Cheminformatics Techniques Research Articles

Molecular Dynamics Study of the Photodegradation of Polymeric Chains

The development of reusable polymeric materials inspires an attempt to combine renewable biomass with upcycling to form a biorenewable closed system. It has been reported that 2,5-furandicarboxylic acid (FDCA) can be recovered for recycling when incorporated as monomers into photodegradable polymeric systems. Here, we conduct density functional theory (DFT) studies with periodic boundary conditions on microscopic structures involved in the photodegradation of polymeric chains incorporating FDCA and 2-nitro-1,3-benzenedimethanol. The photodegradation process of polymeric chains is studied using time-dependent excited-state molecular dynamics (TDESMD) in vacuum and aqueous environments. Changes in the photophysical properties for reaction intermediates are characterized by ground-state observables. The distribution of reaction intermediates and products is obtained from TDESMD trajectories using cheminformatics techniques. Results show that a higher degree of polymeric chain degradation is achieved in the vacuum environment. Additionally, one finds that the FDCA molecule is recoverable in the aqueous environment, in qualitative agreement with experimental findings.

Cheminformatics techniques in antimalarial drug discovery and development from natural products 2: Molecular scaffold and machine learning approaches

Abstract A large number of natural products, especially those used in ethnomedicine of malaria, have shown varying in-vitro antiplasmodial activities. Cheminformatics involves the organization, integration, curation, standardization, simulation, mining and transformation of pharmacology data (compounds and bioactivity) into knowledge that can drive rational and viable drug development decisions. This chapter will review the application of two cheminformatics techniques (including molecular scaffold analysis and bioactivity predictive modeling via Machine learning) to natural products with in-vitro and in-vivo antiplasmodial activities in order to facilitate their development into antimalarial drug candidates and design of new potential antimalarial compounds.

Cheminformatics Analysis of Fluoroquinolones and their Inhibition Potency Against Four Pathogens.

Drug-resistant bacteria are a worldwide public health concern. As the prevalence of multi-drug resistant pathogens outpaces the discovery of new antibacterials, it is of importance to explore the structure-activity relationships for series of known bactericides with proven scaffolds. Herein, we assembled a set of 507 fluoroquinolone analogues all experimentally tested for their inhibition potency against four pathogens: Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and Streptococcus pneumoniae. We relied on cheminformatics techniques to characterize and cluster them based on their structural similarity and analyzed the structure-activity relationships identified for each cluster of fluoroquinolones. Then, we utilized machine learning techniques to develop and validate predictive QSAR models for computing the inhibition potencies (pMIC) of analogues for each pathogen. These QSAR models afforded reasonable external prediction performances (R2≥0.6, MAE∼0.4). This study confirmed that (i) there are both global and local inter-pathogen concordance regarding the antibacterial potency of fluoroquinolones, (ii) small clusters of fluoroquinolone analogues are characterized by unique patterns of strain selectivity and potency, the latter being potentially useful to design new analogues with enhanced potency and/or selectivity towards a given pathogen, and (iii) robust QSAR models were obtained allowing for future design of new bioactive fluoroquinolones.

Cheminformatics techniques in antimalarial drug discovery and development from natural products 1: basic concepts

Abstract A large number of natural products, especially those used in ethnomedicine of malaria, have shown varying in vitro antiplasmodial activities. Facilitating antimalarial drug development from this wealth of natural products is an imperative and laudable mission to pursue. However, limited manpower, high research cost coupled with high failure rate during preclinical and clinical studies might militate against the pursuit of this mission. These limitations may be overcome with cheminformatic techniques. Cheminformatics involves the organization, integration, curation, standardization, simulation, mining and transformation of pharmacology data (compounds and bioactivity) into knowledge that can drive rational and viable drug development decisions. This chapter will review the application of cheminformatics techniques (including molecular diversity analysis, quantitative-structure activity/property relationships and Machine learning) to natural products with in vitro and in vivo antiplasmodial activities in order to facilitate their development into antimalarial drug candidates and design of new potential antimalarial compounds.

