D3R grand challenge 4: blind prediction of proteinxe2x80x93ligand poses, affinity rankings, and relative binding free energies The Drug Design Data Resource (D3R) aims to identify best practice methods for computer aided drug design through blinded ligand pose prediction and affinity challenges. Herein, we report on the results of Grand Challenge 4 (GC4). GC4 focused on proteins beta secretase 1 and Cathepsin S, and was run in an analogous manner to prior challenges. In Stage 1, participant ability to predict the pose and affinity of BACE1 ligands were assessed. Following the completion of Stage 1, all BACE1 cocrystal structures were released, and Stage 2 tested affinity rankings with cocrystal structures. We provide an analysis of the results and discuss insights into determined best practice methods.

DeepDock: Enhancing Ligandprotein Interaction Prediction by a Combination of Ligand and Structure Information The prediction of precise proteinligand binding activities can accelerate drug discovery by virtual screeningxe2x80x94a computational technique that predicts whether a small molecule ligand is able to bind to a specific target. Thus, it is crucial to improve the performance of virtual screening. However, previous models for solving this problem are either ligandbased or structurebased. In this paper, we propose a universal deep neural network model called DeepDock that predicts proteinligand interaction by using both ligand and structure information. Using the combination of two types of information, our model consists of embedding, convolution, max pooling, and fullyconnected layers. In particular, different types of inputs are concatenated before being fed into the fullyconnected layers. In the experiments, we compare our approach to the competing methods against two benchmark datasets under different settings. The experiment results have demonstrated that DeepDock can improve predictive performance by more than 4% on both DUDE and MUV datasets in terms of AUPR