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

DeepAtom: A Framework for ProteinLigand Binding Affinity Prediction The cornerstone of computational drug design is the calculation of binding affinity between two biological counterparts especially a chemical compound, i.e. a ligand, and a protein. Predicting the strength of proteinligand binding with reasonable accuracy is critical for drug discovery. In this paper, we propose a datadriven framework named DeepAtom to accurately predict the proteinligand binding affinity. With 3D Convolutional Neural Network (3DCNN) architecture, DeepAtom could automatically extract binding related atomic interaction patterns from the voxelized complex structure. Compared with the other CNN based approaches, our lightweight model design effectively improves the model representational capacity, even with the limited available training data. With validation experiments on the PDBbind v.2016 benchmark and the independent Astex Diverse Set, we demonstrate that the less feature engineering dependent DeepAtom approach consistently outperforms the other stateoftheart scoring methods. We also compile and propose a new benchmark dataset to further improve the model performances. With the new dataset as training input, DeepAtom achieves Pearsonu0027s  mathrmR0.83  and  textRMSE1.23  pK  units on the PDBbind v.2016 core set. The promising results demonstrate that DeepAtom models can be potentially adopted in computational drug development protocols such as molecular docking and virtual screening.