Scaffold hopping from amodiaquine to novel Nurr1 agonist chemotypes via microscale analogue libraries

Several lines of evidence suggest the ligand‐sensing transcription factor Nurr1 as a promising target to treat neurodegenerative diseases. Nurr1 modulators to validate and exploit this therapeutic potential are rare, however. To identify novel Nurr1 agonist chemotypes, we have employed the Nurr1 activator amodiaquine as template for microscale analogue library synthesis. The first set of analogues was based on the 7‐chloroquiolin‐4‐amine core fragment of amodiaquine and revealed superior N‐substituents compared to diethylaminomethylphenol contained in the template. A second library of analogues was subsequently prepared to replace the chloroquinolineamine scaffold. The two sets of analogues enabled a full scaffold hop from amodiaquine to a novel Nurr1 agonist sharing no structural features with the lead but comprising superior potency on Nurr1. Additionally, pharmacophore modeling based on the entire set of active and inactive analogues suggested key features for Nurr1 agonists.

Virtual Screening and Design with Machine Intelligence Applied to Pim-1 Kinase Inhibitors

Ligand-based virtual screening of large compound collections, combined with fast bioactivity determination, facilitate the discovery of bioactive molecules with desired properties. Here, chemical similarity based machine learning and label-free differential scanning fluorimetry were used to rapidly identify new ligands of the anticancer target Pim-1 kinase. The three-dimensional crystal structure complex of human Pim-1 with ligand bound revealed an ATP-competitive binding mode. Generative de novo design with a recurrent neural network additionally suggested innovative molecular scaffolds. Results corroborate the validity of the chemical similarity principle for rapid ligand prototyping, suggesting the complementarity of similarity-based and generative computational approaches.

Shape Similarity by Fractal Dimensionality: An Application in the de novo Design of (-)-Englerin A Mimetics

Molecular shape and pharmacological function are interconnected. To capture shape, the fractal dimensionality concept was employed, providing a natural similarity measure for the virtual screening of de novo generated small molecules mimicking the structurally complex natural product (-)-englerin A. Two of the top-ranking designs were synthesized and tested for their ability to modulate transient receptor potential (TRP) cation channels which are cellular targets of (-)-englerin A. Intracellular calcium assays and electrophysiological whole-cell measurements of TRPC4 and TRPM8 channels revealed potent inhibitory effects of one of the computer-generated compounds. Four derivatives of this identified hit compound had comparable effects on TRPC4 and TRPM8. The results of this study corroborate the use of fractal dimensionality as an innovative shape-based molecular representation for molecular scaffold-hopping.

Automated De Novo Drug Design: Are We Nearly There Yet?

Medicinal chemistry and, in particular, drug design have often been perceived as more of an art than a science. The many unknowns of human disease and the sheer complexity of chemical space render decision making in medicinal chemistry exceptionally demanding. Computational models can assist the medicinal chemist in this endeavour. Provided here is an overview of recent examples of automated de novo molecular design, a discussion of the concepts and computational approaches involved, and the daring prediction of some of the possibilities and limitations of drug design using machine intelligence.

Discovery of Novel Molecular Frameworks of Farnesoid X Receptor Modulators by Ensemble Machine Learning

The bile acid activated transcription factor farnesoid X receptor (FXR) has revealed therapeutic potential as a molecular drug target for the treatment of hepatic and metabolic disorders. Despite strong efforts in FXR ligand development, the structural diversity among the known FXR modulators is limited. Only four molecular frameworks account for more than 50 % of the FXR modulators annotated in ChEMBL. Here, we leverage machine learning methods to expand the chemical space of FXR-targeting small molecules by employing an ensemble of three complementary machine learning approaches. A counter-propagation artificial neural network, a k-nearest neighbor learner, and a three-dimensional pharmacophore descriptor were combined to retrieve novel FXR ligands from a collection of more than 3 million compounds. The ensemble machine learning model identified six new FXR modulators among ten top-ranked candidates. These active hits comprise both FXR activators and antagonists with micromolar potencies. With four novel FXR ligand scaffolds, these computationally identified bioactive compounds appreciably expand the chemical space of known FXR modulators and may serve as starting points for hit-to-lead expansion.

