The drug development process is a major challenge for the pharmaceutical industry because it requires a lot of time and money to go through all the stages of developing new drugs. One widely used method to minimize the cost and time of the drug development process is computer-aided drug design (CADD). CADD can better focus on experiments, which can reduce the time and cost of researching new drugs. In this case, structure-based virtual screening (SBVS) is both powerful and useful, and is one of the most promising computer technologies for drug design. SBVS tries to predict the best interaction mode between two molecules to form a stable complex, and it uses a scoring function to evaluate the force of the non-covalent interaction between the ligand and the molecular target.
Structure-based virtual screening (SBVS), also known as target-based virtual screening (TBVS), attempts to predict the optimal interaction between ligands to form complexes against molecular targets. As a result, the ligands are ranked according to their affinity to the target, and the most promising compounds are shown at the top of the list. The SBVS method requires knowing the 3D structure of the target protein so that the interaction between the target and each compound can be predicted by the computer. In this strategy, compounds are selected from a database and classified according to their affinity for the receptor site. In SBVS technology, molecular docking is worth noting because of its low computational cost and good results.
First we will design one or more goals. Usually, these targets are closely related to specific diseases, and inhibiting or changing these targets will help treat the disease. Design and synthesize a compound library containing various small molecule drug fragments. The ideal small molecule fragment should have a certain affinity to the active site of the target, such as a target protein or a targeted enzyme. Use pre-designed targeting targets to screen small fragments of synthetic fragments. According to the screening results, molecular fragments with better activity can be obtained, and lead compounds can be obtained by appropriately combining the fragments. Further modify and further optimize the chemical structure of the obtained lead compound to obtain candidate drugs for clinical research.
The method based on AI learning is considered to be an important scoring function. AI-based learning methods have attracted attention because of their reliable predictions. Many researchers have used AI to improve the SBVS algorithm. These techniques have been used in quantitative structure-activity relationship (QSAR) analysis to predict various physical chemistry (for example, molecular hydrophobicity and stereochemistry), biology (for example, activity and selectivity) and drugs (for example, absorption and Metabolism) characteristics. Among these types of scoring functions, modern QSAR analysis can be used to derive statistical models to calculate protein-ligand binding scores.AI-based algorithms include:
|Structure-Based Virtual Screening
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|Depends on the time you need to simulate and the time required for the system to reach equilibrium.
|Product delivery mode
|The simulation results provide you with the raw data and analysis results of molecular dynamics.
Generally, there are three methods for drug screening: animal model, high-throughput screening and virtual screening. This method of screening drugs in animal models has the characteristics of high cost, low efficiency, slow speed, and large sample requirements. The high-throughput screening method achieves automated operation, sensitive and rapid detection, but the cost is also very high. Virtual drug screening has become a classic and efficient method for lead compound discovery. If you need this service, please feel free to contact us.
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