A review on computer-aided chemogenomics and drug repositioning for rational COVID-19 drug discovery.

Application of materials capable of energy harvesting to increase the efficiency and environmental adaptability is sometimes reflected in the ability of discovery of some traces in an environment-either experimentally or computationally-to enlarge practical application window. The emergence of computational methods, particularly computer-aided drug discovery (CADD), provides ample opportunities for the rapid discovery and development of unprecedented drugs. The expensive and time-consuming process of traditional drug discovery is no longer feasible, for nowadays the identification of potential drug candidates is much easier for therapeutic targets through elaborate in silico approaches, allowing the prediction of the toxicity of drugs, such as drug repositioning (DR) and chemical genomics (chemogenomics). Coronaviruses (CoVs) are cross-species viruses that are able to spread expeditiously from the into new host species, which in turn cause epidemic diseases. In this sense, this review furnishes an outline of computational strategies and their applications in drug discovery. A special focus is placed on chemogenomics and DR as unique and emerging system-based disciplines on CoV drug and target discovery to model protein networks against a library of compounds. Furthermore, to demonstrate the special advantages of CADD methods in rapidly finding a drug for this deadly virus, numerous examples of the recent achievements grounded on molecular docking, chemogenomics, and DR are reported, analyzed, and interpreted in detail. It is believed that the outcome of this review assists developers of energy harvesting materials and systems for detection of future unexpected kinds of CoVs or other variants.

Chapter 6: Screening Technologies

Screening advanced compounds enables discovery of direct repurposing candidates, novel drug-like leads for optimization, and informative pharmacological probes. In this chapter, we describe different types of screening collections used in drug repurposing, discuss issues and considerations in preparing and executing a repurposing screen, and present examples of in vitro and in vivo repurposing assays. We further describe various data sources reporting information on de-risked compounds of different types and illustrate how data mining and chemoinformatic and chemogenomic searches can be used to access large numbers of advanced compounds and assemble collections most suitable for screening in a given disease model. We argue that a view of repurposing screening as a large-scale bet on finding candidates for clinical testing is narrow and incomplete. Rather, when thoughtfully executed, screening of re-risked compounds is informed by target pathobiology and offers a means to efficiently convert advances in the development of sophisticated non-clinical models and new insights in disease mechanisms into novel drug-like leads and candidates for development.