Ligand and Structurebased Virtual Screening Studies for the Discovery of Selective Inhibitors New drug design research using computer studies the relationship between 2D3D quantitative structure and activity of target protein, derives pharmacophore model, which is essential for pharmacological activity that substrate should have, proceed screening to select the optimal inhibitor. Computeraided drug design (CADD) studies are divided into structure and ligandbased drug design. Structurebased drug design is to design a pharmacophore model for amino acid residues in active sites based on the 3D structure of the target protein under study. Ligandbased drug design predicts the activity of a new candidate compound by designing a pharmacophore model and a quantitative structureactivity relationship model using information of compounds known to be active against a target protein. In this study, selective inhibitors were designed using both methods. Ligandbased pharmacophore model consisting of ring aromatic, positive ionizable, negative ionizable, and hydrogen bonding receptors were generated from known active inhibitors. In addition, by analyzed the binding modes of known target proteins and ligands, we generated a structurebased pharmacophore model with the addition of hydrogen bonding donors. To increase bioavailability from a total of 1,890,602 commercial drug candidates databases, Lipinskiu0027s rule of five and ADMET analysis were used to filter 178,161 chemical compounds for virtual screening. After that, 20 selective inhibitors were finally selected through the process of selecting compounds with a fit value of 3.54 or higher and eliminating duplication.

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