PRO_SELECT: combining structure-based drug design and array-based chemistry for rapid lead discovery. 2. The development of a series of highly potent and selective factor Xa inhibitors

Emerging structure-based computational methods to screen the exploding accessible chemical space

Structure-based virtual screening can be a valuable approach to computationally select hit candidates based on their predicted interaction with a protein of interest. The recent explosion in the size of chemical libraries increases the chances of hitting high-quality compounds during virtual screening exercises but also poses new challenges as the number of chemically accessible molecules grows faster than the computing power necessary to screen them. We review here two novel approaches rapidly gaining in popularity to address this problem: machine learning-accelerated and synthon-based library screening. We summarize the results from seminal proof-of-concept studies, highlight the latest developments, and discuss limitations and future directions.

A small step towards an important goal: fragment screen of the c-di-AMP-synthesizing enzyme CdaA

CdaA is the most widespread diadenylate cyclase in many bacterial species, including several multidrug-resistant human pathogens. The enzymatic product of CdaA, cyclic di-AMP, is a secondary messenger that is essential for the viability of many bacteria. Its absence in humans makes CdaA a very promising and attractive target for the development of new antibiotics. Here, the structural results are presented of a crystallographic fragment screen against CdaA from Listeria monocytogenes, a saprophytic Gram-positive bacterium and an opportunistic food-borne pathogen that can cause listeriosis in humans and animals. Two of the eight fragment molecules reported here were localized in the highly conserved ATP-binding site. These fragments could serve as potential starting points for the development of antibiotics against several CdaA-dependent bacterial species.

The Benzoylpiperidine Fragment as a Privileged Structure in Medicinal Chemistry: A Comprehensive Review

The phenyl(piperidin-4-yl)methanone fragment (here referred to as the benzoylpiperidine fragment) is a privileged structure in the development of new drugs considering its presence in many bioactive small molecules with both therapeutic (such as anti-cancer, anti-psychotic, anti-thrombotic, anti-arrhythmic, anti-tubercular, anti-parasitic, anti-diabetic, and neuroprotective agents) and diagnostic properties. The benzoylpiperidine fragment is metabolically stable, and it is also considered a potential bioisostere of the piperazine ring, thus making it a feasible and reliable chemical frame to be exploited in drug design. Herein, we discuss the main therapeutic and diagnostic agents presenting the benzoylpiperidine motif in their structure, covering articles reported in the literature since 2000. A specific section is focused on the synthetic strategies adopted to obtain this versatile chemical portion.

Chemical space docking enables large-scale structure-based virtual screening to discover ROCK1 kinase inhibitors

With the ever-increasing number of synthesis-on-demand compounds for drug lead discovery, there is a great need for efficient search technologies. We present the successful application of a virtual screening method that combines two advances: (1) it avoids full library enumeration (2) products are evaluated by molecular docking, leveraging protein structural information. Crucially, these advances enable a structure-based technique that can efficiently explore libraries with billions of molecules and beyond. We apply this method to identify inhibitors of ROCK1 from almost one billion commercially available compounds. Out of 69 purchased compounds, 27 (39%) have K_i values < 10 µM. X-ray structures of two leads confirm their docked poses. This approach to docking scales roughly with the number of reagents that span a chemical space and is therefore multiple orders of magnitude faster than traditional docking.

Drug Discovery by Automated Adaptation of Chemical Structure and Identity

Computer-aided drug design offers the potential to dramatically reduce the cost and effort required for drug discovery. While screening-based methods are valuable in the early stages of hit identification, they are frequently succeeded by iterative, hypothesis-driven computations that require recurrent investment of human time and intuition. To increase automation, we introduce a computational method for lead refinement that combines concerted dynamics of the ligand/protein complex via molecular dynamics simulations with integrated Monte Carlo-based changes in the chemical formula of the ligand. This approach, which we refer to as ligand-exchange Monte Carlo molecular dynamics, accounts for solvent- and entropy-based contributions to competitive binding free energies by coupling the energetics of bound and unbound states during the ligand-exchange attempt. Quantitative comparison of relative binding free energies to reference values from free energy perturbation, conducted in vacuum, indicates that ligand-exchange Monte Carlo molecular dynamics simulations sample relevant conformational ensembles and are capable of identifying strongly binding compounds. Additional simulations demonstrate the use of an implicit solvent model. We speculate that the use of chemical graphs in which exchanges are only permitted between ligands with sufficient similarity may enable an automated search to capture some of the benefits provided by human intuition during hypothesis-guided lead refinement.

