Virtual Drug Screening: A Powerful Tool for Drug Development

Virtual Drug Screening

Innovation in medication research and manufacturing processes has been ongoing throughout the last ten years. One way to minimize the effects on cost and time associated with manufacturing and drug discovery constraints is to employ computer-aided drug design (CADD). Using such a technique, drug design and analysis are carried out in a fully in silico simulation-driven cyclic-assisted process. These simulation techniques can evaluate important aspects of drug research, such as toxicity, activity, biological activity, and bioavailability, before doing in vitro and in vivo clinical trials. In the CADD process, virtual screening (VS) is the initial phase. VS is a molecular classification method based on the chemical or biological properties present in large datasets. It classifies molecules in a database according on how closely their biological characteristics match those of a particular molecular target.

Workflow of Virtual Drug Screening

VS reduces the final number of candidate compounds that could be developed into drugs by a significant margin. The first criteria used in the selection of potentially useful molecules are those that support their mode of action or bear similarities to established therapeutic agents. Following analysis of the candidate ligands, compounds with pharmacophoric groups that suggest toxicity are eliminated. The optimal positions for potential ligands are determined in the following phase. The potential ligands' structures and compositions may be changed during this VS process to improve their characteristics. The composition and structure of potential ligands may need to undergo additional rounds of optimization. After this procedure, the ligands are ready for biological testing.

Fig.1. Virtual screening process.¹

Approaches of Virtual Screening

• Structure-Based Virtual Screening

Predicting the ideal orientation for two molecules to bind together to produce a stable complex is the goal of structure-based virtual screening (SBVS). The 3D structure of the molecular target is explored using techniques included in the SBVS approach. When the molecular target's three-dimensional structure has been experimentally defined, SBVS is the recommended approach. By taking into account the complex's binding strength, SBVS attempts to forecast the possibility of a coupling between potential ligands and the target protein. Molecular anchoring is the most popular SBVS technology because of its good outcomes and cheap computational cost.

• Ligand-Based Virtual Screening

Using virtual libraries of compounds, ligands with known biological activity are used in a process known as ligand-based virtual screening (LBVS) to find molecules with similar structures. The molecular target structure is not taken into account in this method. To uncover more compounds that are biologically active, LBVS looks for molecules that have comparable pharmacophore moieties or molecular scaffolds.

• Fragment-Based Virtual Screening

Finding early hits that can drive progress has also been made possible by the effective use of fragment-based virtual screening (FBVS). The goal of FBVS is to evaluate low-molecular-weight molecule fragments against relevant macromolecular targets. Typically, FBVS turns a chemical fragment with a low molecular weight, low binding affinity, and straightforward chemical structure into a candidate molecule. The discovery of new drugs then begins with these molecules. Fragment hits, however, are often weak binders due to their low molecular weight; therefore, in order to become a lead, they must be converted into larger molecules that bind to the target more firmly. Thus, in fragment-based screening, the techniques employed in FBVS and the fragment binding modes are crucial.

Creative Biolabs' Virtual Drug Screening Service

Creative Biolabs provides high quality virtual drug screening service to identify most promising candidates. Our virtual drug screening technique can decrease the amount of chemicals in the library, increase the likelihood of discovering novel hits, and lower the danger of the lead optimization process failing. We take great pride in using our vast knowledge and cutting-edge technology to provide the best service and the most suitable products to meet every need of our clients.

References

1. Oliveira, Tiago Alves de, et al. "Virtual Screening Algorithms in Drug Discovery: A Review Focused on Machine and Deep Learning Methods." Drugs and Drug Candidates 2.2 (2023): 311-334.
2. Kumar, Ashutosh, and Kam YJ Zhang. "Hierarchical virtual screening approaches in small molecule drug discovery." Methods 71 (2015): 26-37.