I am a lecturer in the Department of Computer and Information Sciences, at the University of Strathclyde, which I joined in June 2024. I have a diverse background in computer science, mathematics, biology, and chemistry. Before jumping into academia, I had experiences as a software developer in industry and as a mathematics teacher in secondary education. I am now avidly interested in interdisciplinary research in the fields of biomolecular artificial intelligence and structural bioinformatics. I also teach courses in bioinformatics and artificial intelligence.

I currently serve on the editorial board of three scientific journals: BMC Bioinformatics, Biomolecules, and Frontiers in Molecular Biosciences. I have published 26 articles (including 15 as first author) in international scientific journals, and 19 articles (including 9 as first author) in peer-reviewed conferences or workshops. Some of my articles have garnered a sizeable number of citations, including four articles with more than 100 citations. So far, I have attracted about £300K in research funding, via two postdoctoral fellowships and small competitive grants. I have delivered 49 invited talks, oral presentations, or poster presentations, across the world. I have supervised 7 graduate and 11 undergraduate students who had either a computer science or a biology background.

If you are a motivated student looking for a supervisor to work on a project related to one of my areas of expertise (listed below), do not hesitate to contact me.

• Artificial intelligence
• Machine learning
• Deep learning
• Parallel algorithms
• Robotics
• Motion planning
• Sampling-based path planning
• Bioinformatics
• Biomedical computing
• Computational structural biology
• Molecular modelling
• Molecular docking
• Molecular caging
• Hydrogen/deuterium exchange

Higher education teaching:

• Bioinformatics
• Artificial intelligence

Past academic advising (University of Edinburgh):

• Zihan Kong, M.Sc. in bioinformatics (2023)
• Tianyu Zhao, M.Sc. in bioinformatics (2023)
• Xinyu Liu, M.Sc.R in integrative biomedical sciences (2023)
• Nicole Li, pharmacology Honours (2023)
• Natalie Cruz, biomedical sciences Honours (2023)
• Erika Lapienyte, biomedical sciences Honours (2023)
• Mengze Zhang, M.Sc.R in biomedical sciences (2022)
• Jie Mei, M.Sc. in drug discovery and translational biology (2022)
• Tiefeng Song, M.Sc. in drug discovery and translational biology (2022)

Past academic advising (Rice University):

• Anja Conev, B.Sc. in computer science (2018)
• Nicole Mitchell, B.Sc. in computer science (2018)
• Elizabeth Palmi, B.Sc. in computer science (2018)
• Stephen Price, B.Sc. in computer science (2018)
• Sarah Hall-Swan, B.Sc. in computer science (2017)
• Angela Hoch, B.Sc. in computer science (2017)
• Yovahn Hoole, B.Sc. in computer science (2017)

In the past two decades, my research has focused on the computational modelling, simulation and analysis of complex physical systems, both in robotics and structural biology. At the algorithmic level, simulating mobile or flexible systems (such as robots and molecules) requires exploring a high-dimensional space: the space of all the possible states of the system. My work has involved developing efficient algorithms and heuristics to address this challenge.

During my PhD at LAAS-CNRS, I developed novel extensions of sampling-based path planning algorithms and created the concept of optimal path planning in a cost space (section 1). As a post-doctoral researcher at Rice University, I developed computational methods to efficiently explore the conformational space of proteins using experimental data as a guide (section 2), and to incrementally dock large ligands to protein receptors (section 3). I also helped develop a robotics-inspired method to predict whether a molecule could cage another one (section 4).

As a research fellow at the University of Edinburgh, I worked on several quantitative biomedical research projects:

• modelling the interaction network of cell-adhesion proteins in cancer cells
• analysing time series of white blood cell data from the Generation Scotland cohort
• improving the coverage of deep mutational scanning experiments using machine learning.

The main application of my current research is to produce clinical interpretations of genetic mutations in people, using machine learning and data from deep mutational scanning experiments. This research is of great significance, as deep mutational scanning is a promising technique in the quest for personalised medicine. Indeed, being able to derive the functional effects of rare mutations in important genes would be a crucial breakthrough for medical practice.

1. Optimal path planning in cost spaces with sampling-based algorithms

During my PhD, I developed novel extensions of sampling-based path planning algorithms. Despite their conceptual simplicity, these algorithms can efficiently explore a high-dimensional space in a probabilistic manner and build a graph representing the topology of this space. They had traditionally been used in simple robotic applications to find feasible (i.e., collision-free) paths, without considering path quality. However, many applications require to compute high-quality (i.e., low-cost) paths or even optimal paths, in the context of cost-space path planning or optimal path planning. To deal with ever more complex applications, I proposed the following contributions:

• I enhanced a cost-space path planning algorithm, called Transition-based Rapidly-exploring Random Tree (T-RRT), by creating bidirectional and multiple-tree variants. I also proposed three parallel versions of T-RRT-like algorithms to improve scalability. Then, I used these algorithms to plan for 6-dimensional manipulation with a towed-cable system involving three aerial robots (in simulation).
• I combined the paradigms of cost-space path planning and optimal path planning to create the concept of optimal path planning in a cost space. In this context, I developed two new algorithms (T-RRT* and Anytime T-RRT) for the Move3D robotic platform and the MoMA molecular modelling library. I also showed that both algorithms were probabilistically complete and asymptotically optimal. I applied them to the planning of industrial inspection tasks performed by flying robots (in simulation) and to the exploration of the energy landscape of small peptides.

