Atom Counting Research Articles

Impact of ion class and time on oral drug molecular properties

Using databases of oral drugs and recent compounds from the patents of major pharmaceutical companies, trends in molecular properties over time are identified. It is shown that the physical property profiles of oral drugs are not absolute, but depend on both ion class and the time of the drug invention. Properties examined include lipophilicity, molecular weight, hydrogen bond donors and acceptors, polar surface area, aromaticity, chirality and tetrahedral (sp3) carbon atom count. Lipophilicity is increasing over time in oral drugs that are neutral or acidic, but not in basic drugs, and has been converging in all ion classes towards a common constant range since the 1960s. In contrast, molecular weight is increasing over time in all ion classes. Hydrogen bond donors (OH + NH group count) are stable over time but hydrogen bond acceptors (O + N atom count) are increasing over time. It is shown that measures using sp3 atom and aromatic atom counts are inversely related and a new parameter, Ar atom count – sp3 atom count (Ar-sp3), describing shape or aromatic/aliphatic balance, is introduced. Ar-sp3 is constant over time in oral drugs, but is higher in patented compounds, independently of molecular weight and lipophilicity. The ion classes in patent compounds occupy distinct molecular weight/lipophilicity/shape chemical space. These properties are significantly increased versus oral drugs, with the exception of the most recently discovered acidic drugs, and occur in most, but not all target classes. While ceilings for lipophilicity in acids and neutral compounds may not have been reached, the design of potential drug molecules with reduced ADMET risk is indicated by a seeking a better balance between lipophilicity, 3-dimensionality and aromaticity.

Improved genome annotation through untargeted detection of pathway-specific metabolites

BackgroundMass spectrometry-based metabolomics analyses have the potential to complement sequence-based methods of genome annotation, but only if raw mass spectral data can be linked to specific metabolic pathways. In untargeted metabolomics, the measured mass of a detected compound is used to define the location of the compound in chemical space, but uncertainties in mass measurements lead to "degeneracies" in chemical space since multiple chemical formulae correspond to the same measured mass. We compare two methods to eliminate these degeneracies. One method relies on natural isotopic abundances, and the other relies on the use of stable-isotope labeling (SIL) to directly determine C and N atom counts. Both depend on combinatorial explorations of the "chemical space" comprised of all possible chemical formulae comprised of biologically relevant chemical elements.ResultsOf 1532 metabolic pathways curated in the MetaCyc database, 412 contain a metabolite having a chemical formula unique to that metabolic pathway. Thus, chemical formulae alone can suffice to infer the presence of some metabolic pathways. Of 248,928 unique chemical formulae selected from the PubChem database, more than 95% had at least one degeneracy on the basis of accurate mass information alone. Consideration of natural isotopic abundance reduced degeneracy to 64%, but mainly for formulae less than 500 Da in molecular weight, and only if the error in the relative isotopic peak intensity was less than 10%. Knowledge of exact C and N atom counts as determined by SIL enabled reduced degeneracy, allowing for determination of unique chemical formula for 55% of the PubChem formulae.ConclusionsTo facilitate the assignment of chemical formulae to unknown mass-spectral features, profiling can be performed on cultures uniformly labeled with stable isotopes of nitrogen (15N) or carbon (13C). This makes it possible to accurately count the number of carbon and nitrogen atoms in each molecule, providing a robust means for reducing the degeneracy of chemical space and thus obtaining unique chemical formulae for features measured in untargeted metabolomics having a mass greater than 500 Da, with relative errors in measured isotopic peak intensity greater than 10%, and without the use of a chemical formula generator dependent on heuristic filtering. These chemical formulae can serve as indicators for the presence of particular metabolic pathways.

A Comparative Assessment of the Physico-Chemical and Microbial Trends in Njaba River, Niger Delta Basin, Southeastern Nigeria

