Figure 3

(A) An illustration of the hit rate versus the number of training examples available to our model. Each point represents a project, with the x-axis denoting the number of active molecules in our training for the target protein or homologs and the y-axis denoting the hit rate of the project (the percentage of molecules tested in the project that were active). The model shows no dependence on the availability of on-target training examples. For 70% of the targets, the AtomNet model training data lacked any active molecules for that target or any similar targets with greater than 70% sequence identity, yet the model achieved a hit rate of 5.3% compared to 6.1% when on-target data was available. (B) The distribution of similarities between hits and their most-similar bioactive compounds in our training data. Our screening protocol ensures that the compounds subjected to physical testing are not similar to known active compounds or close homologs (< 0.5 Tanimoto similarity using ECFP4, 1024 bits). Because 70% of the AIMS targets had no annotated bioactivities in our training dataset, hits identified in these projects have a similarity value of zero.