An Atlas of Forecasted Molecular Data. 2. Vibration Frequencies of Main-Group and Transition-Metal Neutral Gas-Phase Diatomic Molecules in the Ground State

This atlas of diatomic-molecular vibration frequencies parallels the previously offered Atlas of Internuclear Separations. The Atlas was produced by mining the data from Huber and Herzberg and training neural network software to forecast new data. New protocols were employed with the powerful software, which was originally designed for forecasting the financial markets. The Atlas presents 1920 additional vibration frequencies for use until critical tables are available to fill the needs more precisely. The precision of the predictions is characterized by the average fractional 1% confidence limit, that is, 10.66%. The accuracies of the predictions are determined in two ways. First, 221 of the 224 Huber and Herzberg data values used for training and validation fall within the prediction confidence limits or fall outside by less than 10% of the Huber and Herzberg values, and 181 values agree (within the limits). Second, 87 of 101 comparison data values, consisting of literature data and some additional Huber and Herzberg values, fall within the prediction confidence limits or fall outside by less than half the prediction values, and 44 of the 101 values agree (within the limits)

Development and Beta Testing of the Toxmatch Similarity Tool

Toxmatch was developed as a result of a proposal approved within the JRC Innovation Project Competition in 2005. The aim of the project proposal was to develop the prototype of a software tool for supporting the risk assessment of chemical substances. Such a tool will be useful for scientific researchers, for end-users in industry, for regulatory authorities, and in the future EU Chemicals Agency. Toxmatch (Ideaconsult Ltd.) is a flexible user-friendly, computer-based open source application specifically commissioned by ECB which is accessible via internet. It encodes and applies a range of different structural and descriptor based chemical similarity indices. The novelty of this software lies in its ability to calculate similarity measures that are tailored for specific activities/toxicities. Thus, relevant chemical representations can be selected for a given activity and the chemicals of interest can hence be classified into toxicity classes. The present document summarises the beta testing of Toxmatch, reporting general comments and suggestions for further improvement.JRC.I.3-Toxicology and chemical substance

Theoretical Study of Molecular Structure and Physicochemical Properties of Novel Factor Xa Inhibitors and Dual Factor Xa and Factor IIa Inhibitors

The geometries and energies of factor Xa inhibitors edoxaban, eribaxaban, fidexaban, darexaban, letaxaban, and the dual factor Xa and thrombin inhibitors tanogitran and SAR107375 in both the gas-phase and aqueous solution were studied using the Becke3LYP/6-31++G(d,p) or Grimme’s B97D/6-31++G(d,p) method. The fully optimized conformers of these anticoagulants show a characteristic l-shape structure, and the water had a remarkable effect on the equilibrium geometry. According to the calculated pKa values eribaxaban and letaxaban are in neutral undissociated form at pH 7.4, while fidexaban and tanogitran exist as zwitterionic structures. The lipophilicity of the inhibitors studied lies within a large range of log P between 1 and 4. The dual inhibitor SAR107375 represents an improvement in structural, physicochemical and pharmacokinetic characteristics over tanogitran. At blood pH, SAR107375 predominantly exists in neutral form. In contrast with tanogitran, it is better absorbed and more lipophilic and active after oral application

Analysis of Random Fragment Profiles for the Detection of Structure-Activity Relationships