The In Silico Drug Discovery Toolbox: Applications in Lead Discovery and Optimization

BACKGROUND

Discovery and development of a new drug is a long lasting and expensive journey that takes around 20 years from starting idea to approval and marketing of new medication. Despite R&D expenditures have been constantly increasing in the last few years, the number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. To cope with this issue, a number of in silico techniques are currently being used for an early stage evaluation/prediction of potential safety issues, allowing to increase the drug-discovery success rate and reduce costs associated with the development of a new drug.

METHODS

In the present review, we will analyse the early steps of the drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan.

RESULTS

A comprehensive list of widely used in silico tools, databases, and public initiatives that can be effectively implemented and used in the drug discovery pipeline has been provided. A few examples of how these tools can be problem-solving and how they may increase the success rate of a drug discovery and development program have been also provided. Finally, selected examples where the application of in silico tools had effectively contributed to the development of marketed drugs or clinical candidates will be given.

CONCLUSION

The in silico toolbox finds great application in every step of early drug discovery: (i) target identification and validation; (ii) hit identification; (iii) hit-to-lead; and (iv) lead optimization. Each of these steps has been described in details, providing a useful overview on the role played by in silico tools in the decision-making process to speed-up the discovery of new drugs.

(R,S)-5-(4-Chlorophenyl)-3-{(E)-2-[(R,S)-2,6,6-trimethylcyclohex-2-en-1-yl]vinyl}-4,5-dihydro-1H-pyrazole-1-carboximidamide Hydrochloride

Pyrazoline and amidine motifs are important in medicinal chemistry due to their broad spectrum of bioactivities. This work’s goal was to synthesize a new hybrid amidino pyrazoline from terpenyl chalcone. The chosen method consists of making the terpenyl chalcone react with aminoguanidine hydrochloride in the presence of potassium hydroxide using ethanol as solvent. The reaction was carried out under ultrasonic irradiation. The resulting terpenyl amidino pyrazoline was isolated after separation in a silica-gel chromatographic column in 86% of yield. The product structure was confirmed by the analysis of the high resolution mass, 1H and 13C-NMR spectra. The data was consistent with the expected structure. In summary, the method was efficient for the synthesis of a new hybrid terpenyl amidino pyrazolines under sonochemical conditions.

A potent, proteolysis-resistant inhibitor of kallikrein-related peptidase 6 (KLK6) for cancer therapy, developed by combinatorial engineering

Human tissue kallikrein (KLK) proteases are hormone-like signaling molecules with important functions in cancer pathophysiology. KLK-related peptidase 6 (KLK6), specifically, is highly up-regulated in several types of cancer, where its increased activity promotes cancer invasion and metastasis. This characteristic suggests KLK6 as an attractive target for therapeutic interventions. However, inhibitors that specifically target KLK6 have not yet been reported, possibly because KLK6 shares a high sequence homology and structural similarity with other serine proteases and resists inhibition by many polypeptide inhibitors. Here, we present an innovative combinatorial approach to engineering KLK6 inhibitors via flow cytometry-based screening of a yeast-displayed mutant library of the human amyloid precursor protein Kunitz protease inhibitor domain (APPI), an inhibitor of other serine proteases, such as anionic and cationic trypsins. On the basis of this screening, we generated APPI_{M17L,I18F,S19F,F34V} (APPI-4M), an APPI variant with a KLK6 inhibition constant (K_i ) of 160 pm and a turnover time of 10 days. To the best of our knowledge, APPI-4M is the most potent KLK6 inhibitor reported to date, displaying 146-fold improved affinity and 13-fold improved proteolytic stability compared with WT APPI (APPI_WT). We further demonstrate that APPI-4M acts as a functional inhibitor in a cell-based model of KLK6-dependent breast cancer invasion. Finally, the crystal structures of the APPI_WT/KLK6 and APPI-4M/KLK6 complexes revealed the structural and mechanistic bases for the improved KLK6 binding and proteolytic resistance of APPI-4M. We anticipate that APPI-4M will have substantial translational potential as both imaging agent and therapeutic.

