Molecular Topology Research Articles

Integrated Knowledge Graph and Drug Molecular Graph Fusion via Adversarial Networks for Drug-Drug Interaction Prediction.

The Co-administration of multiple drugs can enhance the efficacy of disease treatment by reducing drug resistance and side effects. However, it also raises the risk of adverse drug interactions, presenting a challenging problem in healthcare. Various approaches have been developed to predict drug-drug interactions (DDIs) by leveraging both knowledge graphs and drug attribute information. While these methods have shown promise, they often fail to effectively capture correlations between biomedical information in the knowledge graph and drug properties. This work introduces a novel end-to-end DDI predictor framework based on generative adversarial networks. This framework utilizes a message-passing neural network to capture molecular structure information while employing the knowledge-aware graph attention network to capture the representation of drugs in the knowledge graph through considering the importance of different multihop neighbor nodes and relationships. The dual generative adversarial networks employ two generators and two discriminators in knowledge graph embedding and molecular topology embedding for adversarial training to capture the interrelations and complementary knowledge between molecular structure information and semantic information from the knowledge graph. comprehensive experiments have demonstrated that the proposed method outperforms state-of-the-art algorithms in binary classification, with improvements of 1.0% in accuracy, 0.45% in area under the receiver operating characteristic curve (AUC), 0.24% in area under the precision-recall curve (AUPR), and 0.98% in F1 score. Furthermore, for multiclass classification tasks, improvements were observed across various evaluation metrics, including 0.9% in accuracy, 0.25% in macro precision, 0.2% in macro recall, and 0.28% in macro F1. Additionally, ablation studies were conducted to showcase the effectiveness and robustness of our method in DDI prediction tasks.

Dependence of Thermally Activated Relaxation of Crystalline Stems on the Molecular Topology at Crystalline/Amorphous Interfaces in Polyethylene.

We investigate the relaxation dynamics of crystalline stems in relation to the molecular topology of the crystalline/amorphous interface, employing coarse-grained molecular dynamics. To efficiently generate model semicrystalline systems of linear polyethylene with a realistic interphase morphology, we simplified the Monte Carlo method by introducing molecular dynamics for faster relaxation. The structural properties of the generated systems are validated against experimental measurements, theoretical predictions, and existing simulation data. The models suggest that the probability distribution of loop-entry sites on the lamellar surface can be described by a power law in terms of the distance between the entry sites. By considering realistic interphase morphology, we are able to improve the prediction of the overall activation energy for the relaxation of crystalline stems, aligning it closely with experimental measurements. The largest model predicts that crystalline stems connected via large loops, i.e., those that exceed the entanglement length, and long tails are associated with increased activation energy; whereas stems connected to shorter tails show the lowest activation energy. These predictions can guide the future development of tougher semicrystalline polymers by providing insights into how amorphous chain morphology contributes to the activation energy and the relaxation dynamics of crystalline chains.

Benzofuroxans: A Possible +100 Years Old Misunderstanding?

Since 1912 benzofuroxans have been believed to exhibit a molecular structure characterised by the presence of two fused rings, i.e. a benzene ring and a pentatomic heterocycle containing a N-O-N fragment wherein one of the two N atoms is involved in a further N[[EQUATION]]O bond, namely benzo[1,2-c]1,2,5-oxadiazole N-oxide. In this work, quantum chemical calculation results along with vibrational spectroscopic data raise fundamental questions about the electronic structure and the molecular topology of some 4,6-dinitro-derivatives of this class of compounds. An alternative interpretation is offered corroborated by the experimental vibrational and electronic spectra.

Dicarbatriphyrin(2.1.1) and its Carbacalix[1]phyrin Analogue: Structure-Property Relationships and Application as a Fe(III) Chemosensor.