Optimizing Antimicrobial Peptide Dendrimers in Chemical Space

We used nearest-neighbor searches in chemical space to improve the activity of the antimicrobial peptide dendrimer (AMPD) G3KL and identified dendrimer T7, which has an expanded activity range against Gram-negative pathogenic bacteria including Klebsiellae pneumoniae, increased serum stability, and promising activity in an in vivo infection model against a multidrug-resistant strain of Acinetobacter baumannii. Imaging, spectroscopic studies, and a structural model from molecular dynamics simulations suggest that T7 acts through membrane disruption. These experiments provide the first example of using virtual screening in the field of dendrimers and show that dendrimer size does not limit the activity of AMPDs.

A Gold-Catalyzed Domino Cyclization Enabling Rapid Construction of Diverse Polyheterocyclic Frameworks

We report herein an efficient gold(I)-catalyzed post-Ugi domino dearomatization/ipso-cyclization/Michael sequence that enables access to libraries of diverse (hetero)arene-annulated tricyclic heterocycles. This process affords novel complex polycyclic scaffolds in moderate to good yields from readily available acyclic precursors with excellent chemo-, regio-, and diastereoselectivity. The power of this strategy has been demonstrated by the rapid synthesis of 40 highly functionalized polyheterocycles bearing indole, pyrrole, (benzo)furan, (benzo)thiophene, pyrazole, and electron-rich arene groups in two operational steps.

Optimization of TRPV6 Calcium Channel Inhibitors Using a 3D Ligand-Based Virtual Screening Method

Herein, we report the discovery of the first potent and selective inhibitor of TRPV6, a calcium channel overexpressed in breast and prostate cancer, and its use to test the effect of blocking TRPV6-mediated Ca(2+)-influx on cell growth. The inhibitor was discovered through a computational method, xLOS, a 3D-shape and pharmacophore similarity algorithm, a type of ligand-based virtual screening (LBVS) method described briefly here. Starting with a single weakly active seed molecule, two successive rounds of LBVS followed by optimization by chemical synthesis led to a selective molecule with 0.3 μM inhibition of TRPV6. The ability of xLOS to identify different scaffolds early in LBVS was essential to success. The xLOS method may be generally useful to develop tool compounds for poorly characterized targets.

Deorphaning pyrrolopyrazines as potent multi-target antimalarial agents

The discovery of pyrrolopyrazines as potent antimalarial agents is presented, with the most effective compounds exhibiting EC50 values in the low nanomolar range against asexual blood stages of Plasmodium falciparum in human red blood cells, and Plasmodium berghei liver schizonts, with negligible HepG2 cytotoxicity. Their potential mode of action is uncovered by predicting macromolecular targets through avant-garde computer modeling. The consensus prediction method suggested a functional resemblance between ligand binding sites in non-homologous target proteins, linking the observed parasite elimination to IspD, an enzyme from the non-mevalonate pathway of isoprenoid biosynthesis, and multi-kinase inhibition. Further computational analysis suggested essential P. falciparum kinases as likely targets of our lead compound. The results obtained validate our methodology for ligand- and structure-based target prediction, expand the bioinformatics toolbox for proteome mining, and provide unique access to deciphering polypharmacological effects of bioactive chemical agents.

Drugs by numbers: reaction-driven de novo design of potent and selective anticancer leads

A potent and selective inhibitor of the anticancer target Polo-like kinase 1 was found by computer-based molecular design. This type II kinase inhibitor was synthesized as suggested by the design software DOGS and exhibited significant antiproliferative effects against HeLa cells without affecting nontransformed cells. The study provides a proof-of-concept for reaction-based de novo design as a leading tool for drug discovery.

