Nanograb is a company aiming at designing the best combinations of binders to treat different diseases. They are building a high-throughput computational platform that integrates generative AI and multivalent physics into a seamless framework for the design and optimization of multivalent nanoparticles for targeted drug delivery.My work at Nanograb is focused on the development of computational methods for the prediction of physical-chemical properties of a wide-range bio-molecular complexes (spanning from proteins and nucleic acids to antibodies). These calculations enable application-specific selection and fine-tuning of molecules used to target specific regions of the body, treating diseases more effectively while minimizing side-effects.

Our main research aim is to develop, validate and implement theoretical and simulation frameworks for the study of multivalent interactions in the context of real-life applications. In particular, we want to investigate the conditions in real applications more extensively. We are currently gauging a range of never-before investigated factors and their effects on the selectivity of the multivalent interactions.Most of our work focuses on DNA Coated Colloids (DNACCs) in a diagnostic application setting, but our main results are general and should be readily extendable to capture the behaviour of different multivalent systems. Our two main projects focus, respectively, on the influence of colloidal shape on the adsorption profile of DNACC on a receptor-decorated surface and on the applicability of DNACCs amorphous phase transition as a recognition event in diagnostic devices. In parallel with our main project, we have been and still are collaborating with other academic groups on secondary research projects. In particular:- we have collaborated with members of the Kraft Lab from Leiden University on the development of a novel experimental protocol and a theoretical kinetics model for the adsorption of DNACCs. While our collaborators provided the experimental data we focused on the optimisation and computational implementation of a model to fit, analyse and interpret the data. This collaboration was concluded after the publication of a paper on PNAS (scientific journal).- We are currently collaborating with the Computational Soft Matter research group from Nanyang Technological University in Singapore on the development of a Deep Neural Network for the fast and reliable prediction of the kinetics of reaction-rate based systems. As a possible application of this approach, we are considering the investigation of DNA origami folding kinetics.

Throughout my PhD I have worked part-time as Graduate Teaching Assistant (GTA) in a number of academic courses delivered by the Materials and Chemistry Departments of Imperial College London. As a GTA I have held lectures and presentations, collaborated to the production and revision of notes and slides, marked exams and lab reports and acted as a laboratory assistant helping students with their projects in a number of computational labs. Courses taught (est. 700+ hours of work over 3 years): - Mathematics and Computing, Year 1 & 2, Materials; - Electronic State and Bonding Lab, Year 3, Chemistry; - Characterization Lab, Year 2 Chemistry; - General Chemistry Lab, Year 1, Chemistry; - Quantum Chemistry Lab, Year 3, Chemistry.

- Searching for and evaluating CVs of world leading experts according to clients needs and specifications;- Reviewing scientific literature and research proposals in a range of fields unrelated to personal experience;- Leading brief interviews with experts from industry and academia to evaluate their suitability for technical review of international research proposals;- Compiling quickly short technical reviews of the state-of-the-art in a wide range of fields including AI, scientific computing, software development, aviation, agriculture and chemistry.

During my industrial placement and a few months thereafter I was responsible for a research project in collaboration with the CCP5​ staff at Daresbury Laboratory. The project focused on a multiscale study of reline-based deep eutectic solvents (DES), i.e. a particular type of ionic solvent comprising acidic choline chloride and alkaline urea. There is growing interest in these DES ionic solvents for their carbon dioxide absorption property over a range of different temperatures and pressures.To understand the full potential of reline to capture carbon dioxide requires research at the atomistic level, i.e. modelling the chemical and physical interactions at the scale of atoms and electrons. To learn the necessary modelling and simulation techniques, I spent the three months of my industrial placement working with the​ staff at Daresbury Laboratory, where with Prof. Vlad Sokhan I explored classical simulations using DL_POLY​, and with Dr Thomas Keal I worked with quantum mechanics/molecular mechanics using ​ChemShell.The additional part of the project, focusing on the fine chemistry of significant interactions, took place during the rest of my Masters research at Imperial College. In collaboration with Prof. Tricia Hunt I applied state-of-the-art DFT computations to explore the configurational-energy landscape of the constituent molecules of DES. ​The project concluded with the Imperial Molecular Science Engineering MRes Symposium Day where I was given the opportunity to present my research to academics and other industrial partners (e.g.: P&G, BASF) as well as to discuss the possibility of future collaborations.