Fragment oriented molecular shapes

Molecular shape is an important concept in drug design and virtual screening. Shape similarity typically uses either alignment methods, which dynamically optimize molecular poses with respect to the query molecular shape, or feature vector methods, which are computationally less demanding but less accurate. The computational cost of alignment can be reduced by pre-aligning shapes, as is done with the Volumetric-Aligned Molecular Shapes (VAMS) method. Here, we introduce and evaluate fragment oriented molecular shapes (FOMS), where shapes are aligned based on molecular fragments. FOMS enables the use of shape constraints, a novel method for precisely specifying molecular shape queries that provides the ability to perform partial shape matching and supports search algorithms that function on an interactive time scale. When evaluated using the challenging Maximum Unbiased Validation dataset, shape constraints were able to extract significantly enriched subsets of compounds for the majority of targets, and FOMS matched or exceeded the performance of both VAMS and an optimizing alignment method of shape similarity search.

Role of computer-aided drug design in modern drug discovery

Drug discovery utilizes chemical biology and computational drug design approaches for the efficient identification and optimization of lead compounds. Chemical biology is mostly involved in the elucidation of the biological function of a target and the mechanism of action of a chemical modulator. On the other hand, computer-aided drug design makes use of the structural knowledge of either the target (structure-based) or known ligands with bioactivity (ligand-based) to facilitate the determination of promising candidate drugs. Various virtual screening techniques are now being used by both pharmaceutical companies and academic research groups to reduce the cost and time required for the discovery of a potent drug. Despite the rapid advances in these methods, continuous improvements are critical for future drug discovery tools. Advantages presented by structure-based and ligand-based drug design suggest that their complementary use, as well as their integration with experimental routines, has a powerful impact on rational drug design. In this article, we give an overview of the current computational drug design and their application in integrated rational drug development to aid in the progress of drug discovery research.

Other Related Techniques

With the advances in computational resources, there is an increasing urge among the computational researchers to make the in silico approaches fast, convenient, reproducible, acceptable, and sensible ones. Along with the typical two-dimensional (2D) and three-dimensional (3D) quantitative structure–activity relationship (QSAR) methods, approaches like pharmacophore, structure-based docking studies, and combinations of ligand- and structure-based approaches like comparative residue interaction analysis (CoRIA) and comparative binding energy analysis (COMBINE) have gained a significant popularity in the computational drug design process. A pharmacophore can be developed either in a ligand-based method, by superposing a set of active molecules and extracting common chemical features which are vital for their bioactivity; or in a structure-based manner, by probing probable interaction points between the macromolecular target and ligands. The interaction of protein and ligand molecules with each other is one of the interesting studies in modern molecular biology and molecular recognition. This interaction can well be explained with the conceptof a docking study to show how a molecule can bind to another molecule to exert the bioactivity. Docking and pharmacophore are non-QSAR approaches in in silico drug design that can support the QSAR findings. Approaches like CoRIA and COMBINE can use information generated from the ligand–receptor complexes to extract the critical clue concerning the types of significant interaction at the level of both the receptor and the ligand. Employing the abovementioned ligand- and structure-based methodologies and chemical libraries, virtual screening (VS) emerged as an important tool in the quest to develop novel drug compounds. VS serves as an efficient computational tool that integrates structural data with lead optimization as a cost-effective approach to drug discovery.

