From docking false-positive to active anti-HIV agent

Key essential oil components delocalize Candida albicans Kar3p and impact microtubule structure

BACKGROUND

Treatment of Candida albicans associated infections is often ineffective in the light of resistance, with an urgent need to discover novel antimicrobials. Fungicides require high specificity and can contribute to antifungal resistance, so inhibition of fungal virulence factors is a good strategy for developing new antifungals.

OBJECTIVES

Examine the impact of four plant-derived essential oil components (1,8-cineole, α-pinene, eugenol, and citral) on C. albicans microtubules, kinesin motor protein Kar3 and morphology.

METHODS

Microdilution assays were used to determine minimal inhibitory concentrations, microbiological assays assessed germ tube, hyphal and biofilm formation, confocal microscopy probed morphological changes and localization of tubulin and Kar3p, and computational modelling was used to examine the theoretical binding of essential oil components to tubulin and Kar3p.

RESULTS

We show for the first time that essential oil components delocalize the Kar3p, ablate microtubules, and induce psuedohyphal formation with reduced biofilm formation. Single and double deletion mutants of kar3 were resistant to 1,8-cineole, sensitive to α-pinene and eugenol, but unimpacted by citral. Strains with homozygous and heterozygous Kar3p disruption had a gene-dosage effect for all essential oil components, resulting in enhanced resistance or susceptibility patterns that were identical to that of cik1 mutants. The link between microtubule (αβ-tubulin) and Kar3p defects was further supported by computational modeling, showing preferential binding to αβ-tubulin and Kar3p adjacent to their Mg²⁺-binding sites.

CONCLUSION

This study highlights how essential oil components interfere with the localization of the kinesin motor protein complex Kar3/Cik1 and disrupt microtubules, leading to their destabilization which results in hyphal and biofilm defects.

A molecular evolution algorithm for ligand design in DOCK

As a complement to virtual screening, de novo design of small molecules is an alternative approach for identifying potential drug candidates. Here, we present a new 3D genetic algorithm to evolve molecules through breeding, mutation, fitness pressure, and selection. The method, termed DOCK_GA, builds upon and leverages powerful sampling, scoring, and searching routines previously implemented into DOCK6. Three primary experiments were used during development: Single-molecule evolution evaluated three selection methods (elitism, tournament, and roulette), in four clinically relevant systems, in terms of mutation type and crossover success, chemical properties, ensemble diversity, and fitness convergence, among others. Large scale benchmarking assessed performance across 651 different protein-ligand systems. Ensemble-based evolution demonstrated using multiple inhibitors simultaneously to seed growth in a SARS-CoV-2 target. Key takeaways include: (1) The algorithm is robust as demonstrated by the successful evolution of molecules across a large diverse dataset. (2) Users have flexibility with regards to parent input, selection method, fitness function, and molecular descriptors. (3) The program is straightforward to run and only requires a single executable and input file at run-time. (4) The elitism selection method yields more tightly clustered molecules in terms of 2D/3D similarity, with more favorable fitness, followed by tournament and roulette.

Structure-Based Design of Antivirals against Envelope Glycoprotein of Dengue Virus

Dengue virus (DENV) presents a significant threat to global public health with more than 500,000 hospitalizations and 25,000 deaths annually. Currently, there is no clinically approved antiviral drug to treat DENV infection. The envelope (E) glycoprotein of DENV is a promising target for drug discovery as the E protein is important for viral attachment and fusion. Understanding the structure and function of DENV E protein has led to the exploration of structure-based drug discovery of antiviral compounds and peptides against DENV infections. This review summarizes the structural information of the DENV E protein with regards to DENV attachment and fusion. The information enables the development of antiviral agents through structure-based approaches. In addition, this review compares the potency of antivirals targeting the E protein with the antivirals targeting DENV multifunctional enzymes, repurposed drugs and clinically approved antiviral drugs. None of the current DENV antiviral candidates possess potency similar to the approved antiviral drugs which indicates that more efforts and resources must be invested before an effective DENV drug materializes.

De novo design approaches targeting an envelope protein pocket to identify small molecules against dengue virus

Dengue fever is a mosquito-borne viral disease that has become a major public health concern worldwide. This disease presents with a wide range of clinical manifestations, from a mild cold-like illness to the more serious hemorrhagic dengue fever and dengue shock syndrome. Currently, neither an approved drug nor an effective vaccine for the treatment are available to fight the disease. The envelope protein (E) is a major component of the virion surface. This protein plays a key role during the viral entry process, constituting an attractive target for the development of antiviral drugs. The crystal structure of the E protein reveals the existence of a hydrophobic pocket occupied by the detergent n-octyl-β-d-glucoside (β-OG). This pocket lies at the hinge region between domains I and II and is important for the low pH-triggered conformational rearrangement required for the fusion of the virion with the host's cell. Aiming at the design of novel molecules which bind to E and act as virus entry inhibitors, we undertook a de novo design approach by "growing" molecules inside the hydrophobic site (β-OG). From more than 240000 small-molecules generated, the 2,4 pyrimidine scaffold was selected as the best candidate, from which one synthesized compound displayed micromolar activity. Molecular dynamics-based optimization was performed on this hit, and thirty derivatives were designed in silico, synthesized and evaluated on their capacity to inhibit dengue virus entry into the host cell. Four compounds were found to be potent antiviral compounds in the low-micromolar range. The assessment of drug-like physicochemical and in vitro pharmacokinetic properties revealed that compounds 3e and 3h presented acceptable solubility values and were stable in mouse plasma, simulated gastric fluid, simulated intestinal fluid, and phosphate buffered saline solution.

