Generalizability Improvement of Interpretable Symbolic Regression Models for Quantitative Structure-Activity Relationships

In the pursuit of optimal quantitative structure-activity relationship (QSAR) models, two key factors are paramount: the robustness of predictive ability and the interpretability of the model. Symbolic regression (SR) searches for the mathematical expressions that explain a training data set. Thus, the models provided by SR are globally interpretable. We previously proposed an SR method that can generate interpretable expressions by humans. This study introduces an enhanced symbolic regression method, termed filter-induced genetic programming 2 (FIGP2), as an extension of our previously proposed SR method. FIGP2 is designed to improve the generalizability of SR models and to be applicable to data sets in which cost-intensive descriptors are employed. The FIGP2 method incorporates two major improvements: a modified domain filter to eradicate diverging expressions based on optimal calculation and the introduction of a stability metric to penalize expressions that would lead to overfitting. Our retrospective comparative analysis using 12 structure-activity relationship data sets revealed that FIGP2 surpassed the previously proposed SR method and conventional modeling methods, such as support vector regression and multivariate linear regression in terms of predictive performance. Generated mathematical expressions by FIGP2 were relatively simple and not divergent in the domain of function. Taken together, FIGP2 can be used for making interpretable regression models with predictive ability.

Differences and similarities in cytokine profiles of macrophage activation syndrome in systemic lupus erythematosus and adult-onset Still's disease

To clarify the differences and similarities in the cytokine profiles of macrophage activating syndrome (MAS) between systemic lupus erythematosus (SLE) and adult-onset Still's disease (AOSD). The study participants included 9 patients with MAS-SLE, 22 with non-MAS-SLE, 9 with MAS-AOSD, and 13 with non-MAS-AOSD. Serum cytokine levels were measured using a multiplex bead assay. Cytokine levels were compared between patients with SLE and AOSD with/without MAS. Moreover, cytokine patterns were examined using principal component analysis (PCA) and cluster analysis. IL-6, IL-8, IL-18, and TNF-α levels were elevated in patients with SLE and AOSD. IFN-α levels were elevated in SLE, whereas IL-1β and IL-18 levels were elevated in AOSD. In SLE, IFN-α and IL-10 levels were higher in MAS than in non-MAS and controls. PCA revealed distinctive cytokine patterns in SLE and AOSD, SLE with IFN-α and IP-10, AOSD with IL-1β, IL-6, and IL-18, and enhanced cytokine production in MAS. PCA and cluster analysis showed no differences in cytokine patterns between the MAS and non-MAS groups. However, serum ferritin levels were correlated with IFN-α levels in SLE. Cytokine profiles differed between SLE and AOSD but not between MAS and non-MAS. MAS is induced by the enhancement of underlying cytokine abnormalities rather than by MAS-specific cytokine profiles. Type I IFN may be involved in MAS development in patients with SLE.

Interface State Density Prediction between an Insulator and a Semiconductor by Gaussian Process Regression Models for a Modified Process

During data-driven process condition optimization on a laboratory scale, only a small-size data set is accessible and should be effectively utilized. On the other hand, during process development, new operations are frequently inserted or current operations are modified. These accessible data sets are somewhat related but not exactly the same type. In this study, we focus on the prediction of the quality of the interface between an insulator and GaN as a semiconductor for the potential application of GaN power semiconductor devices. The quality of the interface was represented as the interface state density, Dit, and the inserted operation to the process was the ultraviolet (UV)/O3-gas treatment. Our retrospective evaluation of model-building approaches for Dit prediction from a process condition revealed that for the UV/O3-treated interfaces, data of interfaces without the treatment contributed to performance improvement. Such performance improvement was not observed when using a data set of Si as the semiconductor. As a modeling method, the automatic relevance vector-based Gaussian process regression with the prior distribution of the length-scale parameters exhibited a relatively high predictive performance and represented a reasonable uncertainty of prediction as reflected by the distance to the training data set. This feature is a prerequisite for a potential application of Bayesian optimization. Furthermore, hyperparameters in the prior distribution of the length-scales could be optimized by leave-one-out cross-validation.

