Models of necessity

• Timothy Clark and
• Martin G. Hicks

Beilstein J. Org. Chem. 2020, 16, 1649–1661, doi:10.3762/bjoc.16.137

• more stringent than those of a folk ontology [11], which in many ways resembles Halloun’s personal paradigms [76]. Chemistry is not a solved science, so that the above process must be dynamic. Until 2007, for instance, just about all force fields and computer-aided drug design techniques treated the

G-Protein coupled receptors: answers from simulations

• Timothy Clark

Beilstein J. Org. Chem. 2017, 13, 1071–1078, doi:10.3762/bjoc.13.106

• - and hardware have made MD simulations a powerful tool in GPCR research. This is important because GPCRs are targeted by approximately half of the drugs on the market, so that computer-aided drug design plays a major role in GPCR research. Keywords: computer-aided drug design; GPCR; metadynamicxs

• its distance from them in the G-protein phylogenetic tree [1]. Later, when the β2-adrenergic receptor structure was published [8] it was concluded that homology models would play an increasingly important role in computer-aided drug design (CADD) [24]. With hindsight, this conclusion was perhaps a

• binding) and ligand bias. The simulations are predictive and can therefore be used in prospective computer-aided drug design. The cumulative number of different GPCRs for which X-ray structures were available in a given year. The data represent a total of 174 structures on 91 ligand–receptor complexes for

Computational methods in drug discovery

• Sumudu P. Leelananda and
• Steffen Lindert

Beilstein J. Org. Chem. 2016, 12, 2694–2718, doi:10.3762/bjoc.12.267

• methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein–ligand docking, pharmacophore modeling and QSAR techniques are reviewed. Keywords: ADME; computer-aided drug design; docking; free energy; high-throughput screening; LBDD; lead optimization