Discovery of Novel Aldose Reductase Inhibitors Using a Protein Structure-Based Approach: 3D-Database Search Followed by Design and Synthesis

Natural products: A lead for drug discovery and development

Natural products are used since ancient times in folklore for the treatment of various ailments. Plant-derived products have been recognized for many years as a source of therapeutic agents and structural diversity. A literature survey has been carried out to determine the utility of natural molecules and their modified analogs or derivatives as pharmacological active entities. This review presents a study on the importance of natural products in terms of drug discovery and development. It describes how the natural components can be utilized after small modifications in new perspectives. Various new modifications in structure offer a unique opportunity to establish a new molecular entity with better pharmacological potential. It was concluded that in this current era, new attempts are taken to utilize the compounds derived from natural sources as novel drug candidates, with a focus to find and discover new effective molecules that were referred to as "new entities of natural product drug discovery."

Structure-based molecular modeling in SAR analysis and lead optimization

In silico methods like molecular docking and pharmacophore modeling are established strategies in lead identification. Their successful application for finding new active molecules for a target is reported by a plethora of studies. However, once a potential lead is identified, lead optimization, with the focus on improving potency, selectivity, or pharmacokinetic parameters of a parent compound, is a much more complex task. Even though in silico molecular modeling methods could contribute a lot of time and cost-saving by rationally filtering synthetic optimization options, they are employed less widely in this stage of research. In this review, we highlight studies that have successfully used computer-aided SAR analysis in lead optimization and want to showcase sound methodology and easily accessible in silico tools for this purpose.

Decision Making in Structure-Based Drug Discovery: Visual Inspection of Docking Results

Molecular docking is a computational method widely used in drug discovery. Due to the inherent inaccuracies of molecular docking, visual inspection of binding modes is a crucial routine in the decision making process of computational medicinal chemists. Despite its apparent importance for medicinal chemistry projects, guidelines for the visual docking pose assessment have been hardly discussed in the literature. Here, we review the medicinal chemistry literature with the aim of identifying consistent principles for visual inspection, highlighting cases of its successful application, and discussing its limitations. In this context, we conducted a survey reaching experts in both academia and the pharmaceutical industry, which also included a challenge to distinguish native from incorrect poses. We were able to collect 93 expert opinions that offer valuable insights into visually supported decision-making processes. This perspective shall motivate discussions among experienced computational medicinal chemists and guide young scientists new to the field to stratify their compounds.

In vitro studies of potent aldose reductase inhibitors: Synthesis, characterization, biological evaluation and docking analysis of rhodanine-3-hippuric acid derivatives

Inhibitors of aldose reductase are rate-limiting enzymes and could play a key role to prevent the complications of diabetes. In our attempt to develop novel inhibitors of aldose reductase, the derivatives of rhodanine-3-hippuric acid-pyrazole hybrid were synthesized and characterised by spectral data. The biological studies reveal that all the compounds show an excellent activity against ALR2 with IC₅₀ values ranging from 0.04 to 1.36 µM. Among these the synthesised compounds 6a-m, 6g and 6e showed specific inhibitory activity with IC₅₀ values of 0.04 and 0.06 µM respectively against ALR2 and found to be more potent than epalrestat (IC₅₀ = 0.87 μM), the only aldose reductase inhibitor currently used in the therapy. Molecular docking analysis using the AR-NADP⁺ complex as a receptor was performed with all the synthesized compounds. All the compounds exhibit a well-defined binding mode within the AR active site, similarly to previous described AR inhibitors, with the anion head group bound to the catalytic center, blocking thus its activity. By forming hydrogen bonds with Tyr48 and His110 of the protein from ALR2 (PDB ID: 2FZD), the compounds 6g and 6e interrupt the proton donation mechanism, which is necessary for the catalytic activity of ALR2.

