Computational Methods in Drug Discovery

The design of drugs is the most significant process in the pharmaceutical industry. The emergence of various calculation methods has greatly reduced the time and cost of drug discovery. Promoting the development of computer-related methods will be an inevitable trend in drug progress. Based on our advanced technology and platform, we have been equipped to assist in drug screening and design in your project.

Background of CADD

Bringing drugs to the market is a long and costly process. In recent years, high-throughput screening (HTS) experiments have been playing an important role in the drug screening process. However, HTS is not only expensive but also requires a large amount of target and ligand resources. Moreover, the hit rate of HTS is usually very low. For these reasons, the role of HTS in screening large compound libraries is significantly limited. In the past few decades, computer-aided drug design (CADD) has emerged as a valuable strategy that can significantly narrow the range of compounds required for screening. Computational methods using modeling and visualization strategies can quickly identify potential binders.

Introduction of CADD

In the process of drug discovery, CADD is often applied in many ways. CADD can effectively reduce the large-scale compound library, which lays the foundation for experimental operations. CADD also guides the optimization of lead compounds. The most important thing is that CADD can be used to design new compounds. Commonly used CADD methods can be divided into two categories: structure-based drug design (SBDD) and ligand-based drug design (LBDD). The SBDD method includes ligand docking, ligand design methods and pharmacophore, which need to be based on the target molecular structure. LBDD uses only ligand information to predict activity in the absence of the three-dimensional structure of the potential target. LBDD tools include quantitative structure-activity relationship (QSAR), pharmacophore modeling, and molecular field analysis. It is worth noting that in recent years, in order to solve the problem of neither target structure nor ligand information, sequence-based methods based on bioinformatics methods to analyze and compare multiple sequences have been used to identify potential targets from scratch.

CADD in drug discovery design pipeline. Fig.1 CADD in drug discovery design pipeline. (Sliwoski, 2014)

Features of CADD

As a method to significantly reduce the number of compounds that need to be screened in experimental analysis, CADD also greatly decreases the workload of screening without affecting the discovery of potential customers. At the same time, CADD can also increase the hit rate of new drug compounds. In addition, the application of CADD tools effectively reduces the costs associated with drug exploration, and can also shorten the time required for drugs to enter the consumer market.

Applications of Computational Methods

With the rapid advancement of computing technology, the process of drug discovery benefits from various computational methods. For example, multi-scale biomolecular simulation helps to identify drug binding sites on target macromolecules and clarify the mechanism of drug action. Virtual screening can effectively search for lead compounds in massive chemical databases. Additionally, de novo drug design provides another powerful method to design drug molecules from scratch using the building blocks summarized and abstracted from previous successful drug discoveries. The development of integrated computing methods will help drug detection and help determine effective therapies with new mechanisms of action, which can ultimately be applied to various complex biological systems.

CADD has been successfully applied in multiple cases, which indicates that these methods may further emphasize the role of computational drug discovery in the drug advancement workflow. We have professional scientists to solve your CADD-related problems. Please contact us for more details.


  1. Sliwoski, G.; et al. Computational methods in drug discovery. Pharmacological reviews. 2014, 66(1): 334-395.
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