Chemometrics Approach Based on Wavelet Transforms for the Estimation of Monomer Concentrations from FTIR Spectra

Fourier-transform infrared (FTIR) spectroscopy can detect the presence of functional groups and molecules directly from a mixed solution of organic molecules. Although it is quite useful to monitor chemical reactions, quantitative analysis of FTIR spectra becomes difficult when various peaks of different widths overlap. To overcome this difficulty, we propose a chemometrics approach to accurately predict the concentration of components in chemical reactions, yet interpretable by humans. The proposed method first decomposes a spectrum into peaks with various widths by the wavelet transform. Subsequently, a sparse linear regression model is built using the wavelet coefficients. Models by the method are interpretable using the regression coefficients shown on Gaussian distributions with various widths. The interpretation is expected to reveal the relation of broad regions in spectra to the model prediction. In this study, we conducted the prediction of monomer concentration in copolymerization reactions of five monomers against methyl methacrylate by various chemometric approaches including conventional methods. A rigorous validation scheme revealed that the proposed method overall showed better predictive ability than various linear and non-linear regression methods. The visualization results were consistent with the interpretation obtained by another chemometric approach and qualitative evaluation. The proposed method is found to be useful for calculating the concentrations of monomers in copolymerization reactions and for the interpretation of spectra.

Screening and Validation of Odorants against Influenza A Virus Using Interpretable Regression Models

Influenza is a respiratory infection caused by the influenza virus that is prevalent worldwide. One of the most contagious variants of influenza is influenza A virus (IAV), which usually spreads in closed spaces through aerosols. Preventive measures such as novel compounds are needed that can act on viral membranes and provide a safe environment against IAV infection. In this study, we screened compounds with common fragrances that are generally used to mask unpleasant odors but can also exhibit antiviral activity against a strain of IAV. Initially, a set of 188 structurally diverse odorants were collected, and their antiviral activity was measured in vapor phase against the IAV solution. Regression models were built for the prediction of antiviral activity using this set of odorants by taking into account their structural features along with vapor pressure and partition coefficient (n-octanol/water). The models were interpreted using a feature weighting approach and Shapley Additive exPlanations to rationalize the predictions as an additional validation for virtual screening. This model was used to screen odorants from an in-house odorant data set consisting of 2020 odorants, which were later evaluated using in vitro experiments. Out of 11 odorants proposed using the final model, 8 odorants were found to exhibit antiviral activity. The feature interpretation of screened odorants suggested that they contained hydrophilic substructures, such as hydroxyl group, which might contribute to denaturation of proteins on the surface of the virus. These odorants should be explored as a preventive measure in closed spaces to decrease the risk of infections of IAV.

Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity

Activity cliffs (AC) are formed by pairs of structural analogues that are active against the same target but have a large difference in potency. While much of our knowledge about ACs has originated from the analysis and comparison of compounds and activity data, several studies have reported AC predictions over the past decade. Different from typical compound classification tasks, AC predictions must be carried out at the level of compound pairs representing ACs or nonACs. Most AC predictions reported so far have focused on individual methods or comparisons of two or three approaches and only investigated a few compound activity classes (from 2 to 10). Although promising prediction accuracy has been reported in most cases, different system set-ups, AC definitions, methods, and calculation conditions were used, precluding direct comparisons of these studies. Therefore, we have carried out a large-scale AC prediction campaign across 100 activity classes comparing machine learning methods of greatly varying complexity, ranging from pair-based nearest neighbor classifiers and decision tree or kernel methods to deep neural networks. The results of our systematic predictions revealed the level of accuracy that can be expected for AC predictions across many different compound classes. In addition, prediction accuracy did not scale with methodological complexity but was significantly influenced by memorization of compounds shared by different ACs or nonACs. In many instances, limited training data were sufficient for building accurate models using different methods and there was no detectable advantage of deep learning over simpler approaches for AC prediction. On a global scale, support vector machine models performed best, by only small margins compared to others including simple nearest neighbor classifiers.

Extended Connectivity Fingerprints as a Chemical Reaction Representation for Enantioselective Organophosphorus-Catalyzed Asymmetric Reaction Prediction

Predicting the outcomes of organic reactions using data-driven approaches aids in the acceleration of research. In laboratory-scale experiments, only a small number of reaction data can be accessed for machine learning model construction, where reaction representations play a pivotal role in the success of model construction. Nevertheless, representation comparison for a small data set is not adequate. Herein, focusing on the enantioselectivity of phosphoric-acid-catalyzed reactions, various two-dimensional and three-dimensional reaction representations (descriptors) were compared. Overall, the concatenated form of the extended connectivity fingerprints showed the best predictive capability for the two types of data sets: high-throughput experimental data and manually collected literature data sets. Furthermore, highlighting the substructure contribution to the prediction outcome was shown to be informative for guiding catalyst development.