Binding of peanut allergen Ara h 2 with Vaccinium fruit polyphenols

The potential for 42 different polyphenols found in Vaccinium fruits to bind to peanut allergen Ara h 2 and inhibit IgE binding epitopes was investigated using cheminformatics techniques. Out of 12 predicted binders, delphinidin-3-glucoside, cyanidin-3-glucoside, procyanidin C1, and chlorogenic acid were further evaluated in vitro. Circular dichroism, UV–Vis spectroscopy, and immunoblotting determined their capacity to (i) bind to Ara h 2, (ii) induce protein secondary structural changes, and (iii) inhibit IgE binding epitopes. UV–Vis spectroscopy clearly indicated that procyanidin C1 and chlorogenic acid interacted with Ara h 2, and circular dichroism results suggested that interactions with these polyphenols resulted in changes to Ara h 2 secondary structures. Immunoblotting showed that procyanidin C1 and chlorogenic acid bound to Ara h 2 significantly decreased the IgE binding capacity by 37% and 50%, respectively. These results suggest that certain polyphenols can inhibit IgE recognition of Ara h 2 by obstructing linear IgE epitopes.

Chemical constituents from Paliavana tenuiflora Mansf. (Gesneriaceae)

The study aimed to investigate the chemical composition of Paliavana tenuiflora Mansf., an endemic Brazilian species that belongs to Gesneriaceae family. By chromatographic, spectroscopic and cheminformatics techniques it was possible to isolate and elucidate eleven compounds: a new naphthoquinone named as tenuiflorone (1), besides known compounds 7-metoxy-8-hydroxy-α-dunnione (2), tectoquinone (3), 2-(p-hydroxyphenyl) ethyl hexacosanoate (4), 2-(3′,4′-dihydroxyphenyl) ethyl hexacosanoate (5), caffeic acid (6), presilphiperfolan-9-ol (7), β-sitosterol (8), stigmasterol (9), β-sitosterol-3-O-d-glucopyranoside (10) and stigmasterol-3-O-d-glucopyranoside (11). This is the first time that secondary metabolites were isolated from species belonging to Paliavana genus, with the isolation of a new naphthoquinone named as tenuiflorone. The results obtained contribute to the chemotaxonomy of the Gesneriaceae family.

Across the blood-brain barrier: Neurotherapeutic screening and characterization of naringenin as a novel CRMP-2 inhibitor in the treatment of Alzheimer's disease using bioinformatics and computational tools

The discovery and developmental processes of CNS drugs have been limited by the inability of potential drug molecules to pass through the blood-brain barrier (BBB). This presents a significant setback in the treatment of neurodegenerative disorders such as Alzheimer's disease (AD), hence the need for compounds that can adhere strictly to the selective criteria of suitable CNS drugs. Collapsin response mediator protein-2 (CRMP-2) has been recently identified as a viable target in neurotherapeutics due to its involvement in the etiology of AD. As shown in previous studies, Naringenin (NAR), a small molecule derivative of Drynaria rhizome (DR) extract, specifically binds CRMP-2 and reduces its phosphorylation. This was shown to facilitate axonal regrowth, with improvement in cognition and learning. Herein, we report the first account of the use of cheminformatics techniques to define the CNS drug-suitability of NAR using selective criteria, coupled with the prediction of possible biological activities and toxicities. Also, we evaluated the mechanistic activity of NAR by modeling its molecular interaction with human CRMP-2 (hCRMP-2). Physicochemical analyses revealed the suitability of NAR as a CNS drug and its ability to transverse the BBB. Possible neurogenic, anti-carcinogenic and cardioprotective activities were also predicted. NAR exhibited favorable binding to CRMP-2 and formed strong bonds with active site residues, which accounts for its stabilization and affinity. Moreover, NAR induced notable conformational changes in CRMP-2, an occurrence that could possibly disrupt kinase-mediated phosphorylation. These findings will aid in the optimization of NAR and improve its neurotherapeutic activities in the treatment of AD.

Cheminformatics Driven Development of Novel Therapies for Drug Resistant Prostate Cancer.

Androgen receptor (AR) is a master regulator of prostate cancer (PCa), and therefore is a pivotal drug target for the treatment of PCa including its castration-resistance form (CRPC). The development of acquired resistance is a major challenge in the use of the current antiandrogens. The recent advancements in inhibiting AR activity with small molecules specifically designed to target areas distinct from the receptor's androgen binding site are carefully discussed. Our new classes of AR inhibitors of AF2 and BF3 functional sites and DBD domains designed using cheminformatics techniques are promising to circumvent various AR-dependent resistance mechanisms.

