Topological Distance-Based Electron Interaction Tensor: A Novel Molecular Structure Representation to Bridge Convolutional Neural Network Studies in Computer Vision to Drug-Like Compound Datasets

Owing to the success achieved by deep learning, researchers are exploringthe application of deep learning in drug discovery to improve the accuracy of prediction models. Significant performance improvement has been achieved by diverse convolutional neural network (CNN) models in computer vision, and the preparation of an input format suitable for CNN is one of the major questions required to be answered in order to harness the advancements in using CNNs for chemical data. It was reported that the models achieved improvement in prediction accuracy, in deep learning studies on molecular structure data; however, the improvement was insufficient from an industry perspective. Furthermore, a recent study suggested that conventional machine learning models can outperform deep learning models on chemical data. As only a limited number of feature calculation methods are available for molecules in deep learning studies, it is crucial to develop more methods to calculate features appropriate for deep learning model development.A topological distance-based electron interaction (TDEi) tensor has been introduced in this study to transform a molecular structure into image-like 3D arrays based on electron interactions (Eis) within a molecule. The prediction accuracy of the CNN model with the TDEi tensor was tested with four datasets: MP (275,131), Lipop (4,193), Esol (1,127), and Freesolv (639), and the models achieved desirable prediction accuracy. Ei is the fundamental level of information that determines the chemical properties of a molecule. Feature space variation was visualized by taking outputs from the middle of the CNN architecture as the CNN model exhibited outstanding performance in automatic feature extraction.The correlation between features from the CNN, and target endpoints was strengthened as outputs were extracted from the deeper layer of the CNN.

KORELASI AKSES PERUMAHAN DAN KRIMINALITAS DI PERUMAHAN KOTA SAMARINDA

This research proposes to examine the correlation between access of housing and crime in Samarinda. The findings of this research: the most associated with access of housing to crime was form of access (direct and indirect access from main entrance of housing to a house) and the depths of space (topological distance). Direct access and shorter distance increased crime. While form of access(direct/indirect access) from main street to a house didn’t influenced crime significantly. Less correlated factors were open access (mostly found on housing and zones access). Keywords: access, crime, housingPenelitian ini bertujuan untuk mencari hubungan antara faktor akses dengan kriminalitas di perumahan di kota Samarinda. Hasil penelitian adalah faktor akses yang paling berhubungan dengan kriminalitas adalah bentuk hubungan akses dan kedalaman ruang. Hubungan akses langsung dari rumah menuju entrance keluar masuk perumnas akan meningkatkan kerawanan kriminalitas dibandingkan dengan akses tidak langsung. Semakin dangkal kedalaman ruang dari akses keluar masuk perumnas menuju rumah maka semakin meningkatkan angka kriminalitas.Sedangkanbentuk pencapaian (langsung/tidak langsung) antara rumah dengan jalan utama (bukan akses utama) adalah faktor yang sedikit berhubungan dengan kriminalitas (pencurian). Artinya bahwa rumah berpagar tertutup atau tidak berpagar memiliki sedikit hubungan dengan faktor kriminalitas. Hal yang tidak berhubungan mengenai variabel akses adalah bentuk akses yang terbuka ke Perumnas Air Putih dan zona 1-4. Tidak ditemukan kaitan antara keseragaman bentuk akses ini dengan pola sebaran kriminalitas.

A Note on Ultrametric Spaces, Minimum Spanning Trees and the Topological Distance Algorithm

We relate the definition of an ultrametric space to the topological distance algorithm—an algorithm defined in the context of peer-to-peer network applications. Although (greedy) algorithms for constructing minimum spanning trees such as Prim’s or Kruskal’s algorithm have been known for a long time, they require the complete graph to be specified and the weights of all edges to be known upfront in order to construct a minimum spanning tree. However, if the weights of the underlying graph stem from an ultrametric, the minimum spanning tree can be constructed incrementally and it is not necessary to know the full graph in advance. This is possible, because the join algorithm responsible for joining new nodes on behalf of the topological distance algorithm is independent of the order in which the nodes are added due to the property of an ultrametric. Apart from the mathematical elegance which some readers might find interesting in itself, this provides not only proofs (and clearer ones in the opinion of the author) for optimality theorems (i.e., proof of the minimum spanning tree construction) but a simple proof for the optimality of the reconstruction algorithm omitted in previous publications too. Furthermore, we define a new algorithm by extending the join algorithm to minimize the topological distance and (network) latency together and provide a correctness proof.

