Machine Learning Platform to Discover Novel Growth Inhibitors of Neisseria gonorrhoeae.

PURPOSE: To advance fundamental biological and translational research with the bacterium Neisseria gonorrhoeae through the prediction of novel small molecule growth inhibitors via naïve Bayesian modeling methodology. METHODS: Inspection and curation of data from the publicly available ChEMBL web site for small molecule growth inhibition data of the bacterium Neisseria gonorrhoeae resulted in a training set for the construction of machine learning models. A naïve Bayesian model for bacterial growth inhibition was utilized in a workflow to predict novel antibacterial agents against this bacterium of global health relevance from a commercial library of >105 drug-like small molecules. Follow-up efforts involved empirical assessment of the predictions and validation of the hits. RESULTS: Specifically, two small molecules were found that exhibited promising activity profiles and represent novel chemotypes for agents against N. gonorrrhoeae. CONCLUSIONS: This represents, to the best of our knowledge, the first machine learning approach to successfully predict novel growth inhibitors of this bacterium. To assist the chemical tool and drug discovery fields, we have made our curated training set available as part of the Supplementary Material and the Bayesian model is accessible via the web. Graphical Abstract.

Boosting Docking-Based Virtual Screening with Deep Learning.

In this work, we propose a deep learning approach to improve docking-based virtual screening. The deep neural network that is introduced, DeepVS, uses the output of a docking program and learns how to extract relevant features from basic data such as atom and residues types obtained from protein-ligand complexes. Our approach introduces the use of atom and amino acid embeddings and implements an effective way of creating distributed vector representations of protein-ligand complexes by modeling the compound as a set of atom contexts that is further processed by a convolutional layer. One of the main advantages of the proposed method is that it does not require feature engineering. We evaluate DeepVS on the Directory of Useful Decoys (DUD), using the output of two docking programs: Autodock Vina1.1.2 and Dock 6.6. Using a strict evaluation with leave-one-out cross-validation, DeepVS outperforms the docking programs, with regard to both AUC ROC and enrichment factor. Moreover, using the output of Autodock Vina1.1.2, DeepVS achieves an AUC ROC of 0.81, which, to the best of our knowledge, is the best AUC reported so far for virtual screening using the 40 receptors from the DUD.