Sam Alexander Martino
A5049474095- SARS-CoV-2 and COVID-19 Research
- interferon and immune responses
- Animal Virus Infections Studies
- RNA and protein synthesis mechanisms
- Advanced Graph Neural Networks
- Viral Infections and Immunology Research
- Complex Network Analysis Techniques
- Graph Theory and Algorithms
- Viral gastroenteritis research and epidemiology
- thermodynamics and calorimetric analyses
- Protein Structure and Dynamics
- Machine Learning in Materials Science
- Computational Drug Discovery Methods
- Advanced Clustering Algorithms Research
University College London
2020-2025
King's College London
2020-2021
A critical assessment of computational hit finding experiments (CACHE) challenge was conducted to predict ligands for the SARS-CoV-2 Nsp13 helicase RNA binding site, a highly conserved COVID-19 target. Twenty-three participating teams comprised chemists and data scientists used protein structure from fragment-screening paired with advanced machine learning methods each up 100 inhibitory ligands. Across all teams, 1957 compounds were predicted subsequently procured commercial catalogs...
A critical assessment of computational hit finding experiments (CACHE) challenge was conducted to predict ligands for the SARS-CoV-2 Nsp13 helicase RNA binding site, a highly conserved COVID-19 target. Twenty-three participating teams comprised chemists and data scientists used protein structure from fragment-screening paired with advanced machine learning methods each up 100 inhibitory ligands. Across all teams, 1957 compounds were predicted subsequently procured commercial catalogs...
A critical assessment of computational hit finding experiments (CACHE) challenge was conducted to predict ligands for the SARS-CoV-2 Nsp13 helicase RNA binding site, a highly conserved COVID-19 target. Twenty-three participating teams comprised chemists and data scientists used protein structure from fragment-screening paired with advanced machine learning methods each up 100 inhibitory ligands. Across all teams, 1957 compounds were predicted subsequently procured commercial catalogs...
The RNA helicase (non-structural protein 13, NSP13) of SARS-CoV-2 is essential for viral replication, and it highly conserved among the coronaviridae family, thus a prominent drug target to treat COVID-19. We present here structural models dynamics in complex with its native substrates based on thorough analysis homologous sequences existing experimental structures. performed analysed microseconds molecular (MD) simulations, our model provides valuable insights binding ATP ssRNA at atomic...
SARS-CoV-2 nsp13 is a multifunctional helicase from superfamily 1B. It unwinds the viral RNA genome for replication and thought to play role in 5' mRNA capping produce mature using its triphosphatase activity. The sequence structure are highly conserved nidovirales protein essential infection cycle, acting as standalone enzyme conjunction with other proteins, making promising target structure-based drug design. By inhibiting activity, phosphatase or interaction RNA-dependent polymerase we...
The recent trend in using network and graph structures to represent a variety of different data types has renewed interest the partitioning (GP) problem. This stems from need for general methods that can both efficiently identify communities reduce dimensionality large graphs while satisfying various application-specific criteria. Traditional clustering algorithms often struggle capture complex relationships within generalize arbitrary emergence neural networks (GNNs) as powerful framework...
ABSTRACT Having claimed over 1 million lives worldwide to date, the ongoing COVID-19 pandemic has created one of biggest challenges develop an effective drug treat infected patients. Among all proteins expressed by virus, RNA helicase is a fundamental protein for viral replication, and it highly conserved among coronaviridae family. To there no high-resolution structure bound with ATP RNA. We present here structural insights molecular dynamics (MD) simulation results SARS-CoV-2 both in its...
Traditional clustering algorithms often struggle to capture the complex relationships within graphs and generalise arbitrary criteria. The emergence of graph neural networks (GNNs) as a powerful framework for learning representations data provides new approaches solving problem. Previous work has shown GNNs be capable proposing partitionings using variety criteria, however, these have not yet been extended on Markov chains or kinetic networks. These arise frequently in study molecular...