Machine-learning-assisted materials discovery using failed experiments.

Inorganic-organic hybrid materials such as organically templated metal oxides, metal-organic frameworks (MOFs) and organohalide perovskites have been studied for decades, and hydrothermal and (non-aqueous) solvothermal syntheses have produced thousands of new materials that collectively contain nearly all the metals in the periodic table. Nevertheless, the formation of these compounds is not fully understood, and development of new compounds relies primarily on exploratory syntheses. Simulation- and data-driven approaches (promoted by efforts such as the Materials Genome Initiative) provide an alternative to experimental trial-and-error. Three major strategies are: simulation-based predictions of physical properties (for example, charge mobility, photovoltaic properties, gas adsorption capacity or lithium-ion intercalation) to identify promising target candidates for synthetic efforts; determination of the structure-property relationship from large bodies of experimental data, enabled by integration with high-throughput synthesis and measurement tools; and clustering on the basis of similar crystallographic structure (for example, zeolite structure classification or gas adsorption properties). Here we demonstrate an alternative approach that uses machine-learning algorithms trained on reaction data to predict reaction outcomes for the crystallization of templated vanadium selenites. We used information on 'dark' reactions--failed or unsuccessful hydrothermal syntheses--collected from archived laboratory notebooks from our laboratory, and added physicochemical property descriptions to the raw notebook information using cheminformatics techniques. We used the resulting data to train a machine-learning model to predict reaction success. When carrying out hydrothermal synthesis experiments using previously untested, commercially available organic building blocks, our machine-learning model outperformed traditional human strategies, and successfully predicted conditions for new organically templated inorganic product formation with a success rate of 89 per cent. Inverting the machine-learning model reveals new hypotheses regarding the conditions for successful product formation.

Using Cheminformatics in Drug Discovery.

This chapter illustrates how cheminformatics can be applied to designing novel compounds that are active at the primary target and have good predicted ADMET properties. Examples of various cheminformatics techniques are illustrated in the process of designing inhibitors that inhibit both cyclooxygenase isoforms but are more potent toward COX-2. The first step in the process is to create a knowledge database of cyclooxygenase inhibitors in the public domain. This data was analyzed to find activity cliffs - small structural changes that result in drastic changes in potency. Additional cyclooxygenase potency and selectivity trends were obtained using matched molecular pair analysis. QSAR models were then developed to predict cyclooxygenase potency and selectivity. Next, computational algorithms were used to generate novel scaffolds starting from known cyclooxygenase inhibitors. Nine virtual libraries containing 240 compounds each were constructed. Predictions from the cyclooxygenase QSAR models were used to eliminate molecules with undesirable potency or selectivity. Additionally, the compounds were screened in silico for undesirable ADMET properties, e.g., low solubility, permeability, metabolic stability, or high toxicity, using a liability scoring system known as ADMET Risk™. Eight synthetic candidates were identified from this process after incorporating knowledge gained from activity cliff analysis. Four of the compounds were synthesized and tested to measure their COX-1 and COX-2 IC(50) values as well as several ADME properties. The best compound, SLP0020, had a COX-1 IC(50) of 770 nM and COX-2 IC(50) of 130 nM.

Anti-cancer Drug Development: Computational Strategies to Identify and Target Proteins Involved in Cancer Metabolism

Cancer remains a fundamental burden to public health despite substantial efforts aimed at developing effective chemotherapeutics and significant advances in chemotherapeutic regimens. The major challenge in anti-cancer drug design is to selectively target cancer cells with high specificity. Research into treating malignancies by targeting altered metabolism in cancer cells is supported by computational approaches, which can take a leading role in identifying candidate targets for anti-cancer therapy as well as assist in the discovery and optimisation of anti-cancer agents. Natural products appear to have privileged structures for anti-cancer drug development and the bulk of this particularly valuable chemical space still remains to be explored. In this review we aim to provide a comprehensive overview of current strategies for computer-guided anti-cancer drug development. We start with a discussion of state-of-the art bioinformatics methods applied to the identification of novel anti-cancer targets, including machine learning techniques, the Connectivity Map and biological network analysis. This is followed by an extensive survey of molecular modelling and cheminformatics techniques employed to develop agents targeting proteins involved in the glycolytic, lipid, NAD+, mitochondrial (TCA cycle), amino acid and nucleic acid metabolism of cancer cells. A dedicated section highlights the most promising strategies to develop anti-cancer therapeutics from natural products and the role of metabolism and some of the many targets which are under investigation are reviewed. Recent success stories are reported for all the areas covered in this review. We conclude with a brief summary of the most interesting strategies identified and with an outlook on future directions in anti-cancer drug development.

