction and amplification processes. Here we present a light-triggered supramolecular polymerization system where chiral information can both be transmitted and nonlinearly amplified in a "sergeants-and-soldiers" manner. While matching handedness with CPL resulted in further amplification, we determined that opposite handedness could override molecular information at the supramolecular level when the enantiomeric excess was low. The presence of a critical chiral bias suggests a bifurcation point in the homochirality evolution under random external chiral perturbation. Our results also highlight opportunities for the orthogonal control of supramolecular chirality decoupled from molecular chirality preexisting in the system.

Drug-likeness scoring based on unsupervised learning

Drug-likeness prediction is important for the virtual screening of drug candidates. It is challenging because the drug-likeness is presumably associated with the whole set of necessary properties to pass through clinical trials, and thus no definite data for regression is available. Recently, binary classification models based on graph neural networks have been proposed but with strong dependency of their performances on the choice of the negative set for training. Here we propose a novel unsupervised learning model that requires only known drugs for training. We adopted a language model based on a recurrent neural network for unsupervised learning. It showed relatively consistent performance across different datasets, unlike such classification models. In addition, the unsupervised learning model provides drug-likeness scores that well separate distributions with increasing mean values in the order of datasets composed of molecules at a later step in a drug development process, whereas the classification model predicted a polarized distribution with two extreme values for all datasets presumably due to the overconfident prediction for unseen data. Thus, this new concept offers a pragmatic tool for drug-likeness scoring and further can be applied to other biochemical applications.

Morphology Transformation of Foldamer Assemblies Triggered by Single Oxygen Atom on Critical Residue Switch

The synthesis of morphologically well-defined peptidic materials via self-assembly is challenging but demanding for biocompatible functional materials. Moreover, switching morphology from a given shape to other predictable forms by molecular modification of the identical building block is an even more complicated subject because the self-assembly of flexible peptides is prone to diverge upon subtle structural change. To accomplish controllable morphology transformation, systematic self-assembly studies are performed using congener short β-peptide foldamers to find a minimal structural change that alters the self-assembled morphology. Introduction of oxygen-containing β-amino acid (ATFC) for subtle electronic perturbation on hydrophobic foldamer induces a previously inaccessible solid-state conformational split to generate the most susceptible modification site for morphology transformation of the foldamer assemblies. The site-dependent morphological switching power of ATFC is further demonstrated by dual substitution experiments and proven by crystallographic analyses. Stepwise morphology transformation is shown by modifying an identical foldamer scaffold. This study will guide in designing peptidic molecules from scratch to create complex and biofunctional assemblies with nonspherical shapes.

Reversible disorder-order transitions in atomic crystal nucleation

Nucleation in atomic crystallization remains poorly understood, despite advances in classical nucleation theory. The nucleation process has been described to involve a nonclassical mechanism that includes a spontaneous transition from disordered to crystalline states, but a detailed understanding of dynamics requires further investigation. In situ electron microscopy of heterogeneous nucleation of individual gold nanocrystals with millisecond temporal resolution shows that the early stage of atomic crystallization proceeds through dynamic structural fluctuations between disordered and crystalline states, rather than through a single irreversible transition. Our experimental and theoretical analyses support the idea that structural fluctuations originate from size-dependent thermodynamic stability of the two states in atomic clusters. These findings, based on dynamics in a real atomic system, reshape and improve our understanding of nucleation mechanisms in atomic crystallization.

Graph theory-based reaction pathway searches and DFT calculations for the mechanism studies of free radical-initiated peptide sequencing mass spectrometry (FRIPS MS): a model gas-phase reaction of GGR tri-peptide

