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PubMed2004

Virtual screening in lead discovery and optimization.

Virtual screening by molecular docking, using a protein with an experimentally determined structure as a target, has become an established method for lead discovery and for enhancing efficiency in lead optimization. Generalizations of the quantitative structure-activity relationship concept have led to approaches for virtual screening in the absence of a protein target structure, instead relying upon ligand-based models as surrogates of protein active sites. Recently reported methods for ligand-based virtual screening can achieve similar enrichment rates to those obtained using molecular docking. This review will discuss recent advances in both domains of virtual screening, including theoretical and practical advances and the implications for their application.

Computational Biology↗
PubMed2004

Novel technologies for virtual screening.

There are several methods for virtual screening of databases of small organic compounds to find tight binders to a given protein target. Recent reviews in Drug Discovery Today have concentrated on screening by docking and by pharmacophore searching. Here, we complement these reviews by focusing on virtual screening methods that are based on analyzing ligand similarity on a structural level. Specifically, we concentrate on methods that exploit structural properties of the complete ligand molecules, as opposed to using just partial structural templates, such as pharmacophores. The in silico procedure of virtual screening (VS) and its relationship to the experimental procedure, HTS, is discussed, new developments in the field are summarized and perspectives on future research are offered.

Combinatorial Chemistry Techniques↗
PubMed2006

Virtual screening: are we there yet?

The cost of pharmaceutical development has increased dramatically in recent years, and many assorted approaches have been developed to decrease both the time and costs associated with bringing a drug to the market. Among these methods is the use of in silico screening of compound databases for potential new lead compounds, commonly referred to as virtual screening (VS). Virtual screening has become an integral part of the early discovery process in pharmaceutical development, readily observed by the large number of methodologies that have been published to date. Other reviews have been published detailing the various types of virtual screening methods in use. This work will review some of the virtual screening approaches and strategies that have been attempted to identify compounds to launch medicinal chemistry campaigns. Understanding trends and drivers in VS should help to set expectations about how and when VS could be used and what it can and cannot deliver and how it can be integrated in a successful screening campaign and used in a complementary fashion to HTS.

Computer Simulation↗
PubMed2003

Similarity-based approaches to virtual screening.

Current similarity measures for virtual screening are based on the use of molecular fingerprints and the Tanimoto coefficient. This paper describes two ways in which one can increase the effectiveness of similarity-based virtual screening: using similarity coefficients other than the Tanimoto coefficient for the comparison of molecular fingerprints; and using a graph-theoretic similarity measure based on the largest substructure common to a pair of molecules.

Algorithms↗
PubMed2000

Successful virtual screening of a chemical database for farnesyltransferase inhibitor leads.

Virtual screening of chemical databases is an emerging approach in drug discovery that uses computers to dock chemicals into the active site of a drug target to identify leads through evaluation of binding affinities of the chemicals. However, there are concerns about the validity and scope of the reported virtual screens due to lack of studies to show that randomly selected chemicals are not equally active and due to the fact that metalloproteins were rarely used as drug targets. We have performed a virtual screening of a chemical database to identify prototypic inhibitors of farnesyltransferase (FT) with zinc present in the active site. Among the 21 compounds identified by computers, four inhibited FT in vitro with IC(50) values in the range from 25 to 100 microM. The most potent inhibitor also inhibited FT in human lung cancer cells. In contrast, none of 21 randomly selected compounds have an IC(50) lower than 100 microM. The results demonstrate the validity of virtual screening and the feasibility of applications of this approach to metalloprotein drug targets, such as matrix metalloproteinases, farnesyltransferase, and HIV-1 integrase, for the treatments of cardiovascular diseases, cancers, and AIDS.

Alkyl and Aryl Transferases↗
PubMed2007

Comparison of shape-matching and docking as virtual screening tools.