Substructure- or fragment-type descriptors are effective and widely used tools for chemical similarity searching and other applications in chemoinformatics and computer-aided drug discovery. Therefore, a large number of well-defined computational fragmentation schemes has been devised including hierarchical fragmentation of molecules for the analysis of core structures in drugs or retrosynthetic fragmentation of compounds for de novo ligand design. Furthermore, the generation of dictionaries of structural key-type descriptors that are important tools in pharmaceutical research involves knowledge-based fragment design. Currently more than 5 000 standard descriptors are available for the representation of molecular structures, and therefore the selection of suitable combinations of descriptors for specific chemoinformatic applications is a crucial task. This thesis departs from well-defined substructure design approaches. Randomly generated fragment populations are generated and mined for substructures associated with different compound classes. A novel method termed MolBlaster is introduced for the evaluation of molecular similarity relationships on the basis of randomly generated fragment populations. Fragment profiles of molecules are generated by random deletion of bonds in connectivity tables and quantitatively compared using entropy-based metrics. In test calculations, MolBlaster accurately reproduced a structural key-based similarity ranking of druglike molecules. To adapt the generation and comparison of random fragment populations for largescale compound screening, different fragmentation schemes are compared and a novel entropic similarity metric termed PSE is introduced for compound ranking. The approach is extensively tested on different compound activity classes with varying degrees of intra-class structural diversity and produces promising results in these calculations, comparable to similarity searching using state-of-the-art fingerprints. These results demonstrate the potential of randomly generated fragments for the detection of structure-activity relationships. Furthermore, a methodology to analyze random fragment populations at the molecular level of detail is introduced. It determines conditional probability relationships between fragments. Random fragment profiles are generated for an arbitrary set of molecules, and a frequency vector is assigned for each observed fragment. An algorithm is designed to compare frequency vectors and derive dependencies of fragment occurrence. Using calculated dependency values, random fragment populations can be organized in graphs that capture their relationships and make it possible to map fragment pathways of biologically active molecules. For sets of molecules having similar activity, unique fragment signatures, so-called Activity Class Characteristic Substructures (ACCS), are identified. Random fragment profiles are found to contain compound class-specific information and activity-specific fragment hierarchies. In virtual screening trials, short ACCS fingerprints perform well on many compound classes when compared to more complex state-of-the-art 2D fingerprints. In order to elucidate potential reasons for the high predictive utility of ACCS a thorough systematic analysis of their distribution in active and database compounds have been carried out. This reveals that the discriminatory power of ACCS results from the rare occurrence of individual and combinations of ACCS in screening databases. Furthermore, it is shown that ACCS sets isolated from random populations are typically found to form coherent molecular cores in active compounds. Characteristic core regions are already formed by small numbers of substructures and remain stable when more fragments are added. Thus, classspecific random fragment hierarchies encode meaningful structural information, providing a structural rationale for the signature character of activity-specific fragment hierarchies. It follows that compound-class-directed structural descriptors that do not depend on the application of predefined fragmentation or design schemes can be isolated from random fragment populations

Computational Methods for the Integration of Biological Activity and Chemical Space

One general aim of medicinal chemistry is the understanding of structure-activity relationships of ligands that bind to biological targets. Advances in combinatorial chemistry and biological screening technologies allow the analysis of ligand-target relationships on a large-scale. However, in order to extract useful information from biological activity data, computational methods are needed that link activity of ligands to their chemical structure. In this thesis, it is investigated how fragment-type descriptors of molecular structure can be used in order to create a link between activity and chemical ligand space. First, an activity class-dependent hierarchical fragmentation scheme is introduced that generates fragmentation pathways that are aligned using established methodologies for multiple alignment of biological sequences. These alignments are then used to extract consensus fragment sequences that serve as a structural signature for individual biological activity classes. It is also investigated how defined, chemically intuitive molecular fragments can be organized based on their topological environment and co-occurrence in compounds active against closely related targets. Therefore, the Topological Fragment Index is introduced that quantifies the topological environment complexity of a fragment in a given molecule, and thus goes beyond fragment frequency analysis. Fragment dependencies have been established on the basis of common topological environments, which facilitates the identification of activity class-characteristic fragment dependency pathways that describe fragment relationships beyond structural resemblance. Because fragments are often dependent on each other in an activity class-specific manner, the importance of defined fragment combinations for similarity searching is further assessed. Therefore, Feature Co-occurrence Networks are introduced that allow the identification of feature cliques characteristic of individual activity classes. Three differently designed molecular fingerprints are compared for their ability to provide such cliques and a clique-based similarity searching strategy is established. For molecule- and activity class-centric fingerprint designs, feature combinations are shown to improve similarity search performance in comparison to standard methods. Moreover, it is demonstrated that individual features can form activity-class specific combinations. Extending the analysis of feature cliques characteristic of individual activity classes, the distribution of defined fragment combinations among several compound classes acting against closely related targets is assessed. Fragment Formal Concept Analysis is introduced for flexible mining of complex structure-activity relationships. It allows the interactive assembly of fragment queries that yield fragment combinations characteristic of defined activity and potency profiles. It is shown that pairs and triplets, rather than individual fragments distinguish between different activity profiles. A classifier is built based on these fragment signatures that distinguishes between ligands of closely related targets. Going beyond activity profiles, compound selectivity is also analyzed. Therefore, Molecular Formal Concept Analysis is introduced for the systematic mining of compound selectivity profiles on a whole-molecule basis. Using this approach, structurally diverse compounds are identified that share a selectivity profile with selected template compounds. Structure-selectivity relationships of obtained compound sets are further analyzed