An Overview of Molecular Modeling for Drug Discovery with Specific Illustrative Examples of Applications

In this paper we review the current status of high-performance computing applications in the general area of drug discovery. We provide an introduction to the methodologies applied at atomic and molecular scales, followed by three specific examples of implementation of these tools. The first example describes in silico modeling of the adsorption of small molecules to organic and inorganic surfaces, which may be applied to drug delivery issues. The second example involves DNA translocation through nanopores with major significance to DNA sequencing efforts. The final example offers an overview of computer-aided drug design, with some illustrative examples of its usefulness.

Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations

Accurate in silico prediction of protein-ligand binding affinities has been a primary objective of structure-based drug design for decades due to the putative value it would bring to the drug discovery process. However, computational methods have historically failed to deliver value in real-world drug discovery applications due to a variety of scientific, technical, and practical challenges. Recently, a family of approaches commonly referred to as relative binding free energy (RBFE) calculations, which rely on physics-based molecular simulations and statistical mechanics, have shown promise in reliably generating accurate predictions in the context of drug discovery projects. This advance arises from accumulating developments in the underlying scientific methods (decades of research on force fields and sampling algorithms) coupled with vast increases in computational resources (graphics processing units and cloud infrastructures). Mounting evidence from retrospective validation studies, blind challenge predictions, and prospective applications suggests that RBFE simulations can now predict the affinity differences for congeneric ligands with sufficient accuracy and throughput to deliver considerable value in hit-to-lead and lead optimization efforts. Here, we present an overview of current RBFE implementations, highlighting recent advances and remaining challenges, along with examples that emphasize practical considerations for obtaining reliable RBFE results. We focus specifically on relative binding free energies because the calculations are less computationally intensive than absolute binding free energy (ABFE) calculations and map directly onto the hit-to-lead and lead optimization processes, where the prediction of relative binding energies between a reference molecule and new ideas (virtual molecules) can be used to prioritize molecules for synthesis. We describe the critical aspects of running RBFE calculations, from both theoretical and applied perspectives, using a combination of retrospective literature examples and prospective studies from drug discovery projects. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative binding free energy simulations, with a focus on real-world drug discovery applications. We offer guidelines for improving the accuracy of RBFE simulations, especially for challenging cases, and emphasize unresolved issues that could be improved by further research in the field.

Customizable de novo design strategies for DOCK: Application to HIVgp41 and other therapeutic targets

De novo design can be used to explore vast areas of chemical space in computational lead discovery. As a complement to virtual screening, from-scratch construction of molecules is not limited to compounds in pre-existing vendor catalogs. Here, we present an iterative fragment growth method, integrated into the program DOCK, in which new molecules are built using rules for allowable connections based on known molecules. The method leverages DOCK's advanced scoring and pruning approaches and users can define very specific criteria in terms of properties or features to customize growth toward a particular region of chemical space. The code was validated using three increasingly difficult classes of calculations: (1) Rebuilding known X-ray ligands taken from 663 complexes using only their component parts (focused libraries), (2) construction of new ligands in 57 drug target sites using a library derived from ∼13M drug-like compounds (generic libraries), and (3) application to a challenging protein-protein interface on the viral drug target HIVgp41. The computational testing confirms that the de novo DOCK routines are robust and working as envisioned, and the compelling results highlight the potential utility for designing new molecules against a wide variety of important protein targets. © 2017 Wiley Periodicals, Inc.

Computational methods in drug discovery

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

Computer-aided discovery of anti-HIV agents

A review is provided on efforts in our laboratory over the last decade to discover anti-HIV agents. The work has focused on computer-aided design and synthesis of non-nucleoside inhibitors of HIV-1 reverse transcriptase (NNRTIs) with collaborative efforts on biological assaying and protein crystallography. Numerous design issues were successfully addressed including the need for potency against a wide range of viral variants, good aqueous solubility, and avoidance of electrophilic substructures. Computational methods including docking, de novo design, and free-energy perturbation (FEP) calculations made essential contributions. The result is novel NNRTIs with picomolar and low-nanomolar activities against wild-type HIV-1 and key variants that also show much improved solubility and lower cytotoxicity than recently approved drugs in the class.

Computational drug design strategies applied to the modelling of human immunodeficiency virus-1 reverse transcriptase inhibitors

Reverse transcriptase (RT) is a multifunctional enzyme in the human immunodeficiency virus (HIV)-1 life cycle and represents a primary target for drug discovery efforts against HIV-1 infection. Two classes of RT inhibitors, the nucleoside RT inhibitors (NRTIs) and the nonnucleoside transcriptase inhibitors are prominently used in the highly active antiretroviral therapy in combination with other anti-HIV drugs. However, the rapid emergence of drug-resistant viral strains has limited the successful rate of the anti-HIV agents. Computational methods are a significant part of the drug design process and indispensable to study drug resistance. In this review, recent advances in computer-aided drug design for the rational design of new compounds against HIV-1 RT using methods such as molecular docking, molecular dynamics, free energy calculations, quantitative structure-activity relationships, pharmacophore modelling and absorption, distribution, metabolism, excretion and toxicity prediction are discussed. Successful applications of these methodologies are also highlighted.

