Tackling the conformational sampling of larger flexible compounds and macrocycles in pharmacology and drug discovery.

摘要: Abstract Computational conformational sampling underpins much of molecular modeling and design in pharmaceutical work. The smaller drug-like compounds has been an active area research. However, few studies have tested details the larger more flexible compounds, which are also relevant to drug discovery, including therapeutic peptides, macrocycles, inhibitors protein–protein interactions. Here, we investigate extensively mainstream methods on three carefully curated compound sets, namely ‘Drug-like’, ‘Flexible’, ‘Macrocycle’ compounds. These test molecules chemically diverse with reliable X-ray protein-bound bioactive structures. compared include Stochastic Search recent LowModeMD from MOE, all low-mode based approaches MacroModel, MD/LLMOD recently developed for macrocycles. In addition default settings, key parameters protocols were explored. performance computational was assessed via (i) reproduction structures, (ii) size, coverage diversity output ensembles, (iii) compactness/extendedness conformers, (iv) ability locate global energy minimum. influence stochastic nature searches results examined. Much better obtained by adopting search enhanced over while maintaining tractability. emerged as method choice. Mixed torsional/low-mode MacroModel performed well LowModeMD, yielded very encouraging macrocycle sets. Thus, one can productively tackle