Micellar chromatographic partition coefficients and their application in predicting skin permeability

The major goal for physicochemical screening of pharmaceuticals is to predict human drug absorption, distribution, elimination, excretion and toxicity. These are all dependent on the lipophilicity of the drug, which is expressed as a partition coefficient i.e. a measure of a drug’s preference for the lipophilic or hydrophilic phases. The most common method of determining a partition coefficient is the shake flask method using octanol and water as partitioning media. However, this system has many limitations when modeling the interaction of ionised compounds with membranes, therefore, unreliable partitioning data for many solutes has been reported. In addition to these concerns, the procedure is tedious and time consuming and requires a high level of solute and solvent purity. Micellar liquid chromatography (MLC) has been proposed as an alternative technique for measuring partition coefficients utilising surfactant aggregates, known as micelles. This thesis investigates the application of MLC in determining micelle-water partition coefficients (logPMW) of pharmaceutical compounds of varying physicochemical properties. The effect of mobile phase pH and column temperature on the partitioning of compounds was evaluated. Results revealed that partitioning of drugs solely into the micellar core was influenced by the interaction of charged and neutral species with the surface of the micelle. Furthermore, the pH of the mobile phase significantly influenced the partitioning behaviour and a good correlation of logPMW was observed with calculated distribution coefficient (logD) values. More interestingly, a significant change in partitioning was observed near the dissociation constant of each drug indicating an influence of ionised species on the association with the micelle and retention on the stationary phase. Elevated column temperatures confirmed partitioning of drugs considered in this study was enthalpically driven with a small change in the entropy of the system because of the change in the nature of hydrogen bonding. Finally, a quantitative structure property relationship was developed to evaluate biological relevance in terms of predicting skin permeability of the newly developed partition coefficient values. This study provides a better surrogate for predicting skin permeability based on an easy, fast and cheap experimental methodology, and the method holds the predictive capability for a wider population of drugs. In summary, it can be concluded that MLC has the ability to generate partition coefficient values in a shorter time with higher accuracy, and has the potential to replace the octanol-water system for pharmaceutical compounds