Computational Methods Applied to Rational Drug Design

Due to the synergic relationship between medical chemistry, bioinformatics and molecular simulation, the development of new accurate computational tools for small molecules drug design has been rising over the last years. The main result is the increased number of publications where computational techniques such as molecular docking, de novo design as well as virtual screening have been used to estimate the binding mode, site and energy of novel small molecules. In this work I review some tools, which enable the study of biological systems at the atomistic level, providing relevant information and thereby, enhancing the process of rational drug design.

Docking Screens for Novel Ligands Conferring New Biology

It is now plausible to dock libraries of 10 million molecules against targets over several days or weeks. When the molecules screened are commercially available, they may be rapidly tested to find new leads. Although docking retains important liabilities (it cannot calculate affinities accurately nor even reliably rank order high-scoring molecules), it can often can distinguish likely from unlikely ligands, often with hit rates above 10%. Here we summarize the improvements in libraries, target quality, and methods that have supported these advances, and the open access resources that make docking accessible. Recent docking screens for new ligands are sketched, as are the binding, crystallographic, and in vivo assays that support them. Like any technique, controls are crucial, and key experimental ones are reviewed. With such controls, docking campaigns can find ligands with new chemotypes, often revealing the new biology that may be docking's greatest impact over the next few years.

Identification of novel pyrazole–rhodanine hybrid scaffolds as potent inhibitors of aldose reductase: design, synthesis, biological evaluation and molecular docking analysis

A series of novel pyrazole–rhodanine derivatives was designed, synthesized, and biologically evaluated for their potential inhibitory effect on both aldehyde reductase (ALR1) and aldose reductase (ALR2).

Design, Synthesis, and Biological Evaluation of PKD Inhibitors

Protein kinase D (PKD) belongs to a family of serine/threonine kinases that play an important role in basic cellular processes and are implicated in the pathogenesis of several diseases. Progress in our understanding of the biological functions of PKD has been limited due to the lack of a PKD-specific inhibitor. The benzoxoloazepinolone CID755673 was recently reported as the first potent and kinase-selective inhibitor for this enzyme. For structure-activity analysis purposes, a series of analogs was prepared and their in vitro inhibitory potency evaluated.

Discovery of new selective human aldose reductase inhibitors through virtual screening multiple binding pocket conformations

Aldose reductase reduces glucose to sorbitol. It plays a key role in many of the complications arising from diabetes. Thus, aldose reductase inhibitors (ARI) have been identified as promising therapeutic agents for treating such complications of diabetes, as neuropathy, nephropathy, retinopathy, and cataracts. In this paper, a virtual screening protocol applied to a library of compounds in house has been utilized to discover novel ARIs. IC50's were determined for 15 hits that inhibited ALR2 to greater than 50% at 50 μM, and ten of these have an IC50 of 10 μM or less, corresponding to a rather substantial hit rate of 14% at this level. The specificity of these compounds relative to their cross-reactivity with human ALR1 was also assessed by inhibition assays. This resulted in identification of novel inhibitors with IC50's comparable to the commercially available drug, epalrestat, and greater than an order of magnitude better selectivity.

Structural and Thermodynamic Study on Aldose Reductase: Nitro-substituted Inhibitors with Strong Enthalpic Binding Contribution