Ligand docking is a widely used approach in virtual screening. In recent years a large number of publications have appeared in which docking tools are compared and evaluated for their effectiveness in virtual screening against a wide variety of protein targets. These studies have shown that the effectiveness of docking in virtual screening is highly variable due to a large number of possible confounding factors. Another class of method that has shown promise in virtual screening is the shape-based, ligand-centric approach. Several direct comparisons of docking with the shape-based tool ROCS have been conducted using data sets from some of these recent docking publications. The results show that a shape-based, ligand-centric approach is more consistent than, and often superior to, the protein-centric approach taken by docking.

Binding Sites↗
PubMedDate not supplied

A discussion of measures of enrichment in virtual screening: comparing the information content of descriptors with increasing levels of sophistication.

We have performed virtual screening using some very simple features, by employing the number of atoms per element as molecular descriptors but without regard to any structural information whatsoever. Surprisingly, these atom counts are able to outperform virtual-affinity-based fingerprints and Unity fingerprints in some activity classes. Although molecular weight and other biases were known in target-based virtual screening settings (docking), we report the effect of using very simple descriptors for ligand-based virtual screening, by using clearly defined biological targets and employing a large data set (>100,000 compounds) containing multiple (11) activity classes. Structure-unaware atom count vectors as descriptors in combination with the Euclidean distance measure are able to achieve "enrichment factors" over random selection of around 4 (depending on the particular class of active compounds), putting the enrichment factors reported for more sophisticated virtual screening methods in a different light. They are also able to retrieve active compounds with novel scaffolds instead of merely the expected structural analogues. The added value of many currently used virtual screening methods (calculated as enrichment factors) drops down to a factor of between 1 and 2, instead of often reported double-digit figures. The observed effect is much less profound for simple descriptors such as molecular weight and is only present in cases of atypical (larger) ligands. The current state of virtual screening is not as sophisticated as might be expected, which is due to descriptors still not being able to capture structural properties relevant to binding. This fact can partly be explained by highly nonlinear structure-activity relationships, which represent a severe limitation of the "similar property principle" in the context of bioactivity.

Computer Simulation↗
PubMed2004

Target-biased scoring approaches and expert systems in structure-based virtual screening.

Structure-based virtual screening followed by selection of a top fraction of the rank-ordered result list suffers from many false positives and false negatives because the general scoring functions are not accurate enough. Many approaches have emerged to address this problem by including knowledge about the specific target in the scoring and selection steps. This target bias can include requirements for critical interactions, use of pharmacophore patterns or interaction patterns found in known co-crystal structures, and similarity to known ligands. Such biases are implemented in methods that vary from filtering tools for pre- or post-processing, to expert systems, quantitative (re)scoring functions, and docking tools that generate target-biased poses.

Algorithms↗
PubMed2005

Virtual screening models for finding novel antidepressants.

Virtual screening was carried out against various biological targets related to depression by support vector machine classification using the atom-type descriptors. The models were effective as over 75 and 95% of the molecules in external test datasets could be correctly classified, depending on target. Antidepressant compounds had predicted activity against 2.3 targets, on average. An introduction is given to virtual screening and the results of classification experiments are presented.

Algorithms↗
PubMed2004

Integrating virtual screening in lead discovery.

Target- and ligand-based virtual screening have emerged as resource-saving techniques that have been successfully applied to identify novel chemotypes in biologically active molecules. Eight confirmed virtual screening hits have recently been described and are discussed in this review, with focus on the workflow. These are then evaluated in the light of pharmacokinetics prediction (e.g. Caco-2 permeability, cytochrome P450 inhibition and hERG binding). We anticipate problems for five of these hits (e.g. cardiac toxicity), which warrant further experiments. Future challenges include dynamic tautomer/protonation treatment for both ligands and targets and improved pre- and post- virtual screening filters.

Animals↗
PubMed2006

Minimizing false positives in kinase virtual screens.

