Dr. David Ponting, Principal Scientist
Name: David Ponting
Job Title: Principal Scientist
I joined Lhasa at the beginning of 2016, having undertaken a PhD at the University of Cambridge, with Prof. Jonathan Goodman (where I also studied for my undergraduate degree in Natural Sciences, specialising in Chemistry) and a postdoc at the University of Gothenburg in Sweden with Prof. Ann-Therese Karlberg. Both PhD and postdoc were in the field of predicting skin sensitisation. My main focus was in silico modelling of various kinds, principally ab initio (i.e. quantum-mechanical) models of the reactions of allergens with model peptides, as well as molecular dynamics investigations of the nature of nucleophilic residues in skin proteins. I also ventured back to the lab bench in a variety of roles, ranging from synthesis of test compounds to running LC/MS assays and even working with polymers and their mechanical properties.
Searching for a role in which I could continue developing computational models of chemical behaviour in biological systems, I was unsurprisingly drawn to Lhasa. I’d been aware of Lhasa, and especially Derek (then Derek for Windows), from day one of my PhD – though at the time Derek was the competition to which I would compare my predictions! My current role is split between both aspects of my previous experience:
In terms of toxicology, I’m a core member of the team continually improving the science in Derek Nexus – including of course refining our alerts for skin sensitisation, but also working on other endpoints, from mutagenicity to phototoxicity. I am also involved in Lhasa’s response to the nitrosamines crisis, and work closely with industry members to better understand both the hazard and risk presented by nitrosamine compounds.
Turning to theoretical chemistry and cheminformatics, I am developing a series of prediction models based on the machine learning of quantum mechanical parameters. These are then used in our products to refine the predictions that are generated – for example Zeneth, as of version 8, uses a model for Bond Dissociation Energy that is based on my work. This work stretches from running and interpreting QM calculations to create a training set, through finding the best machine learning models in Python, to finally implementing the best model into our production codebase for colleagues in software development to integrate into the products.
Outside the office, you’ll probably find me either up a mountain (walking, climbing, scrambling or skiing depending on the weather… having multiple National Parks within easy reach is a distinct advantage to living in Leeds) or behind a camera, and often both at once! I also enjoy listening to and making music - I sing bass and play the oboe – cooking, board games and gardening.