Strategies of Virtual Screening in Medicinal Chemistry

Current protein & peptide science, 2007

Virtual screening emerged as an important tool in our quest to access novel drug like compounds. There are a wide range of comparable and contrasting methodological protocols available in screening databases for the lead compounds. The number of methods and software packages which employ the target and ligand based virtual screening are increasing at a rapid pace. However, the general understanding on the applicability and limitations of these methodologies is not emerging as fast as the developments of various methods. Therefore, it is extremely important to compare and contrast various protocols with practical examples to gauge the strength and applicability of various methods. The review provides a comprehensive appraisal on several of the available virtual screening methods to-date. Recent developments of the docking and similarity based methods have been discussed besides the descriptor selection and pharmacophore based searching. The review touches upon the application of statistical, graph theory based methods machine learning tools in virtual screening and combinatorial library design. Finally, several case studies are undertaken where the virtual screening technology has been applied successfully. A critical analysis of these case studies provides a good platform to estimate the applicability of various virtual screening methods in the new lead identification and optimization.

Virtual screening (VS) is a powerful technique for identifying hit molecules as starting points for medicinal chemistry. The number of methods and softwares which use the ligand and target-based VS approaches is increasing at a rapid pace. What, however, are the real advantages and disadvantages of the VS technology and how applicable is it to drug discovery projects? This review provides a comprehensive appraisal of several VS approaches currently available. In the first part of this work, an overview of the recent progress and advances in both ligand-based VS (LBVS) and structurebased VS (SBVS) strategies highlighting current problems and limitations will be provided. Special emphasis will be given to in silico chemogenomics approaches which utilize annotated ligand-target as well as protein-ligand interaction databases and which could predict or reveal promiscuous binding and polypharmacology, the knowledge of which would help medicinal chemists to design more potent clinical candidates with fewer side effects. In the second part, recent case studies (all published in the last two years) will be discussed where the VS technology has been applied successfully. A critical analysis of these case studies provides a good platform in order to estimate the applicability of various VS strategies in the new lead identification and optimization.

Methods (San Diego, Calif.), 2015

Drug discovery faces daunting challenges in the current economic situation, which is further exacerbated by resistance against a large group of available drugs. Development of a new drug with traditional approaches generally takes 12-15years and may cost over $800 millions. Therefore, inexpensive and fast alternatives are required for new drug discovery. Various in silico approaches have shown potential for screening chemical databases against the desired biological targets for the development of new potential leads. Among them, the number of publications on structure based virtual screening has been rapidly mounting in recent years. This increase has led a need to evaluate and compare the performance of different virtual screening methodologies. In the present article, we describe some of the work and addresses the important issues for successful structure-based virtual screening. Moreover, few recent case studies are also discussed, where the virtual screening approaches have been...

2007

Journal of Computer-Aided Molecular Design, 2007

Four different ligand-based virtual screening scenarios are studied: (1) prioritizing compounds for subsequent high-throughput screening (HTS); (2) selecting a predefined (small) number of potentially active compounds from a large chemical database; (3) assessing the probability that a given structure will exhibit a given activity; (4) selecting the most active structure(s) for a biological assay. Each of the four scenarios is exemplified by performing retrospective ligand-based virtual screening for eight different biological targets using two large databases-MDDR and WOMBAT. A comparison between the chemical spaces covered by these two databases is presented. The performance of two techniques for ligand-based virtual screening-similarity search with subsequent data fusion (SSDF) and novelty detection with Self-Organizing Maps (ndSOM) is investigated. Three different structure representations-2,048dimensional Daylight fingerprints, topological autocorrelation weighted by atomic physicochemical properties (sigma electronegativity, polarizability, partial charge, and identity) and radial distribution functions weighted by the same atomic physicochemical properties-are compared. Both methods were found applicable in scenario one. The similarity search was found to perform slightly better in scenario two while the SOM novelty detection is preferred in scenario three. No method/descriptor combination achieved significant success in scenario four.

Biotechnologia, 2011

Virtual screening (VS) overcomes the limitations of traditional high-throughput screening (HTS) by applying computer-based methods in drug discovery. VS takes advantage of fast algorithms to filter chemical space and successfully select potential drug candidates. A key aspect in structure-based VS is the sampling of ligand-receptor conformations and the evaluation of these poses to predict near-native binding modes. The development of fast and accurate algorithms during the last few years has allowed VS to become an important tool in drug discovery and design. Herein, an overview of widely used ligand-based (e.g., similarity, pharmacophore searches) and structure-based (protein-ligand docking) VS methods is discussed. Their strengths and limitations are described, along with many successful stories. This review not only serves as an introductory guide for inexperienced VS users but also presents a general overview of the current state and scope of these powerful tools.

Drug Discovery Today, 2008

Structure-based virtual screening is now an established technology for supporting hit finding and lead optimisation in drug discovery. Recent validation studies have highlighted the poor performance of currently used scoring functions in estimating binding affinity and hence in ranking large datasets of docked ligands. Progress in the analysis of large datasets can be made through the use of appropriate data mining techniques and the derivation of a broader range of descriptors relevant to receptor-ligand binding. In addition, simple scoring functions can be supplemented by simulation-based scoring protocols. Developments in workflow design allow the automation of repetitive tasks, and also encourage the routine use of simulation-based methods and the rapid prototyping of novel modelling and analysis procedures.

Current Opinion in Chemical Biology, 2006

One of the main goals in drug discovery is to identify new chemical entities that have a high likelihood of binding to the target protein to elicit the desired biological response. To this end, virtual screening is being increasingly used as a complement to high-throughput screening to improve the speed and efficiency of the drug discovery and development process. The availability of inexpensive high-performance computing platforms in recent years has transformed this field into one that is highly diverse and rapidly evolving, where large chemical databases have been successfully screened to identify hits for a wide range of targets such as Bcl-2 family proteins, G protein-coupled receptors, kinases, metalloproteins, nuclear hormone receptors, proteases and many more.

The AAPS Journal, 2012

Structure-based virtual screening (SBVS) has been widely applied in early-stage drug discovery. From a problem-centric perspective, we reviewed the recent advances and applications in SBVS with a special focus on docking-based virtual screening. We emphasized the researchers' practical efforts in real projects by understanding the ligand-target binding interactions as a premise. We also highlighted the recent progress in developing target-biased scoring functions by optimizing current generic scoring functions toward certain target classes, as well as in developing novel ones by means of machine learning techniques.

Current Topics in Medicinal Chemistry, 2014

Virtual screening (VS) techniques are well-established tools in the modern drug discovery process, mainly used for hit finding in drug discovery. The availability of knowledge of structural information, which includes an increasing number of 3D protein structures and the readiness of free databases of commercially available smallmolecules, provides a broad platform for VS. This review summarizes the current developments in VS regarding chemical databases and highlights the achievements as well as the challenges with an emphasis on a recent example of the successful application for the identification of new hits for sterol 14 -demethylase (CYP51) of Trypanosoma cruzi.