In spite of recent improvements in docking and scoring methods, high false-positive rates remain a common issue in structure-based virtual screening. In this study, the distinctive features of false positives in kinase virtual screens were investigated. A series of retrospective virtual screens on kinase targets was performed on specifically designed test sets, each combining true ligands and experimentally confirmed inactive compounds. A systematic analysis of the docking poses generated for the top-ranking compounds highlighted key aspects differentiating true hits from false positives. The most recurring feature in the poses of false positives was the absence of certain key interactions known to be required for kinase binding. A systematic analysis of 444 crystal structures of ligand-bound kinases showed that at least two hydrogen bonds between the ligand and the backbone protein atoms in the kinase hinge region are present in 90% of the complexes, with very little variability across targets. Closer inspection showed that when the two hydrogen bonds are present, one of three preferred hinge-binding motifs is involved in 96.5% of the cases. Less than 10% of the false positives satisfied these two criteria in the minimized docking poses generated by our standard protocol. Ligand conformational artifacts were also shown to contribute to the occurrence of false positives in a number of cases. Application of this knowledge in the form of docking constraints and post-processing filters provided consistent improvements in virtual screening performance on all systems. The false-positive rates were significantly reduced and the enrichment factors increased by an average of twofold. On the basis of these results, a generalized two-step protocol for virtual screening on kinase targets is suggested.

Animals↗
PubMed2004

Protein flexibility in ligand docking and virtual screening to protein kinases.

The main complicating factor in structure-based drug design is receptor rearrangement upon ligand binding (induced fit). It is the induced fit that complicates cross-docking of ligands from different ligand-receptor complexes. Previous studies have shown the necessity to include protein flexibility in ligand docking and virtual screening. Very few docking methods have been developed to predict the induced fit reliably and, at the same time, to improve on discriminating between binders and non-binders in the virtual screening process. We present an algorithm called the ICM-flexible receptor docking algorithm (IFREDA) to account for protein flexibility in virtual screening. By docking flexible ligands to a flexible receptor, IFREDA generates a discrete set of receptor conformations, which are then used to perform flexible ligand-rigid receptor docking and scoring. This is followed by a merging and shrinking step, where the results of the multiple virtual screenings are condensed to improve the enrichment factor. In the IFREDA approach, both side-chain rearrangements and essential backbone movements are taken into consideration, thus sampling adequately the conformational space of the receptor, even in cases of large loop movements. As a preliminary step, to show the importance of incorporating protein flexibility in ligand docking and virtual screening, and to validate the merging and shrinking procedure, we compiled an extensive small-scale virtual screening benchmark of 33 crystal structures of four different protein kinases sub-families (cAPK, CDK-2, P38 and LCK), where we obtained an enrichment factor fold-increase of 1.85+/-0.65 using two or three multiple experimental conformations. IFREDA was used in eight protein kinase complexes and was able to find the correct ligand conformation and discriminate the correct conformations from the "misdocked" conformations solely on the basis of energy calculation. Five of the generated structures were used in the small-scale virtual screening stage and, by merging and shrinking the results with those of the original structure, we show an enrichment factor fold increase of 1.89+/-0.60, comparable to that obtained using multiple experimental conformations. Our cross-docking tests on the protein kinase benchmark underscore the necessity of incorporating protein flexibility in both ligand docking and virtual screening. The methodology presented here will be extremely useful in cases where few or no experimental structures of complexes are available, while some binders are known.

Algorithms↗
PubMedDate not supplied

Parallel screening: a novel concept in pharmacophore modeling and virtual screening.

Parallel screening comprises a novel in silico method to predict the potential biological activities of a compound by screening it with a multitude of pharmacophore models. Our aim is to provide a fast, large-scale system that allows for virtual activity profiling. In this proof of principle study, carried out with the software tools LigandScout and Catalyst, we present a model work for the application of parallel pharmacophore-based virtual screening on a set of 50 structure-based pharmacophore models built for various viral targets and 100 antiviral compounds. The latter were screened against all pharmacophore models in order to determine if their biological targets could be correctly predicted via an enrichment of corresponding pharmacophores matching these ligands. The results demonstrate that the desired enrichment, that is, successful virtual activity profiling, was achieved for approximately 90% of all input molecules. We discuss descriptors for output validation, as well as various aspects influencing the analysis of the obtained activity profiles, and the effect of the utilized search modus for screening.

