#Pharmacophore

Pharmacophore Modeling in Drug Discovery Design, Ligand Based Drug Design, Fundamentals of Docking, SAR,QSAR Pharmacophore modeling is a successful yet very diverse subfield of computer-aided drug design. The concept of the pharmacophore has been widely applied to the rational design of novel drugs. In this paper, we review the computational implementation of this concept and its common usage in the drug discovery process. Pharmacophores can be used to represent and identify molecules on a 2D or 3D level by schematically depicting the key elements of molecular recognition. The most common application of pharmacophores is virtual screening, and different strategies are possible depending on the prior knowledge. However, the pharmacophore concept is also useful for ADME-tox modeling, side effect, and off-target prediction as well as target identification. Furthermore, pharmacophores are often combined with molecular docking simulations to improve virtual screening. 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.

Keywords: ADME-tox, computer-aided drug design, pharmacophore fingerprint, protein design, virtual screening


What is computer-aided drug design?

Drug design is an expensive and laborious process of developing new medicine. This process has its origin in herbal remedies dating back millennia.1 Only since the last century have drugs had a (semi)synthetic origin.2 The first hit compounds often lack both potency and safety, and must therefore be optimized. While historically this was a trial-and-error process,3,4 soon rational strategies were developed to improve potency.5,6 As with any data handling procedures, computers have become a more prominent and ubiquitous tool in drug discovery since the 1980s.7 The crossover between computational and pharmaceutical research is typically designated computer-aided drug design (CADD).8,9

CADD covers a broad range of applications spanning the drug discovery pipeline, although these are highly clustered in the early phases. The main purpose of CADD is to speed up and rationalize the drug design process while reducing costs.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 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.11 Later in the drug discovery pipeline the potency of the hit and lead compounds needs to be improved.12 New derivatives are designed with or without a different scaffold at the core of the molecule.13 The ultimate goal is to design highly potent and specific molecules which also have a suitable intellectual property position.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.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. This prioritizes the most promising derivatives from a very wide chemical space in a relatively short time.18,19 However, the potency of the compounds is not the only consideration. 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.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.

The basis of all CADD methods is chemo-informatics, the application of data storage, handling, and retrieval methods to chemical structures, their properties, and biological activity.23,24 Chemo-informatics also covers the calculation of molecular descriptors that describe a chemical or physical property based on the molecules’ structure, and which can be used for filtering compounds.25 In order to be able to compare and quantify (dis)similarity between molecules, molecular fingerprints are often the methods of choice.26

Another very important CADD subfield focuses on quantitative structure activity/property relationship (QSAR/QSPR), in which the physicochemical properties (as calculated by molecular descriptors) of a set of inhibitors are related to the inhibitory activity or toxicity to construct a predictive model for novel inhibitors.27–29 QSAR has become a very popular tool to profile novel inhibitors accurately in silico without going through expensive and time-consuming in vitro and in vivo assays.