Molecular dynamics simulation is a widely employed computational technique for studying the dynamic behavior of molecular systems over time. By simulating macromolecular biological systems consisting of a drug, a receptor and a solvated environment with thousands of water molecules, MD allows for realistic ligand–receptor binding interactions (lrbi) to be studied. In this study, we present MD–ligand–receptor (MDLR), a state-of-the-art software designed to explore the intricate interactions between ligands and receptors over time using molecular dynamics trajectories. Unlike traditional static analysis tools, MDLR goes beyond simply taking a snapshot of ligand–receptor binding interactions (lrbi), uncovering long-lasting molecular interactions and predicting the time-dependent inhibitory activity of specific drugs. With MDLR, researchers can gain insights into the dynamic behavior of complex ligand–receptor systems. Our pipeline is optimized for high-performance computing, capable of efficiently processing vast molecular dynamics trajectories on multicore Linux servers or even multinode HPC clusters. In the latter case, MDLR allows the user to analyze large trajectories in a very short time. To facilitate the exploration and visualization of lrbi, we provide an intuitive Python notebook (Jupyter), which allows users to examine and interpret the results through various graphical representations.

Human Topoisomerase I (hTop1p) is a ubiquitous enzyme that relaxes supercoiled DNA through a conserved mechanism involving transient breakage, rotation, and binding. Htop1p is the molecular target of the chemotherapeutic drug camptothecin (CPT). It causes the hTop1p-DNA complex to slow down the binding process and clash with the replicative machinery during the S phase of the cell cycle, forcing cells to activate the apoptotic response. This gives hTop1p a central role in cancer therapy. Recently, two artesunic acid derivatives (compounds c6 and c7) have been proposed as promising inhibitors of hTop1p with possible antitumor activity. We used several computational approaches to obtain in silico confirmations of the experimental data and to form a comprehensive dynamic description of the ligand-receptor system. We performed molecular docking analyses to verify the ability of the two new derivatives to access the enzyme-DNA interface, and a classical molecular dynamics simulation was performed to assess the capacity of the two compounds to maintain a stable binding pose over time. Finally, we calculated the noncovalent interactions between the two new derivatives and the hTop1p receptor in order to propose a possible inhibitory mechanism like that adopted by CPT.

Purpose: Recent advances in AI, especially in large language models, have created new opportunities to integrate human and artificial agents through shared linguistic capabilities. This paper presents a multi-agent organizational framework in which human agents, LLMs, and specialized agents (narrow AIs) collaborate via dynamic, topic-based group formation. Topic-driven interactions enable agents to coalesce around evolving interests, supported by threshold-based protocols for temporal adaptation, topic emergence, and participation. Methods: Within our framework, human agents guide the overall system objectives, while consultant agents (LLMs) provide semantic analysis and mediation, and specialized agents perform focused domain tasks. By leveraging automated topic modeling, the approach eschews rigid ontologies and instead supports adaptive and interpretable content management. Mathematical properties ensure system coherence—across roles, tasks, and timescales—while allowing the natural evolution of interests and groups. Results: We illustrate the framework’s versatility with example scenarios in emergency response, healthcare research, and financial decision-making, emphasizing how human decision-makers, LLM-based consultants, and specialized worker agents jointly fulfill complex goals through transparent topic alignment and threshold-driven coordination. This formalization advances human-computer interaction as a multi-agent phenomenon that integrates human insight with the strengths of next-generation AI models in a cohesive, evolving system.

Context: Patterns are used in different disciplines as a way to record expert knowledge for problem solving in specific areas. Their systematic use in Software Engineering promotes quality, standardization, reusability and maintainability of software artefacts. The full realisation of their power is however hindered by the lack of a standard formalization of the notion of pattern. Objective: Our goal is to provide a language-independent formalization of the notion of pattern, so that it allows its application to different modelling languages and tools, as well as generic methods to enable pattern discovery, instantiation, composition, and conflict analysis. Method: For this purpose, we present a new visual and formal, language-independent approach to the specification of patterns. The approach is formulated in a general way, based on graphs and category theory, and allows the specification of patterns in terms of (nested) variable submodels, constraints on their allowed variance, and inter-pattern synchronization across several diagrams (e.g. class and sequence diagrams for UML design patterns). Results: We provide a formal notion of pattern satisfaction by models and propose mechanisms to suggest model transformations so that models become consistent with the patterns. We define methods for pattern composition, and conflict analysis. We illustrate our proposal on UML design patterns, and discuss its generality and applicability on different types of patterns, e.g. workflow patterns, enterprise integration patterns and interaction patterns. Conclusion: The approach has proven to be powerful enough to formalize patterns from different domains, providing methods to analyse conflicts and dependencies that usually are expressed only in textual form. Its language independence makes it suitable for integration in meta-modelling tools and for use in Model-Driven Engineering.This work has been supported by the Visiting Professor Programmes of ‘‘Sapienza” University of Rome and its Department of Computer Science, the R&D program of the Community of Madrid (S2009/TIC-1650, project ‘‘e-Madrid”), the CAM-UC3M project ‘‘EXPLORE” (CCG08-UC3M/TIC-4487), as well as by the Spanish Ministry of Science and Innovation, under project ‘‘METEORIC” (TIN2008-02081), and mobility Grants JC2009-00015 and PR2009-0019.Publicad