To prevent diabetic complications derived from enhanced glucose flux via the polyol pathway the development of aldose reductase inhibitors (ARIs) has been established as a promising therapeutic concept. In order to identify novel lead compounds, a virtual screening (VS) was performed successfully suggesting carboxylate-type inhibitors of sub-micromolar to micromolar affinity. Here, we combine a structural characterization of the binding modes observed by X-ray crystallography with isothermal titration calorimetry (ITC) measurements providing insights into the driving forces of inhibitor binding, particularly of the first leads from VS. Characteristic features of this novel inhibitor type include a carboxylate head group connected via an alkyl spacer to a heteroaromatic moiety, which is linked to a further nitro-substituted aromatic portion. The crystal structures of two enzyme-inhibitor complexes have been determined at resolutions of 1.43 A and 1.55 A. Surprisingly, the carboxylic group of the most potent VS lead occupies the catalytic pocket differently compared to the interaction geometry observed in almost all other crystal structures with structurally related ligands and obtained under similar conditions, as an interstitial water molecule is picked up upon ligand binding. The nitro-aromatic moiety of both leads occupies the specificity pocket of the enzyme, however, adopting a different geometry compared to the docking prediction: unexpectedly, the nitro group binds to the bottom of the specificity pocket and provokes remarkable induced-fit adaptations. A peptide group located at the active site orients in such a way that H-bond formation to one nitro group oxygen atom is enabled, whereas a neighbouring tyrosine side-chain performs a slight rotation off from the binding cavity to accommodate the nitro group. Identically constituted ligands, lacking this nitro group, exhibit an affinity drop of one order of magnitude. In addition, thermodynamic data suggest a strongly favourable contribution to binding enthalpy in case the inhibitor is equipped with a nitro group at the corresponding position. To further investigate this phenomenon, we determined crystal structures and thermodynamic data of two similarly constituted IDD-type inhibitors addressing the specificity pocket with either a nitro or halogen-substituted aromatic moiety. As these data suggest, the nitro group provokes the enthalpic contribution, in addition to the H-bond mentioned above, by accepting two "non-classical" H-bonds donated by the aromatic tyrosine side-chain. In summary, this study provides the platform for further structure-guided design hypotheses of novel drug candidates with higher affinity and selectivity.

Exploring Structural Feature of Aldose-Reductase Inhibition by 5-[[2-(.OMEGA.-Carboxyalkoxy)aryl]methylene]-4-oxo-2-thioxothiazolidine Derivatives Employing Fujita-Ban and Hansch Approach

Designing of a highly selective, potent and safe inhibitor of aldose reductase (ALR) capable of potentially blocking the excess glucose flux through the polyol pathway that prevails under diabetic condition has been a long standing challenge. In our study, we did quantitative structure-activity relationship (QSAR) analysis, based on Fujita-Ban and classical Hansch approach, on 5-[[2-(omega-carboxyalkoxy)aryl]methylene]-4-oxo-2-thioxothiazolidine derivatives. Study gave structural insight into the binding mode of the molecules to the aldose reductase enzyme. The Fujita-Ban approach revealed that benzylidene thiazolidine nucleus is more potent as compare to naphthyl-methylene thiazolidine analogs. The bulkierness of naphthyl-methylene might be inquisitive with receptor. Hansch approach suggested that electron-withdrawing groups are conducive to aldose reductase inhibitory activity.

Effective handling of induced-fit motion in flexible docking

For structure-based drug design, where various ligand structures need to be docked to a target protein structure, a docking method that can handle conformational flexibility of not only the ligand, but also the protein, is indispensable. We have developed a simple and effective approach for dealing with the local induced-fit motion of the target protein, and implemented it in our docking tool, ADAM. Our approach efficiently combines the following two strategies: a vdW-offset grid in which the protein cavity is enlarged uniformly, and structure optimization allowing the motion of ligand and protein atoms. To examine the effectiveness of our approach, we performed docking validation studies, including redocking in 18 test cases and foreign-docking, in which various ligands from foreign crystal structures of complexes are docked into a target protein structure, in 22 cases (on five target proteins). With the original ADAM, the correct docking modes (RMSD < 2.0 A) were not present among the top 20 models in one case of redocking and four cases of foreign-docking. When the handling of induced-fit motion was implemented, the correct solutions were acquired in all 40 test cases. In foreign-docking on thymidine kinase, the correct docking modes were obtained as the top-ranked solutions for all 10 test ligands by our combinatorial approach, and this appears to be the best result ever reported with any docking tool. The results of docking validation have thus confirmed the effectiveness of our approach, which can provide reliable docking models even in the case of foreign-docking, where conformational change of the target protein cannot be ignored. We expect that this approach will contribute substantially to actual drug design, including virtual screening.

