20– 22 As well as a battery of in vitro and in vivo experiments, virtual methods have also been developed to predict the ADME-tox profile of drug-like compounds early during the development process. Pharmacokinetic properties (absorption, distribution, metabolism, excretion) and toxicity, referred to as ADME-tox, are also of vital importance if a compound is to be clinically useful. 18, 19 However, the potency of the compounds is not the only consideration. This prioritizes the most promising derivatives from a very wide chemical space in a relatively short time. 15 Computational methods however can also be used to create diverse derivatives based on different scaffolds, 16, 17 and then score them for improved potency. 14 This can be achieved by classical medicinal chemistry approaches, where the design can be based on the observed structure–activity relationships (SAR) or based on structural information. 13 The ultimate goal is to design highly potent and specific molecules which also have a suitable intellectual property position. 12 New derivatives are designed with or without a different scaffold at the core of the molecule. 11 Later in the drug discovery pipeline the potency of the hit and lead compounds needs to be improved. The in silico counterpart of in vitro HTS is referred to as virtual screening and aims at filtering libraries of molecules using computational methods to prioritize those most likely to be active for a given target. 10 The aim of the earliest phase in drug discovery is to identify the first hit compounds, which is sometimes attempted by high-throughput screening (HTS), the testing of many thousands of compounds with a suitable activity assay. The main purpose of CADD is to speed up and rationalize the drug design process while reducing costs. 8, 9ĬADD covers a broad range of applications spanning the drug discovery pipeline, although these are highly clustered in the early phases. 7 The crossover between computational and pharmaceutical research is typically designated computer-aided drug design (CADD).
5, 6 As with any data handling procedures, computers have become a more prominent and ubiquitous tool in drug discovery since the 1980s. While historically this was a trial-and-error process, 3, 4 soon rational strategies were developed to improve potency. 2 The first hit compounds often lack both potency and safety, and must therefore be optimized. 1 Only since the last century have drugs had a (semi)synthetic origin. This process has its origin in herbal remedies dating back millennia. Keywords: ADME-tox, computer-aided drug design, pharmacophore fingerprint, protein design, virtual screeningĭrug design is an expensive and laborious process of developing new medicine. We conclude this review by summarizing the new areas where significant progress may be expected through the application of pharmacophore modeling these include protein–protein interaction inhibitors and protein design. Furthermore, pharmacophores are often combined with molecular docking simulations to improve virtual screening. However, the pharmacophore concept is also useful for ADME-tox modeling, side effect, and off-target prediction as well as target identification. The most common application of pharmacophores is virtual screening, and different strategies are possible depending on the prior knowledge. Pharmacophores can be used to represent and identify molecules on a 2D or 3D level by schematically depicting the key elements of molecular recognition. In this paper, we review the computational implementation of this concept and its common usage in the drug discovery process. The concept of the pharmacophore has been widely applied to the rational design of novel drugs. *These authors contributed equally to this workĪbstract: Pharmacophore modeling is a successful yet very diverse subfield of computer-aided drug design. Xiaoyu Qing, 1,* Xiao Yin Lee, 2,* Joren De Raeymaeker, 1 Jeremy RH Tame, 3 Kam YJ Zhang, 2 Marc De Maeyer, 1 Arnout RD Voet 1,2ġLaboratory for Biomolecular Modelling, Department of Chemistry, Katholieke Universiteit Leuven, Heverlee, Belgium 2Structural Bioinformatics Team, Center for Life Science Technologies, RIKEN, Yokohama, Kanagawa, Japan 3Drug Design Laboratory, Yokohama City University, Yokohama, Kanagawa, Japan
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