Convolutional neural network scoring and minimization in the D3R 2017 community challenge.

We assess the ability of our convolutional neural network (CNN)-based scoring functions to perform several common tasks in the domain of drug discovery. These include correctly identifying ligand poses near and far from the true binding mode when given a set of reference receptors and classifying ligands as active or inactive using structural information. We use the CNN to re-score or refine poses generated using a conventional scoring function, Autodock Vina, and compare the performance of each of these methods to using the conventional scoring function alone. Furthermore, we assess several ways of choosing appropriate reference receptors in the context of the D3R 2017 community benchmarking challenge. We find that our CNN scoring function outperforms Vina on most tasks without requiring manual inspection by a knowledgeable operator, but that the pose prediction target chosen for the challenge, Cathepsin S, was particularly challenging for de novo docking. However, the CNN provided best-in-class performance on several virtual screening tasks, underscoring the relevance of deep learning to the field of drug discovery.

Surf and turf wars online--growing implications of Internet gang violence.

To examine use of the Internet by gangs and its affect on youth, i-SAFE Inc. collected information from more than 100,000 students and 137 gang associates from an urban population. Although individuals who associate with a gang have distinguishable discrepancies in the amount of time spent online, they have similar online behavior as compared with those who are not involved with gangs. Additional research is necessary to develop a substantive link between gangs online and the school and community culture.