Combining docking and molecular dynamic simulations in drug design

A rational approach is needed to maximize the chances of finding new drugs, and to exploit the opportunities of potential new drug targets emerging from genomic and proteomic initiatives, and from the large libraries of small compounds now readily available through combinatorial chemistry. Despite a shaky early history, computer-aided drug design techniques can now be effective in reducing costs and speeding up drug discovery. This happy outcome results from development of more accurate and reliable algorithms, use of more thoughtfully planned strategies to apply them, and greatly increased computer power to allow studies with the necessary reliability to be performed. Our review focuses on applications and protocols, with the main emphasis on critical analysis of recent studies where docking calculations and molecular dynamics (MD) simulations were combined to dock small molecules into protein receptors. We highlight successes to demonstrate what is possible now, but also point out drawbacks and future directions. The review is structured to lead the reader from the simpler to more compute-intensive methods. Thus, while inexpensive and fast docking algorithms can be used to scan large compound libraries and reduce their size, more accurate but expensive MD simulations can be applied when a few selected ligand candidates remain. MD simulations can be used: during the preparation of the protein receptor before docking, to optimize its structure and account for protein flexibility; for the refinement of docked complexes, to include solvent effects and account for induced fit; to calculate binding free energies, to provide an accurate ranking of the potential ligands; and in the latest developments, during the docking process itself to find the binding site and correctly dock the ligand a priori.

A neural networks-based drug discovery approach and its application for designing aldose reductase inhibitors

A novel approach that combines neural networks, computer docking and quantum mechanical method is developed to design potent aldose reductase inhibitors (ARIs). Neural networks is employed to determine the quantitative structure-activity relationship (QSAR) among the known ARIs. The physical descriptors of the neural networks, such as electronegativity and molar volume, are evaluated with first-principles quantum mechanical method. Based on the QSAR, new candidates for ARI are predicted, and subsequently screened via computer docking technique. The surviving candidates are further tested via quantum mechanical calculation for their bindings to aldose reductase. We find that the best 49 predicted ARI candidates have better calculated binding energies than those of experimentally known drug candidates.

Decoys for docking

Molecular docking is widely used to predict novel lead compounds for drug discovery. Success depends on the quality of the docking scoring function, among other factors. An imperfect scoring function can mislead by predicting incorrect ligand geometries or by selecting nonbinding molecules over true ligands. These false-positive hits may be considered "decoys". Although these decoys are frustrating, they potentially provide important tests for a docking algorithm; the more subtle the decoy, the more rigorous the test. Indeed, decoy databases have been used to improve protein structure prediction algorithms and protein-protein docking algorithms. Here, we describe 20 geometric decoys in five enzymes and 166 "hit list" decoys-i.e., molecules predicted to bind by our docking program that were tested and found not to do so-for beta-lactamase and two cavity sites in lysozyme. Especially in the cavity sites, which are very simple, these decoys highlight particular weaknesses in our scoring function. We also consider the performance of five other widely used docking scoring functions against our geometric and hit list decoys. Intriguingly, whereas many of these other scoring functions performed better on the geometric decoys, they typically performed worse on the hit list decoys, often highly ranking molecules that seemed to poorly complement the model sites. Several of these "hits"from the other scoring functions were tested experimentally and found, in fact, to be decoys. Collectively, these decoys provide a tool for the development and improvement of molecular docking scoring functions. Such improvements may, in turn, be rapidly tested experimentally against these and related experimental systems, which are well-behaved in assays and for structure determination.

Design of novel nicotinic ligands through 3D database searching

A series of quinoline derivatives have been designed on the basis of results from a 3D search of the Cambridge Structural Database using the nicotinic pharmacophore as a query and further modified using molecular modeling. Some of the synthesized compounds show nanomolar affinity for the central nicotinic receptor on rat cerebral cortex.