Increasing the structural coverage of tuberculosis drug targets

High-resolution three-dimensional structures of essential Mycobacterium tuberculosis (Mtb) proteins provide templates for TB drug design, but are available for only a small fraction of the Mtb proteome. Here we evaluate an intra-genus "homolog-rescue" strategy to increase the structural information available for TB drug discovery by using mycobacterial homologs with conserved active sites. Of 179 potential TB drug targets selected for x-ray structure determination, only 16 yielded a crystal structure. By adding 1675 homologs from nine other mycobacterial species to the pipeline, structures representing an additional 52 otherwise intractable targets were solved. To determine whether these homolog structures would be useful surrogates in TB drug design, we compared the active sites of 106 pairs of Mtb and non-TB mycobacterial (NTM) enzyme homologs with experimentally determined structures, using three metrics of active site similarity, including superposition of continuous pharmacophoric property distributions. Pair-wise structural comparisons revealed that 19/22 pairs with >55% overall sequence identity had active site Cα RMSD <1 Å, >85% side chain identity, and ≥80% PSAPF (similarity based on pharmacophoric properties) indicating highly conserved active site shape and chemistry. Applying these results to the 52 NTM structures described above, 41 shared >55% sequence identity with the Mtb target, thus increasing the effective structural coverage of the 179 Mtb targets over three-fold (from 9% to 32%). The utility of these structures in TB drug design can be tested by designing inhibitors using the homolog structure and assaying the cognate Mtb enzyme; a promising test case, Mtb cytidylate kinase, is described. The homolog-rescue strategy evaluated here for TB is also generalizable to drug targets for other diseases.

Spectroscopic evaluation for VO(II), Ni(II), Pd(II) and Cu(II) complexes derived from thiosemicarbazide: a special emphasis on EPR study and DNA cleavage

Some thiosemicarbazide complexes were prepared and deliberately investigated by all allowed tools. The ligand coordinates as a mono negative bidentate towards VO(II) and Ni(II) as well as a neutral bidentate towards Pd(II) and Cu(II) ions. Electronic spectral data beside the magnetic measurements facilitate the structural geometry proposal. EPR spectra of Cu(II) and VO(II) complexes were recorded in their solid state. Spin Hamiltonian parameters and molecular orbital coefficient for Cu(II) and VO(II) complexes were calculated and supporting the octahedral geometry of Cu(II) complex and a square pyramidal for VO(II) one. The biological activity investigation was studied by the use of all prepared compounds. The VO(II) and Cu(II) complexes display the susceptible biotoxicity against a gram-positive bacterium. Also, Cu(II) complex displays the same toxicity against gram-negative bacteria used. The effect of all compounds on DNA were photographed. A successive degradation for the DNA target was observed with Pd(II) and Ni(II) complexes beside their original ligand.

Fragment screening by biochemical assay

The use of high concentration biochemical assays to identify weak binding fragment molecules can be an effective method to identify novel starting points for medicinal chemistry programmes. The combination of a high-quality fragment library with sensitive biochemical screening methods is a viable alternative to the more commonly used fragment screening methods such as nuclear magnetic resonance screening or high-throughput X-ray crystallography. Notably, there are a number of literature reports where fragment molecules have been identified by a high concentration biochemical assay. The use of high concentration screening of fragments using a portfolio of single-molecule fluorescence correlation spectroscopy detection techniques to ensure the highest reproducibility and sensitivity have been demonstrated, as well as the use of and X-ray crystallography to determine the binding mode of active fragments.

Synthesis, structure, and biological activity of novel heterocyclic sulfonyl-carboximidamides

Abstract

A series of novel heterocyclic sulfonyl-carboximidamides were synthesized in satisfactory yields via condensation of heterocyclic methyl carbimidates with 2-chlorobenzenesulfonamide and 4-chloropyridine-3-sulfonamide. New structures were confirmed by IR and NMR spectra as well as elemental analyses. X-ray crystallography of two derivatives was performed. The single-crystal structures confirmed the presence of a primary amine group in the amidine moiety. All the compounds were screened for their tuberculostatic, antibacterial, and anticancer activities. Preliminary results indicated that target compounds exhibited weak tuberculostatic and antibacterial activities. Seven compounds inhibited the growth of some cancer cell lines, whereas one of the 2-quinoline derivatives displayed favorable activity against all tested cancer cells with GI₅₀ values of 0.92–13 μM.

