Chat with us, powered by LiveChat

Loading...
 

3 Main Types of Computer-aided Drug Discovery

Publish Date: June 02, 2020

Drug discovery and designing is a time-consuming process which requires varied resources. Scientists and medical experts are making efforts to apply computational power to the combined biological-cum-chemical space in order to restructure the drug discovery process, development, design, and optimization. CADD, nonetheless, is impacting the discovery process of various antidotes that are being used in the treatment of patients.

Computer-aided Drug Discovery (CADD) has been beneficial in inventing and manufacturing pharmaceutical drugs. These drugs have attained the FDA (Food and Drug Administration) approval and entered the consumer market. Throughout recent years, the CADD industry has been improved drastically with the frequent discovery of brand new technologies and methods. CADD is bearing huge potential and promise in the drug discovery industry.

The complex procedure of drug discovery requires an interdisciplinary effort to discover and design commercially viable and effective drugs. The main aim of the drug design process is to invent a chemical compound that can fit to a specific cavity on a protein target both chemically and geometrically. 

Drug discovery and designing is a time-consuming process which requires varied resources. Scientists and medical experts are making efforts to apply computational power to the combined biological-cum-chemical space in order to restructure the drug discovery process, development, design, and optimization. CADD, nonetheless, is impacting the discovery process of various antidotes that are being used in the treatment of patients. 

Structure-based drug discovery

Amongst the three-dimensional structure of a disease-related drug target, structure-based drug discovery (SBDD) is the most frequently used CADD techniques. In SBDD, the therapeutics are designed based on the data of the target structure. 

The two methods in SBDD which are used most frequently are molecular docking approaches and de novo ligand (agonists, antagonists, inhibitors, etc. of a target) design. 

Molecular dynamics (MD) simulations are used in SBDD for two main reasons. 

  • to give insights into how ligands bind with target proteins and
  • the pathways of interaction and to account for target flexibility. 

This is especially important when drug targets are membrane proteins where membrane permeability is considered to be important for drugs to be useful.

Ligand-based drug design (LBDD)

The Ligand-Based Drug Design (LBDD) is used as the main substitute to SBDD i.e. Structure-based Drug Discovery. 

There are cases when the potential drug target structure is unidentified and predicting this structure using methods such as homology modeling or ab initio structure, prediction becomes somewhat stimulating or detrimental. In such cases, ligand-based drug design is used as the alternative procedure. 

An important fact is that LBDD procedure of drug designing is depended on the knowledge of small molecules that bind to the target of interest. 

Some frequently used LBDD approaches are:

  • Pharmacophore modeling, 
  • molecular similarity approaches and 
  • QSAR (quantitative structure–activity relationship) modeling.

In the methods of molecular similarity, the molecular fingerprint of known ligands that bind to a target is used to find molecules with similar fingerprints through screening molecular libraries. In the ligand-based pharmacophore modeling, the screening is done with the common structural features of ligands that bind to a target. QSAR is a computational method that prototypes the relationship between structural features of ligands that bind to a target and the corresponding biological activity effect.

Sequence-based approaches

There are cases when the three-dimensional structures of most proteins have not been determined earlier, and many of the proteins do not even have a known ligand. These are the situations when neither structure-based methods nor ligand-based methods can be implemented to carry out the drug discovery and development research. Here occurs the need of a method which can predict ligand-protein interactions (LPIs) in the absence of 3D or ligand information. 

A sequence-based drug design model for LPI was created recently exclusively on the basis of the primary sequence of proteins and the structural features of small molecules. It was invented using the support vector machine (SVM) approach. This model was trained using 15 000 LPIs between 626 proteins and over 10 000 active compounds collected from the Binding Database. In the validation test of this model, nine original active compounds against four pharmacologically important targets were found using only the sequence of the target. This is the first instance of a successful sequence-based drug design operation.

author

Princy A. J

Princy holds a bachelor’s degree in Civil Engineering from the prestigious Tamil Nadu Dr. M.G.R. University at Chennai, India. After a successful academic record, she pursued her passion for writing. A thorough professional and enthusiastic writer, she enjoys writing on various categories and advancements in the global industries. She plays an instrumental role in writing about current updates, news, blogs, and trends.