Molecular docking is a method of drug design through the characteristics of the receptor and the interaction between the receptor and the drug molecule. It is a theoretical simulation method that mainly studies the interaction between molecules (such as ligands and receptors) and predicts its binding mode and affinity. In recent years, molecular docking methods have become an important technology in the field of computer-aided drug research. Molecular docking is a structure-based drug design method that predicts the binding mode and affinity of organic small molecule ligands and biological macromolecular receptors by studying the interaction. Molecular docking methods have a wide range of applications in the fields of enzymology research and drug design.
Figure 1. Molecular docking. (Tejinder Kaur, et al.2018)
The two major issues of molecular docking methods are spatial recognition and energy recognition between molecules. Spatial matching is the basis for the interaction between molecules, and energy matching is the basis for maintaining stable binding between molecules. Various molecular docking methods have a certain simplification to the system. According to the degree and method of simplification, molecular docking methods can be divided into three categories.
Rigid docking: In the calculation process of the rigid docking method, the conformation of the molecules involved in the docking does not change, only the spatial position and posture of the molecules are changed. The rigid docking method has the highest degree of simplification, and the calculation amount is relatively small.
Semi-flexible docking: The semi-flexible docking method allows the conformation of small molecules to change to a certain extent during the docking process, but usually fixes the conformation of macromolecules. In addition, the adjustment of the conformation of small molecules may also be restricted to a certain extent, such as fixing some For non-critical parts of bond length, bond angle, etc., the semi-flexible docking method takes into account the calculation amount and the predictive ability of the model, and is one of the more widely used docking methods.
Flexible docking: The flexible docking method allows the configuration of the research system to change freely during the docking process. Since the variables increase geometrically with the atomic number of the system, the flexible docking method requires a lot of calculation and consumes a lot of computer time, which is suitable Accurately examine the recognition between molecules.
We adopt the molecular docking method and mainly provide the following services:
If conditions permit, the former needs to examine the reproducibility of the docking software (method), that is, re-dock or self-dock, and the latter needs to examine the screening performance, including Enrichment rate, goodness of fit. Only through rigorous and scientific processing can we ensure that the simple docking process gets accurate calculation results.
In recent years, deep learning (DL) technology in the field of artificial intelligence (AI) has been rapidly developed and has been rapidly introduced into all aspects of the drug discovery and development process. AI-based methods can be used in the early stages of molecular docking. Two types of representations have been used in studying protein-ligand interactions. One is a three-dimensional (3D) grid, which discretizes the protein-ligand complex structure into a 3D grid, and its features are stored on the grid points. Another representation is a graph neural network. Each atom is a vertex, and the atomic characteristics (including atom type, charge, distance, and neighbors) in the molecule are stored on the atom.
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