Mechanism assay of interaction between blood vessels-near infrared probe and cell surface marker proteins of endothelial cells

In vivo blood vessels imaging is crucial to study blood vessels related diseases in real-time. For this purpose, fluorescent based imaging is one of the utmost techniques for imaging a living system. The discovery of a new near-infrared probe (CyA-B2) by screening chemical probe library in our previous report which showed the most specific binding on the blood capillaries of the 3D-tissue models give us interest to study more about the binding site of this probe to the surface of endothelial cells main component cell of blood capillaries. By studying the competition assays of CyA-B2 using several potential surface markers of endothelial cells found through the chemical database (ChEMBL) and manually selected, CD133 gave the lowest IC₅₀ (half maximal inhibitory concentration) value. Hence, CD133 protein which is expressed on the endothelial cell membrane was postulated to be the binding site due to the suppression of CyA-B2 on the blood capillaries by the competition assays. Since, CD133 is also expressed on many types of cancer cells, it would be useful to use CyA-B2 as a bioprobe to monitor or diagnostic tumor growth.

Visualization of Topological Pharmacophore Space with Graph Edit Distance

A topological pharmacophore (TP) is a chemical graph-based pharmacophore representation, where nodes are pharmacophoric features (PF) and edges are topological distances between PFs. Previously proposed sparse pharmacophore graphs (SPhGs) for TPs were shown to be effective in identifying structurally different active compounds while maintaining the interpretability of the graphs. However, one limitation of using SPhGs as queries is that many structurally similar SPhGs can be identified from a set of active compounds, requiring the classification and visualization of SPhGs, followed by an understanding of the pharmacophore hypotheses. In this study, we propose a scheme for SPhG analysis based on dimensionality reduction techniques with the graph edit distance (GED) metric. This metric enables measuring similarities among SPhGs in a quantitative manner. The visualization of SPhGs, which themselves are the graphs shared by active compounds, can help us understand the pharmacophore hypotheses as well as the data set. As a proof-of-concept study, we generated two-dimensional SPhG-maps using three dimensionality reduction techniques for six biological targets. A comparison with other pharmacophore representations was also conducted. We demonstrated knowledge extraction (interpretation of the data set) from the generated maps. Our findings include a suitable mapping algorithm as well as a pharmacophore hypothesis analysis procedure using an SPhG-map.

Prediction of Reaction Yield for Buchwald-Hartwig Cross-coupling Reactions Using Deep Learning

Chemical reaction yield is one of the most important factors for determining reaction conditions. Recently, several machine learning-based prediction models using high-throughput experiment (HTE) data sets were reported for the prediction of reaction yield. However, none of them were at a practical level in terms of predictive ability. In this study, we propose a message passing neural network (MPNN) model for chemical yield prediction, focusing on the Buchwald-Hartwig cross-coupling HTE data set. As an initial atom embedding in MPNN model, we propose to use the Mol2Vec feature vectors pre-trained using a large compound database. Predictive ability of the proposed model was higher than that of previously reported five models for the three out of five data sets. Moreover, visualization of important atoms based on self-attention mechanism was in favor of Mol2Vec as an atom embedding rather than other embeddings including previously employed simple representations.

Interpretation of Ligand-Based Activity Cliff Prediction Models Using the Matched Molecular Pair Kernel

Activity cliffs (ACs) are formed by two structurally similar compounds with a large difference in potency. Accurate AC prediction is expected to help researchers' decisions in the early stages of drug discovery. Previously, predictive models based on matched molecular pair (MMP) cliffs have been proposed. However, the proposed methods face a challenge of interpretability due to the black-box character of the predictive models. In this study, we developed interpretable MMP fingerprints and modified a model-specific interpretation approach for models based on a support vector machine (SVM) and MMP kernel. We compared important features highlighted by this SVM-based interpretation approach and the SHapley Additive exPlanations (SHAP) as a major model-independent approach. The model-specific approach could capture the difference between AC and non-AC, while SHAP assigned high weights to the features not present in the test instances. For specific MMPs, the feature weights mapped by the SVM-based interpretation method were in agreement with the previously confirmed binding knowledge from X-ray co-crystal structures, indicating that this method is able to interpret the AC prediction model in a chemically intuitive manner.