Probing flexibility and "induced-fit" phenomena in aldose reductase by comparative crystal structure analysis and molecular dynamics simulations

Aldose reductase is a promising target for the treatment of diabetic complications, and as such, has become the focus of various drug design projects. As revealed by a survey of available crystal structures, the protein shows pronounced induced-fit effects upon ligand binding. Although helping to explain the enzyme's substrate promiscuity, phenomena of this kind are still responsible for significant complications in structure-based design efforts directed to aldose reductase. Accordingly, a deeper understanding of the principles governing conformational alterations in this enzyme would be of utmost practical importance. As a first step in addressing this issue, molecular dynamics (MD) simulations have been carried out. The ultrahigh resolution crystal structure of aldose reductase complexed with inhibitor IDD594 served as ideal starting point for a set of different simulations of nanosecond time scale: the native complexed state with bound inhibitor, the uncomplexed state (after removal of the inhibitor) at standard temperature, and the uncomplexed state at elevated temperature. The reference simulation of the complex exhibits extraordinary stability of the overall fold, whereas two distinct conformational substates are found for the binding-site region. In contrast, already at standard temperature pronounced changes are observed in the binding region during the simulation of the uncomplexed state. Leu300, for example, closes the access to the pocket opened by IDD594. On the other hand, conformations around the catalytic site are highly conserved, with the His110-Tyr48-NADP+ orientation being stabilized by a water molecule. Detailed analysis of the trajectories allows to reveal a set of distinct conformational substates that may prove useful as alternative structural templates in virtual screening for new aldose reductase inhibitors.

Efficient method for high-throughput virtual screening based on flexible docking: discovery of novel acetylcholinesterase inhibitors

A method of easily finding ligands, with a variety of core structures, for a given target macromolecule would greatly contribute to the rapid identification of novel lead compounds for drug development. We have developed an efficient method for discovering ligand candidates from a number of flexible compounds included in databases, when the three-dimensional (3D) structure of the drug target is available. The method, named ADAM&EVE, makes use of our automated docking method ADAM, which has already been reported. Like ADAM, ADAM&EVE takes account of the flexibility of each molecule in databases, by exploring the conformational space fully and continuously. Database screening has been made much faster than with ADAM through the tuning of parameters, so that computational screening of several hundred thousand compounds is possible in a practical time. Promising ligand candidates can be selected according to various criteria based on the docking results and characteristics of compounds. Furthermore, we have developed a new tool, EVE-MAKE, for automatically preparing the additional compound data necessary for flexible docking calculation, prior to 3D database screening. Among several successful cases of lead discovery by ADAM&EVE, the finding of novel acetylcholinesterase (AChE) inhibitors is presented here. We performed a virtual screening of about 160 000 commercially available compounds against the X-ray crystallographic structure of AChE. Among 114 compounds that could be purchased and assayed, 35 molecules with various core structures showed inhibitory activities with IC(50) values less than 100 microM. Thirteen compounds had IC(50) values between 0.5 and 10 microM, and almost all their core structures are very different from those of known inhibitors. The results demonstrate the effectiveness and validity of the ADAM&EVE approach and provide a starting point for development of novel drugs to treat Alzheimer's disease.

Virtual screening for inhibitors of human aldose reductase

The inhibition of aldose reductase (AR) provides an interesting strategy to prevent the complications of chronic diabetes. Although a large number of different AR inhibitors are known, very few of these compounds exhibit sufficient efficacy in clinical trials. We performed a virtual screening based on the ultrahigh resolution crystal structure of the inhibitor IDD594 in complex with human AR. AR operates on a large scale of structurally different substrates. To achieve this pronounced promiscuity, the enzyme can adapt rather flexibly to its substrates. Likewise, it has a similar adaptability for the binding of inhibitors. We applied a protocol of consecutive hierarchical filters to search the Available Chemicals Directory. In the first selection step, putative ligands were chosen that exhibit functional groups to anchor the anion-binding pocket of AR. Subsequently, a pharmacophore model based on the binding geometry of IDD594 and the mapping of the binding pocket in terms of putative "hot spots" of binding was applied as a second consecutive filter. In a third and final filtering step, the remaining candidate molecules were flexibly docked into the binding pocket of IDD594 with FlexX and ranked according to their estimated DrugScore values. Out of 206 compounds selected by this search and complemented by a cluster analysis and visual inspection, 9 compounds were selected and subjected to biological testing. Of these, 6 compounds showed IC50 values in the micromolar range. According to the proposed binding mode, the two inhibitors BTB02809 (IC50 = 2.4 +/- 0.5 microM) and JFD00882 (IC50 = 4.1 +/- 1.0 microM) both place a nitro group into the hydrophobic specificity pocket of human AR in an orientation coinciding with the position of the bromine atom of IDD594. The interaction of this Br with Thr113 has been identified as a key feature that is responsible for selectivity enhancement.