The investigation of unsaturated or π-conjugated hydrocarbons, particularly within the macrocyclic framework, has garnered significant interest due to their fundamental properties and practical applications. This research focuses on the synthesis and characterization of a novel stable dicarbatriphyrin(2.1.1) which was constructed by amending the [14]triphyrin(2.1.1) framework by, replacing two pyrrole rings with meta-connected phenyl rings and introducing an o-benzene bridge at the C2 position. The structural modification resulted in a saddle-shaped macrocyclic core with negative Gaussian curvature, as confirmed by crystal structure analysis. This unique molecular topology offers acumens into the structure-property relationships in the curved locally π-conjugated contracted porphyrinoids. The macrocycle exhibits fluorescent emission in solution, which is selectively quenched by Fe(III) ions, highlighting its potential as a chemosensor for Fe(III) cations. Additionally, the formation of carbacalix[1]phyrin(2.1.1), a phlorin analogue of dicarbatriphyrin(2.1.1), which exhibits a chair conformation in contrast to the saddle-shaped structure of its oxidized counterpart. Remarkably, the emergence of a mono-pyrrolic calixbenziphyrin marks an unprecedented advancement in the field of calixphyrin chemistry. Moreover, theoretical studies provide strong support for the experimental findings, reinforcing the conclusions drawn from the structural and functional analyses.

Oxidation Potential of 2,6-Dimethyl-1,4-dihydropyridine Derivatives Estimated by Structure Descriptors

Linear relationships, expressing the electrochemical properties of molecules as functions of structure, give insight into the associated electrochemical process and are a tool for prediction. Many biological activities rely on water-based dissociation, making electrochemical properties a bridge between structure and activity. Motivated by a previous study, a replica is made here on a different dataset in order to validate/invalidate the previously reported results. There are several methods for obtaining structure-based descriptors. Some of the methods have been devised to account for molecular topology, some to account for molecular geometry, and others to account for both. Two methods are involved here to derive structure-based descriptors and further obtain structure–property relationships (FMPI and ChPE). In order to express structure descriptors, both FMPI and ChPE express first the topology of the molecule, using the heavy atoms identity matrix and the heavy atoms adjacency matrix, both square symmetric matrices in the belief that symmetry is one major factor of molecular stability. A set of 2,6-dimethyl-1,4-dihydropyridine derivatives with oxidation peak potentials and coulometrically determined number of electrons experimental data is subjected to the search for structure–activity relationships. Even if the 2,6-dimethyl-1,4-dihydropyridine is a symmetric compound (of Cs point group), their derivatives are generally not symmetric (9 out of 24 are asymmetric). The dataset is subjected to descriptive and inferential statistics in order to filter out the most relevant structure–activity relationship. The geometry is built using three levels of theory (one from molecular mechanics and two others from density functionals, of which one accounts for the interaction with water as solvent). One challenge of picking one out of two reported measured values is dealt with by calculating the likelihood associated with the two choices. Relevant structure–activity models are extracted and discussed. The use of in vivo (in water, SM8 model) models in geometry optimization (from MMFF94 and B3LYP and to M06 + Water SM8) results in a precision gain, but this is, in most of the cases, not statistically significant, and this can be considered a negative result.

Molecular Topology Index of a Zero Divisor Graph on a Ring of Integers Modulo Prime Power Order

In chemistry, graph theory has been widely utilized to address molecular problems, with numerous applications in graph theory and ring theory within this field. One of these applications involves topological indices that represent chemical structures with numerical values. Various types of topological indices exist, including the Wiener index, the first Zagreb index, and the hyper-Wiener index. In the context of this research, the values of the Wiener index, the first Zagreb index, and the hyper-Wiener index for zero-divisor graphs on the ring of integers modulo a prime power order will be explored through a literature review and conjecture.