Antiviral Agents↗
PubMed2005

Frontal affinity chromatography with MS detection of EphB2 tyrosine kinase receptor. 2. Identification of small-molecule inhibitors via coupling with virtual screening.

We have integrated two complementary methods, high-throughput virtual screening with a "high-content" wet screening technique based on frontal affinity chromatography with mass spectrometry detection (FAC-MS), for identification of hits against the erythropoietin-producing hepatocellular B2 (EphB2) receptor tyrosine kinase domain. Both an EphB2-directed virtual screen combining docking and scoring and a kinase-directed pharmacophore search strategy were used to identify a compound set enriched in bioactive compounds against EphB2. The coupling of virtual screening methodologies with FAC-MS is a unique hybrid approach that can be used to increase the efficacy of both hit discovery and optimization efforts in drug discovery and has successfully identified hits, in particular 19a (36% shift, IC(50) = 5.2 microM, K(d) = 3.3 microM), as inhibitors for EphB2, a potential cancer target.

Antineoplastic Agents↗
PubMed2002

A neural network based virtual screening of cytochrome P450 3A4 inhibitors.

A virtual screening test to identify potential CP450 3A4 inhibitors has been developed. Molecular structures of inhibitors and non-inhibitors available in the Genetest database were represented using 2D Unity fingerprints and a feedforward neural network was trained to classify molecules regarding their inhibitory activity. Validation tests revealed that our neural net recognizes at least 89% of 3A4 inhibitors and suggest using this methodology in our virtual screening protocol.

Artificial Intelligence↗
PubMed2001

Virtual screening: an effective tool for lead structure discovery?

Different methods of virtual screening as a tool for lead structure discovery are described. They range from structure based docking procedures to ligand based methods such as the chemical features based pharmacophore hypothesis approach. A review on several successful applications of virtual screening is given. Different approaches have been described to derive pharmacophore models, which were subsequently used for 3D database searching. The studies so far published cover a wide range of pharmacological applications. The results hereby obtained clearly indicate that focused assessment of corporate databases by virtual screening using well validated pharmacophore models yield to a significant improvement in lead structure determination compared to high throughput screening.

Animals↗
PubMedDate not supplied

Similarity search profiles as a diagnostic tool for the analysis of virtual screening calculations.

An analysis method termed similarity search profiling has been developed to evaluate fingerprint-based virtual screening calculations. The analysis is based on systematic similarity search calculations using multiple template compounds over the entire value range of a similarity coefficient. In graphical representations, numbers of correctly identified hits and other detected database compounds are separately monitored. The resulting profiles make it possible to determine whether a virtual screening trial can in principle succeed for a given compound class, search tool, similarity metric, and selection criterion. As a test case, we have analyzed virtual screening calculations using a recently designed fingerprint on 23 different biological activity classes in a compound source database containing approximately 1.3 million molecules. Based on our predefined selection criteria, we found that virtual screening analysis was successful for 19 of 23 compound classes. Profile analysis also makes it possible to determine compound class-specific similarity threshold values for similarity searching.

Databases, Factual↗
PubMed2005

New methodologies for ligand-based virtual screening.

Computational screening of compound databases has become increasingly popular in pharmaceutical research. Virtual screening approaches can roughly be divided into target structure-based screening (often referred to as docking) and screening using active compounds as templates (ligand-based virtual screening). Ligand-based screening techniques essentially focus on comparative molecular similarity analysis of compounds with known and unknown activity, regardless of the methods or algorithms used. In this review, we first provide an overview of widely used ligand-based virtual screening approaches including various database filters and then discuss recent trends in this field and new methodological developments.

Database Management Systems↗