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We are most known for the Open Source Malaria project, our open science work more generally and for the use of click-derived triazoles in chemical sensing. However, there are plenty of other projects that interest us.

Open Science

We live in the age of the internet, one of the most transformative inventions of our time. Computer scientists understood what the internet meant - working together without barriers. Scientists are just catching up. We could collaborate in fantastically productive ways if only we released out secrets.

We have adopted open source principles to experimental lab science. The first project successfully used this idea to find a way of producing the important drug praziquantel (used to treat the dreadful disease Schistosomiasis) as a single enantiomer. All data and ideas were freely shared and anybody could take part. People did - about 30, and the problem was more quickly solved than we could have alone because strangers came along and contributed where they were able to. The science was published here (check out the awesome links to actual lab notebook pages) and the way we did it was published separately in Nature Chemistry.

This made us think, what about drug discovery? Could we discover new drugs using an open approach and without patents? So, we started a project to see if this works and we're now driving the Open Source Malaria project, a fully open, borderless, patentless drug discovery project for malaria that aims to discover a compound that will enter Phase I clinical trials. It's a fantastically exciting project made possible by the continual contributions of a scientists around the world.

Synthetic Methodology

The group's motto is To make the right molecule in the right place at the right time. While nobody understands what this means it is crucial that we know how to make molecules. The group is mainly interested in developing methods for the construction of new bonds in small molecules, i.e. the development of ways of making bonds that cannot currently be made.

New project in Late Stage Functionalisation. Relevant to this: Late stage azidation, catalyst-controlled site-selective bond activation

Asymmetric Catalysis

One of the most value but difficult things an organic chemist can do is to selectively synthesise one enantiomer of a molecule, and most impressively via catalysis. We are interested in asymmetric catalysis and the rational development of new catalysts for asymmetric reactions. Recently we ran an Open Source Catalysis Project and this is awaiting the right student to reboot it.

Catalyst prediction: 2017 review, Use of noncovalent interactions, Computational catalyst design

Autocatalysis: speculations on ways of symmetry-breaking in synthesis

Chemical Education

We made up a very cool Treasure Hunt for chemical education that you can read about here. Essentially the answers to questions guide you round a campus so that you find certain objects, and when you have found all the objects you draw them out on a campus map and the shape gives you the structure of a molecule, which is the "treasure". This could work for lots of other disciplines too and it'd be possible to use Google Maps to make a global version, though we've not yet tried.

We also like chemical animations. We like getting students involved with making real molecules in large numbers - we have done this through the Open Source Malaria project with some success.

In response to the price hike of HIV/AIDS drug, pyrimethamine (Daraprim), by Turing Pharmaceuticals, we helped a small team of high school students from Sydney Grammar School to synthesise the drug. The team produced 3.7 grams of Daraprim for $US20, which would be worth between $US35,000 and $US110,000 in the United States based on hiked prices. This received significant media attention and was featured on ABC, BBC, CNN, The Guardian, and Time.

ALTC teaching project blog

Automated Synthesis Planning

Alphachem. Prediction of reaction outcomes, Neural networks for reaction prediction

Machine Learning and AI. AI assistants in chemistry, Reaction prediction based on knowledge graph of chemistry


Impact of AI in Drug Discovery: Neural networks for generation of libraries