Sparse Topological Pharmacophore Graphs for Interpretable Scaffold Hopping

The aim of scaffold hopping (SH) is to find compounds consisting of different scaffolds from those in already known active compounds, giving an opportunity for unexplored regions of chemical space. We previously demonstrated the usefulness of pharmacophore graphs (PhGs) for this purpose through proof-of-concept virtual screening experiments. PhGs consist of nodes and edges corresponding to pharmacophoric features (PFs) and their topological distances. Although PhGs were effective in SH, they are hard to interpret as they are complete graphs. Herein, we introduce an intuitive representation of a molecule, termed as sparse pharmacophore graphs (SPhG) by keeping the topological distances among PFs as much as possible while reducing the number of edges in the graphs. Several benchmark calculations quantitatively confirmed the sparseness of the graphs and the preservation of topological distances among pharmacophoric points. As proof-of-concept applications, virtual screening (VS) trials for SH were conducted using active and inactive compounds from ChEMBL and PubChem databases for three biological targets: thrombin, tyrosine kinase ABL1, and κ-opioid receptor. The performances of VS were comparable with using fully connected PhGs. Furthermore, highly ranked SPhGs were interpretable for the three biological targets, in particular for thrombin, for which selected SPhGs were in agreement with the structure-based interpretation.

Ranking-Oriented Quantitative Structure-Activity Relationship Modeling Combined with Assay-Wise Data Integration

In ligand-based drug design, quantitative structure-activity relationship (QSAR) models play an important role in activity prediction. One of the major end points of QSAR models is half-maximal inhibitory concentration (IC₅₀). Experimental IC₅₀ data from various research groups have been accumulated in publicly accessible databases, providing an opportunity for us to use such data in predictive QSAR models. In this study, we focused on using a ranking-oriented QSAR model as a predictive model because relative potency strength within the same assay is solid information that is not based on any mechanical assumptions. We conducted rigorous validation using the ChEMBL database and previously reported data sets. Ranking support vector machine (ranking-SVM) models trained on compounds from similar assays were as good as support vector regression (SVR) with the Tanimoto kernel trained on compounds from all the assays. As effective ways of data integration, for ranking-SVM, integrated compounds should be selected from only similar assays in terms of compounds. For SVR with the Tanimoto kernel, entire compounds from different assays can be incorporated.

Comparing predictive ability of QSAR/QSPR models using 2D and 3D molecular representations

Quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) models predict biological activity and molecular property based on the numerical relationship between chemical structures and activity (property) values. Molecular representations are of importance in QSAR/QSPR analysis. Topological information of molecular structures is usually utilized (2D representations) for this purpose. However, conformational information seems important because molecules are in the three-dimensional space. As a three-dimensional molecular representation applicable to diverse compounds, similarity between a test molecule and a set of reference molecules has been previously proposed. This 3D representation was found to be effective on virtual screening for early enrichment of active compounds. In this study, we introduced the 3D representation into QSAR/QSPR modeling (regression tasks). Furthermore, we investigated relative merits of 3D representations over 2D in terms of the diversity of training data sets. For the prediction task of quantum mechanics-based properties, the 3D representations were superior to 2D. For predicting activity of small molecules against specific biological targets, no consistent trend was observed in the difference of performance using the two types of representations, irrespective of the diversity of training data sets.

Ligand-based Activity Cliff Prediction Models with Applicability Domain

Activity cliffs (ACs) are formed by pairs of structurally similar compounds with large differences in potency. Predicting ACs is of high interest in lead optimization for drug discovery. Previous AC prediction models that focused on matched molecular pair (MMP) cliffs produced adequate performances. However, the extrapolation ability of these models is unclear because the main scaffold for MMPs, the core structure, could exist in both training and test data sets. Also, representation of MMPs did not consider the attachment points where the core and R-group substituents are connected. In this study, we aimed to improve a ligand-based AC prediction method using molecular fingerprints. We incorporated applicability domain, which was defined using R-path fingerprints to consider the local environment around an attachment point. Rigorous evaluation of the extrapolation ability of AC prediction models showed that MMP-cliffs were accurately predicted for nine biological targets. Furthermore, incorporation of training MMPs with cores distinct from those of test MMPs improved the predictability compared with using training MMPs with only similar cores.

Large-Scale Comparison of Alternative Similarity Search Strategies with Varying Chemical Information Contents

Similarity searching (SS) is a core approach in computational compound screening and has a long tradition in pharmaceutical research. Over the years, different approaches have been introduced to increase the information content of search calculations and optimize the ability to detect compounds having similar activity. We present a large-scale comparison of distinct search strategies on more than 600 qualifying compound activity classes. Challenging test cases for SS were identified and used to evaluate different ways to further improve search performance, which provided a differentiated view of alternative search strategies and their relative performance. It was found that search results could not only be improved by increasing compound input information but also by focusing similarity calculations on database compounds. In the presence of multiple active reference compounds, asymmetric SS with high weights on chemical features of target compounds emerged as an overall preferred approach across many different activity classes. These findings have implications for practical virtual screening applications.