A Fluorescence Polarization Assay for Binding to Macrophage Migration Inhibitory Factor and Crystal Structures for Complexes of Two Potent Inhibitors

Human macrophage migration inhibitory factor (MIF) is both a keto–enol tautomerase and a cytokine associated with numerous inflammatory diseases and cancer. Consistent with observed correlations between inhibition of the enzymatic and biological activities, discovery of MIF inhibitors has focused on monitoring the tautomerase activity using l-dopachrome methyl ester or 4-hydroxyphenyl pyruvic acid as substrates. The accuracy of these assays is compromised by several issues including substrate instability, spectral interference, and short linear periods for product formation. In this work, we report the syntheses of fluorescently labeled MIF inhibitors and their use in the first fluorescence polarization-based assay to measure the direct binding of inhibitors to the active site. The assay allows the accurate and efficient identification of competitive, noncompetitive, and covalent inhibitors of MIF in a manner that can be scaled for high-throughput screening. The results for 22 compounds show that the most potent MIF inhibitors bind with K_d values of ca. 50 nM; two are from our laboratory, and the other is a compound from the patent literature. X-ray crystal structures for two of the most potent compounds bound to MIF are also reported here. Striking combinations of protein–ligand hydrogen bonding, aryl–aryl, and cation−π interactions are responsible for the high affinities. A new chemical series was then designed using this knowledge to yield two more strong MIF inhibitors/binders.

1,6-Bis[(benzyloxy)methyl]uracil derivatives-Novel antivirals with activity against HIV-1 and influenza H1N1 virus

A series of 1,6-bis[(benzyloxy)methyl]uracil derivatives combining structural features of both diphenyl ether and pyridone types of NNRTIs were synthesized. Target compounds were found to inhibit HIV-1 reverse transcriptase at micro- and submicromolar levels of concentrations and exhibited anti-HIV-1 activity in MT-4 cell culture, demonstrating resistance profile similar to first generation NNRTIs. The synthesized compounds also showed profound activity against influenza virus (H1N1) in MDCK cell culture without detectable cytotoxicity. The lead compound of this assay appeared to exceed rimantadine, amantadine, ribavirin and oseltamivir carboxylate in activity. The mechanism of action of 1,6-bis[(benzyloxy)methyl]uracils against influenza virus is currently under investigation.

Design, discovery, modelling, synthesis, and biological evaluation of novel and small, low toxicity s-triazine derivatives as HIV-1 non-nucleoside reverse transcriptase inhibitors

A set of top-ranked compounds from a multi-objective in silico screen was experimentally tested for toxicity and the ability to inhibit the activity of HIV-1 reverse transcriptase (RT) in cell-free assay and in cell-based assay using HIV-1 based virus-like particles. Detailed analysis of a commercial sample that indicated specific inhibition of HIV-1 reverse transcription revealed that a minor component that was structurally similar to that of the main compound was responsible for the strongest inhibition. As a result, novel s-triazine derivatives were proposed, modelled, discovered, and synthesised, and their antiviral activity and cellular toxicity were tested. Compounds 18a and 18b were found to be efficient HIV-1 RT inhibitors, with an IC50 of 5.6±1.1μM and 0.16±0.05μM in a cell-based assay using infectious HIV-1, respectively. Compound 18b also had no detectable toxicity for different human cell lines. Their binding mode and interactions with the RT suggest that there was strong and adaptable binding in a tight (NNRTI) hydrophobic pocket. In summary, this iterative study produced structural clues and led to a group of non-toxic, novel compounds to inhibit HIV-RT with up to nanomolar potency.

Synthesis of 2-amino-1,3,4-oxadiazoles and 2-amino-1,3,4-thiadiazoles via sequential condensation and I2-mediated oxidative C–O/C–S bond formation

2-Amino-substituted 1,3,4-oxadiazoles and 1,3,4-thiadiazoles were synthesized via condensation of semicarbazide/thiosemicarbazide and the corresponding aldehydes followed by I2-mediated oxidative C–O/C–S bond formation. This transition-metal-free sequential synthesis process is compatible with aromatic, aliphatic, and cinnamic aldehydes, providing facile access to a variety of diazole derivatives bearing a 2-amino substituent in an efficient and scalable fashion.

Pd-catalyzed oxidative annulation of hydrazides with isocyanides: synthesis of 2-amino-1,3,4-oxadiazoles

An efficient palladium-catalyzed oxidative annulation reaction was developed through sequential isocyanide insertions into N-H and O-H bonds of hydrazides, which provides an efficient access to valuable 2-amino-1,3,4-oxadiazoles and their derivatives.

