QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery

Abstract

Virtual screening (VS) has emerged in drug discovery as a powerful computational approach to screen large libraries of small molecules for new hits with desired properties that can then be tested experimentally. Similar to other computational approaches, VS intention is not to replace in vitro or in vivo assays, but to speed up the discovery process, to reduce the number of candidates to be tested experimentally, and to rationalize their choice. Moreover, VS has become very popular in pharmaceutical companies and academic organizations due to its time-, cost-, resources-, and labor-saving. Among the VS approaches, quantitative structure–activity relationship (QSAR) analysis is the most powerful method due to its high and fast throughput and good hit rate. As the first preliminary step of a QSAR model development, relevant chemogenomics data are collected from databases and the literature. Then, chemical descriptors are calculated on different levels of representation of molecular structure, ranging from 1D to nD, and then correlated with the biological property using machine learning techniques. Once developed and validated, QSAR models are applied to predict the biological property of novel compounds. Although the experimental testing of computational hits is not an inherent part of QSAR methodology, it is highly desired and should be performed as an ultimate validation of developed models. In this mini-review, we summarize and critically analyze the recent trends of QSAR-based VS in drug discovery and demonstrate successful applications in identifying perspective compounds with desired properties. Moreover, we provide some recommendations about the best practices for QSAR-based VS along with the future perspectives of this approach.

Ellagic Acid, Kaempferol, and Quercetin from Acacia nilotica: Promising Combined Drug With Multiple Mechanisms of Action

“…The computational methods including the virtual screening are not to substitute the in vitro and in vivo methods, however, to reduce the time, cost, and the difficultness in the drug target identification [91]. Therefore, this study is an attempt to Table 10 The predicted lead likeness, drug likeness, and synthetic accessibility score of phytochemical constituents having higher affinity scores Phytochemical identify the best A. nilotica's phytochemical constituents that contribute to its pharmacological activity as well as their targets.…”

Affinity Effects on the Release of Non-Conventional Antifibrotics from Polymer Depots

“…The use of virtual screening in drug discovery cuts the time and costs of finding new potential therapies [49]. Leveraging these software and binding equations, the affinity of drug molecules for candidate polymers can be pre-assessed to speed translation for affinity-based delivery systems (an advantage over standard degradable polymer matrices or diffusion-reliant systems).…”

In silico Methods for Design of Kinase Inhibitors as Anticancer Drugs

“…Compounds are ranked based on the similarity score and those at the top are selected as virtual hit molecules for further optimization and synthesis. Modern VS protocols include additional filtering steps in order to exclude compounds that e.g., have low similarity score, do not fall within the Lipinski's rule of five, are not feasible for synthesis or are not available for purchase (Neves et al, 2018).…”