Exploring docking methods for virtual screening: application to the identification of neuraminidase and Ftsz potential inhibitors

Virtual Screening Meets Deep Learning

BACKGROUND

Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence.

OBJECTIVE

This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

Identification of novel antilipogenic agents targeting fatty acid biosynthesis through structure-based virtual screening

An Asinex Gold Platinium chemical library subset of 12 055 compounds was screened employing docking simulations in the active site of the human FAS KS domain. Among them, 13 compounds were further evaluated for their ability to inhibit fatty acid biosynthesis. Four compounds were found to be active in particular ASN05064661 and ASN05374526 with IC50 values of 6.6 and 10.5 μm, respectively. A binding mode study was further conducted with these two compounds structurally related to benzene sulfonamide and aromatic polyamide. This study showed that they fit tightly with the active site with several interactions, notably with the key residues Cys161, His293, and His331.