Graph theory-based reaction pathway searches (ACE-Reaction program) and density functional theory calculations were performed to shed light on the mechanisms for the production of [a_n + H]⁺, x_n⁺, y_n⁺, z_n⁺, and [y_n + 2H]⁺ fragments formed in free radical-initiated peptide sequencing (FRIPS) mass spectrometry measurements of a small model system of glycine-glycine-arginine (GGR). In particular, the graph theory-based searches, which are rarely applied to gas-phase reaction studies, allowed us to investigate reaction mechanisms in an exhaustive manner without resorting to chemical intuition. As expected, radical-driven reaction pathways were favorable over charge-driven reaction pathways in terms of kinetics and thermodynamics. Charge- and radical-driven pathways for the formation of [y_n + 2H]⁺ fragments were carefully compared, and it was revealed that the [y_n + 2H]⁺ fragments observed in our FRIPS MS spectra originated from the radical-driven pathway, which is in contrast to the general expectation. The acquired understanding of the FRIPS fragmentation mechanism is expected to aid in the interpretation of FRIPS MS spectra. It should be emphasized that graph theory-based searches are powerful and effective methods for studying reaction mechanisms, including gas-phase reactions in mass spectrometry.

Molecular Generative Model Based on an Adversarially Regularized Autoencoder

Deep generative models are attracting great attention as a new promising approach for molecular design. A variety of models reported so far are based on either a variational autoencoder (VAE) or a generative adversarial network (GAN), but they have limitations such as low validity and uniqueness. Here, we propose a new type of model based on an adversarially regularized autoencoder (ARAE). It basically uses latent variables like VAE, but the distribution of the latent variables is estimated by adversarial training like in GAN. The latter is intended to avoid both the insufficiently flexible approximation of posterior distribution in VAE and the difficulty in handling discrete variables in GAN. Our benchmark study showed that ARAE indeed outperformed conventional models in terms of validity, uniqueness, and novelty per generated molecule. We also demonstrated a successful conditional generation of drug-like molecules with ARAE for the control of both cases of single and multiple properties. As a potential real-world application, we could generate epidermal growth factor receptor inhibitors sharing the scaffolds of known active molecules while satisfying drug-like conditions simultaneously.

Scaffold-based molecular design with a graph generative model

Searching for new molecules in areas like drug discovery often starts from the core structures of known molecules. Such a method has called for a strategy of designing derivative compounds retaining a particular scaffold as a substructure. On this account, our present work proposes a graph generative model that targets its use in scaffold-based molecular design. Our model accepts a molecular scaffold as input and extends it by sequentially adding atoms and bonds. The generated molecules are then guaranteed to contain the scaffold with certainty, and their properties can be controlled by conditioning the generation process on desired properties. The learned rule of extending molecules can well generalize to arbitrary kinds of scaffolds, including those unseen during learning. In the conditional generation of molecules, our model can simultaneously control multiple chemical properties despite the search space constrained by fixing the substructure. As a demonstration, we applied our model to designing inhibitors of the epidermal growth factor receptor and show that our model can employ a simple semi-supervised extension to broaden its applicability to situations where only a small amount of data is available.

A Bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification

Deep neural networks have been increasingly used in various chemical fields. In the nature of a data-driven approach, their performance strongly depends on data used in training. Therefore, models developed in data-deficient situations can cause highly uncertain predictions, leading to vulnerable decision making. Here, we show that Bayesian inference enables more reliable prediction with quantitative uncertainty analysis. Decomposition of the predictive uncertainty into model- and data-driven uncertainties allows us to elucidate the source of errors for further improvements. For molecular applications, we devised a Bayesian graph convolutional network (GCN) and evaluated its performance for molecular property predictions. Our study on the classification problem of bio-activity and toxicity shows that the confidence of prediction can be quantified in terms of the predictive uncertainty, leading to more accurate virtual screening of drug candidates than standard GCNs. The result of log P prediction illustrates that data noise affects the data-driven uncertainty more significantly than the model-driven one. Based on this finding, we could identify artefacts that arose from quantum mechanical calculations in the Harvard Clean Energy Project dataset. Consequently, the Bayesian GCN is critical for molecular applications under data-deficient conditions.

Clinical evaluation of anesthesia for high-risk cesarean section at a tertiary medical center: retrospective study for 8 years (2009-2016)

Objective

The number of high-risk pregnancies is increasing in tertiary medical centers. Therefore, we investigated perioperative outcomes based on risk factors to ascertain proper maternal and neonatal management.