Iterative Screening Methods for Identification of Chemical Compounds with Specific Values of Various Properties

Identification of chemical compounds having desirable properties is a central goal of screening campaigns. Iterative screening is a means of surveying a set of compounds, during which their property values are determined and used as feedback for regression models. Quantitative models that assess the relationships between chemical structures and property/activity are repeatedly updated through this type of cycle, and the efficient sampling of compounds for the subsequent test is a key factor in the early identification of target compounds. Nevertheless, methodological approaches to comparisons and to establishing the degree of extrapolation of sampled compounds, including the effects of applicability domains, are still required. In the present study, we conducted a series of virtual experiments to assess the characteristics of different iterative screening methods. Genetic algorithm-based partial least-squares regression, support vector regression, Bayesian optimization with Gaussian Process (GP), and batch-based Bayesian optimization with GP (GP_batch) were all compared, based on the analysis of one million compounds extracted from the ZINC database. Our results show that, irrespective of the diversity of the initial set of compounds, it was possible to identify a compound having the desired property value using the appropriate screening method. However, overall, the GP_batch method was found to be preferable when evaluating properties either which are difficult to predict or for which a key factor is present in the set of molecular descriptors.

Th2 cell differentiation from naive CD4+ T cells is enhanced by autocrine CC chemokines in atopic diseases

BACKGROUND

Chemokines are involved not only in regulating leucocyte recruitment, but also in other activities. However, functions other than cell recruitment remain poorly understood. We have already shown that the production of CC chemokine ligand (CCL)17 and CCL22 by antigen-stimulated naïve CD4⁺  T cells was higher in asthmatic patients than in healthy controls. However, the role of these chemokines in stimulated naïve CD4⁺ T cells remains unclear.

OBJECTIVE

To clarify the biological function of CCL17 and CCL22 on naïve CD4⁺ T, we examined effects of these two chemokines on naïve CD4⁺ T cells expressing CC chemokine receptor (CCR)4 (a receptor for CCL17 and CCL22) during differentiation of Th2 cells in asthmatic patients as allergic subjects.

METHODS

Naïve CD4⁺ T cells were prepared from healthy controls and patients with asthma. We analysed effect of CCL17 and CCL22, and blocking their receptor on differentiation of Th2 cells.

RESULTS

Production of CCL17 and CCL22 by activated naive CD4⁺ T cells under Th2 condition was much more in asthmatic patients than in healthy controls. Proliferation and survival of the Th2 differentiating cells and restimulation-induced IL-4 production were much greater in asthmatic patients than in healthy controls. These cell biological phenomena were inhibited by blockade of CCR4. The biological effects of exogenous CCL17 and CCL22 were apparently observed in both healthy controls and asthmatic patients. The effectiveness of these chemokines on naïve CD4⁺ T cells from healthy controls was stronger than those from asthmatic patients. We found that thymic stromal lymphopoietin (TSLP), a Th2 promoting chemokine, is involved in the activation of CD4⁺ naïve T cells via production of CCL17 and CCL22.

CONCLUSIONS AND CLINICAL RELEVANCE

These data suggest that CCL17 and CCL22 produced by TSLP-primed naïve CD4⁺ T cells in asthma might contribute to an increase in Th2 cells via autocrine loops.

Computational Assessment of Chemical Saturation of Analogue Series under Varying Conditions

Assessing the degree to which analogue series are chemically saturated is of major relevance in compound optimization. Decisions to continue or discontinue series are typically made on the basis of subjective judgment. Currently, only very few methods are available to aid in decision making. We further investigate and extend a computational concept to quantitatively assess the progression and chemical saturation of a series. To these ends, existing analogues and virtual candidates are compared in chemical space and compound neighborhoods are systematically analyzed. A large number of analogue series from different sources are studied, and alternative chemical space representations and virtual analogues of different designs are explored. Furthermore, evolving analogue series are distinguished computationally according to different saturation levels. Taken together, our findings provide a basis for practical applications of computational saturation analysis in compound optimization.

Prediction of Compound Profiling Matrices Using Machine Learning

Screening of compound libraries against panels of targets yields profiling matrices. Such matrices typically contain structurally diverse screening compounds, large numbers of inactives, and small numbers of hits per assay. As such, they represent interesting and challenging test cases for computational screening and activity predictions. In this work, modeling of large compound profiling matrices was attempted that were extracted from publicly available screening data. Different machine learning methods including deep learning were compared and different prediction strategies explored. Prediction accuracy varied for assays with different numbers of active compounds, and alternative machine learning approaches often produced comparable results. Deep learning did not further increase the prediction accuracy of standard methods such as random forests or support vector machines. Target-based random forest models were prioritized and yielded successful predictions of active compounds for many assays.

