3D Quantitative Structure-activity Relationship Research Articles

Comparative Molecular Field Analysis (CoMFA) for Thiotetrazole Alkynylacetanilides, a Non‐Nucleoside Inhibitor of HIV‐1 Double Mutant K103N/Y181C Reverse Transcriptase

Non‐nucleoside reverse transcriptase inhibitors (NNRTIs) are allosteric inhibitors of the HIV‐1 reverse transcriptase. HIV‐1 resistance to nucleoside RT inhibitors such as AZT can arise through mutations in the coding region of RT. Recently a series of thioterazolyl acetanilides were reported as potent inhibitors of HIV‐1 wild type and double mutant K103N/Y181C reverse transcriptase. In the present study 3D quantitative structure activity relationship (3D QSAR) studies involving comparative molecular field analysis (CoFMA) were performed on 28 thiotetrazole alkynylacetanilides. CoMFA was performed using sibyl software 6.7v. The molecules were divided into training set and test set. The developed model based on training set containing 20 molecules gave Q2 value of 0.587 and non cross validation R2 value of 0.961 and standard error of estimate 0.098 for CoMFA. The steric and electrostatic contributions are 42.7% and 57.3% The cross validation on test set gave R2 value of 0.74. The CoMFA model provided the most significant correlation of steric and electrostatic fields with biological activities. The information rendered by 3D QSAR model may afford valuable clues to optimize the lead and design new potential inhibitors

Exploring the binding of HIV-1 integrase inhibitors by comparative residue interaction analysis (CoRIA)

Since the recognition of HIV-1 integrase as a novel and rational target for HIV therapeutics, remarkable progress has been made in the development of integrase inhibitors. Computational techniques have played a critical role in accelerating research in this area. However, most previous computational studies were based solely on ligand information. In the present work, we describe the application of one of our recently developed receptor-based 3D-quantitative structure activity relationships (QSAR) methods, i.e. comparative residue interaction analysis (CoRIA), in exploring the events involved in ligand-integrase binding. In this methodology, the non-bonded interaction energies (van der Waals and Coulombic) of the inhibitors with individual active site residues of the integrase enzyme are calculated and, along with other thermodynamic descriptors, are correlated with biological activity using chemometric methods. Different combinations of descriptors were used to develop three types of QSAR models, all of which were found to be statistically significant by internal and external validation. This is the first report of such a dedicated receptor-based 3D-QSAR approach being applied to comprehend the integrase-inhibitor recognition process. In addition, the study was performed on 13-different series of inhibitors, thereby exploring the most structurally diverse data set ever used in understanding the inhibition of HIV-1 integrase. The major advantage of this technique is that it can quantitatively extract crucial residues and identify the nature of interactions between the ligand and receptor that modulate activity. The models suggest that Asp64, Thr66, Val77, Asp116, Glu152 and Lys159 are the key residues influencing the binding of ligands with the integrase enzyme, and the majority of these results are in line with earlier studies. The approach facilitates easy lead-to-hit conversion and design of novel inhibitors by optimisation of the interaction of ligands with these specific residues of the integrase enzyme.

Multimode Methods Applied on MIA Descriptors in QSAR

Since the introduction of physicochemical descriptors to derive useful QSAR (quantitative structure-activity relationship) models, some regression methods have been applied to linearly correlate dependent (bioactivities) and independent variables. Multiple linear regression (MLR) has been widely used when the number of samples (rows) exceed the amount of descriptors (columns), whilst partial least squares (PLS) is the most commonly applied regression method in 3D QSAR (e.g. CoMFA and related methods), where a large number of descriptors are generated. The recently implemented MIA-QSAR (Multivariate Image Analysis applied to QSAR) method is a especial (not only) case in which the descriptors (pixels) for each active compound result in a three-way array after grouping samples to give a data set. Such array may be properly treated by using N-way methods, such as multilinear PLS (N-PLS) and parallel factor analysis (PARAFAC). However, these methods have not been appropriately explored in QSAR studies, despite their supposed advantages over well established methods. Thus, this review formally details the MIA-QSAR approach prior to presenting two promising multimode methods to be applied on MIA descriptors, namely N-PLS and PARAFAC. Also, the suitability of such methods is discussed in terms of application to a case study (a series of anti-HIV compounds) and comparison to traditional (bilinear) PLS and docking studies.