Computing Zagreb Connection Indices for the Cartesian Product of Path and Cycle Graphs

Topological indices ( ) are a class of graph-based descriptors widely used in chemiformatics and Quantitative Structure-Activity Relationship (QSAR) studies. TIs capture key structural features of molecules by encoding graph-theoretic information, offering a quantitative representation of molecular topology. Each mathematical network is associated with a specific numerical value determined by a function. Among the Zagreb connection indices have been extensively researched. This study delves into the Cartesian product of cycle graphs with path graphs, elucidating the comprehensive implications of . These indices encompass the first , second , and third . Furthermore, comprehensive results of the modified first , modified second , and modified third are presented, along with the first multiplicative ZCI second multiplicative ZCI (SMZCI), and third multiplicative ZCI Moreover, modified first multiplicative ZCI ), modified second multiplicative ZCI and modified third multiplicative ZCI ( are also calculated. To provide precision, both the graphical and numerical analyses of the computed findings are aligned for the two Cartesian products. The foundation for mathematically modeling complex networks and chemical structures lies in graph theory. Topological indices ( ) are a class of graph-based descriptors widely used in chemiformatics and quantitative structure-activity relationship (QSAR) studies. TIs capture key structural features of molecules by encoding graph-theoretic information, offering a quantitative representation of molecular topology. Each mathematical network is associated with a specific numerical value determined by a topological index function. Among these the Zagreb connection indices are extensively researched TIs. This article delves into the Cartesian product of cycle graphs with path graphs, elucidating the comprehensive implications of . These indices encompass the first, Second and third. Furthermore, we present the comprehensive results of the modified , modified and modified , and also present the first multiplicative Zagreb connection index , and , along with their modified counterparts like modified first multiplicative Zagreb connection index , and . These analysis encompass two distinct types of graphs: cycle graphs and path graphs. To provide further precision, we align both the graphical and numerical analysis of our computed findings for these two Cartesian products.

On statistical evaluation of reverse degree based topological indices for iron telluride networks

In the context of graph theory and chemical graph theory, this research conducts a detailed mathematical investigation of reverse topological indices as they relate to iron telluride networks, clarifying their complex interactions. Graph theory is a branch of abstract mathematics that carefully studies the connections and structural features of graphs made up of edges and vertices. These theoretical ideas are expanded upon in chemical graph theory, which models molecular architectures with atoms acting as vertices and chemical bonds as edges. By extending these concepts, this work investigates the reverse topological indices in the context of Iron Telluride networks and outlines their significant effects on chemical reactivity, molecular topology and statistical modeling. By navigating intricate mathematical formalisms and algorithmic approaches, the analysis provides profound insights into the reactivity patterns and structural dynamics of Iron Telluride compounds, enhancing our knowledge of solid-state chemistry and materials science.

One pot multi-component synthesis of novel functionalized pyrazolo furan-2(5H)-one derivatives: in vitro, DFT, molecular docking, and pharmacophore studies, as coronavirus inhibitors.

New and facile one-pot approach for the syntheses of 12 derivatives of 3,5-disubstituted furane-2(5H)-one (4a-l) from easily available starting materials. The suitable synthetic procedures for selective synthesis of diverse furane-2(5H)-one derivatives were achieved via multi-component condensation of 1,3-diphenyl-1H-pyrazole-4-carbaldehyde (1), pyruvic acid and different aromatic amines 3a-l in good to high yields and short reaction time by refluxing in acetic acid as well as obtained by another method (method B) when unsaturated arylidene pyruvic acid 6 was refluxed with different aromatic amines in acetic acid but in smaller yield than method A. Structures of the prepared compounds were elucidated by elemental analysis and spectral data as mass, IR, 1H-NMR and 13C-NMR spectroscopy. The antiviral efficacy of compounds 4a-l against SARS-CoV-2 was evaluated using the MTT assay. It was demonstrated that synthetic compounds 4c-e and 4h-j have a potent and selective inhibitory effect on SARS-CoV-2, a strain obtained from Egyptian patients. We utilized density-functional theory (DFT) analyses to deduce the molecular structures and topologies of the more energetic molecules. Molecular docking studies were performed against the SARS-CoV-2 main protease (PDB ID: 6Y84) and the SARS-CoV-2 Nsp9 RNA binding protein (PDB ID: 6W4B) to study the binding mechanism, non-bonding interactions, and binding affinity. Lastly, a hypothetical pharmacophore model was constructed by applying the Molecular Operating Environment (MOE) tool and eleven pharmaceuticals with proven antiviral activity.

