# Protein-Ligand Scoring with Convolutional Neural Networks Source: [Protein-Ligand Scoring with Convolutional Neural Networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T19:06:52.680Z Authors: Matthew Ragoza, Joshua Hochuli, Elisa Idrobo, Jocelyn Sunseri, David Ryan Koes (1) \_\_INSTITUTION\_5\_\_ University of Pittsburgh, (2) The College of New Jersey) Published: 2016-12-08T18:18:29.000Z ## Abstract Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. Structure-based drug design methods rely on scoring functions to rank and predict binding affinities and poses. The ever-expanding amount of protein-ligand binding and structural data enables the use of deep machine learning techniques for protein-ligand scoring. We describe convolutional neural network (CNN) scoring functions that take as input a comprehensive 3D representation of a protein-ligand interaction. A CNN scoring function automatically learns the key features of protein-ligand interactions that correlate with binding. We train and optimize our CNN scoring functions to discriminate between correct and incorrect binding poses and known binders and non-binders. We find that our CNN scoring function outperforms the AutoDock Vina scoring function when ranking poses both for pose prediction and virtual screening. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Virtual Screening on DUD-E 10 diverse targets](): Virtual Screening; DUD-E 10 diverse targets; Mean AUC: 83. ## Original sources and implementation [Original paper]() [PDF]()