Comparison of benzil and trifluoromethyl ketone (TFK)-mediated carboxylesterase inhibition using classical and 3D-quantitative structure–activity relationship analysis

Carboxylesterases are enzymes that hydrolyze a broad suite of endogenous and exogenous ester-containing compounds to the corresponding alcohol and carboxylic acid. These enzymes metabolize a number of therapeutics including the anti-tumor agent CPT-11, the anti-viral drug oseltamivir, and the anti-thrombogenic agent clopidogrel as well as many agrochemicals. In addition, carboxylesterases are involved in lipid homeostasis, including cholesterol metabolism and transport with a proposed role in the development of atherosclerosis. Several different scaffolds capable of inhibiting carboxylesterases have been reported, including organophosphates, carbamates, trifluoromethyl ketone-containing structures (TFKs), and aromatic ethane-1,2-diones. Of these varied groups, only the 1,2-diones evidence carboxylesterase isoform-selectivity, which is an important characteristic for therapeutic application and probing biological mechanisms. This study constructed a series of classical and 3D-QSAR models to examine the physiochemical parameters involved in the observed selectivity of three mammalian carboxylesterases: human intestinal carboxylesterase (hiCE), human carboxylesterase 1 (hCE1), and rabbit carboxylesterase (rCE). CoMFA-based models for the benzil-analogs described 88%, 95% and 76% of observed activity for hiCE, hCE1 and rCE, respectively. For TFK-containing compounds, two distinct models were constructed using either the ketone or gem-diol form of the inhibitor. For all three enzymes, the CoMFA ketone models comprised more biological activity than the corresponding gem-diol models; however the differences were small with described activity for all models ranging from 85–98%. A comprehensive model incorporating both benzil and TFK structures described 92%, 85% and 87% of observed activity for hiCE, hCE1 and rCE, respectively. Both classical and 3D-QSAR analysis showed that the observed isoform-selectivity with the benzil-analogs could be described by the volume parameter. This finding was successfully applied to examine substrate selectivity, demonstrating that the relative volumes of the alcohol and acid moieties of ester-containing substrates were predictive for whether hydrolysis was preferred by hiCE or hCE1. Based upon the integrated benzil and TFK model, the next generation inhibitors should combine the A-ring and the 1,2-dione of the benzil inhibitor with the long alkyl chain of the TFK-inhibitor in order to optimize selectivity and potency. These new inhibitors could be useful for elucidating the role of carboxylesterase activity in fatty acid homeostasis and the development of atherosclerosis as well as effecting the controlled activation of carboxylesterase-based prodrugs in situ.

A 3D linear solvation energy model to quantify the affinity of flavonoid derivatives toward P-glycoprotein

P-glycoprotein (Pgp/ABCB1) both accounts for multidrug resistance (MDR) in chemotherapy and contributes to reduce oral bioavailability and brain distribution of drugs. Flavonoids, reported as potent Pgp inhibitors, are able to bind to the cytosolic ATP-binding site and a vicinal hydrophobic pocket. In order to explore the interaction forces governing the affinity of flavonoids towards Pgp, the 3D quantitative structure–activity relationship (QSAR) approach was used to analyze a set of flavonoid derivatives. The variation of affinity towards Pgp was investigated by VolSurf descriptors of Molecular Interaction Fields (MIFs) related to hydrophobic interaction forces, polarizability, and hydrogen-bonding capacity. The 3D linear solvation energy VolSurf model developed here identifies shape parameters and hydrophobicity as the major physicochemical parameters responsible for the affinity of flavonoid derivatives towards Pgp and hydrogen-bonding capacities as minor modulators of this activity. Furthermore, this predictive model (q2 of 0.71) was also validated by use of an external set of 10 flavones.

Molecule Kernels: A Descriptor- and Alignment-Free Quantitative Structure–Activity Relationship Approach

Quantitative structure activity relationship (QSAR) analysis is traditionally based on extracting a set of molecular descriptors and using them to build a predictive model. In this work, we propose a QSAR approach based directly on the similarity between the 3D structures of a set of molecules measured by a so-called molecule kernel, which is independent of the spatial prealignment of the compounds. Predictors can be build using the molecule kernel in conjunction with the potential support vector machine (P-SVM), a recently proposed machine learning method for dyadic data. The resulting models make direct use of the structural similarities between the compounds in the test set and a subset of the training set and do not require an explicit descriptor construction. We evaluated the predictive performance of the proposed method on one classification and four regression QSAR datasets and compared its results to the results reported in the literature for several state-of-the-art descriptor-based and 3D QSAR approaches. In this comparison, the proposed molecule kernel method performed better than the other QSAR methods.