Methods

We reviewed the medical records of patients receiving cesarean sections over an 8-year period. Clinical parameters for anesthesia and the neonatal outcome were compared among high-risk groups after subdivision by the number of clinical risk factors. The groups were as follows: group A (one risk factor), group B (two risk factors), and group C (three or more risk factors).

Results

Patient age, estimated blood loss (EBL), and volume of transfused red blood cell (RBC) were higher in group B than group A. Birth weight, 1- and 5-minute Apgar scores, and gestational age were lower while the frequency of neonatal intensive care unit (NICU) admission was higher in group B than group A. Group C patients were significantly older than group A or B patients. Birth weight, 1- and 5-minute Apgar scores and gestational age were significantly lower while frequency of NICU admission was higher in group C than group A and B.

Conclusion

The number of maternal risk factors was positively associated with adverse outcomes in the neonates.

Quantum transport properties of single-crystalline Ag2Se0.5Te0.5 nanowires as a new topological material

We report a ternary silver chalcogenide, Ag2Se0.5Te0.5, as a new topological material with improved quantum transport properties. Single-crystalline nanostructures of ternary silver chalcogenides Ag2SexTe1-x are synthesized with a tunable chemical composition via the chemical vapor transport method. Quantum transport studies reveal that Ag2Se0.5Te0.5 nanowires present topological surface states with higher electron mobility and longer mean free path compared to binary Ag-chalcogenides. First-principles calculations also indicate that Ag2Se0.5Te0.5 is a topological insulator, and the observed enhancement in transport properties could imply reduced bulk carrier contribution in the new ternary silver chalcogenide.

Study of Li Adsorption on Graphdiyne Using Hybrid DFT Calculations

Promising applications of graphdiyne have often been initiated by theoretical predictions especially using DFT known as the most powerful first-principles electronic structure calculation method. However, there is no systematic study on the reliability of DFT for the prediction of the electronic properties of the graphdiyne. Here, we performed a study of Li adsorption on the graphdiyne using hybrid DFT with LC-ωPBE and compared the results with those of PBE, because accurate prediction of the Li adsorption is important for performance as a Li storage that was first theoretically suggested and then experimentally realized. Our results show that PBE overestimates the adsorption energy inside a pore and the barrier height at the transition state of in-plane diffusion compared to the those of LC-ωPBE. In particular, LC-ωPBE predicted almost barrier-less in-plane diffusion of Li on the graphdiyne because of the presence of both in-plane and out-of-plane π orbitals. Also, LC-ωPBE favors a high spin state due to the exact exchange energy when several Li atoms are adsorbed on the graphdiyne, whereas PBE favors a low spin state. Thus, the use of the hybrid DFT is critical for reliable predictions on the electronic properties of the graphdiyne.

Poly(amide-imide) materials for transparent and flexible displays

The key component currently missing for the next generation of transparent and flexible displays is a high-performance polymer material that is flexible, while showing optical and thermal properties of glass. It must be transparent to visible light and show a low coefficient of thermal expansion (CTE). While specialty plastics such as aromatic polyimides are promising, reducing their CTE and improving transparency simultaneously proved challenging, with increasing coloration the main problem to be resolved. We report a new poly(amide-imide) material that is flexible and displays glass-like behavior with a CTE value of 4 parts per million/°C. This novel polymer was successfully used as a substrate to fabricate transparent and flexible indium-gallium-zinc oxide thin-film transistors.

Molecular generative model based on conditional variational autoencoder for de novo molecular design

We propose a molecular generative model based on the conditional variational autoencoder for de novo molecular design. It is specialized to control multiple molecular properties simultaneously by imposing them on a latent space. As a proof of concept, we demonstrate that it can be used to generate drug-like molecules with five target properties. We were also able to adjust a single property without changing the others and to manipulate it beyond the range of the dataset.

