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Review
. 2020 Apr 28:8:343.
doi: 10.3389/fchem.2020.00343. eCollection 2020.

Structure-Based Virtual Screening: From Classical to Artificial Intelligence

Eduardo Habib Bechelane Maia  1   2 , Letícia Cristina Assis  1 , Tiago Alves de Oliveira  2 , Alisson Marques da Silva  2 , Alex Gutterres Taranto  1
Affiliations
Review

Structure-Based Virtual Screening: From Classical to Artificial Intelligence

Eduardo Habib Bechelane Maia et al. Front Chem. .

Abstract

The drug development process is a major challenge in the pharmaceutical industry since it takes a substantial amount of time and money to move through all the phases of developing of a new drug. One extensively used method to minimize the cost and time for the drug development process is computer-aided drug design (CADD). CADD allows better focusing on experiments, which can reduce the time and cost involved in researching new drugs. In this context, structure-based virtual screening (SBVS) is robust and useful and is one of the most promising in silico techniques for drug design. SBVS attempts to predict the best interaction mode between two molecules to form a stable complex, and it uses scoring functions to estimate the force of non-covalent interactions between a ligand and molecular target. Thus, scoring functions are the main reason for the success or failure of SBVS software. Many software programs are used to perform SBVS, and since they use different algorithms, it is possible to obtain different results from different software using the same input. In the last decade, a new technique of SBVS called consensus virtual screening (CVS) has been used in some studies to increase the accuracy of SBVS and to reduce the false positives obtained in these experiments. An indispensable condition to be able to utilize SBVS is the availability of a 3D structure of the target protein. Some virtual databases, such as the Protein Data Bank, have been created to store the 3D structures of molecules. However, sometimes it is not possible to experimentally obtain the 3D structure. In this situation, the homology modeling methodology allows the prediction of the 3D structure of a protein from its amino acid sequence. This review presents an overview of the challenges involved in the use of CADD to perform SBVS, the areas where CADD tools support SBVS, a comparison between the most commonly used tools, and the techniques currently used in an attempt to reduce the time and cost in the drug development process. Finally, the final considerations demonstrate the importance of using SBVS in the drug development process.

Keywords: SBVS; computer-aided drug design; consensus virtual screening; homology modeling; scoring functions.

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Figures

Figure 1
Figure 1
Examples of structures identified by HTS. (A) cyclosporine A, (B) Neviparine, (C) Gefitinib, (D) Clioquinol, and (E) Maraviroc.
Figure 2
Figure 2
Drug development timeline.
Figure 3
Figure 3
Drugs that came to the market with the assistance of VS: (A) Captopril, (B) Saquinavir, (C) Tirofiban, (D) Indinavir, (E) Ritonavir.
Figure 4
Figure 4
Drugs that came to the market with the assistance of VS. (A) Dorzolamide, (B) Zanamivir, (C) Aliskiren, (D) Boceprevir, (E) Nolatrexid.
Figure 5
Figure 5
VS scheme.
Figure 6
Figure 6
Identification of a ligand candidate by using a typical scoring function. The hydrogens were omitted for better visualization. (A) Inactive ligand, (B) celecoxib.
Figure 7
Figure 7
RMSD between the ligand FCP with a protein (PDB ID: 1VZK) after redocking using DOCK6.
Figure 8
Figure 8
ROC curve example.
See this image and copyright information in PMC

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