kGCN: a graph-based deep learning framework for chemical structures

Deep learning is developing as an important technology to perform various tasks in cheminformatics. In particular, graph convolutional neural networks (GCNs) have been reported to perform well in many types of prediction tasks related to molecules. Although GCN exhibits considerable potential in various applications, appropriate utilization of this resource for obtaining reasonable and reliable prediction results requires thorough understanding of GCN and programming. To leverage the power of GCN to benefit various users from chemists to cheminformaticians, an open-source GCN tool, kGCN, is introduced. To support the users with various levels of programming skills, kGCN includes three interfaces: a graphical user interface (GUI) employing KNIME for users with limited programming skills such as chemists, as well as command-line and Python library interfaces for users with advanced programming skills such as cheminformaticians. To support the three steps required for building a prediction model, i.e., pre-processing, model tuning, and interpretation of results, kGCN includes functions of typical pre-processing, Bayesian optimization for automatic model tuning, and visualization of the atomic contribution to prediction for interpretation of results. kGCN supports three types of approaches, single-task, multi-task, and multi-modal predictions. The prediction of compound-protein interaction for four matrixmetalloproteases, MMP-3, -9, -12 and -13, in the inhibition assays is performed as a representative case study using kGCN. Additionally, kGCN provides the visualization of atomic contributions to the prediction. Such visualization is useful for the validation of the prediction models and the design of molecules based on the prediction model, realizing “explainable AI” for understanding the factors affecting AI prediction. kGCN is available at https://github.com/clinfo

A prospective compound screening contest identified broader inhibitors for Sirtuin 1

Potential inhibitors of a target biomolecule, NAD-dependent deacetylase Sirtuin 1, were identified by a contest-based approach, in which participants were asked to propose a prioritized list of 400 compounds from a designated compound library containing 2.5 million compounds using in silico methods and scoring. Our aim was to identify target enzyme inhibitors and to benchmark computer-aided drug discovery methods under the same experimental conditions. Collecting compound lists derived from various methods is advantageous for aggregating compounds with structurally diversified properties compared with the use of a single method. The inhibitory action on Sirtuin 1 of approximately half of the proposed compounds was experimentally accessed. Ultimately, seven structurally diverse compounds were identified

Discutindo a educação ambiental no cotidiano escolar: desenvolvimento de projetos na escola formação inicial e continuada de professores

A presente pesquisa buscou discutir como a Educação Ambiental (EA) vem sendo trabalhada, no Ensino Fundamental e como os docentes desta escola compreendem e vem inserindo a EA no cotidiano escolar., em uma escola estadual do município de Tangará da Serra/MT, Brasil. Para tanto, realizou-se entrevistas com os professores que fazem parte de um projeto interdisciplinar de EA na escola pesquisada. Verificou-se que o projeto da escola não vem conseguindo alcançar os objetivos propostos por: desconhecimento do mesmo, pelos professores; formação deficiente dos professores, não entendimento da EA como processo de ensino-aprendizagem, falta de recursos didáticos, planejamento inadequado das atividades. A partir dessa constatação, procurou-se debater a impossibilidade de tratar do tema fora do trabalho interdisciplinar, bem como, e principalmente, a importância de um estudo mais aprofundado de EA, vinculando teoria e prática, tanto na formação docente, como em projetos escolares, a fim de fugir do tradicional vínculo “EA e ecologia, lixo e horta”.Facultad de Humanidades y Ciencias de la Educació

Intermolecular Interaction Among Remdesivir, RNA and RNA-Dependent RNA Polymerase of SARS-CoV-2 Analyzed by Fragment Molecular Orbital Calculation

COVID-19, a disease caused by a new strain of coronavirus (SARS-CoV-2) originating from Wuhan, China, has now spread around the world, triggering a global pandemic, leaving the public eagerly awaiting the development of a specific medicine and vaccine. In response, aggressive efforts are underway around the world to overcome COVID-19. In this study, referencing the data published on the Protein Data Bank (PDB ID: 7BV2) on April 22, we conducted a detailed analysis of the interaction between the complex structures of the RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 and Remdesivir, an antiviral drug, from the quantum chemical perspective based on the fragment molecular orbital (FMO) method. In addition to the hydrogen bonding and intra-strand stacking between complementary strands as seen in normal base pairs, Remdesivir bound to the terminus of an primer-RNA strand was further stabilized by diagonal π-π stacking with the -1A base of the complementary strand and an additional hydrogen bond with an intra-strand base, due to the effect of chemically modified functional group. Moreover, stable OH/π interaction is also formed with Thr687 of the RdRp. We quantitatively revealed the exhaustive interaction within the complex among Remdesivir, template-primer-RNA, RdRp and co-factors, and published the results in the FMODB database.</p

