Pharmacophore modeling: advances, limitations, and current utility in drug discovery

Abstract

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. 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

Virtual Screening on Indonesian Herbal Compounds as COVID-19 Supportive Therapy: Machine Learning and Pharmacophore Modelling Approaches

“…Pharmacophore models are very suitable as queries for virtual screening of databases. Pharmacophore models are often utilised as a filter to identify compounds that fulfil simple geometric and chemical functionality requirements of the query, before more complicated and computationally demanding approaches such as molecular docking [59]. Thus, using two approaches in the methodology, i.e.…”

Main protease inhibitors and drug surface hotspots for the treatment of COVID-19: A drug repurposing and molecular docking approach

“…Virtual screening is another computational technique that is widely used in drug discovery to search libraries of small compounds to identify molecules that are most likely to bind to a drug target (e.g., protein receptor or enzyme) [35] , [36] . Among different types of virtual screening methods, pharmacophore-based modeling is most often applied to virtual screening, resulting in an abstract description of the molecular features necessary for recognition of a ligand by a biological macromolecule [37] . Despite the abundance of associated successful cases, several demerits are encountered, such as the absence of good scoring metrics [37] .…”

“…Among different types of virtual screening methods, pharmacophore-based modeling is most often applied to virtual screening, resulting in an abstract description of the molecular features necessary for recognition of a ligand by a biological macromolecule [37] . Despite the abundance of associated successful cases, several demerits are encountered, such as the absence of good scoring metrics [37] . Pharmacophore modeling is also impossible if the target structure or ligands are unknown.…”

Design of novel F-ATP synthase inhibitors of mycobacterium tuberculosis

“…Secondly, a library containing only few of several possible conformations, so the potential good ligands might be filtered out. Third, the fast and efficient screening comes in cost of overgeneralisation, and not being able to count in complex interactions and mechanisms, which might again lead in filtering out the active compounds [165].…”

“…Mycobacterium has an extra loop of 13 amino acids (165)(166)(167)(168)(169)(170)(171)(172)(173)(174)(175)(176)(177)(178), which is predicted to be located near the interface of the γ subunit and the c-ring [149].…”