Anti-cancer Drug Development: Computational Strategies to Identify and Target Proteins Involved in Cancer Metabolism

Cancer remains a fundamental burden to public health despite substantial efforts aimed at developing effective chemotherapeutics and significant advances in chemotherapeutic regimens. The major challenge in anti-cancer drug design is to selectively target cancer cells with high specificity. Research into treating malignancies by targeting altered metabolism in cancer cells is supported by computational approaches, which can take a leading role in identifying candidate targets for anti-cancer therapy as well as assist in the discovery and optimisation of anti-cancer agents. Natural products appear to have privileged structures for anti-cancer drug development and the bulk of this particularly valuable chemical space still remains to be explored. In this review we aim to provide a comprehensive overview of current strategies for computer-guided anti-cancer drug development. We start with a discussion of state-of-the art bioinformatics methods applied to the identification of novel anti-cancer targets, including machine learning techniques, the Connectivity Map and biological network analysis. This is followed by an extensive survey of molecular modelling and cheminformatics techniques employed to develop agents targeting proteins involved in the glycolytic, lipid, NAD+, mitochondrial (TCA cycle), amino acid and nucleic acid metabolism of cancer cells. A dedicated section highlights the most promising strategies to develop anti-cancer therapeutics from natural products and the role of metabolism and some of the many targets which are under investigation are reviewed. Recent success stories are reported for all the areas covered in this review. We conclude with a brief summary of the most interesting strategies identified and with an outlook on future directions in anti-cancer drug development.

Simple Structure-Based Approach for Predicting the Activity of Inhibitors of Beta-Secretase (BACE1) Associated with Alzheimer’s Disease

Beta-site amyloid precursor protein cleaving enzyme-1 (BACE1) is a target of interest for treating patients with Alzheimer's disease (AD). Inhibition of BACE1 may prevent amyloid-ß (Aß) plaque formation and the development or progression of Alzheimer's disease. Known BACE1 inhibitors were analyzed using computational chemistry and cheminformatics techniques to search for quantitative structure-activity relationships (QSAR). A remarkable relationship was found with only two simple descriptors. The square of the linear correlation coefficient r(2) is 0.75. The main descriptor is the number of hydrophobic contacts in the range 4-5 Å between the atoms of the ligand and active site. The other descriptor is the number of short (<2.8 Å) hydrogen bonds. Our approach uses readily available structural data on protein-inhibitor complexes in the Protein Data Bank (PDB) but would be equally applicable to proprietary structural biology data. The findings can aid structure-based design of improved BACE-1 inhibitors. If an inhibitor has less observed activity than predicted by our correlation, the compound should be retested because the first assay may have underestimated the compound's true activity.

Anti-cancer Drug Development: Computational Strategies to Identify and Target Proteins Involved in Cancer Metabolism

Cancer remains a fundamental burden to public health despite substantial efforts aimed at developing effective chemotherapeutics and significant advances in chemotherapeutic regimens. The major challenge in anti-cancer drug design is to selectively target cancer cells with high specificity. Research into treating malignancies by targeting altered metabolism in cancer cells is supported by computational approaches, which can take a leading role in identifying candidate targets for anti-cancer therapy as well as assist in the discovery and optimisation of anti-cancer agents. Natural products appear to have privileged structures for anti-cancer drug development and the bulk of this particularly valuable chemical space still remains to be explored. In this review we aim to provide a comprehensive overview of current strategies for computer-guided anti-cancer drug development. We start with a discussion of state-of-the art bioinformatics methods applied to the identification of novel anti-cancer targets, including machine learning techniques, the Connectivity Map and biological network analysis. This is followed by an extensive survey of molecular modelling and cheminformatics techniques employed to develop agents targeting proteins involved in the glycolytic, lipid, NAD+, mitochondrial (TCA cycle), amino acid and nucleic acid metabolism of cancer cells. A dedicated section highlights the most promising strategies to develop anti-cancer therapeutics from natural products and the role of metabolism and some of the many targets which are under investigation are reviewed. Recent success stories are reported for all the areas covered in this review. We conclude with a brief summary of the most interesting strategies identified and with an outlook on future directions in anti-cancer drug development.