Computational method for estimating progression saturation of analog series

In lead optimization, it is difficult to estimate when an analog series might be saturated and synthesis of additional compounds would be unlikely to yield further progress. Rather than terminating a series, one often continues to generate analogs hoping to reach the final optimization goal, even if obstacles that are faced ultimately prove to be unsurmountable. Clearly, methodologies to better understand series progression and saturation are highly desirable. However, only a few approaches are currently available to monitor series progression and aid in decision making. Herein, we introduce a new computational method to assess progression saturation of an analog series by relating the properties of existing compounds to those of synthetic candidates and comparing their distributions in chemical space. The neighborhoods of analogs are analyzed and the distance relationships between existing and candidate compounds quantified. An intuitive dual scoring scheme makes it possible to characterize analog series and their degree of progression saturation.

Structure Modification toward Applicability Domain of a QSAR/QSPR Model Considering Activity/Property

In drug and material design, the activity and property values of the designed chemical structures can be predicted by quantitative structure-activity and structure-property relationship (QSAR/QSPR) models. When a QSAR/QSPR model is applied to chemical structures, its applicability domain (AD) must be considered. The predicted activity/property values are only reliable for chemical structures inside the AD. Chemical structures outside the AD are usually neglected, as the predicted values are unreliable. The purpose of this study is to develop a methodology for obtaining novel chemical structures with the desired activity or property based on a QSAR/QSPR model by making use of the neglected structures. We propose a structure modification strategy for the AD that considers the activity and property simultaneously. The AD is defined by a one-class support vector machine and the structure modification is guided by a partial derivative of the AD model and matched molecular pairs analysis. Three proof-of-concept case studies generate novel chemical structures inside the AD that exhibit preferable activity/property values according to the QSAR/QSPR model.

Exploring differential evolution for inverse QSAR analysis

Inverse quantitative structure-activity relationship (QSAR) modeling encompasses the generation of compound structures from values of descriptors corresponding to high activity predicted with a given QSAR model. Structure generation proceeds from descriptor coordinates optimized for activity prediction. Herein, we concentrate on the first phase of the inverse QSAR process and introduce a new methodology for coordinate optimization, termed differential evolution (DE), that originated from computer science and engineering. Using simulation and compound activity data, we demonstrate that DE in combination with support vector regression (SVR) yields effective and robust predictions of optimized coordinates satisfying model constraints and requirements. For different compound activity classes, optimized coordinates are obtained that exclusively map to regions of high activity in feature space, represent novel positions for structure generation, and are chemically meaningful.

[Ring-system-based Chemical Structure Enumeration for de Novo Design]

  De novo molecular design aims to propose molecules exhibiting desired properties and/or activities, as constructed from scratch. Although this approach opposes the widely used virtual screening (VS), the same criteria should be applied, such as ones based on substructure filters, and quantitative structure-property relationship (QSPR) and quantitative structure-activity relationship (QSAR) regression models. QSPR/QSAR, which enables us to predict properties/activities by making use of experimental data, are widely used in academia as well as in industry. Herewith, we present a novel chemical structure generation system by combining fragments whose final chemical structures satisfy the aforementioned criteria. Using inverse analysis, QSPR/QSAR models determine a specific region in chemical space corresponding to a set of desired values by a designer. Chemical structures are generated by combining ring systems, as well as atom fragments, in every possible way until violating the upper bounds of that region. We also show the results of inverse-QSAR analysis for the human Alpha-2A adrenergic receptor. This suggests that our system has features preferable to VS-like methods in terms of the number of generated structures.

Exhaustive Structure Generation for Inverse-QSPR/QSAR

Chemical structure generation based on quantitative structure property relationship (QSPR) or quantitative structure activity relationship (QSAR) models is one of the central themes in the field of computer-aided molecular design. The objective of structure generation is to find promising molecules, which according to statistical models, are considered to have desired properties. In this paper, a new method is proposed for the exhaustive generation of chemical structures based on inverse-QSPR/QSAR. In this method, QSPR/QSAR models are constructed by multiple linear regression method, and then the conditional distribution of explanatory variables given the desired properties is estimated by inverse analysis of the models using the framework of a linear Gaussian model. Finally, chemical structures are exhaustively generated by a sophisticated algorithm that is based on a canonical construction path method. The usefulness of the proposed method is demonstrated using a dataset of the boiling points of acyclic hydrocarbons containing up to 12 carbon atoms. The QSPR model was constructed with 600 hydrocarbons and their boiling points. Using the proposed method, chemical structures which had boiling points of 100, 150, or 200 °C were exhaustively generated.