Feasibility of Activation Energy Prediction of Gas-Phase Reactions by Machine Learning

Machine learning based on big data has emerged as a powerful solution in various chemical problems. We investigated the feasibility of machine learning models for the prediction of activation energies of gas-phase reactions. Six different models with three different types, including the artificial neural network, the support vector regression, and the tree boosting methods, were tested. We used the structural and thermodynamic properties of molecules and their differences as input features without resorting to specific reaction types so as to maintain the most general input form for broad applicability. The tree boosting method showed the best performance among others in terms of the coefficient of determination, mean absolute error, and root mean square error, the values of which were 0.89, 1.95, and 4.49 kcal mol^-1 , respectively. Computation time for the prediction of activation energies for 2541 test reactions was about one second on a single computing node without using accelerators.

Fragment-orbital tunneling currents and electronic couplings for analysis of molecular charge-transfer systems

In theoretical charge-transfer research, calculation of the electronic coupling element is crucial for examining the degree of the electronic donor-acceptor interaction. The tunneling current (TC), representing the magnitudes and directions of electron flow, provides a way of evaluating electronic couplings, along with the ability of visualizing how electrons flow in systems. Here, we applied the TC theory to π-conjugated organic dimer systems, in the form of our fragment-orbital tunneling current (FOTC) method, which uses the frontier molecular-orbitals of system fragments as diabatic states. For a comprehensive test of FOTC, we assessed how reasonable the computed electronic couplings and the corresponding TC densities are for the hole- and electron-transfer databases HAB11 and HAB7. FOTC gave 12.5% mean relative unsigned error with regard to the high-level ab initio reference. The shown performance is comparable with that of fragment-orbital density functional theory, which gave the same error by 20.6% or 13.9% depending on the formulation. In the test of a set of nucleobase π stacks, we showed that the original TC expression is also applicable to nondegenerate cases under the condition that the overlap between the charge distributions of diabatic states is small enough to offset the energy difference. Lastly, we carried out visual analysis on the FOTC densities of thiophene dimers with different intermolecular alignments. The result depicts an intimate topological connection between the system geometry and electron flow. Our work provides quantitative and qualitative grounds for FOTC, showing it to be a versatile tool in characterization of molecular charge-transfer systems.

Efficient prediction of reaction paths through molecular graph and reaction network analysis

Despite remarkable advances in computational chemistry, prediction of reaction mechanisms is still challenging, because investigating all possible reaction pathways is computationally prohibitive due to the high complexity of chemical space. A feasible strategy for efficient prediction is to utilize chemical heuristics. Here, we propose a novel approach to rapidly search reaction paths in a fully automated fashion by combining chemical theory and heuristics. A key idea of our method is to extract a minimal reaction network composed of only favorable reaction pathways from the complex chemical space through molecular graph and reaction network analysis. This can be done very efficiently by exploring the routes connecting reactants and products with minimum dissociation and formation of bonds. Finally, the resulting minimal network is subjected to quantum chemical calculations to determine kinetically the most favorable reaction path at the predictable accuracy. As example studies, our method was able to successfully find the accepted mechanisms of Claisen ester condensation and cobalt-catalyzed hydroformylation reactions.

Efficient structural elucidation of microhydrated biomolecules through the interrogation of hydrogen bond networks

Pattern analysis of H-bond networks through a graph-theoretic method is very effective in determining the global minima of microhydrated biomolecules.

Non-empirical atomistic dipole-interaction-model for quantum plasmon simulation of nanoparticles

Plasmonic nanoparticles in the quantum regime exhibit characteristic optical properties that cannot be described by classical theories. Time-dependent density functional theory (TDDFT) is rising as a versatile tool for study on such systems, but its application has been limited to very small clusters due to rapidly growing computational costs. We propose an atomistic dipole-interaction-model for quantum plasmon simulations as a practical alternative. Namely the atomic dipole approximation represents induced dipoles with atomic polarizabilities obtained from TDDFT without empirical parameters. It showed very good agreement with TDDFT for plasmonic spectra of small silver clusters at much lower computational cost, though it is not appropriate for molecular-like excitations. It could also reproduce the plasmonic band shift experimentally observed in sub-10 nm silver particles.