Chemometric Modeling of the Ecotoxicity of Industrial Chemicals to an Avian Species Anas Platyrhynchos

Birds or avians have been imperative species in the ecology, having been evaluated in an effort to understand the toxic effects of endocrine disruption. The ecotoxicity of 56 industrial chemicals classified as endocrine disruptors were modeled employing classification and regression-based quantitative structure-activity relationship (QSAR) models to an important avian species, Anas platyrhynchos. The classification- and regression-based QSAR models were developed using linear discriminant analysis (LDA) and partial least squares (PLS) tools, respectively. All models were validated meticulously by employing internal and external validation metrics followed by randomization test, applicability domain (AD) study, and intelligent consensus prediction of all individual models. Features like topological distance of 1, 3, and 5 between atoms O-P, C-P, and N-S, correspondingly, along with the CR3X fragment, can be responsible for an increase in toxicity. On the contrary, the presence of S-Cl with topological distance 6 is accountable for lowering the toxicity of towards A. platyrhynchos. The developed chemometric models can offer significant evidence and guidance in the framework of virtual screening as well as a toxicity prediction of new and/or untested chemical libraries towards this specific avian species.

Combinatorial Design of Molecule using Activity-Linked Substructural Topological Information as Applied to Antitubercular Compounds

Background: Generating a large number of compounds using combinatorial methods increases the possibility of finding novel bioactive compounds. Although some combinatorial structure generation algorithms are available, any method for generating structures from activity-linked substructural topological information is not yet reported. Objective: To develop a method using graph-theoretical techniques for generating structures of antitubercular compounds combinatorially from activity-linked substructural topological information, predict activity and prioritize and screen potential drug candidates. </P><P> Methods: Activity related vertices are identified from datasets composed of both active and inactive or, differently active compounds and structures are generated combinatorially using the topological distance distribution associated with those vertices. Biological activities are predicted using topological distance based vertex indices and a rule based method. Generated structures are prioritized using a newly defined Molecular Priority Score (MPS). Results: Studies considering a series of Acid Alkyl Ester (AAE) compounds and three known antitubercular drugs show that active compounds can be generated from substructural information of other active compounds for all these classes of compounds. Activity predictions show high level of success rate and a number of highly active AAE compounds produced high MPS score indicating that MPS score may help prioritize and screen potential drug molecules. A possible relation of this work with scaffold hopping and inverse Quantitative Structure-Activity Relationship (iQSAR) problem has also been discussed. The proposed method seems to hold promise for discovering novel therapeutic candidates for combating Tuberculosis and may be useful for discovering novel drug molecules for the treatment of other diseases as well.

Specificity and robustness of long-distance connections in weighted, interareal connectomes

Brain areas’ functional repertoires are shaped by their incoming and outgoing structural connections. In empirically measured networks, most connections are short, reflecting spatial and energetic constraints. Nonetheless, a small number of connections span long distances, consistent with the notion that the functionality of these connections must outweigh their cost. While the precise function of long-distance connections is unknown, the leading hypothesis is that they act to reduce the topological distance between brain areas and increase the efficiency of interareal communication. However, this hypothesis implies a nonspecificity of long-distance connections that we contend is unlikely. Instead, we propose that long-distance connections serve to diversify brain areas’ inputs and outputs, thereby promoting complex dynamics. Through analysis of five weighted interareal network datasets, we show that long-distance connections play only minor roles in reducing average interareal topological distance. In contrast, areas’ long-distance and short-range neighbors exhibit marked differences in their connectivity profiles, suggesting that long-distance connections enhance dissimilarity between areal inputs and outputs. Next, we show that—in isolation—areas’ long-distance connectivity profiles exhibit nonrandom levels of similarity, suggesting that the communication pathways formed by long connections exhibit redundancies that may serve to promote robustness. Finally, we use a linearization of Wilson–Cowan dynamics to simulate the covariance structure of neural activity and show that in the absence of long-distance connections a common measure of functional diversity decreases. Collectively, our findings suggest that long-distance connections are necessary for supporting diverse and complex brain dynamics.