Development of in silico filters to predict activation of the pregnane X receptor (PXR) by structurally diverse drug-like molecules

The pregnane X receptor (PXR), a member of the nuclear hormone superfamily, regulates the expression of several enzymes and transporters involved in metabolically relevant processes. The significant induction of CYP450 enzymes by PXR, in particular CYP3A4, might significantly alter the metabolism of prescribed drugs. In order to early identify molecules in drug discovery with a potential to activate PXR as antitarget, we developed fast and reliable in silico filters by ligand-based QSAR techniques. Two classification models were established on a diverse dataset of 434 drug-like molecules. A second augmented set allowed focusing on interesting regions in chemical space. These classifiers are based on decision trees combined with a genetic algorithm based variable selection to arrive at predictive models. The classifier for the first dataset on 29 descriptors showed good performance on a test set with a correct classification of both 100% for PXR activators and non-activators plus 87% for activators and 83% for non-activators in an external dataset. The second classifier then correctly predicts 97% activators and 91% non-activators in a test set and 94% for activators and 64% non-activators in an external set of 50 molecules, which still qualifies for application as a filter focusing on PXR activators. Finally a quantitative model for PXR activation for a subset of these molecules was derived using a regression-tree approach combined with GA variable selection. This final model shows a predictive r(2) of 0.774 for the test set and 0.452 for an external set of 33 molecules. Thus, the combination of these filters consistently provide guidelines for lowering PXR activation in novel candidate molecules.

Dual-target virtual screening by pharmacophore elucidation and molecular shape filtering

Dual-target inhibitors gained increased attention in the past years. A novel in silico approach was employed for the discovery of dual 5-lipoxygenase/soluble epoxide hydrolase inhibitors. The ligand-based approach uses excessive pharmacophore elucidation and pharmacophore alignment in conjunction with shape-based scoring. The virtual screening results were verified in vitro, leading to nine novel inhibitors including a dual-target compound.

Virtual Screening for Selective Allosteric mGluR1 Antagonists and Structure–Activity Relationship Investigations for Coumarine Derivatives

A virtual screening study towards novel noncompetitive antagonists of the metabotropic glutamate receptor 1 (mGluR1) is described. Alignment-free topological pharmacophore descriptors (CATS) were used to encode the screening compounds. All virtual hits were characterized with respect to their allosteric antagonistic effect on mGluR1 in both functional and binding assays. Exceptionally high hit rates of up to 26 % were achieved, confirming the applicability of this virtual screening concept. Most of the compounds were found to be moderately active, however, one potent and subtype selective mGluR1 antagonist, 13 (IC(50): 0.362 microM, SEM +/-0.031; K(i): 0.753 microM, SEM +/-0.048), based on a coumarine scaffold was discovered. In a following activity optimization program a series of coumarine derivatives was synthesized. This led to the discovery of potent (60, IC(50): 0.058 microM, SEM +/-0.008; K(i): 0.293 microM, SEM +/-0.022) and subtype selective (rmGluR5 IC(50): 28.6 microM) mGluR1 antagonists. From our homology model of mGluR1 we derived a potential binding mode within the allosteric transmembrane region. Potential interacting patterns are proposed considering the difference of the binding pockets between rat and human receptors. The study demonstrates the applicability of ligand-based virtual screening for noncompetitive antagonists of a G-protein coupled receptor, resulting in novel, potent, and selective agents.

Comparison of 2D Fingerprint Methods for Multiple-Template Similarity Searching on Compound Activity Classes of Increasing Structural Diversity

We studied the similarity search performance of differently designed molecular fingerprints using multiple reference structures and different search strategies. For this purpose, nine compound activity classes were assembled that exclusively consisted of molecules with different core structures and that represented different levels of intra-class structural diversity. Thus, there was a strict one-to-one correspondence between test compounds and core structures. Analysis of unique core structures was found to be a better measure of class diversity than distributions of simplified scaffolds. On increasingly diverse classes, a trainable fingerprint using a unique search strategy performed better than others tested herein. Overall, clear preferences were detected for nearest-neighbor search strategies over fingerprint-averaging techniques. Nearest-neighbor searching that relied on selecting database compounds most similar to one of the reference structures often improved compound recovery over other averaging methods, but at the cost of decreasing the ability to detect hits that were structurally distinct from reference molecules.