LIBSA--a method for the determination of ligand-binding preference to allosteric sites on receptor ensembles

Incorporation of receptor flexibility into computational drug discovery through the relaxed complex scheme is well suited for screening against a single binding site. In the absence of a known pocket or if there are multiple potential binding sites, it may be necessary to do docking against the entire surface of the target (global docking). However no suitable and easy-to-use tool is currently available to rank global docking results based on the preference of a ligand for a given binding site. We have developed a protocol, termed LIBSA for LIgand Binding Specificity Analysis, that analyzes multiple docked poses against a single or ensemble of receptor conformations and returns a metric for the relative binding to a specific region of interest. By using novel filtering algorithms and the signal-to-noise ratio (SNR), the relative ligand-binding frequency at different pockets can be calculated and compared quantitatively. Ligands can then be triaged by their tendency to bind to a site instead of ranking by affinity alone. The method thus facilitates screening libraries of ligand cores against a large library of receptor conformations without prior knowledge of specific pockets, which is especially useful to search for hits that selectively target a particular site. We demonstrate the utility of LIBSA by showing that it correctly identifies known ligand binding sites and predicts the relative preference of a set of related ligands for different pockets on the same receptor.

In silico pharmacology for a multidisciplinary drug discovery process

The process of bringing new and innovative drugs, from conception and synthesis through to approval on the market can take the pharmaceutical industry 8-15 years and cost approximately $1.8 billion. Two key technologies are improving the hit-to-drug timeline: high-throughput screening (HTS) and rational drug design. In the latter case, starting from some known ligand-based or target-based information, a lead structure will be rationally designed to be tested in vitro or in vivo. Computational methods are part of many drug discovery programs, including the assessment of ADME (absorption-distribution-metabolism-excretion) and toxicity (ADMET) properties of compounds at the early stages of discovery/development with impressive results. The aim of this paper is to review, in a simple way, some of the most popular strategies used by modelers and some successful applications on computational chemistry to raise awareness of its importance and potential for an actual multidisciplinary drug discovery process.

Designing a ligand for pharmaceutical purposes

BACKGROUND

Drug approval applications to the FDA have shown a remarkably small increment compared with what was expected. In the last few years several efforts have been made to improve the results of rational drug design approaches and in particular to predict inhibitor-target structure and to evaluate the free energy of binding. Virtual database screening, combined with other computational methods, is one of the most promising methods to overcome this key issue.

OBJECTIVE

It is possible to understand how computational medicinal chemistry is changing, improving from its errors and moving towards becoming a more important tool for drug development.

METHODS

Some of the most recent modeling techniques have been presented and in particular the benefits of combining these techniques are highlighted.

RESULTS/CONCLUSION

At present computational chemists can understand the peculiar problems associated with the study of biological systems and on this basis they can choose the right collection of complementary in silico approaches to solve the medicinal chemistry problem in a better manner.

Grid-based molecular footprint comparison method for docking and de novo design: application to HIVgp41

Scoring functions are a critically important component of computer-aided screening methods for the identification of lead compounds during early stages of drug discovery. Here, we present a new multigrid implementation of the footprint similarity (FPS) scoring function that was recently developed in our laboratory which has proven useful for identification of compounds which bind to a protein on a per-residue basis in a way that resembles a known reference. The grid-based FPS method is much faster than its Cartesian-space counterpart, which makes it computationally tractable for on-the-fly docking, virtual screening, or de novo design. In this work, we establish that: (i) relatively few grids can be used to accurately approximate Cartesian space footprint similarity, (ii) the method yields improved success over the standard DOCK energy function for pose identification across a large test set of experimental co-crystal structures, for crossdocking, and for database enrichment, and (iii) grid-based FPS scoring can be used to tailor construction of new molecules to have specific properties, as demonstrated in a series of test cases targeting the viral protein HIVgp41. The method is available in the program DOCK6.

Identification of HIV inhibitors guided by free energy perturbation calculations

Free energy perturbation (FEP) theory coupled to molecular dynamics (MD) or Monte Carlo (MC) statistical mechanics offers a theoretically precise method for determining the free energy differences of related biological inhibitors. Traditionally requiring extensive computational resources and expertise, it is only recently that its impact is being felt in drug discovery. A review of computer-aided anti-HIV efforts employing FEP calculations is provided here that describes early and recent successes in the design of human immunodeficiency virus type 1 (HIV-1) protease and non-nucleoside reverse transcriptase inhibitors. In addition, our ongoing work developing and optimizing leads for small molecule inhibitors of cyclophilin A (CypA) is highlighted as an update on the current capabilities of the field. CypA has been shown to aid HIV-1 replication by catalyzing the cis/trans isomerization of a conserved Gly-Pro motif in the Nterminal domain of HIV-1 capsid (CA) protein. In the absence of a functional CypA, e.g., by the addition of an inhibitor such as cyclosporine A (CsA), HIV-1 has reduced infectivity. Our simulations of acylurea-based and 1-indanylketone-based CypA inhibitors have determined that their nanomolar and micromolar binding affinities, respectively, are tied to their ability to stabilize Arg55 and Asn102. A structurally novel 1-(2,6-dichlorobenzamido) indole core was proposed to maximize these interactions. FEP-guided optimization, experimental synthesis, and biological testing of lead compounds for toxicity and inhibition of wild-type HIV-1 and CA mutants have demonstrated a dose-dependent inhibition of HIV-1 infection in two cell lines. While the inhibition is modest compared to CsA, the results are encouraging.

