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.
['Sunseri|Jocelyn|J|', 'King|Jonathan E|JE|', 'Francoeur|Paul G|PG|', 'Koes|David Ryan|DR|0000-0002-9043-6267']
D000465:Algorithms D001665:Binding Sites D002403:Cathepsins D030562:Databases, Protein D055808:Drug Discovery D008024:Ligands D062105:Molecular Docking Simulation D016571:Neural Networks, Computer D011485:Protein Binding D011487:Protein Conformation D013329:Structure-Activity Relationship
Neural networks D3R Virtual screening Drug design data Machine learning Protein–ligand scoring