Advancements in Predictive In Silico Models for ADME

This comprehensive review describes contemporary computational (in silico) quantitative structure-activity relationship (QSAR) approaches that have been used to elucidate the molecular features that influence the Absorption, Distribution, Metabolism and Elimination (ADME) of drugs. Recent studies have applied 2D and 3D QSAR, pharmacophore approaches and nonlinear techniques (for example: recursive partitioning, neural networks and support vector machines) to model ADME processes. Furthermore, this review highlights some of the challenges and opportunities for future research; the need to develop ‘global’ models and to extend the QSAR for the protein transporters that influence ADME. Keywords: In silico, QSAR, ADME, drug metabolism, pharmacokinetics, statistical methods, protein transporters

Relationships Between Molecular Structure and Perceived Odor Quality of Ligands for a Human Olfactory Receptor

Perception of thousands of odors by a few hundreds of olfactory receptors (ORs) results from a combinatorial coding, in which one OR recognizes multiple odorants and an odorant is recognized by a specific group of ORs. Moreover, odorants could act both as agonists or antagonists depending on the OR. This dual agonist-antagonist combinatorial coding is in good agreement with behavioral and psychophysical observations of mixture perception. We previously described the odorant repertoire of a human OR, OR1G1, identifying both agonists and antagonists. In this paper, we performed a 3D-quantitative structure-activity relationship (3D-QSAR) study of these ligands. We obtained a double-alignment model explaining previously reported experimental activities and permitting to predict novel agonists and antagonists for OR1G1. These model predictions were experimentally validated. Thereafter, we evaluated the statistical link between OR1G1 response to odorants, 3D-QSAR categorization of OR1G1 ligands, and their olfactory description. We demonstrated that OR1G1 recognizes a group of odorants that share both 3D structural and perceptual qualities. We hypothesized that OR1G1 contributes to the coding of waxy, fatty, and rose odors in humans.

Structural basis for ligand recognition at the benzodiazepine binding site of GABA A α3 receptor, and pharmacophore-based virtual screening approach

Given the heterogeneity of GABA A receptor, the pharmacological significance of identifying subtype selective modulators is increasingly being recognized. Thus, drugs selective for GABA A α 3 receptors are expected to display fewer side effects than the drugs presently in clinical use. Hence we carried out 3D QSAR (three-dimensional quantitative structure–activity relationship) studies on a series of novel GABA A α 3 subtype selective modulators to gain more insight into subtype affinity. To identify the 3D functional attributes required for subtype selectivity, a chemical feature-based pharmacophore, primarily based on selective ligands representing diverse structural classes was generated. The obtained pseudo receptor model of the benzodiazepine binding site revealed a binding mode akin to “Message–Address” concept. Scaffold hopping was carried out across multi-conformational May Bridge database for the identification of novel chemotypes. Further a focused data reduction approach was employed to choose a subset of enriched compounds based on “Drug likeness” and “Similarity-based” methods. These results taken together could provide impetus for rational design and optimization of more selective and high affinity leads with a potential to have decreased adverse effects.

3D Quantitative and Qualitative Structure-Activity Relationships of the δ -Opioid Receptor Antagonists

Antagonists of the d -opioid receptor are effective in overcoming resistance against analgesic drugs such as morphine. To identify novel antagonists of the d -opioid receptor that display high potency and low resistance, we performed 3D-QSAR analysis using chemical feature-based pharmacophore models. Chemical features for d -opioid receptor antagonists were generated using quantitative (Catalyst/HypoGen) and qualitative (Catalyst/HipHop) approaches. For HypoGen analysis, we collected 16 peptide and 16 non-peptide antagonists as the training set. The best-fit pharmacophore hypotheses of the two antagonist models comprised identical features, including a hydrophobic aromatic (HAR), a hydrophobic (HY), and a positive ionizable (PI) function. The training set of the HipHop model was constructed with three launched opioid drugs. The best hypothesis from HipHop included four features: an HAR, an HY, a hydrogen bond donor (HBD), and a PI function. Based on these results, we confirm that HY, HAR and PI features are essential for effective antagonism of the d -opioid receptor, and determine the appropriate pharmacophore to design such antagonists.