Legislative Documents

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Molecular Recognition of SARS-CoV-2 Spike Glycoprotein: Quantum Chemical Hot Spot and Epitope Analyses

Due to the COVID-19 pandemic, researchers have attempted to identify complex structures of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein (S-protein) with angiotensin-converting enzyme 2 (ACE2) or a blocking antibody. However, the molecular recognition mechanism - critical information for drug and antibody design - has not been fully clarified at the amino acid residue level. Elucidating such a microscopic mechanism in detail requires a more accurate molecular interpretation that includes quantum mechanics to quantitatively evaluate hydrogen bonds, XH/π interactions (X = N, O, and C), and salt bridges. In this study, we applied the fragment molecular orbital (FMO) method to characterize the SARS-CoV-2 S-protein binding interactions with not only ACE2 but also the B38 Fab antibody involved in ACE2-inhibitory binding. By analyzing FMO-based interaction energies along a wide range of binding inter-faces carefully, we identified amino acid residues critical for molecular recognition between S-protein and ACE2 or B38 Fab antibody. Importantly, hydrophobic residues that attribute to weak interactions such as CH-O and XH/π interactions, as well as polar residues that construct conspicuous hydrogen bonds, play important roles in molecular recognition and binding ability. Moreover, through these FMO-based analyses, we also clarified novel hot spots and epitopes that had been overlooked in previous studies by structural and molecular mechanical approaches. Altogether, these hot spots/epitopes identified between S-protein and ACE2/B38 Fab antibody may provide useful information for future anti-body design and small or medium drug design against the SARS-CoV-2. </div

Bioisostere Identification by Determining the Amino Acid Binding Preferences of Common Chemical Fragments

To assist in the structural optimization of hit/lead compounds during drug discovery, various computational approaches to identify potentially useful bioisosteric conversions have been reported. Here, the preference of chemical fragments to hydrogen bonds with specific amino acid residues was used to identify potential bioisosteric conversions. We first compiled a data set of chemical fragments frequently occurring in complex structures contained in the Protein Data Bank. We then used a computational approach to determine the amino acids to which these chemical fragments most frequently hydrogen bonded. The results of the frequency analysis were used to hierarchically cluster chemical fragments according to their amino acid preferences. The Euclid distance between amino acid preferences of chemical fragments for hydrogen bonding was then compared to MMP information in the ChEMBL database. To demonstrate the applicability of the approach for compound optimization, the similarity of amino acid preferences was used to identify known bioisosteric conversions of the epidermal growth factor receptor inhibitor gefitinib. The amino acid preference distance successfully detected bioisosteric fragments corresponding to the morpholine ring in gefitinib with a higher ROC score compared to those based on topological similarity of substituents and frequency of MMP in the ChEMBL database

Selective Inhibitor Design for Kinase Homologs Using Multiobjective Monte Carlo Tree Search

Designing highly selective molecules for a drug target protein is a challenging task in drug discovery. This task can be regarded as a multiobjective problem that simultaneously satisfies a criteria of various objectives, such as selectivity for a target protein, pharmacokinetics endpoints, and drug-like indices. Recent breakthroughs in artificial intelligence have accelerated the development of molecular structure generation methods, and various researchers have applied them to computational drug designs and successfully proposed promising drug candidates. However, designing efficient selective inhibitors with releasing activities against various homologs of a target protein remains a difficult issue. In this study, we developed a de novo structure generator based on reinforcement learning that is capable of simultaneously optimizing multiobjective problems. Our structure generator successfully proposed selective inhibitors for tyrosine-kinases while optimizing 18 objectives consisting of the inhibitory activities against nine tyrosine kinases, three pharmacokinetics endpoints, and six other important properties. These results show that our structure generator and optimization strategy for selective inhibitors will contribute to the further development of practical structure generators for drug designs