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research-statement.html
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---
layout: default
---
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<article id="main">
<section id="cta">
<header class="special container">
<span class="icon fa-book"></span>
<h2><strong>Research</strong></h2>
<p>
My goal is to improve the technology behind computer-aided drug design
</p>
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<h3>Atomistic simulations for drug discovery</h3>
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<p>
Much of my research revolves around atomistic simulations of biomolecules, which
include proteins, drugs, solvent (e.g. water), and other small molecules that
are of biological interest. The simulations model the forces between atoms to
predict configurations and dynamical processes. These in turn can be used to
estimate quantities that can be verified (or falsified) experimentally. The
simulations are atomistic in resolution, giving us an unprecedented level of
detail on the systems in question. We can use them to generate hypotheses or to
address questions like ‘how does this particular drug bind?’ and ‘how does this
particular protein function?’.
</p>
<p>
Despite being simplified models of the atomistic world, the simulations are
highly complex and time-intensive. For a typically-sized protein immersed in
water, the simulations have to evaluate the forces between tens of thousands of
atoms over hundreds of thousands of iterations. Depending on the system and the
questions one is trying to answer, simulations can take days to weeks. Despite
the increasing power of computer processors and smarter use of hardware,
sampling the important states of the biomolecules still remains a challenge.
</p>
Traditionally, the two main problems my field has grappled with are 1) improving
the accuracy of the modelled atomistic forces, and 2) improving the thoroughness
of the sampling. Much of my research has been on the latter, with particular
focus on <strong>enhancing the sampling of water in buried protein cavities </strong>,
particularly in the context of protein-ligand <strong>binding free energy calculations</strong>.
See, for instance
see <ul style="padding-left:40px">
<li type="square"><a href="https://doi.org/10.1021/acs.jctc.0c00660"> Enhancing water sampling in free energy calculations with grand canonical Monte Carlo </a></li>
<li type="square"><a href="https://doi.org/10.1021/acs.jctc.8b00614"> Ligand binding free energies with adaptive water networks: two-dimensional grand canonical alchemical perturbations</a></li>
<li type="square"><a href="https://doi.org/10.1021/acs.jctc.7b00738"> Replica-exchange and standard state binding free energies with Grand Canonical Monte Carlo</a></li>
<li type="square"><a href="https://doi.org/10.1021/jacs.5b07940"> Water sites, networks, and free energies with grand canonical Monte Carlo</a></li>
</ul>
Binding free energies are a measure of strongly one molecule attaches to another,
so they are really important quantities to be able to calculate to aid drug
discovery. Experimentally, binding free energies are measured as
equilibrium constants and inhibition constants.
</p>
<p>
I am also interested in reducing the barriers to using atomistic simulations.
They require expertise to set up, run, and analyse. One large barrier is the
complexity of the software we use, a problem that is compounded by
the frequent augmentations and modifications that are made to
the software as the science progresses. This in turn raises issues around the
sustainability of the software itself. At Schrodinger, I work a lot on the FEP+ package,
which aims to be the most accurate software for calculating binding free energies
as well as the most robust and user friendly.
Prior to joining Schrodinger, I strove to make all my software easy to
use, free, open-source, and written in an interpretable way. I developed methods for
<a href="https://www.essexgroup.soton.ac.uk/ProtoMS">ProtoMS</a>
and worked on tools for <a href="https://openmm.org">OpenMM</a>.
</p>
<p>
Another barrier to using atomistic simulations is knowing how make reliable inferences from the
large and complex data that is produced. It is important to ask the
<a href="https://pubs.acs.org/doi/abs/10.1021/ct4004228">right questions</a>
from the data, and have the best tools to address them. To this end, I'm very
interested in statistics (with a Bayesian flavour) and machine learning.
</p>
<p>
We are entering an exciting phase of computer aided drug-design. Modern calculation
tools, like FEP+, have demonstrated that atomistic simulations really can drive
drug discovery projects forward and make the process more efficient. The central
questions of the field no longer center around "do these methods work?", but
"how can we make these methods even faster?". The faster we make our methods, the
larger volume of chemical space can be rapidly assayed <i>in silico</i>. As
more and more binding free energy data predictions are made, the greater the possibility
of training machine learning models that can side-step more and more of these
expensive simulations.
</p>
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