Water quality monitoring at five (5) different gauge stations on the course of Njaba River was undertaken to understand the variability of the physico-chemical and microbial contents of the river water within a specified period of time (2003-2008). Collected water samples were analyzed using Atomic Absorption Spectrophotometer (AAS), Digital Meters and Standard Plate Counts. Results of the analyses indicated that average pH, electrical conductivity and the Total Dissolved Solids (TDS) of the Njaba River in 2003 were 6.3, 22 µS/cm and 13.5 mg/l, respectively. Mean values in 2008 for the same parameters were 6.4, 24.4µS/cm and 14.7 mg/l, respectively. Mean concentrations of analyzed cations (Ca2+, Mg2+, Na+ and K+) in 2003 were 4.10, 0.15, 5.00 and 1.20 mg/l, respectively, and that obtained for same parameters in 2008 were 4.40, 0.18, 6.40 and 1.30 mg/l, respectively. The mean concentrations of analyzed anions (HCO3 - , SO4 2-, Cl- and NO3 - ) in 2003 were 20.0, 4.0, 1.30 and 0.20 mg/l, respectively and in 2008 the mean concentrations were 24.5, 4.20, 1.60 and 0.22 mg/l, respectively. Characterization of the river water followed the trend: Na+ > Ca2+ > K+ > Mg2+ (for the cations) and HCO3 - > SO4 2- > Cl- > NO3 - (for anions), showing the Njaba River is NaHCO3 water. Mean concentrations of Dissolved Oxygen (DO) and Biochemical Oxygen Demand (BOD) of the river water were 7.2 and 2.2 mg/l, respectively in 2003, and 8.0 and 4.0 mg/l respectively, in 2008. Total Coliform Counts of the river water in 2003 ranged from 70 - 90 cfu/100ml with a mean value of 80 cfu/100ml, while the counts in 2008 ranged from 100 - 120 cfu/100ml with a mean value of 110 cfu/100 ml. Calculated Pollution Index (PI) slightly increased from 0.72 in 2003 to 0.73 in 2008. These water quality determinants revealed gradual rise in the concentrations of the respective physico-chemical parameters and bacteriological constituents of the Njaba River water. Sodium Adsorption Ratio (SAR) of 1.37 in 2003 and 1.54 in 2008, however, indicated that the river remained excellent for irrigation purposes. Except for the observed low pH and poor bacteriological quality, all other measured parameters of the Njaba River water conformed to the World Health Organization (WHO) standards for safe drinking water. The generally decreasing low pH value and progressively increasing Coliform Counts and nutrient contents (although still low) were the major environmental problems observed in the Njaba River water. These problems are essentially associated with increased anthropogenic activities on the Njaba River Watershed within the period in focus.

Field evaporation behavior in [0 0 1] FePt thin films

Though the atom probe has provided unprecedented atomic identification and spatial imaging capability, the basic reconstruction assumption of a smooth hemispherical tip shape creates significant challenges in yielding high fidelity chemical information for atomic species with extreme differences in fields required for field evaporation. In the present study, the evaporation behavior and accompanying artifacts are examined for the super-cell lattice structure of L1(0) FePt, where alternating Fe and Pt planes exist in the [0 0 1] orientation. Elemental Fe and Pt have significant differences in field strengths providing a candidate system to quantify these issues. Though alloys can result in changes in the elemental field strength, the intrinsic nature of elemental planes in [0 0 1] L1(0) provides a system to determine to what extent basic assumptions of elemental field strengths can break down in understanding reconstruction artifacts in this intermetallic alloy. The reconstruction of field evaporation experiments has shown depletion of Fe at the (0 0 2) pole and zone axes. Compositional profiles revealed an increase in Fe and atom count moving outward from the pole. The depletion at the low indexed pole and zone axes was determined to be the result of local magnification and electrostatic effects. The experimental results are compared to an electrostatic simulation model.

ChemInform Abstract: Abiological Catalysis for Synthetic Efficiency

Synthetic efficiency requires reactions that are both selective and economical in terms of atom count. The invention of new transition metal catalyzed reactions provides a powerful approach to both problems. Palladium complexes have been particularly fruitful in creating new reactions. By use of such complexes to resolve a chemoselectivity problem, cyclizations to medium sized rings becomes efficient. An approach for controlling regioselectivity in palladium catalyzed allylic alkylations invokes tethering two reactants either by hydrogen bonding or covalently. The metal complex may become a template to impose diastereoselectivity that violates the thermodynamic preferences of the product. A new type of chiral environment to promote enantioselectivity now allows asymmetric C-C bond formation in excess of 95% ee. These methods evolve efficient syntheses of glycosidase inhibitors allosamidine and mannostatin A. While many of these methods are atom economical, this goal requires exploration of other processes. Transition metal catalyzed reactions of acetylenes are particularly fruitful territory for exploration. Palladium catalyzed isomerization of acetylenes to allenes and insertion into acetylenic C-H bonds provide two new processes, both of which when applied intramolecularly effect cycloisomerization to macrocycles. Palladium complexes are not unique in their synthetic utility. A ruthenium catalyzed condensation of ally1 alcohols and terminal acetylenes constitutes an effective synthesis of p,y- unsaturated ketones which is a simple addition of the two reactants with reorganization of the oxidation pattern.

