Figure 4 from the paper. (c) 2020 The authors. Reproduced under the CC BY-NC-ND 4.0 license.
While there are many ML-based design studies in the literature it is quite rare so see one with experimental verification. The Open Source Malaria (OSM) made two rounds of antimalarial activity data available and invited researchers to use this data to develop predictive models and identify molecules with high potency for synthesis. Here I'll focus on the second round that started in 2019, were the participants worked with a ~400 compound dataset.
Here 10 teams from both industry and academia submitted models (classifiers) that were judged by a panel of experts using a held-back dataset. The four teams with the highest scoring models (with a precision between 81% and 91%) were then asked to submit two new molecules each for experimental verification: one possessing a triazolopyrazine core and one without. However, the latter compounds all proved synthetically inaccessible, as did two with the triazolopyrazine core. Thus, a total of six molecules were synthesised and tested and ...
Three of the six compounds were found to be active(<1 μM) or moderately active (1–2.5 μM) in in vitro growth assays with asexual blood-stage P. falciparum (3D7) parasites, representing a hit rate of 50% on a small sample size. Up to this point a total of 398 compounds had been made and evaluated for in vitro activity in OSM Series 4, with the design of these compounds driven entirely by the intuition of medicinal chemists. By setting a potency cut-off of 2.5 μM (the upper limit of reasonable activity), the tally of active compounds discovered in this series stands at 165, representing a comparable human intuition-derived hit rate of 41% on a larger sample size.
Interestingly, the Optibrium/Intelligens candidate was initially met with a great deal of scepticism by the expert panel but turned out to be the best overall candidate.