3D‐Quantitative Structure–Activity Relationship Studies on Benzothiadiazepine Hydroxamates as Inhibitors of Tumor Necrosis Factor‐α Converting Enzyme

A set of 29 benzothiadiazepine hydroxamates having selective tumor necrosis factor-alpha converting enzyme inhibitory activity were used to compare the quality and predictive power of 3D-quantitative structure-activity relationship, comparative molecular field analysis, and comparative molecular similarity indices models for the atom-based, centroid/atom-based, data-based, and docked conformer-based alignment. Removal of two outliers from the initial training set of molecules improved the predictivity of models. Among the 3D-quantitative structure-activity relationship models developed using the above four alignments, the database alignment provided the optimal predictive comparative molecular field analysis model for the training set with cross-validated r(2) (q(2)) = 0.510, non-cross-validated r(2) = 0.972, standard error of estimates (s) = 0.098, and F = 215.44 and the optimal comparative molecular similarity indices model with cross-validated r(2) (q(2)) = 0.556, non-cross-validated r(2) = 0.946, standard error of estimates (s) = 0.163, and F = 99.785. These models also showed the best test set prediction for six compounds with predictive r(2) values of 0.460 and 0.535, respectively. The contour maps obtained from 3D-quantitative structure-activity relationship studies were appraised for activity trends for the molecules analyzed. The comparative molecular similarity indices models exhibited good external predictivity as compared with that of comparative molecular field analysis models. The data generated from the present study helped us to further design and report some novel and potent tumor necrosis factor-alpha converting enzyme inhibitors.

Comparative molecular similarity indices analysis for predicting anti-HIV activity of phenyl ethyl thiourea (PET) derivatives

Human immunodeficiency virus type-1 (HIV-1) is a pathogenic retrovirus and the causative agent of acquired immunodeficiency syndrome (AIDS). HIV-1 RT is one of the key enzymes in the duplication of HIV-1. Inhibitors of HIV-1 RT are classified as nonnucleoside reverse transcriptase inhibitors (NNRTIs) and NRTIs. NNRTIs bind in a region not associated with the active site of the enzyme. Within the NNRTIs category, there is a set of inhibitors commonly referred to as phenyl or pyridyl ethyl thiourea (PET) derivatives. The present three-dimensional (3D) quantitative structure–activity relationship (QSAR) study attempts to explore the structural requirements of (phenyl ethyl thiourea) PET derivatives for anti-HIV activity. Based on the structures and biodata of previous PET analogs, comparative molecular similarity indices analysis (CoMSIA) is performed to explain the structural requirements for the anti-HIV activity of PET derivatives. The CoMSIA studies resulted in reliable computational models. The obtained CoMSIA model has high predictive ability, q 2 = 0.584, r 2 = 0.882 and standard error of estimation (SEE) = 0.352, explaining the majority of the variance in the data with four partial least square (PLS) components. The predictive ability of the developed models (3D QSAR) was also confirmed by using a test (external validation) set comprised of 11 molecules with a predicted r 2 value of 0.871. It is shown that the steric, electrostatic, hydrophobic, and hydrogen-bond acceptor and donor properties predicted by CoMSIA contours can be related to anti-HIV activity. This model is a significant guide to trace the features that really matter especially with respect to the design of novel compounds. Comparative molecular similarity indices analysis for predicting anti-HIV activity of phenyl ethyl thiourea (PET) derivatives The parameters indicated in the substitution position of compound are responsible for anti-HIV activity.

Pharmacophore mapping of flavone derivatives for aromatase inhibition

Aromatase, which catalyses the final step in the steroidogenesis pathway of estrogen, has been target for the design of inhibitor in the treatment of hormone dependent breast cancer for postmenopausal women. The extensive SAR studies performed in the last 30 years to search for potent, selective and less toxic compounds, have led to the development of second and third generation of non-steroidal aromatase inhibitors (AI). Besides the development of synthetic compounds, several naturally occurring and synthetic flavonoids, which are ubiquitous natural phenolic compounds and mediate the host of biological activities, are found to demonstrate inhibitory effects on aromatase. The present study explores the pharmacophores, i.e., the structural requirements of flavones (Fig. 1) for inhibition of aromatase activity, using quantitative structure activity relationship (QSAR) and space modeling approaches. The classical QSAR studies generate the model (R (2) = 0.924, Q (2) = 0.895, s = 0.233) that shows the importance of aromatic rings A and C, along with substitutional requirements in meta and para positions of ring C for the activity. 3D QSAR of Comparative Molecular Field Analysis (CoMFA, R (2) = 0.996, R(2)(cv) = 0.791) and Comparative Molecular Similarity Analysis (CoMSIA, R (2) = 0.992, R(2)(cv) = 0.806) studies show contour maps of steric and hydrophobic properties and contribution of acceptor and donor of the molecule, suggesting the presence of steric hindrance due to ring C and R''-substituent, bulky hydrophobic substitution in ring A, along with acceptors at positions 11, and alpha and gamma of imidazole ring, and donor in ring C favor the inhibitory activity. Further space modeling (CATALYST) study (R = 0.941, Delta( cost ) = 96.96, rmsd = 0.876) adjudge the presence of hydrogen bond acceptor (keto functional group), hydrophobic (ring A) and aromatic rings (steric hindrance) along with critical distance among features are important for the inhibitory activity.