Optimal π-Stacking Interaction Energies in Parallel-Displaced Aryl/Aryl Dimers are Predicted by the Dimer Heavy Atom Count

It is generally accepted that large basis sets are required for the accurate calculation of interaction energies for weakly bound aryl dimers, but it has recently been reported that MP2(full)/6-31G* energies, although inaccurate in absolute terms, are well-correlated with estimated CCSD(T)/CBS results. It is now shown that this correlation holds for MP2/aug-cc-pVDZ and SCS-MP2/aug-cc-pVDZ values. Linear regression of published CCSD(T)/CBS results with MP2 or SCS-MP2 results has been used to correct systematic errors observed with both MP2 theories, and these corrections are applied to 27 parallel-displaced aromatic dimers of interest in medicinal chemistry. The optimal computed interaction energies are found to be strongly correlated with the heavy atom counts of the aryl/aryl dimers. This relationship between heavy atom count and interaction energy also applies to a series of 12 aryl/non-aryl dimers such that a single linear regression equation accounts for all of the dimers studied.

Sub-Projection-Noise Sensitivity in Broadband Atomic Magnetometry

We demonstrate sub-projection-noise sensitivity of a broadband atomic magnetometer using quantum nondemolition spin measurements. A cold, dipole-trapped sample of rubidium atoms provides a long-lived spin system in a nonmagnetic environment, and is probed nondestructively by paramagnetic Faraday rotation. The calibration procedure employs as known reference state, the maximum-entropy or "thermal" spin state, and quantitative imaging-based atom counting to identify electronic, quantum, and technical noise in both the probe and spin system. The measurement achieves a sensitivity 1.6 dB (2.8 dB) better than projection-noise (thermal state quantum noise) and will enable squeezing-enhanced broadband magnetometry.

Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling

The Carcinogenicity Reliability Database (CRDB) was constructed by collecting experimental carcinogenicity data on about 1,500 chemicals from six sources, including IARC, and NTP databases, and then by ranking their reliabilities into six unified categories. A wide variety of 911 organic chemicals were selected from the database for QSAR modeling, and 1,504 kinds of different molecular descriptors were calculated, based on their 3D molecular structures as modeled by the Dragon software. Positive (carcinogenic) and negative (non-carcinogenic) chemicals containing various substructures were counted using atom and functional group count descriptors, and the statistical significance of ratios of positives to negatives was tested for those substructures. Very few were judged to be strongly related to carcinogenicity, among substructures known to be responsible for carcinogens as revealed from biomedical studies. In order to develop QSAR models for the prediction of the carcinogenicities of a wide variety of chemicals with a satisfactory performance level, the relationship between the carcinogenicity data with improved reliability and a subset of significant descriptors selected from 1,504 Dragon descriptors was analyzed with a support vector machine (SVM) method: the classification function (SVC) for weighted data in LIBSVM program was used to classify chemicals into two carcinogenic categories (positive or negative), where weights were set depending on the reliabilities of the carcinogenicity data. The quality and stability of the models presented were tested by performing a dual cross-validation procedure. A single SVM model as the first step was developed for all the 911 chemicals using 250 selected descriptors, achieving an overall accuracy level, i.e., positive and negative correct estimate, of about 70%. In order to improve the accuracy of the final model, the 911 chemicals were classified into 20 mutually overlapping subgroups according to contained substructures, a specific SVM model was optimized for each subgroup, and the predicted carcinogenicities of the 911 chemicals were determined by the majorities of the outputs of the corresponding SVM models. The model developed on the basis of grouping of chemicals into 20 substructures predicts the carcinogenicities of a wide variety of chemicals with a satisfactory overall accuracy of approximately 80%.

Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis

There is an urgent need for new drugs against tuberculosis which annually claims 1.7-1.8 million lives. One approach to identify potential leads is to screen in vitro small molecules against Mycobacterium tuberculosis (Mtb). Until recently there was no central repository to collect information on compounds screened. Consequently, it has been difficult to analyze molecular properties of compounds that inhibit the growth of Mtb in vitro. We have collected data from publically available sources on over 300 000 small molecules deposited in the Collaborative Drug Discovery TB Database. A cheminformatics analysis on these compounds indicates that inhibitors of the growth of Mtb have statistically higher mean logP, rule of 5 alerts, while also having lower HBD count, atom count and lower PSA (ChemAxon descriptors), compared to compounds that are classed as inactive. Additionally, Bayesian models for selecting Mtb active compounds were evaluated with over 100 000 compounds and, they demonstrated 10 fold enrichment over random for the top ranked 600 compounds. This represents a promising approach for finding compounds active against Mtb in whole cells screened under the same in vitro conditions. Various sets of Mtb hit molecules were also examined by various filtering rules used widely in the pharmaceutical industry to identify compounds with potentially reactive moieties. We found differences between the number of compounds flagged by these rules in Mtb datasets, malaria hits, FDA approved drugs and antibiotics. Combining these approaches may enable selection of compounds with increased probability of inhibition of whole cell Mtb activity.

Arsenic toxicity: an atom counting and electrophilicity-based protocol

The atomic number (Z) and electrophilicity index (omega) have been utilized to explain the toxicity of various alkali and transition-metal ions as well as to predict that of the arsenic ions. The toxicity of two different training sets of arsenic derivatives is described using the global electrophilicity (omega) and number of nonhydrogenic atoms (N (NH)) along with the local philicity (omega(As)+) and the atomic charge (Q (As)) on the arsenic atom. Applying the regression models from the training sets, toxicity of some unknown arsenic derivatives is predicted.

Efficient Calculation of Heats of Formation

An efficient procedure has been devised for calculating heats of formation of uncharged, closed-shell molecules comprising H, C, N, O, F, S, Cl, and Br. Known as T1, it follows the G3(MP2) recipe, by substituting an HF/6-31G* for the MP2/6-31G* geometry, eliminating both the HF/6-31G* frequency and QCISD(T)/6-31G* energy and approximating the MP2/G3MP2large energy using dual basis set RI-MP2 techniques. Taken together, these changes reduce computation time by 2-3 orders of magnitude. Atom counts, Mulliken bond orders, and HF/6-31G* and RI-MP2 energies are introduced as variables in a linear regression fit to a set of 1126 G3(MP2) heats of formation. The T1 procedure reproduces these values with mean absolute and rms errors of 1.8 and 2.5 kJ/mol, respectively. It reproduces experimental heats of formation for a set of 1805 diverse organic molecules from the NIST thermochemical database with mean absolute and rms errors of 8.5 and 11.5 kJ/mol, respectively. Heats of formation of flexible molecules have been approximated by the heats of formation of their lowest-energy conformer as given by the T1 recipe. This has been identified by examining all conformers for molecules with fewer than 100 conformers and by examining a random sample of 100 conformers for molecules with more than 100 conformers. While this approximation necessarily yields heats of formation that are too negative, the error for typical organic molecules with less than 10 degrees of conformational freedom (several thousand conformers) is <2-3 kJ/mol. T1 heats of formation have been used to calculate energy differences for a variety of structural, positional, and stereoisomers, as well as energy differences between conformers in a variety of simple acyclic and cyclic molecules for which reliable experimental data are available. In terms of both overall error and errors for individual systems, T1 provides a better account of the experimental thermochemistry than any practical quantum chemical method that we have previously examined. A database of approximately 40,000 T1 calculations for both rigid and flexible organic molecules has been produced and is available as part of the Spartan Molecular Database (SMD) in the current version of the Spartan electronic structure program (Spartan'08). (A subset of approximately 5000 molecules is provided as part of the standard release, and the full T1 database can be licensed.). This collection differs from the other components of SMD in that the lowest-energy conformation for each molecule has been assigned using a high-level quantum chemical method and not molecular mechanics. Thus, it is not only a source of "high-quality" calculated heats of formation for organic molecules but also a source of conformational preferences.