Graphical abstract

 

Fragment-based screening by protein crystallography: successes and pitfalls

Fragment-based drug discovery (FBDD) concerns the screening of low-molecular weight compounds against macromolecular targets of clinical relevance. These compounds act as starting points for the development of drugs. FBDD has evolved and grown in popularity over the past 15 years. In this paper, the rationale and technology behind the use of X-ray crystallography in fragment based screening (FBS) will be described, including fragment library design and use of synchrotron radiation and robotics for high-throughput X-ray data collection. Some recent uses of crystallography in FBS will be described in detail, including interrogation of the drug targets β-secretase, phenylethanolamine N-methyltransferase, phosphodiesterase 4A and Hsp90. These examples provide illustrations of projects where crystallography is straightforward or difficult, and where other screening methods can help overcome the limitations of crystallography necessitated by diffraction quality.

Synthesis and antimicrobial activity of novel heterocyclic sulfamoyl-phenyl-carboximidamides derived from clinically applied sulfonamides

A series of novel heterocyclic sulfamoyl-phenyl-carboximidamides were synthesized in satisfactory yields via condensation of clinically applied sulfonamides with heterocyclic methyl carbimidates. New structures were confirmed by IR and NMR spectra as well as elemental analyses. All the compounds were screened for their antibacterial, antifungal, and tuberculostatic activities. Preliminary results indicated that some target compounds exhibited promising antibacterial potency. Especially, N-[4-(thiazol-2-sulfamoyl)phenyl]pyrazine-2-carboximidamide (16) was found to be as potent as clinically applied sulfamethoxypyridazine.

Synthesis, spectroscopic characterization and X-ray structure of [1,2a]benzimidazol-2-yl amidine

[1,2a]Benzimidazol-2-yl amidine was synthesized by adding cyclopentanamine to iminoester in ethanol. The structure of amidine 1 was characterized by IR, 1H NMR, 1H-1H NOESY, 13C NMR, DEPT, XHCOR spectra, thermogravimetric analysis (TGA), differential thermal analysis (DTA), differential scanning calorimetry thermograms (DSC), elementary analysis as well as by X-ray diffraction. The single crystals suitable for X-ray measurement were obtained by recrystallization at room temperature. The amidine group of a model was found to have Z configuration in the crystal. This compound crystallizes in a P2(1)/n monoclinic unit cell with parameters a=12.679(2) Å, b=8.468(3) Å, c=13.108(2) Å, β=96.538(2)°, V=1398.2 Å3 and Z=4.

CSAR benchmark exercise of 2010: combined evaluation across all submitted scoring functions

As part of the Community Structure-Activity Resource (CSAR) center, a set of 343 high-quality, protein–ligand crystal structures were assembled with experimentally determined K_d or K_i information from the literature. We encouraged the community to score the crystallographic poses of the complexes by any method of their choice. The goal of the exercise was to (1) evaluate the current ability of the field to predict activity from structure and (2) investigate the properties of the complexes and methods that appear to hinder scoring. A total of 19 different methods were submitted with numerous parameter variations for a total of 64 sets of scores from 16 participating groups. Linear regression and nonparametric tests were used to correlate scores to the experimental values. Correlation to experiment for the various methods ranged R² = 0.58–0.12, Spearman ρ = 0.74–0.37, Kendall τ = 0.55–0.25, and median unsigned error = 1.00–1.68 pK_d units. All types of scoring functions—force field based, knowledge based, and empirical—had examples with high and low correlation, showing no bias/advantage for any particular approach. The data across all the participants were combined to identify 63 complexes that were poorly scored across the majority of the scoring methods and 123 complexes that were scored well across the majority. The two sets were compared using a Wilcoxon rank-sum test to assess any significant difference in the distributions of >400 physicochemical properties of the ligands and the proteins. Poorly scored complexes were found to have ligands that were the same size as those in well-scored complexes, but hydrogen bonding and torsional strain were significantly different. These comparisons point to a need for CSAR to develop data sets of congeneric series with a range of hydrogen-bonding and hydrophobic characteristics and a range of rotatable bonds.

