The reverse virtual screening method based on molecular docking has important application prospects in the field of drug target determination, new use of old drugs, and drug side effects/toxicology research. It has attracted extensive attention from researchers in the field of drug discovery. This method is the opposite of the traditional virtual screening method. For a given ligand molecule, it is necessary to select the target protein that can bind to it from the database, hence the name. The IVS method is favored by new drug developers.

Figure 1. Reverse Virtual Screening. (Zhang H.; et al.2019)

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CD ComputaBio can offer you but not limited to:

• Ligand-based Virtual Screening (LBVS) Service
• Structure-based Virtual Screening (SBVS) Service
• 3D-QSAR Service

CD ComputaBio' reverse virtual screening can significantly reduce the cost and labor of the subsequent experiments. Virtual screening technology service is a personalized and customized innovative scientific research service. Each project needs to be evaluated before the corresponding analysis plan and price can be determined. If you want to know more about service prices or technical details, please feel free to contact us.

Reference:

1. Zhang H.; et al. IVS2vec: A tool of Inverse Virtual Screening based on word2vec and deep learning techniques. Methods, 2019.

Reverse Virtual Screening FAQs

• • Q: What is the process of reverse virtual screening (RVS)?

    • A: The RVS process begins with a database of compounds or ligands that are screened against a large collection of proteins or enzymes. The aim is to find novel binding sites on the proteins or enzymes that have not been previously identified. This process allows for the discovery of new targets for drug development. The first step in RVS is to create a library of small molecules that can potentially interact with a protein target. Once the ligand library has been assembled, it is screened against a large database of protein structures to identify potential targets. The next step is to evaluate the potential for each ligand-protein interaction using computational methods. These methods include molecular docking, molecular dynamics simulations, and machine learning techniques.

  • Q: What is the algorithm of reverse virtual screening?

    • A: The algorithm used in RVS typically consists of several steps. The first step is to generate a library of ligands or small molecules that have the potential to interact with the protein target. The next step is to use a molecular docking algorithm to dock each ligand in the library to the protein target. The docking algorithm predicts the binding affinity of each ligand to the protein target and produces a low-energy binding pose. In the third step, molecular dynamics simulations are used to study the conformational changes that occur during binding. Finally, machine learning techniques can be used to predict the binding affinity of each ligand, based on its chemical structure and the properties of the protein target. This step identifies the ligand with the highest binding potential to the protein target.

  • Q: What is the difference between reverse and traditional virtual screening?

    • A: The main difference between RVS and traditional virtual screening is the starting point of the process. A traditional virtual screen starts with a known protein target and seeks to identify the small molecules that bind to it. In contrast, RVS starts with a library of small molecules and tries to predict which proteins they might interact with.

  • Q: What are the reverse virtual screening service items?

    • A: Reverse virtual screening services include ligand library generation, protein target selection, molecular docking, molecular dynamics simulation, and machine learning-based binding affinity prediction.

  • Q: What is the application of reverse virtual screening service?

    • A: RVS in can be used to identify new drug targets, which can lead to the development of new therapies for diseases for which there is currently no effective treatment. RVS can also be used to identify new uses for existing drugs by predicting their potential interactions with other proteins or enzymes. another application of rvs is the optimization of lead compounds.