[1-(3,5-Difluoro-4-hydroxyphenyl)-1H-pyrrol-3-yl]phenylmethanone as a Bioisostere of a Carboxylic Acid Aldose Reductase Inhibitor

[1-(3,5-Difluoro-4-hydroxyphenyl)-1H-pyrrol-3-yl]phenylmethanone (6) was synthesized as a putative bioisostere of the known aldose reductase (AR) inhibitor (3-benzoylpyrrol-1-yl)acetic acid (I). It was found that 6 is approximately 5 times more potent as an in vitro inhibitor of AR than I, with an IC(50) value in the submicromolar range. Furthermore, 6 showed considerable activity in an in vitro experimental glycation model of diabetes mellitus. Our results support the notion that 6 might become a useful lead structure.

Rational design of an indolebutanoic acid derivative as a novel aldose reductase inhibitor based on docking and 3D QSAR studies of phenethylamine derivatives

A series of 45 phenethylamine derivatives were synthesized and evaluated for their inhibitory activity against pig kidney aldose reductase (ALR2, EC 1.1.1.21). Their IC(50) values ranged from 400 microM to 24 microM. The binding modes of compounds at the active site of ALR2 were examined using flexible docking. The results indicated that phenethylamine derivatives nicely fit into the active pocket of ALR2 by forming various hydrogen bonding and hydrophobic interactions. 3D-QSAR analysis was also conducted using FlexX-docked alignment of the compounds. The best prediction was obtained by CoMSIA combined with hydrophobic and hydrogen bond donor/acceptor field (q(2) = 0.557, r(2) = 0.934). A new derivative, 4-oxo-4-(4-hydroxyindole)butanoic acid, was designed, taking into account the CoMSIA field and the binding mode derived by FlexX docking. This rationally designed compound exhibits an ALR2 inhibition with an IC(50) value of 7.4 microM, which compares favorably to that of a well-known ALR2 inhibitor, tolrestat (IC(50) = 16 microM) and represents a potency approximately 240-fold higher than that of an original phenethylamine lead compound, YUA001.

Molecular Modeling of the Three-Dimensional Structure of Dopamine 3 (D3) Subtype Receptor: Discovery of Novel and Potent D3 Ligands through a Hybrid Pharmacophore- and Structure-Based Database Searching Approach

The dopamine 3 (D3) subtype receptor has been implicated in several neurological conditions, and potent and selective D3 ligands may have therapeutic potential for the treatment of drug addiction, Parkinson's disease, and schizophrenia. In this paper, we report computational homology modeling of the D3 receptor based upon the high-resolution X-ray structure of rhodopsin, extensive structural refinement in the presence of explicit lipid bilayer and water environment, and validation of the refined D3 structural models using experimental data. We further describe the development, validation, and application of a hybrid computational screening approach for the discovery of several classes of novel and potent D3 ligands. This computational approach employs stepwise pharmacophore and structure-based searching of a large three-dimensional chemical database for the identification of potential D3 ligands. The obtained hits are then subjected to structural novelty screening, and the most promising compounds are tested in a D3 binding assay. Using this approach we identified four compounds with K(i) values better than 100 nM and eight compounds with K(i) values better than 1 microM out of 20 compounds selected for testing in the D3 receptor binding assay. Our results suggest that the D3 structural models obtained from this study may be useful for the discovery and design of novel and potent D3 ligands. Furthermore, the employed hybrid approach may be more effective for lead discovery from a large chemical database than either pharmacophore-based or structure-based database screening alone.