A ligand-based approach for the in silico discovery of multi-target inhibitors for proteins associated with HIV infection

Acquired immunodeficiency syndrome (AIDS) is a dangerous disease, which damages the immune system cells to the point that the immune system can no longer fight against other infections that it would usually be able to prevent. The causal agent is the human immunodeficiency virus (HIV), and for this reason, the search for more effective chemotherapies against HIV is a challenge for the scientific community. Chemoinformatics and Quantitative Structure-Activity Relationship (QSAR) studies have played an essential role in the design of potent inhibitors for proteins associated with the HIV infection. However, all previous studies took into consideration the discovery of future drug candidates using homogeneous series of compounds against only one protein. This fact limits the use of more efficient anti-HIV chemotherapies. In this work, we develop the first ligand-based approach for the in silico design of multi-target (mt) inhibitors for seven key proteins associated with the HIV infection. Two mt-QSAR models were constructed from a large and heterogeneous database of compounds. The first model was based on linear discriminant analysis (mt-QSAR-LDA) employing fragment-based descriptors. The second model was obtained using artificial neural networks (mt-QSAR-ANN) with global 2D descriptors. Both models correctly classified more than 90% of active and inactive compounds in training and prediction sets. Some fragments were extracted and their contributions to anti-HIV activity through inhibition of the different proteins were calculated using the mt-QSAR-LDA model. New molecules designed from fragments with positive contributions were suggested and correctly predicted by the two models as possible potent and versatile anti-HIV agents.

In silico fragment-based drug design using a PASS approach

Fragment-based drug design integrates different methods to create novel ligands using fragment libraries focused on particular biological activities. Experimental approaches to the preparation of fragment libraries have some drawbacks caused by the need for target crystallization (X-ray and nuclear magnetic resonance) and careful immobilization (surface plasmon resonance). Molecular modelling (docking) requires accurate data on protein-ligand interactions, which are difficult to obtain for some proteins. The main drawbacks of QSAR application are associated with the need to collect large homogeneous datasets of chemical structures with experimentally determined self-consistent quantitative values (potency). We propose a ligand-based approach to the selection of fragments with positive contribution to biological activity, developed on the basis of the PASS algorithm. The robustness of the PASS algorithm for heterogeneous datasets has been shown earlier. PASS estimates qualitative (yes/no) prediction of biological activity spectra for over 4000 biological activities and, therefore, provides the basis for the preparation of a fragment library corresponding to multiple criteria. The algorithm for fragment selection has been validated using the fractions of intermolecular interactions calculated for known inhibitors of nine enzymes extracted from the Protein Data Bank database. The statistical significance of differences between fractions of intermolecular interactions corresponds, for several enzymes, to the estimated positive and negative contribution of fragments in enzyme inhibition.

Rational prediction with molecular dynamics for hit identification

Although the motions of proteins are fundamental for their function, for pragmatic reasons, the consideration of protein elasticity has traditionally been neglected in drug discovery and design. This review details protein motion, its relevance to biomolecular interactions and how it can be sampled using molecular dynamics simulations. Within this context, two major areas of research in structure-based prediction that can benefit from considering protein flexibility, binding site detection and molecular docking, are discussed. Basic classification metrics and statistical analysis techniques, which can facilitate performance analysis, are also reviewed. With hardware and software advances, molecular dynamics in combination with traditional structure-based prediction methods can potentially reduce the time and costs involved in the hit identification pipeline.

Classification of anti-HIV compounds using counterpropagation artificial neural networks and decision trees

The main aim of the present work was to collect and categorize anti-HIV molecules in order to identify general structure-activity relationships. In this respect, a total of 5580 drugs and drug-like molecules was collected from 256 different articles published between 1992 and 2010. An algorithm called genetic algorithm-pattern search counterpropagation artificial neural networks (GPS-CPANN) was proposed for the classification of compounds. In addition, the CART (classification and regression trees) method was used for construction of decision trees and finding the best molecular descriptors. The results revealed that the developed CPANN models and decision tree can correctly classify the molecules according to their inhibition mechanisms and activities. Some general parameters such as molecular weight, average molecular weight, number of hydrogen atoms and number of hydroxyl groups were found to be important for describing the inhibition behaviour of anti-HIV agents. The developed classifier models in this work can be used to screen large libraries of compounds to identify those likely to display activity as anti-HIV agents.

