The other type of task in current study was to build models for predicting proteinligand interaction based on complex structures. Accurate prediction of ligand binding affinity can help to prioritize compound synthesis and speed up the drug screening process . Typical scoring functions are divided into: physics-based methods , empirical-based methods and knowledge-based methods . They were used in popular docking software such as AutoDock Vina and GOLD etc. to rank 3D ligand poses. While the docking calculation can identify the binding pose of the protein-ligand complexes with relatively high accuracy, prediction of binding affinity and ranking compounds in the database are rather challenging . To improve the performance of these classical scoring functions, first wave of efforts focused primarily on applying Machine Learning (ML) method to develop scoring function. Ballester et al. introduced the RF-Score , which was the first scoring function relating the structural descriptors of complex with binding affinities using Random Forest regression model. The RF-Score achieved impressive scoring power comparing with classic scoring functions. Besides RF, other ML algorithms model were also proposed for training scoring function . One limitation of these machine learning scoring functions is their reliance on the feature engineering. Some recent efforts in developing descriptors for representing the chemical environ-ment of protein-ligand binding site were reported. Roitberg et al. modified the Behler and Parrinello symmetry functions to build single-atom atomic environment vectors (AEV), which were used as the input descriptors to construct a neural network potential named ANAKIN-ME (Accurate NeurAl networK engINe for Molecular Energies) . Barril et al. introduced Extended Connectivity Interaction Features (ECIF) to describe the types of protein-ligand atom pair and scoring functions using ECIF achieved outstanding scoring power.