Research on the molecular structure, solvent effects, quantum computing, topology, and biological aspects of 4-phenylcoumarin-7-yl-methacrylate as an anticancer agent

The objective of this research is to conduct both experimental and theoretical analyses on the synthesis and characterization of a new coumarin derivative known as 4-phenylcoumarin-7-yl-methacrylate (PCMA). To accomplish this, several methods including 1H-NMR, FT-IR, and UV-visible spectroscopy were employed, and atomic charges and bond parameters were calculated using the DFT-B3LYP/6-311G++(d,p) basis set. Additionally, topological properties such as Electron Localized Function, Localized Orbital Locator and Non-Covalent interactions (RDG-NCI) were thoroughly examined. The TD-DFT approach was used to calculate the UV-Vis absorption technique in various solvent media, and the Light Harvesting Efficiency was also investigated. An analysis of Frontier Molecular Orbitals (FMO), Molecular Electrostatic Potential (MEP), and Average Local Ionization Energy (ALIE) simulations were conducted to identify the optimal sites for both electrophilic and nucleophilic attacks. Nonlinear Optical (NLO) behavior was also examined by determining the dipole moment (μ), the linear polarizability (α), first hyperpolarizability (β) and second hyperpolarizability (γ) through the use of the same basis set. To better understand molecular stability and interactions, Natural Bond Orbital (NBO) analysis was performed, providing insights into hyperconjugative interactions, charge delocalization, and electron density (ED) among the molecular components. Electron-hole and TDM analysis were also conducted to investigate the various types of electron-excited states. This research will additionally explore the drug-likeness and docking potential of PCMA, with the goal of determining its suitability as a potential candidate for cancer treatment. This comprehensive study offers valuable insights into the structural, electronic, and biological properties of 4-phenylcoumarin-7-yl-methacrylate (PCMA).

BEROLECMI: a novel prediction method to infer circRNA-miRNA interaction from the role definition of molecular attributes and biological networks

Circular RNA (CircRNA)–microRNA (miRNA) interaction (CMI) is an important model for the regulation of biological processes by non-coding RNA (ncRNA), which provides a new perspective for the study of human complex diseases. However, the existing CMI prediction models mainly rely on the nearest neighbor structure in the biological network, ignoring the molecular network topology, so it is difficult to improve the prediction performance. In this paper, we proposed a new CMI prediction method, BEROLECMI, which uses molecular sequence attributes, molecular self-similarity, and biological network topology to define the specific role feature representation for molecules to infer the new CMI. BEROLECMI effectively makes up for the lack of network topology in the CMI prediction model and achieves the highest prediction performance in three commonly used data sets. In the case study, 14 of the 15 pairs of unknown CMIs were correctly predicted.

An insight into the cryptic diversity of Fragilariaceae (Bacillariophyta), with the description of a new Antarctic species, Gedaniella antarctica sp. nov.

ABSTRACT Fragilariaceae is a paraphyletic family of araphid diatoms commonly used as bio-indicators, in environmental assessments and in paleoenvironmental reconstructions, and with various potential industrial applications. Recent molecular taxonomic research has highlighted significant limitations in traditional morphology-based investigations of these diatom species. Most descriptions of species and genera of the Fragilariaceae present broad morphological character definitions and many diagnostic characters are indistinguishable by light microscopy. In this sense, taxon misidentification is common and could have serious implications for environmental surveys and laboratory experiments. To better understand the diversity of the Fragilariaceae, we (1) performed phylogenetic analyses of the small subunit ribosomal RNA (18S rRNA), rbcL and psbC gene sequences, (2) inferred a cladogram from key morphological features used in the traditional identification of Fragilariaceae, (3) tested if the topologies of the tree recovered from the molecular phylogeny, of the tree based on morphology and of the trees constraining to monophyly the genera Sarcophagodes, Pseudostaurosira and Nanofrustulum (together and separately) were significantly different and (4) mapped morphological character states on the ML tree and inferred their evolution based on maximum parsimony. Our results supported the monophyly of a group of Fragilariaceae within small araphid diatoms including the genera Cratericulifera, Plagiostriata, Castoridens, Opephora, Staurosira, Staurosirella, Punctastriata, Psammotaenia, Hendeyella, Stauroforma, Pseudostaurosira sensu Li, Nanofrustulum sensu Li, Serratifera sensu Li and Gedaniella sensu Li. Molecular phylogeny and topology tests suggested that the latest circumscriptions of the genera Sarcophagodes, Pseudostaurosira and Nanofrustulum sensu Morales were not monophyletic. Analyses of the Antarctic strain IMA070A collected during the XXXIV Italian Antarctic Expedition using fine structural features of the frustule and molecular data revealed that this diatom belongs to a distinct lineage within Gedaniella, which we describe here as Gedaniella antarctica sp. nov.

