Cluster Identification Using Modularity Optimization to Uncover Chemical Heterogeneity in Complex Solutions

Network Models of BACE-1 Inhibitors: Exploring Structural and Biochemical Relationships

This study investigates the clustering patterns of human β-secretase 1 (BACE-1) inhibitors using complex network methodologies based on various distance functions, including Euclidean, Tanimoto, Hamming, and Levenshtein distances. Molecular descriptor vectors such as molecular mass, Merck Molecular Force Field (MMFF) energy, Crippen partition coefficient (ClogP), Crippen molar refractivity (MR), eccentricity, Kappa indices, Synthetic Accessibility Score, Topological Polar Surface Area (TPSA), and 2D/3D autocorrelation entropies are employed to capture the diverse properties of these inhibitors. The Euclidean distance network demonstrates the most reliable clustering results, with strong agreement metrics and minimal information loss, indicating its robustness in capturing essential structural and physicochemical properties. Tanimoto and Hamming distance networks yield valuable clustering outcomes, albeit with moderate performance, while the Levenshtein distance network shows significant discrepancies. The analysis of eigenvector centrality across different networks identifies key inhibitors acting as hubs, which are likely critical in biochemical pathways. Community detection results highlight distinct clustering patterns, with well-defined communities providing insights into the functional and structural groupings of BACE-1 inhibitors. The study also conducts non-parametric tests, revealing significant differences in molecular descriptors, validating the clustering methodology. Despite its limitations, including reliance on specific descriptors and computational complexity, this study offers a comprehensive framework for understanding molecular interactions and guiding therapeutic interventions. Future research could integrate additional descriptors, advanced machine learning techniques, and dynamic network analysis to enhance clustering accuracy and applicability.

Tracking Molecular Transport Across Oil/Aqueous Interfaces: Insight into "Antagonistic" Binding in Solvent Extraction

Liquid/liquid (L/L) interfaces play a key, yet poorly understood, role in a range of complex chemical phenomena where time-evolving interfacial structures and transient supramolecular assemblies act as gatekeepers to function. Here, we employ surface-specific vibrational sum frequency generation combined with neutron and X-ray scattering methods to track the transport of dioctyl phosphoric acid (DOP) and di-(2-ethylhexyl) phosphoric acid (DEHPA) ligands used in solvent extraction at buried oil/aqueous interfaces away from equilibrium. Our results show evidence for a dynamic interfacial restructuring at low ligand concentrations in contrast to expectation. These time-varying interfaces arise from the transport of sparingly soluble interfacial ligands into the neighboring aqueous phase. These results support a proposed "antagonistic" role of ligand complexation in the aqueous phase that could serve as a holdback mechanism in kinetic liquid extractions. These findings provide new insights into interfacially controlled chemical transport at L/L interfaces and how these interfaces vary chemically, structurally, and temporally in a concentration-dependent manner and present potential avenues to design selective kinetic separations.

Dynamic Community Detection Decouples Multiple Time Scale Behavior of Complex Chemical Systems

Although community or cluster identification is becoming a standard tool within the simulation community, traditional algorithms are challenging to adapt to time-dependent data. Here, we introduce temporal community identification using the Δ-screening algorithm, which has the flexibility to account for varying community compositions, merging and splitting behaviors within dynamically evolving chemical networks. When applied to a complex chemical system whose varying chemical environments cause multiple time scale behavior, Δ-screening is able to resolve the multiple time scales of temporal communities. This computationally efficient algorithm is easily adapted to a wide range of dynamic chemical systems; flexibility in implementation allows the user to increase or decrease the resolution of temporal features by controlling parameters associated with community composition and fluctuations therein.

Modularity in Biological Networks

Network modeling, from the ecological to the molecular scale has become an essential tool for studying the structure, dynamics and complex behavior of living systems. Graph representations of the relationships between biological components open up a wide variety of methods for discovering the mechanistic and functional properties of biological systems. Many biological networks are organized into a modular structure, so methods to discover such modules are essential if we are to understand the biological system as a whole. However, most of the methods used in biology to this end, have a limited applicability, as they are very specific to the system they were developed for. Conversely, from the statistical physics and network science perspective, graph modularity has been theoretically studied and several methods of a very general nature have been developed. It is our perspective that in particular for the modularity detection problem, biology and theoretical physics/network science are less connected than they should. The central goal of this review is to provide the necessary background and present the most applicable and pertinent methods for community detection in a way that motivates their further usage in biological research.

The Middle Science: Traversing Scale In Complex Many-Body Systems

A roadmap is developed that integrates simulation methodology and data science methods to target new theories that traverse the multiple length- and time-scale features of many-body phenomena.