HIV-1 NNRTIs: structural diversity, pharmacophore similarity, and implications for drug design

Nonnucleoside reverse transcriptase inhibitors (NNRTIs) nowadays represent very potent and most promising anti-AIDS agents that specifically target the HIV-1 reverse transcriptase (RT). However, the effectiveness of NNRTI drugs can be hampered by rapid emergence of drug-resistant viruses and severe side effects upon long-term use. Therefore, there is an urgent need to develop novel, highly potent NNRTIs with broad spectrum antiviral activity and improved pharmacokinetic properties, and more efficient strategies that facilitate and shorten the drug discovery process would be extremely beneficial. Fortunately, the structural diversity of NNRTIs provided a wide space for novel lead discovery, and the pharmacophore similarity of NNRTIs gave valuable hints for lead discovery and optimization. More importantly, with the continued efforts in the development of computational tools and increased crystallographic information on RT/NNRTI complexes, structure-based approaches using a combination of traditional medicinal chemistry, structural biology, and computational chemistry are being used increasingly in the design of NNRTIs. First, this review covers two decades of research and development for various NNRTI families based on their chemical scaffolds, and then describes the structural similarity of NNRTIs. We have attempted to assemble a comprehensive overview of the general approaches in NNRTI lead discovery and optimization reported in the literature during the last decade. The successful applications of medicinal chemistry strategies, crystallography, and computational tools for designing novel NNRTIs are highlighted. Future directions for research are also outlined.

Some Trends in Chem(o)informatics

This introductory chapter gives a brief overview of the history of cheminformatics, and then summarizes some recent trends in computing, cultures, open systems, chemical structure representation, docking, de novo design, fragment-based drug design, molecular similarity, quantitative structure-activity relationships (QSAR), metabolite prediction, the use of phamacophores in drug discovery, data reduction and visualization, and text mining. The aim is to set the scene for the more detailed exposition of these topics in the later chapters.

Effects of Water Placement on Predictions of Binding Affinities for p38α MAP Kinase Inhibitors

Monte Carlo free energy perturbation (MC/FEP) calculations have been applied to compute the relative binding affinities of 17 congeneric pyridazo-pyrimidinone inhibitors of the protein p38α MAP kinase. Overall correlation with experiment was found to be modest when the complexes were hydrated using a traditional procedure with a stored solvent box. Significant improvements in accuracy were obtained when the MC/FEP calculations were repeated using initial solvent distributions optimized by the water placement algorithm JAWS. The results underscore the importance of accurate placement of water molecules in a ligand binding site for the reliable prediction of relative free energies of binding.

De novo design: balancing novelty and confined chemical space

IMPORTANCE OF THE FIELD

De novo drug design serves as a tool for the discovery of new ligands for macromolecular targets as well as optimization of known ligands. Recently developed tools aim to address the multi-objective nature of drug design in an unprecedented manner.

AREAS COVERED IN THIS REVIEW

This article discusses recent advances in de novo drug design programs and accessory programs used to evaluate compounds post-generation.

WHAT THE READER WILL GAIN

The reader is introduced to the challenges inherent in de novo drug design and will become familiar with current trends in de novo design. Furthermore, the reader will be better prepared to assess the value of a tool, and be equipped to design more elegant tools in the future.

TAKE HOME MESSAGE

De novo drug design can assist in the efficient discovery of new compounds with a high affinity for a given target. The inclusion of existing chemoinformatic methods with current structure-based de novo design tools provides a means of enhancing the therapeutic value of these generated compounds.

Eastern extension of azoles as non-nucleoside inhibitors of HIV-1 reverse transcriptase; cyano group alternatives

Design of non-nucleoside inhibitors of HIV-1 reverse transcriptase is being pursued with the assistance of free energy perturbation (FEP) calculations to predict relative free energies of binding. Extension of azole-containing inhibitors into an 'eastern' channel between Phe227 and Pro236 has led to the discovery of potent and structurally novel derivatives.

Discovery of novel fibroblast growth factor receptor 1 kinase inhibitors by structure-based virtual screening

Fibroblast growth factors (FGFs) play important roles in embryonic development, angiogenesis, wound healing, and cell proliferation and differentiation. In search of inhibitors of FGFR1 kinase, 2.2 million compounds were docked into the ATP binding site of the protein. A co-crystal structure, which shows two alternative conformations for the nucleotide binding loop, is reported. Docking was performed on both conformations and, ultimately, 23 diverse compounds were purchased and assayed. Following hit validation, two compounds 10 and 16, a benzylidene derivative of pseudothiohydantoin and a thienopyrimidinone derivative, respectively, were discovered that inhibit FGFR1 kinase with IC(50) values of 23 and 50 microM. Initial optimization of 16 led to the more unsaturated 40, which has significantly enhanced potency, 1.9 microM. The core structures represent new structural motifs for FGFR1 kinase inhibitors. The study also illustrates complexities associated with the choice of protein structures for docking, possible use of multiple kinase structures to seek selectivity, and hit identification.

Energetics of displacing water molecules from protein binding sites: consequences for ligand optimization

A strategy in drug design is to consider enhancing the affinity of lead molecules with structural modifications that displace water molecules from a protein binding site. Because success of the approach is uncertain, clarification of the associated energetics was sought in cases where similar structural modifications yield qualitatively different outcomes. Specifically, free-energy perturbation calculations were carried out in the context of Monte Carlo statistical mechanics simulations to investigate ligand series that feature displacement of ordered water molecules in the binding sites of scytalone dehydratase, p38-alphaMAP kinase, and EGFR kinase. The change in affinity for a ligand modification is found to correlate with the ease of displacement of the ordered water molecule. However, as in the EGFR example, the binding affinity may diminish if the free-energy increase due to the removal of the bound water molecule is not more than compensated by the additional interactions of the water-displacing moiety. For accurate computation of the effects of ligand modifications, a complete thermodynamic analysis is shown to be needed. It requires identification of the location of water molecules in the protein-ligand interface and evaluation of the free-energy changes associated with their removal and with the introduction of the ligand modification. Direct modification of the ligand in free-energy calculations is likely to trap the ordered molecule and provide misleading guidance for lead optimization.