Fragment-based and classical quantitative structure–activity relationships for a series of hydrazides as antituberculosis agents

Worldwide, tuberculosis (TB) is the leading cause of death among curable infectious diseases. Multidrug-resistant Mycobacterium tuberculosis is an emerging problem of great importance to public health, and there is an urgent need for new anti-TB drugs. In the present work, classical 2D quantitative structure-activity relationships (QSAR) and hologram QSAR (HQSAR) studies were performed on a training set of 91 isoniazid derivatives. Significant statistical models (classical QSAR, q (2) = 0.68 and r (2) = 0.72; HQSAR, q (2) = 0.63 and r (2) = 0.86) were obtained, indicating their consistency for untested compounds. The models were then used to evaluate an external test set containing 24 compounds which were not included in the training set, and the predicted values were in good agreement with the experimental results (HQSAR, r(2)(pred) = 0.87; classical QSAR, r(2)(pred) = 0.75).

2D-QSAR of purine-scaffold novel class of Hsp90 binders that inhibit the proliferation of cancer cells

The chaperone heat shock protein 90 (Hsp90) is an emerging target for designing anticancer agent due to its important roles in maintaining transformation and modulating the survival and growth potential of cancer cells. Quantitative structure–activity relationship (QSAR) studies have been carried out in a series of purine derivatives with Hsp90 inhibitory activities. The two-diemensional (2D) QSAR studies revealed that the activity is positively influenced by the presence of a halo substituent in the trimethoxyphenyl moiety and by electronic parameter (field effect) of the 9-N substituent where the contribution of the steric parameter (MR) is negative. The best QSAR model with good correlation coefficient (r 2 = 0.738), of high statistical significance (>99.9%) well explained the variance in activity.

QSAR study on aminophenylbenzamides and acrylamides as histone deacetylase inhibitors: An insight into the structural basis of antiproliferative activity

Histone deacetylases have emerged as important drug target with a multitude of therapeutic potentials for their inhibitors. With the purpose of designing new chemical entities with enhanced inhibitory potencies against histone deacetylases, a two-dimensional (2D) quantitative structure-activity relationship (QSAR) study was carried out on aminophenylbenzamides and acrylamide derivatives as inhibitors of these deacetylases. The developed model was validated by standard QSAR parameters and through a detailed structural analysis of how it reproduces and explains the differences in the experimentally known activity data. The model showed a good correlative and predictive ability, with a cross-validated correlation coefficient of 0.594. The conventional and predictive correlation coefficients were found to be 0.725 and 0.577 for the antiproliferative activity of aminophenylbenzamides and acrylamide series, respectively. The study indicated that their antiproliferative activity is largely explained by the steric factors of the substituents, highlighting the role of the size and shape of the inhibitor in forming effective binding interactions with histone deacetylase. The models could be usefully employed to design inhibitors with greatly enhanced potency and selectivity.

Stereochemical and Steric Control of the UDP‐Glucuronosyltransferase‐Catalyzed Conjugation Reaction: A Rational Approach for the Design of Inhibitors for the Human UGT2B7

A set of 76 derivatives of the epimeric tricyclic sesquiterpenols longifolol and isolongifolol was subjected to inhibition and glucuronidation assays employing the human UDP-glucuronosyltransferase (UGT) 2B7. Detailed structure-activity relationships (SARs) with respect to functionality, stereochemical properties, and steric features were derived. To gain further insight into the SARs of UGT2B7 ligands herein, we have developed a 3D-quantitative structure-activity relationship (3D-QSAR) using Comparative Molecular Similarity Analysis (CoMSIA). The formation of the enzyme-inhibitor complex was predominantly controlled by spatially directed hydrophobic interactions. The glucuronidation rate was significantly influenced by the steric demand of substituents in proximity of the nucleophilic hydroxy group. The glucuronidation of the compounds was prevented by the introduction of bulky substituents such as isopropyl, tert-butyl, and phenyl groups. The epimeric longifolol derivatives of series D were the best inhibitors displaying IC(50) values as low as 4.6 nM. This study shows that high-potency substrates can be turned into potent inhibitors by addressing functional, stereochemical, and steric properties.