Development of a Nanoelectronic 3-D (NEMO 3-D ) Simulator for Multimillion Atom Simulations and Its Application to Alloyed Quantum Dots

Material layers with a thickness of a few nanometers are common-place in today's semiconduc- tor devices. Before long, device fabrication methods will reach a point at which the other two device dimensions are scaled down to few tens of nanometers. The total atom count in such deca-nano devices is reduced to a few million. Only a small finite number of free electrons will operate such nano-scale devices due to quantized electron energies and electron charge. This work demon- strates that the simulation of electronic structure and electron transport on these length scales must not only be fundamentally quantum mechanical, but it must also include the atomic granularity of the device. Various el- ements of the theoretical, numerical, and software foun- dation of the prototype development of a Nanoelectronic Modeling tool (NEMO 3-D) which enables this class of device simulation on Beowulf cluster computers are pre- sented. The electronic system is represented in a sparse complex Hamiltonian matrix of the order of hundreds of millions. A custom parallel matrix vector multiply al- gorithm that is coupled to a Lanczos and/or Rayleigh- Ritz eigenvalue solver has been developed. Benchmarks of the parallel electronic structure and the parallel strain calculation performed on various Beowulf cluster com- puters and a SGI Origin 2000 are presented. The Be- owulf cluster benchmarks show that the competition for memory access on dual CPU PC boards renders the util- ity of one of the CPUs useless, if the memory usage per node is about 1-2 GB. A new strain treatment for the

Approaching nanoscale oxides: models and theoretical methods

This tutorial review deals with the rapidly developing area of modelling oxide materials at the nanoscale. Top-down and bottom-up modelling approaches and currently used theoretical methods are discussed with the help of a selection of case studies. We show that the critical oxide nanoparticle size required to be beyond the scale where every atom counts to where structural and chemical properties are essentially bulk-like (the scalable regime) strongly depends on the structural and chemical parameters of the material under consideration. This oxide-dependent behaviour with respect to size has fundamental implications with respect to their modelling. Strongly ionic materials such as MgO and CeO(2), for example, start to exhibit scalable-to-bulk crystallite-like characteristics for nanoparticles consisting of about 100 ions. For such systems there exists an overlap in nanoparticle size where both top-down and bottom-up theoretical techniques can be applied and the main problem is the choice of the most suitable computational method. However, for more covalent systems such TiO(2) or SiO(2) the onset of the scalable regime is still unclear and for intermediate sized nanoparticles there exists a gap where neither bottom-up nor top-down modelling are fully adequate. In such difficult cases new efforts to design adequate models are required. Further exacerbating these fundamental methodological concerns are oxide nanosystems exhibiting complex electronic and magnetic behaviour. Due to the need for a simultaneous accurate treatment of the atomistic, electronic and spin degrees of freedom for such systems, the top-down vs. bottom-up separation is still large, and only few studies currently exist.

Fermion and spin counting in strongly correlated systems

We apply the atom counting theory to strongly correlated Fermi systems and spin models, which can be realized with ultracold atoms. The counting distributions are typically sub-Poissonian and remain smooth at quantum phase transitions, but their moments exhibit critical behavior, and characterize quantum statistical properties of the system. Moreover, more detailed characterizations are obtained with experimentally feasible spatially resolved counting distributions.

Quick prediction of the retention of solutes in 13 thin layer chromatographic screening systems on silica gel by classification and regression trees

The use of classification and regression trees (CART) was studied in a quantitative structure-retention relationship (QSRR) context to predict the retention in 13 thin layer chromatographic screening systems on a silica gel, where large datasets of interlaboratory determined retention are available. The response (dependent variable) was the rate mobility (RM) factor, while a set of atomic contributions and functional substituent counts was used as an explanatory dataset. The trees were investigated against optimal complexity (number of the leaves) by external validation and internal crossvalidation. Their predictive performance is slightly lower than full atomic contribution model, but the main advantage is the simplicity. The retention prediction with the proposed trees can be done without computer or even pocket calculator.

Thermochemical and Physical Properties of Element 112

Every atom counts: New data for the adsorption chromatographic behavior of single atoms of element 112 confirm a metallic character involved in their interaction with a gold surface, similar to the lighter homologues Zn, Cd, and Hg. The elemental volatility of element 112 is much higher than that of lighter homologues of Group 12 of the periodic table, as empirically deduced from the experimental results.