Towards selective antagonists of T-type calcium channels: design, characterization and potential applications of NNC 55-0396

NNC 55-0396 is a structural analog of mibefradil (Ro 40-5967) that inhibits both T-type and high-voltage-activated (HVA) Ca2+ channels with a higher selectivity for T-type Ca2+ channels. The inhibitory effect of mibefradil on HVA Ca2+ channels can be attributed to a hydrolyzed metabolite of the drug: the methoxy acetate side chain of mibefradil is removed by intracellular enzymes, thus it forms (1S,2S)-2-(2-(N-[(3-benzoimidazol-2-yl)propyl]-N-methylamino)ethyl)-6-fluoro-1,2,3,4-tetrahydro-1-isopropyl-2-naphtyl hydroxy dihydrochloride (dm-mibefradil), which causes potent inhibition of HVA Ca2+ currents. By replacing the methoxy acetate chain of mibefradil with cyclopropanecarboxylate, a more stable analog was developed (NNC 55-0396). The acute IC50 of NNC 55-0396 to block recombinant Cav3.1 T-type channels expressed in HEK293 cells is approximately 7 muM, whereas 100 microM NNC 55-0396 has no detectable effect on high voltage-activated currents in INS-1 cells. Block of T-type Ca2+ current was partially reduced by membrane hyperpolarization and was enhanced at high stimulus frequency. Washing NNC 55-0396 out of the recording chamber did not reverse the T-type Ca2+ current activity, suggesting that the compound dissolves in or passes through the plasma membrane to exert its effect; however, intracellular perfusion of the compound did not block T-type Ca2+ currents, arguing against a cytoplasmic route of action. We conclude that NNC 55-0396, by virtue of its modified structure, does not produce the metabolite that causes inhibition of L-type Ca2+ channel channels, thus rendering it more selective to T-type Ca2+ channels.

New inhibitors of the Tat-TAR RNA interaction found with a "fuzzy" pharmacophore model

TAR RNA is a potential target for AIDS therapy. Ligand-based virtual screening was performed to retrieve novel scaffolds for RNA-binding molecules capable of inhibiting the Tat-TAR interaction, which is essential for HIV replication. We used a "fuzzy" pharmacophore approach (SQUID) and an alignment-free pharmacophore method (CATS3D) to carry out virtual screening of a vendor database of small molecules and to perform "scaffold-hopping". A small subset of 19 candidate molecules were experimentally tested for TAR RNA binding in a fluorescence resonance energy transfer (FRET) assay. Both methods retrieved molecules that exhibited activities comparable to those of the reference molecules acetylpromazine and chlorpromazine, with the best molecule showing ten times better binding behavior (IC50 = 46 microM). The hits had molecular scaffolds different from those of the reference molecules.

Prediction of CNS activity of compound libraries using substructure analysis

An in silico ADME/Tox prediction tool based on substructural analysis has been developed. The tool called SUBSTRUCT has been used to predict CNS activity. Data sets with CNS active and nonactive drugs were extracted from the World Drug Index (WDI). The SUBSTRUCT program predicts CNS activity as good as a much more complicated artificial neural network model. SUBSTRUCT separates the data sets with approximately 80% accuracy. Substructural analysis also shows surprisingly large differences in substructure profiles between CNS active and nonactive drugs.

High-throughput, in silico prediction of aqueous solubility based on one- and two-dimensional descriptors

An aqueous solubility model has been developed. The model is based solely on one- and two-dimensional descriptors and an artificial neural network to ensure fast execution. 63 descriptors expressing physicochemical and topological properties were used. The final model consisted of a training set of 3042 molecules, a test set of 309 molecules and an independent validation set of 307 molecules. The squared correlation coefficients were 0.91 for the training set, 0.89 for the test set and 0.86 for the independent validation set.