Abstract of Project

[mattodd]

We could use a simple abstract of the project, suitable for the paper, but also suitable for the base Readme on this repo.
@edwintse could you please take the text that is currently on the wiki landing page (https://github.com/OpenSourceMalaria/Series4_PredictiveModel/wiki) and put that on the landing page for the repo (the "code" page) and then tweak it suitable for interested scientists who may not be familiar with what's going on? Two paras - what has happened up to this point and then what round 2 is hoping to achieve. We can copy and paste it into the paper when done.

[edwintse]

Here's a first pass at the abstract. The last bit is uncompleted as we don't know the results yet.

The discovery of new antimalarial medicines with novel mechanisms of action is key to combating the increasing reports of resistance to our frontline treatments. The Open Source Malaria consortium have been developing a series of triazolopyrazine-based compounds which possess potent activity against Plasmodium falciparum in vitro and in vivo and have been implicated to act through the inhibition of PfATP4, an essential ion transporter in the parasite membrane that regulates intracellular Na+ and H+ concentrations. In the absence of structural information about this target, a public competition was created to better utilise the expertise of the wider scientific community. Two rounds were run, in 2016 and 2019, with participants from academia and industry taking part, applying methods involving machine learning and artificial intelligence for the prediction of new potent compounds for the series. A number of high quality models were submitted by the community and the predictions validated against experimental data. Outcome…

[mattodd]

Great, thanks Ed. Tweaked below. Pasted onto the landing "Code" page. Tweak as appropriate, but can form basis for paper abstract too.

The discovery of new antimalarial medicines with novel mechanisms of action is key to combating the increasing reports of resistance to our frontline treatments. The Open Source Malaria consortium have been developing compounds ("Series 4") which possess potent activity against Plasmodium falciparum in vitro and in vivo and have been suggested to act through the inhibition of PfATP4, an essential ion pump in the parasite membrane that regulates intracellular Na+ and H+ concentrations. This pump has not yet been crystallised, so in the absence of structural information about this target, a public competition was created to develop a model that would allow us to predict when compounds in Series 4 are likely to be active.

In the first round in 2016, six participants used the open data collated by OSM to develop moderately predictive models using diverse methods. Notably all submitted models were available to all other participants in real time. Since then further bioactivity data have been acquired and machine learning methods have rapidly developed, so a second round of the competition is now underway. The best-performing models from this second round will be used to predict novel analogs in Series 4 that will be synthesised and evaluated against the parasite. As such the project will openly demonstrate the abilities of new machine learning algorithms in the prediction of active compounds where there is no confirmed target, frequently the central problem in phenotypic drug discovery.

[mattodd]

Closing since further edits to abstract can take place on landing page readme or in paper.