Inverse QSPR/QSAR Analysis for Chemical Structure Generation (from y to x)

Retrieving descriptor information (x information) from a value of an objective variable (y) is a fundamental problem in inverse quantitative structure-property relationship (inverse-QSPR) analysis but challenging because of the complexity of the preimage function. Herewith, we propose using a cluster-wise multiple linear regression (cMLR) model as a QSPR model for inverse-QSPR analysis. x information is acquired as a probability density function by combining cMLR and the prior distribution modeled with a mixture of Gaussians (GMMs). Three case studies were conducted to demonstrate various aspects of the potential of cMLR. It was found that the predictive power of cMLR was superior to that of MLR, especially for data with nonlinearity. Moreover, it turned out that the applicability domain could be considered since the posterior distribution inherits the prior distribution's feature (i.e., training data feature) and represents the possibility of having the desired property. Finally, a series of inverse analyses with the GMMs/cMLR was demonstrated with the aim to generate de novo structures having specific aqueous solubility.

Chemography of natural product space

We present the application of the generative topographic map algorithm to visualize the chemical space populated by natural products and synthetic drugs. Generative topographic maps may be used for nonlinear dimensionality reduction and probabilistic modeling. For compound mapping, we represented the molecules by two-dimensional pharmacophore features (chemically advanced template search descriptor). The results obtained suggest a close resemblance of synthetic drugs with natural products in terms of their pharmacophore features, despite pronounced differences in chemical structure. Generative topographic map-based cluster analysis revealed both known and new potential activities of natural products and drug-like compounds. We conclude that the generative topographic map method is suitable for inferring functional similarities between these two classes of compounds and predicting macromolecular targets of natural products.

Ring-System-Based Exhaustive Structure Generation for Inverse-QSPR/QSAR

Inverse-QSPR/QSAR aims to solve the inverse problem of chemical structure generation based on QSPR/QSAR models, once the properties or activities are specified. To efficiently solve this problem, an exhaustive ring-system-based structure generation methodology was developed. The concept of the applicability domain (AD) is automatically acknowledged within the proposed strategy. The local AD is considered by introducing the probability distribution of a given data set, and the universal AD is considered using ring-system-based fragments in the training data set. Structures with desired properties or activities are enumerated by assembling fragments, including atomic elements, in a tree-like way. The usefulness of the proposed method is demonstrated through a case study of ligand design for the human alpha 2A adrenergic receptor (ADR2A_HUMAN). We succeeded in generating structures focusing only on a pre-defined region in chemical space, resulting in structures whose desired activity has a high likelihood being efficiently generated. In addition, the limitations of our proposed method and future challenges are discussed.

Generation of New Acidic Sites by Dispersing Zinc Oxide Fine Particles on Silica

New acidic sites were generated on finely dispersed ZnO particles supported on silica prepared by impregnation method. Ammonia-TPD, XPS, XRD, TEM and catalytic measurements revealed that the acidic properties on the nano-sized amorphous particles of ZnO were quite different from homogeneous ZnO-SiO

Deletion of the RNA polymerase subunit RPB4 acts as a global, not stress-specific, shut-off switch for RNA polymerase II transcription at high temperatures

We used whole genome expression analysis to investigate the changes in the mRNA profile in cells lacking the Saccharomyces cerevisiae RNA polymerase II subunit RPB4 (Delta RPB4). Our results indicated that an essentially complete shutdown of transcription occurs upon temperature shift of this conditionally lethal mutant; 98% of mRNA transcript levels decrease at least 2-fold, 96% at least 4-fold. This data was supported by in vivo experiments that revealed a rapid and greater than 5-fold decline in steady state poly(A) RNA levels after the temperature shift. Expression of several individual genes, measured by Northern analysis, was also consistent with the whole genome expression profile. Finally we demonstrated that the loss of RNA polymerase II activity causes secondary effects on RNA polymerase I, but not RNA polymerase III, transcription. The transcription phenotype of the Delta RPB4 mutant closely mirrors that of the temperature-sensitive rpb1-1 mutant frequently implemented as a tool to inactivate the RNA polymerase II in vivo. Therefore, the Delta RPB4 mutant can be used to easily design strains that enable the study of distinct post-transcriptional cellular processes in the absence of RNA polymerase II transcription.