Fragment-Based Screening by Biochemical Assays

Fragment-based screening (FBS) has gained acceptance in the pharmaceutical industry as an attractive approach for the identification of new chemical starting points for drug discovery programs in addition to classical strategies such as high-throughput screening. There is the concern that screening of fragments at high µM concentrations in biochemical assays results in increased false-positive and false-negative rates. Here the authors systematically compare the data quality of FBS obtained by enzyme activity-based fluorescence intensity, fluorescence lifetime, and mobility shift assays with the data quality from surface plasmon resonance (SPR) and nuclear magnetic resonance (NMR) methods. The serine protease trypsin and the matrix metalloprotease MMP12 were selected as model systems. For both studies, 352 fragments were selected each. From the data generated, all 3 biochemical protease assay methods can be used for screening of fragments with low false-negative and low false-positive rates, comparable to those achieved with the SPR-based assays. It can also be concluded that only fragments with a solubility higher than the screening concentration determined by means of NMR should be used for FBS purposes. Extrapolated to 10,000 fragments, the biochemical assays speed up the primary FBS process by approximately a factor of 10 and reduce the protease consumption by approximately 10,000-fold compared to NMR protein observation experiments.

Docking, virtual high throughput screening and in silico fragment-based drug design

The drug discovery process has been profoundly changed recently by the adoption of computational methods helping the design of new drug candidates more rapidly and at lower costs. In silico drug design consists of a collection of tools helping to make rational decisions at the different steps of the drug discovery process, such as the identification of a biomolecular target of therapeutical interest, the selection or the design of new lead compounds and their modification to obtain better affinities, as well as pharmacokinetic and pharmacodynamic properties. Among the different tools available, a particular emphasis is placed in this review on molecular docking, virtual high-throughput screening and fragment-based ligand design.

Novel inhibitors of anthrax edema factor

Several pathogenic bacteria produce adenylyl cyclase toxins, such as the edema factor (EF) of Bacillus anthracis. These disturb cellular metabolism by catalyzing production of excessive amounts of the regulatory molecule cAMP. Here, a structure-based method, where a 3D-pharmacophore that fit the active site of EF was constructed from fragments, was used to identify non-nucleotide inhibitors of EF. A library of small molecule fragments was docked to the EF-active site in existing crystal structures, and those with the highest HINT scores were assembled into a 3D-pharmacophore. About 10,000 compounds, from over 2.7 million compounds in the ZINC database, had a similar molecular framework. These were ranked according to their docking scores, using methodology that was shown to achieve maximum accuracy (i.e., how well the docked position matched the experimentally determined site for ATP analogues in crystal structures of the complex). Finally, 19 diverse compounds with the best AutoDock binding/docking scores were assayed in a cell-based assay for their ability to reduce cAMP secretion induced by EF. Four of the test compounds, from different structural groups, inhibited in the low micromolar range. One of these has a core structure common to phosphatase inhibitors previously identified by high-throughput assays of a diversity library. Thus, the fragment-based pharmacophore identified a small number of diverse compounds for assay, and greatly enhanced the selection process of advanced lead compounds for combinatorial design.

Small, potent, and selective diaryl phosphonate inhibitors for urokinase-type plasminogen activator with in vivo antimetastatic properties

A set of small nonpeptidic diaryl phosphonate inhibitors was prepared. Some of these inhibitors show potent and highly selective irreversible uPA inhibition. The biochemical and modeling data prove that the combination of a benzylguanidine moiety with a diaryl phosphonate ester results in optimized molecules for derivatizing the serine alcohol in the uPA active site. Selected compounds show significant antimetastatic effects in the BN-472 rat mammary carcinoma model. We report in this paper a preclinical proof of concept that selective, irreversible uPA inhibitors could be valuable in antimetastatic therapy.

d-Phenylglycinol-derived non-covalent factor Xa inhibitors: Effect of non-peptidic S4 linkage elements on affinity and anticoagulant activity

Analogs to a series of D-phenylglycinamide-derived factor Xa inhibitors were discovered. It was found that the S4 amide linkage can be replaced with an ether linkage to reduce the peptide character of the molecules and that this substitution leads to an increase in binding affinity that is not predicted based on modeling. Inhibitors which incorporate ether, amino, or alkyl S4 linkage motifs exhibit similar levels of binding affinity and also demonstrate potent in vitro functional activity, however, binding affinity in this series is strongly dependent on the nature of the S1 binding element.