Prediction of the water content in protein binding sites

An efficient molecular simulation methodology has been developed to determine the positioning of water molecules in the binding site of a protein or protein-ligand complex. Occupancies and absolute binding free energies of water molecules are computed using a statistical thermodynamics approach. The methodology, referred to as Just Add Water Molecules (JAWS), features "theta-water" molecules that can appear and disappear on a binding-site grid. Key approximations render the technique far more efficient than conventional free energy simulations. Testing of JAWS on five diverse examples (neuraminidase, scytalone dehydratase, major urinary protein 1, beta-lactoglobulin, and COX-2) demonstrates its accuracy in locating hydration sites in comparison to results from high-resolution crystal structures. Possible applications include aid in refinement of protein crystal structures, drug lead optimization, setup of docking calculations, and simulations of protein-ligand complexes.

Discovery of wild-type and Y181C mutant non-nucleoside HIV-1 reverse transcriptase inhibitors using virtual screening with multiple protein structures

To discover non-nucleoside inhibitors of HIV-1 reverse transcriptase (NNRTIs) that are effective against both wild-type (WT) virus and variants that encode the clinically troublesome Tyr181Cys (Y181C) RT mutation, virtual screening by docking was carried out using three RT structures and more than 2 million commercially available compounds. Two of the structures are for WT-virus with different conformations of Tyr181, while the third structure incorporates the Y181C modification. Eventually nine compounds were purchased and assayed. Three of the compounds show low-micromolar antiviral activity toward either or both the wild-type and Y181C HIV-1 strains. The study illustrates a viable protocol to seek anti-HIV agents with enhanced resistance profiles.

Efficient drug lead discovery and optimization

During the 1980s, advances in the abilities to perform computer simulations of chemical and biomolecular systems and to calculate free energy changes led to the expectation that such methodology would soon show great utility for guiding molecular design. Important potential applications included design of selective receptors, catalysts, and regulators of biological function including enzyme inhibitors. This time also saw the rise of high-throughput screening and combinatorial chemistry along with complementary computational methods for de novo design and virtual screening including docking. These technologies appeared poised to deliver diverse lead compounds for any biological target. As with many technological advances, realization of the expectations required significant additional effort and time. However, as summarized here, striking success has now been achieved for computer-aided drug lead generation and optimization. De novo design using both molecular growing and docking are illustrated for lead generation, and lead optimization features free energy perturbation calculations in conjunction with Monte Carlo statistical mechanics simulations for protein-inhibitor complexes in aqueous solution. The specific applications are to the discovery of non-nucleoside inhibitors of HIV reverse transcriptase (HIV-RT) and inhibitors of the binding of the proinflammatory cytokine MIF to its receptor CD74. A standard protocol is presented that includes scans for possible additions of small substituents to a molecular core, interchange of heterocycles, and focused optimization of substituents at one site. Initial leads with activities at low-micromolar concentrations have been advanced rapidly to low-nanomolar inhibitors.

C-2-aryl O-substituted HI-236 derivatives as non-nucleoside HIV-1 reverse-transcriptase inhibitors

Several novel thiourea derivatives of the NNRTI HI-236 substituted at the C-2 oxygen of the phenyl ring have been synthesized and evaluated for their inhibitory activity against HIV-1 (IIIB) replication in MT-2 cell cultures. The compounds were synthesized in order to fine-tune the activity of HI-236 as well as to gain insight into spatial characteristics in the pocket pertaining to the positional choice of tether in the design of [NRTI]-tether-[HI-236] bifunctional inhibitors. Two of the thiourea derivatives bearing a butynyl (6c) or hydroxyethyl tether (6n) were endowed with improved anti-HIV activity compared to HI-236. NNRTI activity was confirmed by a cell-free RT assay on six of the derivatives in which 6c returned an IC(50) of 3.8 nM compared to 28 nM for HI-236, establishing it as an improved lead for HI-236. The structure-activity profile is discussed in terms of potential interactions in the NNRTI pocket as suggested by a docking model using AutoDock, which have a bearing on the bifunctional drug design.

Virtual screening, identification, and biochemical characterization of novel inhibitors of the reverse transcriptase of human immunodeficiency virus type-1

The reverse transcriptase (RT) of human immunodeficiency virus type-1 (HIV-1) is a leading target in current antiretroviral therapy. Unfortunately, drug-resistant RT mutants evolve under the pressure of these drugs, and therefore, new anti-RT inhibitors are constantly required for HIV-1/AIDS treatment. We virtually screened a large chemical library of compounds against two crystal structures of HIV-1 RT to identify novel inhibitors. Top-scoring compounds were tested experimentally; 71 inhibited the RT-associated DNA polymerase, while several also inhibited HIV-1 pseudovirus infection in a cell-based assay. A combination of substituents from two structurally related inhibitors in a single molecule improved the inhibition efficacy. This compound strongly suppressed the RT-associated activity also protecting human lymphocytes from HIV-1 infection. RT inhibition by this compound was reversible and noncompetitive. This molecule and another structurally unrelated potent compound inhibited a known drug-resistant mutant of HIV-1 RT and affected moderately the HIV-2 RT-associated DNA polymerase. These inhibitors may serve as promising anti-HIV lead compounds.

