Development of anti-viral agents using molecular modeling and virtual screening techniques.

Computational chemistry has always played a key role in anti-viral drug development. The challenges and the quickly rising public interest when a virus is becoming a threat has significantly influenced computational drug discovery. The most obvious example is anti-AIDS research, where HIV protease and reverse transcriptase have triggered enormous efforts in developing and improving computational methods. Methods applied to anti-viral research include (i) ligand-based approaches that rely on known active compounds to extrapolate biological activity, such as machine learning techniques or classical QSAR, (ii) structure-based methods that rely on an experimentally determined 3D structure of the targets, such as molecular docking or molecular dynamics, and (iii) universal approaches that can be applied in a structure- or ligand-based way, such as 3D QSAR or 3D pharmacophore elucidation. In this review we summarize these molecular modeling approaches as they were applied to fight anti-viral diseases and highlight their importance for anti-viral research. We discuss the role of computational chemistry in the development of small molecules as agents against HIV integrase, HIV-1 protease, HIV-1 reverse transcriptase, the influenza virus M2 channel protein, influenza virus neuraminidase, the SARS coronavirus main proteinase and spike protein, thymidine kinases of herpes viruses, hepatitis c virus proteins and other flaviviruses as well as human rhinovirus coat protein and proteases, and other picornaviridae. We highlight how computational approaches have helped in discovering anti-viral activities of natural products and give an overview on polypharmacology approaches that help to optimize drugs against several viruses or help to optimize the metabolic profile of and anti-viral drug.

Discovery of novel CB2 receptor ligands by a pharmacophore-based virtual screening workflow.

Cannabinoid receptor 2 (CB(2) receptor) ligands are potential candidates for the therapy of chronic pain, inflammatory disorders, atherosclerosis, and osteoporosis. We describe the development of pharmacophore models for CB(2) receptor ligands, as well as a pharmacophore-based virtual screening workflow, which resulted in 14 hits for experimental follow-up. Seven compounds were identified with K(i) values below 25 microM. The CB(2) receptor-selective pyridine tetrahydrocannabinol analogue 8 (K(i) = 1.78 microM) was identified as a CB(2) partial agonist. Acetamides 12 (K(i) = 1.35 microM) and 18 (K(i) = 2.1 microM) represent new scaffolds for CB(2) receptor-selective antagonists and inverse agonists, respectively. Overall, our pharmacophore-based workflow yielded three novel scaffolds for the chemical development of CB(2) receptor ligands.