Virtual screening of virtual libraries

Virtual screening of virtual libraries (VSVL) is a rapidly changing area of research. Great efforts are being made to produce better algorithms, selection methods and infrastructure. Yet, the number of successful examples in the literature is not impressive, although the quality of work certainly is high. Why is this? One reason is that these methods tend to be applied at the lead generation stage and therefore there is a large lead-time before successful examples appear in the literature. However, any computational chemist would confirm that these methods are successful and there exists a glut of start-up companies specialising in virtual screening. Moreover, the scientific community would not be focussing so much attention on this area if it were not yielding results. Even so, the paucity of literature data is certainly a hindrance to the development of better methods. The VSVL process is unique within the discovery process, in that it is the only method that can screen the > 10(30) genuinely novel molecules out there. Already, some VSVL methods are evaluating 10(13) compounds, a capacity that high throughput screening can only dream of. There is a huge potential advantage for the company that develops efficient and effective methods, for lead generation, lead hopping and optimization of both potency and ADME properties. To do this, it requires more than the software, it requires confidence to exploit the methodology, to commit synthesis on the basis of it, and to build this approach into the medicinal chemistry strategy. It is a fact that these tools remain quite daunting for the majority of scientists working at the bench. The routine use of these methods is not simply a matter of education and training. Integration of these methods into accessible and robust end user software, without dilution of the science, must be a priority. We have reached a coincidence, where several technologies have the required level of maturity predictive computational chemistry methods, algorithms that manage the combinatorial explosion, high throughput crystallography and ADME measurements and the massive increase in computational horsepower from distributed computing. The author is confident that the synergy of these technologies will bring great benefit to the industry, with more efficient production of higher quality clinical candidates. The future is bright. The future is virtual!

Lead discovery using molecular docking

As the structures of more and more proteins and nucleic acids become available, molecular docking is increasingly considered for lead discovery. Recent studies consider the hit-rate enhancement of docking screens and the accuracy of docking structure predictions. As more structures are determined experimentally, docking against homology-modeled targets also becomes possible for more proteins. With more docking studies being undertaken, the 'drug-likeness' and specificity of docking hits is also being examined.

Discovery of new inhibitors of aldose reductase from molecular docking and database screening

Aldose reductase (ALR2) is a target enzyme for the treatment of diabetic complications. Owing to the limited number of currently available drugs for the treatment of diabetic complications, the discovery of new inhibitors of ALR2 that can potentially be optimized as drugs appears highly desirable. In this study, a molecular docking analysis of the structures of more than 127,000 organic compounds contained in the National Cancer Institute database was performed to find and score molecules that are complementary to ALR2. Besides retrieving several carboxylic acid derivatives, which are known to generally inhibit aldose reductase, docking proposed other families of putative inhibitors such as sulfonic acids, nitro-derivatives, sulfonamides and carbonyl derivatives. Twenty-five compounds, chosen as the highest-scoring representatives of each of these families, were tested as aldose reductase inhibitors. Five of them were found to inhibit aldose reductase in the micromolar range. For these active compounds, selectivity with respect to the closely-related aldehyde reductase was determined by measuring the corresponding inhibitory activities. The structures of the complexes between the new lead inhibitors and aldose reductase, here refined with molecular mechanics and molecular dynamics calculations, suggest that new pharmacophoric groups can bind aldose reductase very efficiently. In the case of the family of the nitro-derivative inhibitors, a class of particularly interesting compounds, a round of optimizations was performed with the synthesis and biological evaluation of a series of derivatives aimed at testing the proposed binding mode and at improving interaction with active site residues. Starting from a hit compound having an IC(50) of 42 microM, the most potent compound synthesized showed a 10-fold increase in inhibitory activity and 10-fold selectivity with respect to ALR1, and structure--activity relationships of the designed compounds were in agreement with the proposed mode of binding at the active site.

Virtual screening and fast automated docking methods

Recent advances in high-throughput protein structure determination and in computational chemistry have refocused attention on virtual screening and fast automated docking methods. This review provides a brief introduction to the basic ideas and outlines computational tools currently used. We also provide several examples of where virtual screening has proved successful, highlighting the usefulness of the approach.