Combinatorial × Computational × Cheminformatics (C3) Approach to Characterization of Congeneric Libraries of Organic Pollutants

Congeners are molecules based on the same carbon skeleton but are different by the number of substituents and/or a substitution pattern. Examples are 1-chloronaphthalene, 1,4-dichloronaphthalene, and 1,3,8-trichloronaphthalene. Various persistent organic pollutants (POPs) exist in the environment as families of congeners. Very large numbers of possible congeners make their experimental characterization and risk assessment unfeasible. Computational high-throughput and quantitative structure-property relationship (QSPR) modeling has been limited by the lack of tools and approaches facilitating analysis of such POP families. We present a comprehensive approach that enables modeling of extremely large congeneric libraries. The approach involves three steps: (1) combinatorial generation of a library of congeners, (2) quantum chemical characterization of each structure at the PM6 semiempirical level to obtain molecular descriptors, and (3) analysis of the information generated in step 2. In steps 1-3, we employ combinatorial, computational, and cheminformatics techniques, respectively. Therefore, this hybrid approach is named "Combinatorial × Computational × Cheminformatics", or just abbreviated as C(3) (or C-cubed) approach. We demonstrate the usefulness of this approach by generating and characterizing Br- and Cl-substituted congeneric families of 23 typical POPs. The analysis of the resulting set of 1 840 951 congeners that includes Cl-, Br-, and mixed Br/Cl-substituted species, proves that, based on structural similarities defined by the molecular descriptors' values, the existing QSPR models developed originally for Cl- and Br-substituted congeners can be applied also to mixed Br/Cl-substituted ones. Thus, the C(3) approach may serve as a tool for exploring structural applicability domains of the existing QSPR models for congeneric sets.

Computational metabolomics – a field at the boundaries of cheminformatics and bioinformatics

Metabolomics studies the occurrence and change of concentrations of small molecular weight chemical compounds (metabolites) in organisms, organs, tissues, cells and ultimately cell compartments in the context of environmental changes, disease or other boundary conditions. It does this by means of spectroscopic and chromatographic techniques and by observing at once not only a few but all compounds visible to the particular technique used. As such, it is not only the third large Omics area of research next to Genomics and Proteomics, but also a field at the boundary between chemistry and biology, helping to answer biological questions using analytical chemistry and cheminformatics techniques. This talk gives an overview about the field of Metabolomics and the challenges we face with respect to computing, informatics as well as chemical information aspects of metabolomics, including the analysis of metabolomics experiments, metabolomics databases, computer-assisted structure elucidation of metabolites and more. We give examples from our own research where we have for instance used machine learning techniques to predict NMR spectra of biological metabolites and applied those in computer-assisted structure elucidation systems for metabolites [1-3] – a classical area of cheminformatics which is now seeing a renaissance in the context of metabolomics. We further outline the data-collection challenges currently being address by the development of the MetaboLights database in our group at the European Bioinformatics Institute.

Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research

Molecular modelers and cheminformaticians typically analyze experimental data generated by other scientists. Consequently, when it comes to data accuracy, cheminformaticians are always at the mercy of data providers who may inadvertently publish (partially) erroneous data. Thus, dataset curation is crucial for any cheminformatics analysis such as similarity searching, clustering, QSAR modeling, virtual screening, etc., especially nowadays when the availability of chemical datasets in public domain has skyrocketed in recent years. Despite the obvious importance of this preliminary step in the computational analysis of any dataset, there appears to be no commonly accepted guidance or set of procedures for chemical data curation. The main objective of this paper is to emphasize the need for a standardized chemical data curation strategy that should be followed at the onset of any molecular modeling investigation. Herein, we discuss several simple but important steps for cleaning chemical records in a database including the removal of a fraction of the data that cannot be appropriately handled by conventional cheminformatics techniques. Such steps include the removal of inorganic and organometallic compounds, counterions, salts and mixtures; structure validation; ring aromatization; normalization of specific chemotypes; curation of tautomeric forms; and the deletion of duplicates. To emphasize the importance of data curation as a mandatory step in data analysis, we discuss several case studies where chemical curation of the original “raw” database enabled the successful modeling study (specifically, QSAR analysis) or resulted in a significant improvement of model's prediction accuracy. We also demonstrate that in some cases rigorously developed QSAR models could be even used to correct erroneous biological data associated with chemical compounds. We believe that good practices for curation of chemical records outlined in this paper will be of value to all scientists working in the fields of molecular modeling, cheminformatics, and QSAR studies.