Main references:

• Optimal path planning in complex cost spaces with sampling-based algorithms; IEEE Transactions on Automation Science and Engineering; 2016; DOI: 10.1109/TASE.2015.2487881
• MoMA-LigPath: A web server to simulate protein-ligand unbinding; Nucleic Acids Research; 2013; DOI: 10.1093/nar/gkt380
• Parallelizing RRT on large-scale distributed-memory architectures; IEEE Transactions on Robotics; 2013; DOI: 10.1109/TRO.2013.2239571

2. Protein structural sampling guided by experimental hydrogen-exchange data

Gathering experimental data about a protein’s three-dimensional structure allows understanding its function and possible dysfunctions. In addition, computational techniques exist to explore a protein's conformational space, i.e., the space of all possible states (or conformations) of the protein. However, experimentally observing and computationally modelling large proteins remain critical challenges for structural biology. To address this issue, I developed a novel approach integrating an experimental technique and a computational method to analyse large proteins. I studied how the computational exploration of a protein’s conformational space could be guided by sparse structural information, such as the experimental data obtained through hydrogen exchange (HX) monitoring. For that, I extended a computational framework called Structured Intuitive Move Selector (SIMS) performing coarse-grained structural sampling, i.e., in which not all perturbations (or moves) applied to protein conformations consider a protein in its full atomistic resolution.

SIMS combines robotics-inspired structural sampling algorithms with the popular Rosetta library for protein modelling. I published three applications of my method:

• I showed that my method yields a better fit between HX data and computationally-generated protein conformations than other HX-guided conformational sampling methods.
• I showed that I could analyse the inherent variability of a protein's native state (i.e., its equilibrium state in solution).
• I showed that I could generate structural models for protein states described only by HX data.

Main references:

• Computational modeling of molecular structures guided by hydrogen-exchange data; Journal of the American Society for Mass Spectrometry; 2022; DOI: 10.1021/jasms.1c00328
• Revealing unknown protein structures using computational conformational sampling guided by experimental hydrogen-exchange data; International Journal of Molecular Sciences; 2018; DOI: 10.3390/ijms19113406
• Coarse-grained conformational sampling of protein structure improves the fit to experimental hydrogen-exchange data; Frontiers in Molecular Biosciences; 2017; DOI: 10.3389/fmolb.2017.00013

3. Molecular docking of large ligands to protein receptors

Although there is a variety of software for the molecular docking of protein-ligand complexes, most docking tools can only deal with small drug-like ligands. The docking of large ligands, including peptides, is still considered a challenge in computational structural biology. To address this issue, I developed a molecular docking tool, called DINC, specifically aimed at dealing with large ligands, following a parallelized incremental meta-docking approach. DINC is a meta-docking tool in the sense that it uses existing docking software at its core. Following the divide-and-conquer paradigm, it was conceived as an incremental method that iteratively docks larger and larger overlapping fragments of a ligand in the protein’s binding site. This research was motivated by the study of molecular complexes important in cancer immunotherapy.

I extended this approach to address a limitation of DINC and numerous other docking tools: the fact that they do not account for receptor flexibility when docking a flexible ligand. Because of COVID-19, my collaborators and I chose to specifically implement a computational tool for ensemble docking with SARS-CoV-2 proteins. We extracted representative ensembles of protein conformations from the Protein Data Bank and from computer simulations. Twelve pre-computed ensembles of SARS-CoV-2 protein conformations are available for ensemble docking via a user-friendly webserver called DINC-COVID. We validated DINC-COVID using tested inhibitors of two SARS-CoV-2 proteins, obtaining good correlations between docking-derived binding energies and experimental binding affinities.

Main references:

• DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins; Computers in Biology and Medicine; 2021; DOI: 10.1016/j.compbiomed.2021.104943
• Using parallelized incremental meta-docking can solve the conformational sampling issue when docking large ligands to proteins; BMC Molecular and Cell Biology; 2019; DOI: 10.1186/s12860-019-0218-z
• General prediction of peptide-MHC binding modes using incremental docking: A proof of concept; Scientific Reports; 2018; DOI: 10.1038/s41598-018-22173-4

4. Robotics-inspired screening for molecular caging prediction

A molecular caging complex is defined as a pair of molecules in which a so-called host (or cage) features an internal cavity that can enclose a so-called guest, preventing its escape. In synthetic biochemistry, a host molecule is usually created with dynamic covalent bonds allowing its self-assembly around a guest molecule and its later disassembly in response to a specific stimulus (such as temperature, pH, or light). This paradigm has produced exciting biomedical applications, for example in targeted drug delivery, virus trapping, or medical imaging. Despite its promises, the use of molecular caging complexes remains challenging, with the discovery or synthesis of host molecules being the main bottleneck. There is thus a need for computational screening methods that can predict whether a given pair of molecules form a caging complex.

We proposed such a method, based on a caging verification algorithm that was initially designed for applications in robotic manipulation. We tested our algorithm on three pairs of molecules that were previously described in a pioneering work on molecular caging complexes and found that our results were fully consistent with previously reported ones. We also performed a screening experiment on a data set consisting of 46 hosts and four guests and used our algorithm to predict which pairs were likely to form caging complexes. Our method is computationally efficient and can be integrated into a screening pipeline to complement experimental techniques. This is important because the possibility of performing computational screening studies would propel biomedical applications even further and deliver substantial impact.

Main reference:

• A robotics-inspired screening algorithm for molecular caging prediction; Journal of Chemical Information and Modeling; 2020; DOI: 10.1021/acs.jcim.9b00945

• Ph.D. in Artificial Intelligence, University of Toulouse, France
• M.Sc. in Computer Science, Claude Bernard University, Lyon, France
• B.Sc. in Computer Science, Blaise Pascal University, Clermont-Ferrand, France
• Teacher certification in Mathematics, IUFM of Auvergne, Clermont-Ferrand, France