Modulating the Photophysical Properties of Twisted Donor-Acceptor-Donor π-Conjugated Molecules: Effect of Heteroatoms, Molecular Conformation, and Molecular Topology.

ConspectusModulating the photophysical properties of organic emitters through molecular design is a fundamental endeavor in materials science. A critical aspect of this process is the control of the excited-state energy, which is essential for the development of triplet exciton-harvesting organic emitters, such as those with thermally activated delayed fluorescence and room-temperature phosphorescence. These emitters are pivotal for developing highly efficient organic light-emitting diodes and bioimaging probes. A particularly promising class of these emitters consists of twisted donor-acceptor organic π-conjugated scaffolds. These structures facilitate a spatial separation of the frontier molecular orbitals, which is crucial for achieving a narrow singlet-triplet energy gap. This narrow gap is necessary to overcome the endothermic reverse intersystem crossing process, enhancing the efficiency of thermally activated delayed fluorescence. To precisely modulate the photophysical properties of these emitting materials, it is essential to understand the electronic structures of new donor-acceptor scaffolds, especially those influenced by heteroatoms, as well as their conformations and topologies. This understanding not only improves the efficiency of these emitters but also expands their potential applications in advance technologies.In 2014, the Takeda group made a significant breakthrough by discovering a novel method for synthesizing U-shaped diazaacenes (dibenzo[a,j]phenazine) through an oxidative skeletal rearrangement of 1,1'-binaphthalene-2,2'-diamines. This class of compounds is typically challenging to synthesize using conventional organic reactions. The resulting unique geometric and electronic structure of U-shaped diazaacenes opened new possibilities for photophysical applications. Leveraging the U-shaped structure, photoluminescent properties, and high electron affinity, we developed twisted donor-acceptor-donor compounds. These compounds exhibit efficient thermally activated delayed fluorescence, stimuli-responsive luminochromism, heavy atom-free room-temperature phosphorescence, and anion-responsive red shifts. These innovative emitters have demonstrated significant potential in various practical applications, including organic light-emitting diode devices and advanced sensing systems.In this Account, I summarize our achievements in modulating the photofunctions of dibenzo[a,j]phenazine-cored twisted donor-acceptor-donor compounds by controlling excited-state singlet-triplet energy gaps through conformational regulation. Our comprehensive studies revealed the significant impact of heteroatoms, molecular conformations, and topologies on the photophysics of these compounds. These findings highlight the importance of molecular engineering in tailoring the photophysical properties of organic donor-acceptor π-conjugated materials for specific applications. Our research has demonstrated that incorporating heteroatoms into the molecular framework effectively tunes the electronic properties and, consequently, the photophysical behavior of the compounds. Understanding the influence of heteroatoms, conformational dynamics, and molecular topology on excited-state behavior will open new avenues for next-generation optoelectronic devices and biological technologies. These advancements include ultra-low-power displays, photonic communication, and super-resolution biomedical imaging. Ultimately, our work highlights the potential of strategic molecular design in driving innovation across various fields, paving the way for the development of cutting-edge technologies that leverage the unique properties of organic emitters.

