Chemical Graph-Based Transformer Models for Yield Prediction of High-Throughput Cross-Coupling Reaction Datasets.

The chemical reaction yield is an important factor to determine the reaction conditions. Recently, many data-driven models for yield prediction using high-throughput experimentation datasets have been reported. In this study, we propose a neural network architecture based on the chemical graphs of the reaction components to predict the reaction yield.

Bridging Structure- and Ligand-Based Virtual Screening through Fragmented Interaction Fingerprint.

Ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS), and their combinations, are frequently conducted in modern drug discovery campaigns. As a form of combination, an amalgamation of methods from ligand- and structure-based information, termed hybrid VS approaches, has been extensively investigated such as using interaction fingerprints (IFPs) in combination with machine learning (ML) models. This approach has the potential to prioritize active compounds in terms of protein-ligand binding and ligand structural characteristics, which is assumed to be difficult using either one of the approaches.

Scaffold-Hopped Compound Identification by Ligand-Based Approaches with a Prospective Affinity Test.

Scaffold-hopped (SH) compounds are bioactive compounds structurally different from known active compounds. Identifying SH compounds in the ligand-based approaches has been a central issue in medicinal chemistry, and various molecular representations of scaffold hopping have been proposed. However, appropriate representations for SH compound identification remain unclear.

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.

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.

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.

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.

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.

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.

Changing patterns of pulmonary abnormalities in rheumatoid arthritis.

Background: As a first step in identifying the developmental pathways of pulmonary abnormalities in rheumatoid arthritis (RA), we sought to determine the existing and changing patterns of pulmonary abnormalities.

Methods: We conducted a retrospective cohort study of consecutive patients with RA who underwent high-resolution computed tomography before and during biologic therapy. The presence of 20 pulmonary abnormalities and the changes in those abnormalities were recorded.

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.

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

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.

Ligand-based approaches to activity prediction for the early stage of structure-activity-relationship progression.

The retrospective evaluation of virtual screening approaches and activity prediction models are important for methodological development. However, for fair comparison, evaluation data sets must be carefully prepared. In this research, we compiled structure-activity-relationship matrix-based data sets for 15 biological targets along with many diverse inactive compounds, assuming the early stage of structure-activity-relationship progression.

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.

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.

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.

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 (IC). Experimental IC 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.

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.

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.

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.

Evaluation of different virtual screening strategies on the basis of compound sets with characteristic core distributions and dissimilarity relationships.

In this work, computational compound screening strategies on the basis of two- and three-dimensional (2D and 3D) molecular representations were investigated including similarity searching and support vector machine (SVM) ranking. Calculations based on topological fingerprints and molecular shape queries and features were compared. A unique aspect of the analysis setting apart from previous comparisons of 2D and 3D virtual screening approaches has been the design of compound reference, training, and test data sets with controlled incremental increases in intra-set structural diversity and different categories of structural relationships between reference/training and test sets.

Development of R-Group Fingerprints Based on the Local Landscape from an Attachment Point of a Molecular Structure.

Molecular fingerprints are indispensable in medicinal chemistry for quantifying chemical structures. Fingerprints can be calculated for substructures with attachment points, which are positions where a substructure and a corresponding core structure connect. Because structures with attachment points can be crucial for understanding structure-activity relationships, fingerprints specialized for representing this structural feature are required.