Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

Abstract

Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.

Resources and computational strategies to advance small molecule SARS-CoV-2 discovery: Lessons from the pandemic and preparing for future health crises

“…In modern days, many different in silico approaches can be used to assist the design of a drug candidate or drug, from data (e.g., text and image) mining (e.g., annotated drug databases, antiviral peptide databases, electronic health patient records…), genome analysis, comparative genomics, multiple sequence alignments, visualization tools for epidemiological studies, analysis of macromolecular interaction networks, structural predictions (e.g., comparative modeling, protein folding…), antibody-drug conjugate, analysis of point mutations, protein docking, various types of molecular simulation engines (e.g., for proteins, peptides, small molecules, cell membrane, DNA, RNA, glycans, and interactions among these molecules…), binding pocket predictions, PROTACs (e.g., degradation of viral protein capsids), transcriptomic profile analysis, virtual screening (from small collections of approved drugs as in drug repositioning or repurposing projects to the screening of ultra-large virtual libraries), hit to lead optimization, drug combination, computational polypharmacology and compound profiling, ADMET prediction, multiparameter optimization methods associated with novel data visualization approaches, systems biology, systems pharmacology, with or without the use of machine learning and artificial intelligence (AI) algorithms depending on the type of methods, available data and the stage of the projects [19] , [77] , [80] , [127] , [128] , [129] , [130] , [131] , [132] , [133] , [134] , [135] , [136] , [137] , [138] , [139] , [140] , [141] , [142] , [143] , [144] , [145] , [146] , [147] , [148] , [149] , [150] , [151] , [152] , [153] , [154] , [155] , [156] , [157] , [158] , [159] , [160] , [161] , [162] , [163] , [164] , [165] , [166] , [167] , [168] , [169] , …”

“…Indeed, these methods are known to play a direct role in drug discovery by enabling the identification/optimization of hit or lead compounds that can exert therapeutic effect by binding to one or more targets (mainly proteins but other macromolecules are also considered, such as RNA and DNA) (e.g., [186] , [187] ). Virtual screening methods allow for the screening of large databases of compounds (e.g., real or virtual small chemical molecules, approved and investigational drugs, short peptides…) [134] , [141] , [188] , [189] , [190] . A short list of molecules selected after the VS computations are then validated experimentally, providing insights into the underlying mechanism of action and providing interesting starting points for further developments.…”

“…Different types of scoring functions can then be used to select the molecules for experimental assays [155] , [157] , [160] , [207] , [208] , [209] , [210] , [211] . LBVS and SBVS can be combined if the right data are available [134] . Then, as new knowledge is being reported, various types of data mining approaches and workflows can be developed and used [212] .…”

Sulfated and Sulfur-Containing Steroids and Their Pharmacological Profile

“…The concept, which was formed more than 150 years ago, that the biological activity of natural and synthetic compounds depends on their chemical structure has been confirmed at the current time [ 165 ]. Using this concept, it is generally accepted that the biological activity of both natural and synthetic compounds depends on their chemical structure [ 166 , 167 ]. Apart from the sharp jumps in biological activity that are observed for some medicinal compounds [ 168 ], this can be considered a violation of this rule; however, for most chemical compounds, the structure–activity ratio (SAR) is widely used in medicinal chemistry and pharmacology to search for and optimize new pharmacological agents [ 169 ].…”

Antiprotozoal and Antitumor Activity of Natural Polycyclic Endoperoxides: Origin, Structures and Biological Activity

“…It is currently accepted that the biological activity of both natural and synthetic compounds depends on their structure [ 33 , 251 , 252 ]. Despite the activity cliffs observed for some drug-like compounds [ 253 ], which can be considered as a violation of this rule, structure-activity relationships (SAR) are widely used in medicinal chemistry for finding and optimization new pharmacological agents [ 254 ].…”