AttenGpKa: A Universal Predictor of Solvation Acidity Using Graph Neural Network and Molecular Topology.

Rapid and accurate calculation of acid dissociation constant (pKa) is crucial for designing chemical synthesis routes, optimizing catalysts, and predicting chemical behavior. Despite recent progress in machine learning, predicting solvation acidity, especially in nonaqueous solvents, remains challenging due to limited experimental data. This challenge arises from treating experimental values in different solvents as distinct data domains and modeling them separately. In this work, we treat both the solutes and solvents equally from a perspective of molecular topology and propose a highly universal framework called AttenGpKa for predicting solvation acidity. AttenGpKa is trained using 26,522 experimental pKa values from 60 pure and mixed solvents in the iBonD database. As a result, our model can simultaneously predict the pKa values of a compound in various solvents, including pure water, pure nonaqueous, and mixed solvents. AttenGpKa achieves universality by using graph neural networks and attention mechanisms to learn complex effects within solute and solvent molecules. Furthermore, encodings of both solute and solvent molecules are adaptively fused to simulate the influence of the solvent on acid dissociation. AttenGpKa demonstrates robust generalization in extensive validations. The interpretability studies further indicate that our model has effectively learnt electronic and solvent effects. A free-to-use software is provided to facilitate the use of AttenGpKa for pKa prediction.

Orchestrated Octuple C−H Activation: A Bottom‐Up Topology Engineering Approach toward Stimuli‐Responsive Double‐Heptagon‐Embedded Wavy Polycyclic Heteroaromatics

AbstractCuriosity‐driven innovations on the design and synthesis of nonplanar polycyclic aromatic/heteroaromatic compounds with new molecular topologies unfold exciting opportunities for harnessing their intriguing supramolecular properties and thereby the development of novel functional organic materials. This work presents such an innovative synthetic concept of a bottom‐up molecular topology engineering through a unique orchestrated octuple C−H activation reaction, toward the rapid synthesis of a novel class of double heptagon‐incorporated nitrogen‐doped laterally‐fused polycyclic compounds with rarely reported wavy structural configuration. The profound impact of the molecular wavy structures of these compounds on their properties is manifested by weak and tunable solid‐state intermolecular interactions controlling the electronic properties of the materials, leading to reversibly switchable fluorochromism in the solid state and thin films with mechanical force and solvent vapors as external stimuli, thereby indicating their potential applicability in rewritable fluorescent optical recording media, security papers, mechanosensors, volatile organic compound (VOC) sensors etc.

Orchestrated Octuple C-H Activation: A Bottom-Up Topology Engineering Approach toward Stimuli-Responsive Double-Heptagon-Embedded Wavy Polycyclic Heteroaromatics.

Curiosity-driven innovations on the design and synthesis of nonplanar polycyclic aromatic/heteroaromatic compounds with new molecular topologies unfold exciting opportunities for harnessing their intriguing supramolecular properties and thereby the development of novel functional organic materials. This work presents such an innovative synthetic concept of a bottom-up molecular topology engineering through a unique orchestrated octuple C-H activation reaction, toward the rapid synthesis of a novel class of double heptagon-incorporated nitrogen-doped laterally-fused polycyclic compounds with rarely reported wavy structural configuration. The profound impact of the molecular wavy structures of these compounds on their properties is manifested by weak and tunable solid-state intermolecular interactions controlling the electronic properties of the materials, leading to reversibly switchable fluorochromism in the solid state and thin films with mechanical force and solvent vapors as external stimuli, thereby indicating their potential applicability in rewritable fluorescent optical recording media, security papers, mechanosensors, volatile organic compound (VOC) sensors etc.

