If anyone's wondering in what way AI/ML was used, here's the relevant part of the article: >>> We first screened a diverse collection of 7,684 small molecules

If anyone's wondering in what way AI/ML was used, here's the relevant part of the article: >>> We first screened a diverse collection of 7,684 small molecules at 50µM for those that inhibited the growth of A. baumannii ATCC 17978 in Lysogeny Broth (LB) medium (Fig. 1b and Extended Data Fig. 1a). This chemical collection consisted of both off-patent drugs (2,341 mol-ecules) and synthetic chemicals (5,343 molecules) curated from various high-throughput screening sub-libraries at the Broad Institute. Using a conventional hit cutoff of one standard deviation below the mean growth of the entire dataset resulted in 480 molecules being defined as ‘active’ and 7,204 being defined as ‘inactive’ (Supplementary Data 1).Next, this dataset was used to train a binary classifier to predict whether structurally new molecules may display activity against A. baumannii. Briefly, we leveraged a directed message-passing neural network architecture, which translates the graph structure of a mol-ecule into a continuous vector18 (Fig. 1a).This type of model operates by iteratively exchanging informa-tion of local chemistry between adjacent atoms and bonds in a series of ‘message-passing’ steps. Each iteration of message passing propa-gates information about local chemistry across the molecule, thereby allowing the model to build a more holistic representation of the mol-ecule. After a defined number of message-passing steps, the vector representations of various local chemical regions of a molecule are summed into a single continuous vector that captures the complexity of the entire compound. This learned final vector is then supplemented with fixed molecular features computed using RDKit19. A final vector containing both learned and computed features is then used as an input vector for a feed-forward neural network that predicts antibacterial properties. The model was further optimized by using an ensemble of ten classifiers, increasing its robustness. Our final model achieved an area under the precision-recall curve of 0.337±0.088 and an area under the receiver-operating characteristic curve of 0.792±0.042, providing confidence in leveraging the model for predictions in new chemical spaces.