Simple method for the prediction of the separation of racemates with high-performance liquid chromatography on Whelk-O1 chiral stationary phase

A simple method for the prediction of whether or not a racemate can be separated on a Whelk-O1 chiral stationary phase has been developed. In this approach, molecules are represented by counting the number of atom types of the neighbors spheres of the chiral center. A decision tree is then used to decide based on a few of these atom count descriptors whether a given racemate can be separated. High values of correct prediction were obtained, namely with more than 94% for training sets and of about 90% for cross-validation results. The same rate of correct prediction was also obtained on an external data set. The descriptors can be rapidly and easily retrieved by just counting the atom types around the chiral center by inspecting the chemical diagram of the molecule. Furthermore, the decision tree model can be applied through the use of a small set of rules that eventually predicts whether or not a racemate is separated. Due to its computational simplicity, the procedure is of interest for experimentalists that need to make rapid assessment of the separation without having to program or input complex formulas.

Probing the structural and topological requirements for CCR2 antagonism: Holographic QSAR for indolopiperidine derivatives

CCR2 is the major family of chemokine receptors which involve in the pathophysiology of the acute or chronic inflammatory conditions such as rheumatoid arthritis, atherosclerosis, asthma, obesity, and type-2 diabetes. Herein, we report the results of HQSAR model, developed for CCR2 antagonistic activity of indolopiperidine derivatives. The best HQSAR model with r(2)=0.916, q(2)=0.562 with atom count=4-7 was used to predict the activity of the test set molecules. The predicted values are in good agreement with experimental results and show the potential of the model for untested compounds. Analysis of molecular fragments throws light on essential structural and topological features of indolopiperidine derivatives for antagonist activity. The analysis shows that the presence of tertiary hydrogen bond acceptor groups is important for CCR2 antagonism. Fragments containing benzene ring substituted with one or more chlorine atoms show the positive effect of electron withdrawing group for favorable activity.

A Force Sensor with a Diameter of 20 nm-Characterization of a Single Elasticoluminescence Nanoparticle Using AFM-Photon Measurement System-

Force sensing in nano-scale can be realized by using nanoparticles of “elastico-luminescence” (a new inorganic material which emits light in response to externally applied mechanical stress in elastic region). By applying the material as a coating on the surface of subject, a technology for two-dimensional visualization of stress distribution has been innovated. In order to characterize the stress-induced luminescence from a single particle, we have developed an evaluation equipment utilizing an atomic force microscope (AFM) and photon counting system, and have succeeded in capturing the stress-induced luminescence from a single nanoparticle. The luminescence intensity increases with the increasing force on the nanoparticle, demonstrating that elastico-luminescence nanoparticle can act as force/strain sensor in nanometer region. A force sensor with a diameter of 20 nm has been developed.

Fragment-Based Approach to Drug??Lead Discovery

In the last 20 years the advent of new technologies, such as high-throughput screening (HTS) and combinatorial chemistry, has produced new tools for the discovery of biologically active molecules. In the past decade, fragment-based drug discovery has emerged as a more rational and focused approach that concentrates on the quality, rather than the quantity, of hits and leads. The principles behind this strategy are different from those that represented the basis of conventional HTS. The starting point of this approach is always a small chemical entity (typically MW 150-200), a fragment, with low affinity for the selected target. Fragments should satisfy key features such as diversity, reduced structural complexity, aqueous solubility and availability. Because of their small size, they occupy a smaller region of chemical space if compared with classical HTS compounds; hence, fragment libraries provide a good diversity with a relatively low number of compounds. Classical biochemical assays are often not suitable to detect the low binding affinities involved, so some well known biophysical techniques, such as nuclear magnetic resonance and x-ray, have been opportunely modified in order to render them able to perform the task. When selecting fragments suitable for subsequent optimization, a useful parameter has been introduced, the ligand efficiency, which is defined as the free energy of binding divided by the non-hydrogen atom count. Once selected, a fragment must undergo a heavy elaboration to improve binding affinity, at the same time acquiring drug-like properties. There are two main ways to go on at this point. The most common one is the so-called 'fragment evolution', consisting of a stepwise and systematic addition of chemical functionalities to the starting fragment core, together with a continuous feedback for pharmacological and physicochemical properties. The second one, less common but with great potential, is 'fragment linking': when two or more fragment hits are found to bind in adjacent regions of the target protein, they can be linked through appropriate spacers to rapidly produce a single molecule with much higher binding affinity. Two representative case histories are described: Abbott's ABT 518, an MMP (matrix metalloproteinase) inhibitor, and Eli-Lilly's LY-517717, an inhibitor of factor Xa serine protease. In addition, a list of molecules claimed to be derived from fragment approach and currently undergoing clinical trials is presented.