Structure-based pharmacophore approaches have become widely used in drug discovery and design. This can be attributed to the development of new tools and methods over the past decade. Various tools based on different premises have been developed, including active site information in traditional pharmacophores. These tools have been widely used in virtual screening, de novo design, and lead optimization and been proven to be highly successful. Studies based on simultaneous use of structure-based pharmacophores, ligand-based pharmacophores, and docking have also come into the picture recently. Here, the development of structure-based pharmacophores as an alternative to traditional drug discovery approaches is discussed, with emphasis on the advances and latest developments in tools and success stories involving their application.

Inhibition of phosphodiesterase 4 (PDE4) is a promising therapeutic approach for the treatment of inflammatory pulmonary disorders, i.e. asthma and chronic obstructive pulmonary disease. However, the treatment with non-selective PDE4 inhibitors is associated with side effects such as nausea and vomiting. Among the subtypes of PDE4 inhibited by these inhibitors, PDE4B is expressed in immune, inflammatory and airway smooth muscle cells, whereas, PDE4D is expressed in the area postrema and nucleus of the solitary tract. Thus, PDE4D inhibition is responsible for the emetic response. In this regard, a selective PDE4B inhibitor is expected to be a potential drug candidate for the treatment of inflammatory pulmonary disorders. Therefore, a shared feature pharmacophore model was developed and used as a query for the virtual screening of Maybridge and SPECS databases. A number of filters were applied to ensure only compounds with drug-like properties were selected. Accordingly, nine compounds have been identified as final hits, where HTS04529 showed the highest affinity and selectivity for PDE4B over PDE4D in molecular docking. The docked complexes of HTS04529 with PDE4B and PDE4D were subjected to molecular dynamics simulations for 100ns to assess their binding stability. The results showed that HTS04529 was bound tightly to PDE4B and formed a more stable complex with it than with PDE4D.

Over the last two decades computational technologies have always played a crucial role in anti-viral drug development. Whenever a virus spreads and becomes a threat to global health it brings along the challenge to develop new therapeutics and prophylactics. Computational drug and vaccine discovery have evolved at a breakneck pace over the years. Some interesting examples of computational drug discovery are anti-AIDS drugs, where HIV protease and reverse transcriptase have been targeted by agents developed using computational methods. Various computational methods that have been applied to anti-viral research include, ligand-based methods that rely on known active compounds i.e., pharmacophore modeling, machine learning or classical QSAR; structure-based methods that rely on an experimentally determined 3D structure of the targets i.e., molecular docking and molecular dynamics and methods for development of vaccines such as reverse vaccinology; structural vaccinology and vaccine epitope prediction. In this review we summarize these approaches as they were applied to battle viral diseases and underscore their importance for anti-viral research. We discuss the role of computational methods in the development of small molecules and vaccines against, human immunodeficiency virus, yellow fever, human papilloma virus, SARS-CoV-2, and other viruses. Various computational tools available for abovementioned purposes have been listed and described. A discussion on application of artificial intelligence-based methods for antiviral drug discovery has also been included.