85Kr measurement system for continuous monitoring at the Meteorological Research Institute, Japan

A 85Kr measurement system for continuous monitoring based principally on the BfS-IAR method (activity measurement of 85Kr by gas counting coupled with gas chromatographic separation, using pure CH4 as carrier and Counting gas) was implemented for the first time in Japan. In this paper, a detailed description of the system and procedures is given and the inter-comparison results of our system with the BfS-IAR system are presented. A consistent temporal concentration change with high accuracy and consistency of the respective data with the BfS-IAR data (maximum difference of 5%) were achieved with the Meteorological Research Institute (MRI) system, which shows that the system is valid and reliable for the purpose of background monitoring for 85Kr in air. Also, the 85Kr monitoring record at the MRI during 1995-2001 is described. The record distinctively shows the Northern Hemispheric background 85Kr concentrations at the mid-latitude and the elevated concentrations affected by the operation of a nuclear fuel reprocessing plant in Tokai-mura, Ibaraki. Japan.

T cell apoptosis causes peripheral T cell depletion in mice transgenic for the HIV-1 vpr gene

Vpr, an accessory protein of HIV, is known to affect viral replication as well as cell growth, differentiation, and apoptosis in vitro. To investigate its pathogenicity in vivo, we have produced mice transgenic for the HIV-1 vpr gene with the CD4 enhancer/promoter. Interestingly, apoptotic death of T lymphocytes was enhanced in those mice, causing marked reduction of T cells in lymphatic organs and peripheral blood. Involvement of Bcl-x, Bax, and Caspase-1, but not of the Fas-Fas ligand system, was suggested in the apoptotic processes. These observations suggest that Vpr is involved in the pathogenesis of T cell depletion in HIV-infected people.

Multiple mechanisms of suppression circumvent transcription defects in an RNA polymerase mutant

Using a high-copy-number suppressor screen to obtain clues about the role of the yeast RNA polymerase II subunit RPB4 in transcription, we identified three suppressors of the temperature sensitivity resulting from deletion of the RPB4 gene (DeltaRPB4). One suppressor is Sro9p, a protein related to La protein, another is the nucleosporin Nsp1p, and the third is the RNA polymerase II subunit RPB7. Suppression by RPB7 was anticipated since its interaction with RPB4 is well established both in vitro and in vivo. We examined the effect of overexpression of each suppressor gene on transcription. Interestingly, suppression of the temperature-sensitive phenotype correlates with the correction of a characteristic transcription defect of this mutant: each suppressor restored the level of promoter-specific, basal transcription to wild-type levels. Examination of the effects of the suppressors on other in vivo transcription aberrations in DeltaRPB4 cells revealed significant amelioration of defects in certain inducible genes in Sro9p and RPB7, but not in Nsp1p, suppressor cells. Analysis of mRNA levels demonstrated that overexpression of each of the three suppressors minimally doubled the mRNA levels during stationary phase. However, the elevated mRNA levels in Sro9p suppressor cells appear to result from a combination of enhanced transcription and message stability. Taken together, these results demonstrate that these three proteins influence transcription and implicate Sro9p in both transcription and posttranscription events.

Anthropogenic radionuclides in seawater of the Far Eastern Seas

Large quantities of radioactive wastes have been dumped in the Far Eastern Seas by the former Soviet Union and the Russian Federation, and small amounts of radioactive wastes have been dumped by Japan and the Republic of Korea. In order to investigate the concentrations of anthropogenic radionuclides in the nine dumping areas, a second expedition was conducted in 1995 by Japan, the Republic of Korea, the Russian Federation and IAEA, following the first expedition in 1994. The results show that 137Cs, 90Sr and 239 + 240Pu concentrations in surface and bottom waters at dumping areas do not significantly differ from the values observed in background areas, and from historical values. There is no clear effect of possible contamination due to radioactive waste dumping. The concentrations and water column inventories of 137Cs, 90Sr and 239 + 240Pu in the Far Eastern seas are controlled by physical oceanic processes such as horizontal transport and biogeochemical processes such as scavenging.

Air concentration of radiocaesium in Tsukuba, Japan following the release from the Tokai waste treatment plant: comparisons of observations with predictions

On March 11, 1997 a fire and explosion accident occurred at the bituminization facility of the Power Reactor and Nuclear Fuel Development, Tokai, Japan. As a result of this accident, 134,137Cs was detected in an air filter sample collected at the Meteorological Research Institute, Tsukuba during March 10 to 12. The 134,137Cs air concentration was about 100 and 10 muBq m-3, respectively. This result suggests that there was little radiation exposure of the residents in the area. The average 137Cs air concentration during this period was about two orders of magnitude higher than "baseline" air (sub-muBq m-3) during February to April, 1997, measured by ultra-low background gamma-spectrometry. By a simple calculation using a Gaussian plume model with the measured data, we estimated the minimum emission of the radioactivity by the PNC accident to be in the range 60 MBq to around 600 MBq. The meteorological condition during the week of the accident are also described.