We report here that nanofluids of copper capped by graphene have an improved thermal conductivity compared to the Cu nanofluids, being up to close 30 times higher for the graphene-trilayered NPs.

The Student Council of the International Society for Computational Biology (ISCB-SC) is a student-focused organization for researchers from all early career levels of training (undergraduates, masters, PhDs and postdocs) that organizes bioinformatics and computational biology activities across the globe. Among its activities, the ISCB-SC organizes several symposia in different continents, many times, with the help of the Regional Student Groups (RSGs) that are based on each region. In this editorial we highlight various key moments and learned lessons from the 14th Student Council Symposium (SCS, Chicago, USA), the 5th European Student Council Symposium (ESCS, Athens, Greece) and the 3rd Latin American Student Council Symposium (LA-SCS, Viña del Mar, Chile).

This editorial summarises the organisation, activities, and scientific content of the 6th European Student Council Symposium (ESCS) organised by the International Society for Computational Biology Student Council (ISCB-SC). The event was held on September 6, 2020, as a satellite event preceding the ISCB’s 19th European Conference in Computational Biology. Both events were first planned to be held in-person in Sitges, Spain, but moved virtually as a strategy to face the SARS-CoV2 sanitary crisis. This completely unforeseen situation has posed several challenges that have been successfully addressed thanks to the robust ISCB Student Council community structure and the strong commitment of the organisers. Despite all the obstacles and challenges, we have found that virtuality has several advantages that can continue to be kept to improve in-person meetings in the future and make conferences more inclusive allowing a larger audience to participate.

Since 2004, the ISCB Student Council has been organizing different symposia worldwide, gathering together the community of young computational biologists. Due to the coronavirus disease 2019 (COVID-19) pandemic situation, the world scientific community was forced to cancel in-person meetings for almost two years, imposing the adoption of virtual formats instead. After the successful editions of our continental symposia in 2020 in the USA, Latin America, and Europe, we organized our flagship global event, the Student Council Symposium (SCS) 2021, trying to apply all previous lessons learned and to exploit the advantages that virtuality has to offer.

In this study, we investigated the mechanical behavior of pristine copper (Cu) nanoparticles (NPs) and Cu@graphene (Cu@G) hybrid NPs using molecular dynamics simulations.

Structure-based virtual screening (SBVS) is a fundamental approach in drug discovery, yet its predictive accuracy is highly dependent on methodological choices, scoring functions, and data processing strategies. This study systematically evaluates five protocol variants integrating molecular docking, induced-fit docking (IFD), quantum-polarized ligand docking (QPLD), ensemble docking (ED), and molecular mechanics/generalized Born surface area (MM-GBSA) in Helicobacter pylori urease employing four distinct crystallographic structures obtained from the protein data bank (PDB). We assess their predictive performance using statistical correlation metrics (Spearman and Pearson) and error-based measures (mean absolute error, root-mean-squared error, and inlier ratio metric). Additionally, we investigate the influence of data fusion techniquesminimum, median, arithmetic, geometric, harmonic, and Euclidean meansand varying numbers of docking poses (ranging from 1 to 100) on ligand ranking accuracy. Results indicate that MM-GBSA and ED consistently outperform other methods in compound ranking, although MM-GBSA exhibits higher errors in absolute binding energy predictions. While increasing the number of poses generally reduces predictive accuracy, the minimum fusion approach remains robust across all conditions. Comparisons between IC₅₀ and pIC₅₀ as experimental reference values reveal that pIC₅₀ provides higher Pearson correlations, reinforcing its suitability for affinity prediction, while both metrics perform similarly in Spearman rankings. These findings refine SBVS workflows by optimizing scoring and pose aggregation strategies, highlighting the importance of method selection and data fusion techniques. The proposed framework enhances ligand prioritization in virtual screening campaigns and can be adapted to other therapeutic targets. Future research should explore adaptive scoring frameworks and machine-learning approaches to further improve the SBVS predictive reliability.