Optimization of azoles as anti-human immunodeficiency virus agents guided by free-energy calculations

Efficient optimization of an inactive 2-anilinyl-5-benzyloxadiazole core has been guided by free energy perturbation (FEP) calculations to provide potent non-nucleoside inhibitors of human immunodeficiency virus (HIV) reverse transcriptase (NNRTIs). An FEP "chlorine scan" was performed to identify the most promising sites for substitution of aryl hydrogens. This yielded NNRTIs 8 and 10 with activities (EC50) of 820 and 310 nM for protection of human T-cells from infection by wild-type HIV-1. FEP calculations for additional substituent modifications and change of the core heterocycle readily led to oxazoles 28 and 29, which were confirmed as highly potent anti-HIV agents with activities in the 10-20 nM range. The designed compounds were also monitored for possession of desirable pharmacological properties by use of additional computational tools. Overall, the trends predicted by the FEP calculations were well borne out by the assay results. FEP-guided lead optimization is confirmed as a valuable tool for molecular design including drug discovery; chlorine scans are particularly attractive since they are both straightforward to perform and highly informative.

Possible New Reaction Mechanisms of Dideoxynucleosides as Anti-Aids Drugs

Evidence is presented that a major class of drugs, the dideoxynucleosides (ddNs) and nucleoside/nucleotide analogues, may inhibit the symptoms of acquired immunodeficiency syndrome (AIDS) by initiation of inactivation at the ribonucleotide reductase (RNR) enzyme stage and/or inactivation of reverse transcriptase enzyme or at a stage more initial than that of the currently accepted DNA chain termination hypothesis. For example, it has been previously shown that ribonucleotide diphosphate reductase (RDPR) and ribonucleotide triphosphate reductase (RTPR) are inactivated with 2′-chloro-2 ‘-deoxyuridine 5′-diphosphate-([3′-3H]ClUDP) and triphosphate ([3′-3H]ClUTP) by reaction with an intermediate furanone, Scheme 2. RDPR has also been inactivated by 2‘-azido-2‘-deoxyuridine 5‘-diphosphate (N3UDP). Furthermore, addition of hydroxyurea to RNR can inhibit DNA synthesis which results in a rapid depletion of limiting deoxynucleotide triphosphate (dNTP) pools. There are similar perturbations of dNTP pools upon interaction of human RNR with 3‘-azido-2‘,3 ‘-dideoxythymidine (AZT), in human cell studies involving AZT/HIV and in adenosine/coformycin experiments in relation to inherited immunodeficiency, Table 1. Also, the herein proposed reduction mechanisms of nucleotides by RNR ( e.g., a single electron transfer from the nucleotide base to the phenol moiety of the tyrosyl radical of RNR via a pathway involving the thiyl radical of a cysteine residue) can also account for the chemistry of some antiretroviral drugs, the ddNs. Analyses are presented that the RNR reductions of regular unsubstituted nucleotides may occur predominantly via initial 2’ C-H abstraction instead of the originally proposed 3’ C-H abstraction mechanism. Also, it is noted that the fate of the phenol moiety of the tyrosyl unit in some RNR reactions with 2‘-halo-2‘-deoxynucleotides is not clear. The proposed reaction mechanisms may provide guidance for the development of potentially effective anti-AIDS drugs.

Search for non-nucleoside inhibitors of HIV-1 reverse transcriptase using chemical similarity, molecular docking, and MM-GB/SA scoring

A virtual screening protocol has been applied to seek non-nucleoside inhibitors of HIV-1 reverse transcriptase (NNRTIs) and its K103N mutant. First, a chemical similarity search on the Maybridge library was performed using known NNRTIs as reference structures. The top-ranked molecules obtained from this procedure plus 26 known NNRTIs were then docked into the binding sites of the wild-type reverse transcriptase (HIV-RT) and its K103N variant (K103N-RT) using Glide 3.5. The top-ranked 100 compounds from the docking for both proteins were post-scored with a procedure using molecular mechanics and continuum solvation (MM-GB/SA). The validity of the virtual screening protocol was supported by (i) testing of the MM-GB/SA procedure, (ii) agreement between predicted and crystallographic binding poses, (iii) recovery of known potent NNRTIs at the top of both rankings, and (iv) identification of top-scoring library compounds that are close in structure to recently reported NNRTI HTS hits. However, purchase and assaying of selected top-scoring compounds from the library failed to yield active anti-HIV agents. Nevertheless, the highest-ranked database compound, S10087, was pursued as containing a potentially viable core. Subsequent synthesis and assaying of S10087 analogues proposed by further computational analysis yielded anti-HIV agents with EC50 values as low as 310 nM. Thus, with the aid of computational tools, it was possible to evolve a false positive into a true active.