Molecular Similarity and Xenobiotic Metabolism

MetaPrint2D, a new software tool implementing a data-mining approach for predicting sites of xenobiotic metabolism has been developed. The algorithm is based on a statistical analysis of the occurrences of atom centred circular fingerprints in both substrates and metabolites. This approach has undergone extensive evaluation and been shown to be of comparable accuracy to current best-in-class tools, but is able to make much faster predictions, for the first time enabling chemists to explore the effects of structural modifications on a compound’s metabolism in a highly responsive and interactive manner.MetaPrint2D is able to assign a confidence score to the predictions it generates, based on the availability of relevant data and the degree to which a compound is modelled by the algorithm.In the course of the evaluation of MetaPrint2D a novel metric for assessing the performance of site of metabolism predictions has been introduced. This overcomes the bias introduced by molecule size and the number of sites of metabolism inherent to the most commonly reported metrics used to evaluate site of metabolism predictions.This data mining approach to site of metabolism prediction has been augmented by a set of reaction type definitions to produce MetaPrint2D-React, enabling prediction of the types of transformations a compound is likely to undergo and the metabolites that are formed. This approach has been evaluated against both historical data and metabolic schemes reported in a number of recently published studies. Results suggest that the ability of this method to predict metabolic transformations is highly dependent on the relevance of the training set data to the query compounds.MetaPrint2D has been released as an open source software library, and both MetaPrint2D and MetaPrint2D-React are available for chemists to use through the Unilever Centre for Molecular Science Informatics website.----Boehringer-Ingelhie

Mass Transport and Wetting Resistance in Membranes for Advanced Water Treatment

1 in 4 people on Earth face a lack of clean and safe water sources for drinking, irrigation, sanitation, and economic development. The urgent need for water motivates the use of unconventional water resources, such as seawater and wastewater. Advanced water treatment technologies that allow us to access these unconventional resources are drawing increasing attention. Membrane processes including reverse osmosis (RO) and nanofiltration (NF) have been rapidly growing as advanced water treatment technologies for desalination and water reuse due to high productivity, costeffectiveness, and scalability. The objective of this dissertation research is to further understand mass transport of water and solutes across membranes and increase the effectiveness of innovative distillation-based membrane technologies.&nbsp; Numerous potentially harmful compounds exist in feed streams entering advanced water treatment facilities, and thus, understanding membrane rejection in reverse osmosis and nanofiltration for hundreds of compounds is critical for securing high quality product water. A large rejection dataset was compiled, and machine learning techniques enhanced by molecular fingerprints were used to predict membrane rejection of organic compounds. These techniques allowed us to interpret the relationship between the molecular structure of the solute and its rejection in membrane processes. The machine learning models showed high prediction accuracy (Spearman and Pearson coefficients of 0.86-0.99) both with training and test sets. Then, the trained models were analyzed using Shapley values to study the effects of sub-structures of organic compounds on membrane rejection.&nbsp; The second study in this dissertation focused on addressing the low water flux of the osmotic distillation (OD) process. We found that the high membrane thickness (typically 30-100 m) of current membranes was the main cause of low water fluxes. An optimal membrane thickness of 0.073 m was derived with element-scale simulations, and it was able to achieve water fluxes exceeding those of current commercial forward osmosis (FO) membranes. In addition, comparison of module-scale performance with OD and FO membranes found that optimized OD membranes can outcompete high-performance FO membranes in maximum achievable water flux (25.3 vs. 18.6 kg m-2h-1 for OD and FO membranes, respectively) and water recovery (0.28 vs. 0.18).&nbsp; The third study in this dissertation focused on the transport of volatile compounds in membrane distillation (MD). Rejection of volatile compounds in MD is highly varied and poorly understood. This study analyzed a variety of volatile and semi-volatile organic compounds to yield a comprehensive understanding of transport in MD. The effects of different molecular properties on transport were studied first, and we found the Henry&rsquo;s constant and diffusion coefficient were important in determining solute flux. Then, the transport resistances across MD membranes were quantified and two distinct transport regimes (membrane resistance regime and boundary layer resistance regime) were defined. Hydrophobic membranes are susceptible to membrane pore wetting, which results in failure of the system. To overcome this issue in pressure-driven distillation treating low surface tension liquids, the final study in the dissertation focused on fabricating omniphobic, wetting resistant membranes. Nanoporous membranes were modified with re-entrant structures and low surface energy. The results showed the liquid entry pressure values of the modified membranes were much higher than those of conventional membranes with cylindrical pores, allowing for the desalination at 16 bar with a 15% water-ethanol mixture. This low surface tension feed solution wetted the membrane with cylindrical pores.&nbsp;</p