Cheminformatics Analysis of Assertions Mined from Literature That Describe Drug-Induced Liver Injury in Different Species

Drug-induced liver injury is one of the main causes of drug attrition. The ability to predict the liver effects of drug candidates from their chemical structures is critical to help guide experimental drug discovery projects toward safer medicines. In this study, we have compiled a data set of 951 compounds reported to produce a wide range of effects in the liver in different species, comprising humans, rodents, and nonrodents. The liver effects for this data set were obtained as assertional metadata, generated from MEDLINE abstracts using a unique combination of lexical and linguistic methods and ontological rules. We have analyzed this data set using conventional cheminformatics approaches and addressed several questions pertaining to cross-species concordance of liver effects, chemical determinants of liver effects in humans, and the prediction of whether a given compound is likely to cause a liver effect in humans. We found that the concordance of liver effects was relatively low (ca. 39-44%) between different species, raising the possibility that species specificity could depend on specific features of chemical structure. Compounds were clustered by their chemical similarity, and similar compounds were examined for the expected similarity of their species-dependent liver effect profiles. In most cases, similar profiles were observed for members of the same cluster, but some compounds appeared as outliers. The outliers were the subject of focused assertion regeneration from MEDLINE as well as other data sources. In some cases, additional biological assertions were identified, which were in line with expectations based on compounds' chemical similarities. The assertions were further converted to binary annotations of underlying chemicals (i.e., liver effect vs no liver effect), and binary quantitative structure-activity relationship (QSAR) models were generated to predict whether a compound would be expected to produce liver effects in humans. Despite the apparent heterogeneity of data, models have shown good predictive power assessed by external 5-fold cross-validation procedures. The external predictive power of binary QSAR models was further confirmed by their application to compounds that were retrieved or studied after the model was developed. To the best of our knowledge, this is the first study for chemical toxicity prediction that applied QSAR modeling and other cheminformatics techniques to observational data generated by the means of automated text mining with limited manual curation, opening up new opportunities for generating and modeling chemical toxicology data.

Natural product-likeness score and its applications in the drug discovery process

Natural products (NPs) – i.e. secondary metabolites of plants or lower organisms - have been optimized in a very long natural selection process for optimal interactions with biological macromolecules. NPs are therefore an excellent source of validated substructures for the design of novel bioactive molecules. Indeed, many drugs in the current pharmacopeias are NPs, NP derivatives, or are of NP origin. Various cheminformatics techniques can provide useful help in design of new bioactive molecules by taking into account information extracted from structures of NPs. In this presentation we describe a method to calculate natural product-likeness score - a Bayesian measure that characterizes similarity of molecules to the structural space covered by natural products. This score is shown to efficiently separate NPs from synthetic molecules in a cross-validation experiment. Possible applications of the NP-likeness score in drug discovery process are discussed, including virtual screening, prioritization of compound libraries towards NP-likeness and design of building blocks for the synthesis of NP-like combinatorial libraries.

Natural Product-likeness Score and Its Application for Prioritization of Compound Libraries

Natural products (NPs) have been optimized in a very long natural selection process for optimal interactions with biological macromolecules. NPs are therefore an excellent source of validated substructures for the design of novel bioactive molecules. Various cheminformatics techniques can provide useful help in analyzing NPs, and the results of such studies may be used with advantage in the drug discovery process. In the present study we describe a method to calculate the natural product-likeness score--a Bayesian measure which allows for the determination of how molecules are similar to the structural space covered by natural products. This score is shown to efficiently separate NPs from synthetic molecules in a cross-validation experiment. Possible applications of the NP-likeness score are discussed and illustrated on several examples including virtual screening, prioritization of compound libraries toward NP-likeness, and design of building blocks for the synthesis of NP-like libraries.