A comparative molecular field and comparative molecular similarity indices analyses (CoMFA and CoMSIA) of N-phenyl- N′-(2-chloroethyl)ureas targeting the colchicine-binding site as anticancer agents

To decipher the mechanism underlying the covalent binding of N-phenyl- N′-(2-chloroethyl)ureas (CEU) to the colchicine-binding site on β II-tubulin and to design new and selective antimitotic drugs, we developed 3D quantitative structure–activity relationships (3D-QSAR) models using CoMFA and CoMSIA analyses. The present study correlates the cell growth inhibition activities of 56 structurally related CEU derivatives to several physicochemical parameters representing steric, electrostatic, and hydrophobic fields. Both CoMFA and CoMSIA models using two different optimum numbers of components (ONC) 10 and 4, respectively, gave good internal predictions and their cross-validated r 2 values were between 0.639 and 0.743. These comprehensive CoMFA and CoMSIA models are useful in understanding the structure–activity relationships of CEU. The two models were compared to the X-ray crystal structure of the complex of tubulin–colchicine and analyzed for similarities between the two modes of analysis. These models will inspire the design of new CEU derivatives with enhanced inhibition of tumor cell growth and targeting specificity of β II-tubulin and the cytoskeleton.

Characterization of a novel class of antimalarials and its applicability to plasmodial target identification

Candidate drugs related to the lead compound propafenone are highly effective inhibitors of P. falciparum growth with 50% inhibitory concentrations (IC(50)s) in the sub-micromolar range. The parental compound propafenone is a cardiac sodium channel blocker which is in clinical use for the treatment of ventricular arrhythmia. An in house library of more than 400 compounds with systematically varied structures is available for 2D and 3D quantitative structure activity relationship studies. In a first step selected compounds were evaluated for their antimalarial activity using the histidine-rich protein 2 drug sensitivity assay. Propafenone analogues contain an inherently photoactive aryl-carbonyl substructure, which allows their use in photolabeling studies. Labelling efficiency is increased for compounds in which the phenylpropiophenone core structure is replaced by a benzophenone substructure. However, the phenylpropiophenone substructure represents part of the pharmacophore of the compounds. Benzophenone-type analogues show IC(50) values that are higher than their congeneric phenylpropiophenones. Nevertheless, one of the photoligands shows an IC(50) value in the low micro-molar range. Use of this photoligand is thus expected to allow identification of candidate targets by mass spectrometry following two dimensional separation of the plasmodial proteome. The Malaria Genome Project has advanced our understanding of parasite biology and development of novel drugs can mount on data made available by the recently completed sequencing effort of P. falciparum. The lead compound propafenone is a registered drug and this compound class might therefore have a major potential as an antimalarial drug, either alone, or in combination with conventional antimalarials.

Discovery of Small Molecule CXCR4 Antagonists

In light of a proposed molecular mechanism for the C-X-C chemokine receptor type 4 (CXCR4) antagonist 1 (AMD3100), a template with the general structure 2 was designed, and 15 was identified as a lead by means of an affinity binding assay against the ligand-mimicking CXCR4 antagonist 3 (TN14003). Following a structure-activity profile around 15, the design and synthesis of a series of novel small molecular CXCR4 antagonists led to the discovery of 32 (WZ811). The compound shows subnanomolar potency (EC50 = 0.3 nM) in an affinity binding assay. In addition, when subjected to in vitro functional evaluation, 32 efficiently inhibits CXCR4/stromal cell-derived factor-1 (SDF-1)-mediated modulation of cyclic adenosine monophophate (cAMP) levels (EC50 = 1.2 nM) and SDF-1 induced Matrigel invasion (EC50 = 5.2 nM). Molecular field topology analysis (MFTA), a 2D quantitative structure-activity relationship (QSAR) approach based on local molecular properties (Van der Waals radii (VdW), atomic charges, and local lipophilicity), applied to the 32 series suggests structural modifications to improve potency.