Effects of the locality of a potential derived from hybrid density functionals on Kohn-Sham orbitals and excited states

Density functional theory (DFT) has been an essential tool for electronic structure calculations in various fields. In particular, its hybrid method including the Hartree-Fock (HF) exchange term remarkably improves the reliability of DFT for chemical applications and computational material design. There are two different types of exchange-correlation potential that can be derived from hybrid functionals. The conventional approach adopts a non-multiplicative potential including the non-local HF exchange operator. Herein, we propose to use a local multiplicative potential as an alternative for accurate excited state calculations. We show that such a local potential can be derived from existing global hybrid functionals using the optimized effective potential method. As a proof-of-concept, we chose PBE0 and investigated its performance for the Caricato benchmark set. Unlike the conventional one, the local potential produced orbital energy gaps with no strong dependence on the mixing ratio as a good approximation for optical excitations. Furthermore, its time-dependent DFT resulted in a surprisingly small mean absolute error even with a local density approximation kernel, surpassing all reported values with various popular functionals. In particular, most excitations were dictated by single orbital transitions due to physically meaningful virtual orbitals, which is beneficial to clear interpretations in the molecular orbital picture.

Performance of heterogeneous computing with graphics processing unit and many integrated core for hartree potential calculations on a numerical grid

We investigated the performance of heterogeneous computing with graphics processing units (GPUs) and many integrated core (MIC) with 20 CPU cores (20×CPU). As a practical example toward large scale electronic structure calculations using grid-based methods, we evaluated the Hartree potentials of silver nanoparticles with various sizes (3.1, 3.7, 4.9, 6.1, and 6.9 nm) via a direct integral method supported by the sinc basis set. The so-called work stealing scheduler was used for efficient heterogeneous computing via the balanced dynamic distribution of workloads between all processors on a given architecture without any prior information on their individual performances. 20×CPU + 1GPU was up to ∼1.5 and ∼3.1 times faster than 1GPU and 20×CPU, respectively. 20×CPU + 2GPU was ∼4.3 times faster than 20×CPU. The performance enhancement by CPU + MIC was considerably lower than expected because of the large initialization overhead of MIC, although its theoretical performance is similar with that of CPU + GPU. © 2016 Wiley Periodicals, Inc.

Effect of Nicardipine on Haemodynamic and Bispectral Index Changes following Endotracheal Intubation

We investigated the effect of IV nicardipine on haemodynamic and bispectral index responses to the induction of general anaesthesia and intubation. Forty patients were randomly allocated to two groups of 20 to receive normal saline or nicardipine 15 μg/kg IV 30 s after induction. Ninety seconds later, tracheal intubation was performed. Systolic blood pressure, heart rate and bispectral index were measured at baseline, 1 min after induction, pre-intubation, and every minute until 5 min after endotracheal intubation. Rate–pressure product values were calculated. In the nicardipine group, systolic blood pressure decreased compared with the control group, and heart rate increased compared with the control group. Bispectral index and rate–pressure product showed no differences between the two groups. In conclusion, the administration of 15 μg/kg nicardipine IV does not affect anaesthetic depth in response to the induction of general anaesthesia and intubation.

Effect of Administration of Ketorolac and Local Anaesthetic Infiltration for Pain Relief after Laparoscopic-assisted Vaginal Hysterectomy

The efficacy of local anaesthetic infiltration and/or non-steroidal anti-inflammatory drugs for post-operative analgesia following laparoscopic-assisted vaginal hysterectomy (LAVH) was investigated in 83 patients, randomized into four groups in this double-blind, placebo-controlled study: group BK, local infiltration with bupivacaine and pre-incisional intramuscular (IM) ketorolac; group NN, saline local infiltration IM; group BN, local infiltration with bupivacaine and saline IM; group NK, local infiltration with saline and ketorolac IM. Post-operative pain scores were assessed at 1 h, 3 h, 6 h, 12 h and 24 h using a visual analogue scale (VAS). The major pain site, first analgesic request time and incidence of analgesic requests were also recorded. At 1 h, 3 h and 6 h after surgery, group BK patients had significantly lower VAS pain scores than group NN patients. The first analgesic request time was significantly longer in group BK than in groups NN, BN and NK. Pre-incisional treatment with ketorolac IM and local infiltration with bupivacaine reduced post-operative pain after LAVH.