Computing the Hosoya Index of Some Nanostar Dendrimers

Dendrimers are highly branched macromolecules built up from a monomer, with new branches added in steps until a tree structure is created. The various biological characteristics of dendrimers are a good choice in chemistry, biology, the medical field, and nano-science. A topological index is a type of molecular descriptor that is calculated based on the molecular graph of a chemical structure. The Hosoya index is a well-known topological index that is used to predict some of their physico-chemical properties from the structure of molecules. The Hosoya index of a graph is defined as the total number of its independent edge sets. In this paper, we study the Hosoya index of some families of nanostar dendrimers. We obtain the formulas of the Hosoya index for two infinite classes of dendrimers, namely, nanostar dendrimer and tetrathiafulvalene dendrimer. We use the Mathematica program to evaluate the results and accuracy of calculations. Our results can be used in analyzing the molecular topology of these families of nanostar dendrimers.

Rational design and efficient synthesis of cyclic polymers with visualized molecular topology and unique dielectric properties

Cyclic polymers without chain ends have attracted increasing attention because of the unique physical properties. However, the direct visualization of cyclic topology remains a formidable challenge, because the cyclic chains with flexible backbone or strong intramolecular interaction usually existed in a coiled state rather than cyclic topology. Herein, monocyclic and bicyclic polymers were prepared via blocking-cyclization technique, and the molecular topology of cyclic polymer containing phenyl pendant was directly observed depending on the reduced intrachain entanglement. The structure and characteristics of cyclic polymers were investigated, and the difference between cyclic polymer and linear counterpart was demonstrated. Compared to linear polymer, cyclic polymer exhibited improved dielectric properties, and bicyclic polymer displayed further increased dielectric constant and energy storage density than monocyclic polymer possessing the same repeating units. Therefore, this research presents a facile strategy in the design and construction of cyclic polymers with directly visualized topology and unique dielectric properties.

Deep graph contrastive learning model for drug-drug interaction prediction.

Drug-drug interaction (DDI) is the combined effects of multiple drugs taken together, which can either enhance or reduce each other's efficacy. Thus, drug interaction analysis plays an important role in improving treatment effectiveness and patient safety. It has become a new challenge to use computational methods to accelerate drug interaction time and reduce its cost-effectiveness. The existing methods often do not fully explore the relationship between the structural information and the functional information of drug molecules, resulting in low prediction accuracy for drug interactions, poor generalization, and other issues. In this paper, we propose a novel method, which is a deep graph contrastive learning model for drug-drug interaction prediction (DeepGCL for brevity). DeepGCL incorporates a contrastive learning component to enhance the consistency of information between different views (molecular structure and interaction network), which means that the DeepGCL model predicts drug interactions by integrating molecular structure features and interaction network topology features. Experimental results show that DeepGCL achieves better performance than other methods in all datasets. Moreover, we conducted many experiments to analyze the necessity of each component of the model and the robustness of the model, which also showed promising results. The source code of DeepGCL is freely available at https://github.com/jzysj/DeepGCL.

Molecular Packing Topology and Interactions to Decipher Mechanical Compliances in Dicyano-Distyrylbenzene Derivatives.

Flexible optoelectronics is the need of the hour as the market moves toward wearable and conformable devices. Crystalline π-conjugated materials offer high performance as active materials compared to their amorphous counterpart, but they are typically brittle. This poses a significant challenge that needs to be overcome to unfold their potential in optoelectronic devices. Unveiling the molecular packing topology and identifying interaction descriptors that can accommodate strain offers essential guiding principles for developing conjugated materials as active components in flexible optoelectronics. The molecular packing and interaction topology of eight crystal systems of dicyano-distyrylbenzene derivatives are investigated. Face-to-face π-stacks in an inclined orientation relative to the bending surface can accommodate expansion and compression with minimal molecular motion from their equilibrium positions. This configuration exhibits good compliance towards mechanical strain, while a similar structure with a criss-cross arrangement capable of distributing applied strain equally in opposite directions enhances the flexibility. Molecular arrangements that cannot reversibly undergo expansion and compression exhibit brittleness. In the isometric CT crystals, the disproportionate strength of the interactions along the bending plane and orthogonal directions makes these materials sustain a moderate bending strain. These results provide an updated explanation for the elastic bending in semiconducting π-conjugated crystals.