Synthesis, anti-inflammatory and analgesic activity evaluation of some amidine and hydrazone derivatives

A number of amidine derivatives (3a-i) were synthesized by condensation of cyanopyridine and cyanopyrazine with sulfonylhydrazides in the presence of sodium methoxide. 2-Acetylpyridine and 4-acetylpyridine were condensed with sulfonylhydrazides by microwave irradiation in solid phase to give corresponding hydrazones (5a-d). Indole-3-carboxaldehyde was condensed with sulfonylhydrazides by refluxing in acetic acid to give corresponding condensation product (5e and f). All the compounds, that is, 3a-i and 5a-f were purified by crystallization or by column chromatography. Structures of all the synthesized compounds are supported by correct IR, (1)H NMR, mass spectral and analytical data. Anti-inflammatory activity evaluation was carried out using carrageenin-induced paw oedema assay and compounds 3e,f and 5e exhibited good anti-inflammatory activity, that is 52%, 37% and 38% at 50 mg/kg po, respectively. Analgesic activity evaluation was carried out using acetic acid writhing assay and compounds 3a,c,e and 5f showed good analgesic activity, that is, 50%, 50%, 50% and 60% at 50 mg/kg po, respectively.

Structural biology and drug discovery

It has long been recognized that knowledge of the 3D structures of proteins has the potential to accelerate drug discovery, but recent developments in genome sequencing, robotics and bioinformatics have radically transformed the opportunities. Many new protein targets have been identified from genome analyses and studied by X-ray analysis or NMR spectroscopy. Structural biology has been instrumental in directing not only lead optimization and target identification, where it has well-established roles, but also lead discovery, now that high-throughput methods of structure determination can provide powerful approaches to screening.

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.

Fragment-based lead discovery: leads by design

Fragment-based lead discovery (also referred to as needles, shapes, binding elements, seed templates or scaffolds) is a new lead discovery approach in which much lower molecular weight (120-250 Da) compounds are screened relative to HTS campaigns. Fragment-based hits are typically weak inhibitors (10 microM-mM), and therefore need to be screened at higher concentration using very sensitive biophysical detection techniques such as protein crystallography and NMR as the primary screening techniques, rather than bioassays. Compared with HTS hits, these fragments are simpler, less functionalized compounds with correspondingly lower affinity. However, fragment hits typically possess high 'ligand efficiency' (binding affinity per heavy atom) and so are highly suitable for optimization into clinical candidates with good drug-like properties.

Enzyme Binding Selectivity Prediction: α-Thrombin vs Trypsin Inhibition

In the present work we explore the possibility of an in-depth computational analysis of available experimental X-ray structures in the specific case of a series of alpha-thrombin and trypsin complexes with their respective inhibitors for the development of a novel scoring function based on molecular electrostatic potential computed at the contact surface in the enzyme-inhibitor molecular complex. We subsequently employ the chemometrical approach to determine which are the interactions in the large volume of data that determine the resulting experimental binding constant between ligand and receptor. The results of the model evaluated with molecules in the independent validation set show that a reasonable average error of 1.30 log units of the difference between experimental and calculated binding constants was achieved in the system thrombin-trypsin, which is comparable with those of methods from the literature. Furthermore, by a careful preparation of the Kohonen top layer in the artificial neural network approach that is normally perceived as a "black box device", we have been able to follow the implications of the structure of the inhibitor-enzyme complex for the inhibitor's binding constant. The method appears to be suitable for evaluation of selectivity in structurally similar enzymatic systems, which is currently an important problem in drug design.