3D QSAR pharmacophore-based virtual screening for the identification of potential inhibitors of tyrosinase

Abstract

Tyrosinase plays an important role in melanin biosynthesis and protects skin against ultraviolet radiations. Functional deficiency of tyrosinase results in serious dermatological diseases. Tyrosinase also participates in neuromelanin formation in the human brain, which leads to neurodegeneration resulting in Parkinson’s disease. In fruits and vegetables, tyrosinase plays a critical role in senescence, causing undesired browning that results in faster deterioration and shorter shelf lines. The only commercially available tyrosinase is mushroom tyrosinase and it shows the highest homology to the mammalian tyrosinase. Although kojic acid is currently used as a tyrosinase inhibitor, they have serious side effects such as dermatitis, carcinogenesis and hepatotoxicity. Therefore, in order to develop a more active and safer tyrosinase inhibitor, 3D QSAR pharmacophore models were generated based on experimentally known inhibitors. The pharmacophore model, Hypo1, was developed with a large cost difference, high correlation coefficient and low RMS deviation. Hypo1 showed a good spatial arrangement; consisting of five-point features including two hydrogen bond acceptor, one hydrogen bond donor and two hydrophobic features. Hypo1 was further validated by cost analysis, test set and Fisher’s randomisation method. Hypo1 was used as a 3D query for screening the in-house drug-like databases, and the hits were further selected by applying ADMET, Lipinski’s rule of five and fit value criteria. To identify binding conformations, the obtained hits were subjected to molecular docking. Finally, molecular dynamics simulations revealed the appropriate binding modes of hit compounds. To conclude, we propose the final three hit compounds with new structural scaffolds as a virtual candidate as tyrosinase inhibitors.

Communicated by Ramaswamy H. Sarma     

Pharmacophore modeling study based on known Spleen tyrosine kinase inhibitors together with virtual screening for identifying novel inhibitors

In this investigation, chemical features based 3D pharmacophore models were developed based on the known inhibitors of Spleen tyrosine kinase (Syk) with the aid of hiphop and hyporefine modules within catalyst. The best quantitative pharmacophore model, Hypo1, was used as a 3D structural query for retrieving potential inhibitors from chemical databases including Specs, NCI, MayBridge, and Chinese Nature Product Database (CNPD). The hit compounds were subsequently subjected to filtering by Lipinski’s rule of five and docking studies to refine the retrieved hits. Finally 30 compounds were selected from the top ranked hit compounds and conducted an in vitro kinase inhibitory assay. Six compounds showed a good inhibitory potency against Syk, which have been selected for further investigation.     

Pharmacophore modeling and in silico screening for new KDR kinase inhibitors

In order to elucidate the essential structural features for KDR kinase inhibitors, three-dimensional pharmacophore hypotheses were built on the basis of a set of known KDR kinase inhibitors selected from the literature with CATALYST program. Several methods tools used in validation of pharmacophore hypothsis were presented, and the first hypothesis (Hypo1) was considered to be the best pharmacophore hypothesis. The model (Hypo1) was then employed as 3D search query to screen the Traditional Chinese Medicine Database (TCMD) for other potential lead compounds. One hit illustrated high binding affinity with KDR kinase measured by the surface plasmon resonance biosensor. Docking studies may help elucidate the mechanisms of KDR kinase receptor-ligand interactions.     

A specific pharmacophore model of sodium-dependent glucose co-transporter 2 (SGLT2) inhibitors

Sodium-dependent glucose co-transporter 2 (SGLT2) plays a pivotal role in maintaining glucose equilibrium in the human body, emerging as one of the most promising targets for the treatment of diabetes mellitus type 2. Pharmacophore models of SGLT2 inhibitors have been generated with a training set of 25 SGLT2 inhibitors using Discovery Studio V2.1. The best hypothesis (Hypo1(SGLT2)) contains one hydrogen bond donor, five excluded volumes, one ring aromatic and three hydrophobic features, and has a correlation coefficient of 0.955, cost difference of 68.76, RMSD of 0.85. This model was validated by test set, Fischer randomization test and decoy set methods. The specificity of Hypo1(SGLT2) was evaluated. The pharmacophore features of Hypo1(SGLT2) were different from the best pharmacophore model (Hypo1(SGLT1)) of SGLT1 inhibitors we developed. Moreover, Hypo1(SGLT2) could effectively distinguish selective inhibitors of SGLT2 from those of SGLT1. These results indicate that a highly predictive and specific pharmacophore model of SGLT2 inhibitors has been successfully obtained. Then Hypo1(SGLT2) was used as a 3D query to screen databases including NCI and Maybridge for identifying new inhibitors of SGLT2. The hit compounds were subsequently subjected to filtering by Lipinski's rule of five. And several compounds selected from the top ranked hits have been suggested for further experimental assay studies.     

Pharmacophore modeling, molecular docking, and molecular dynamics simulation approaches for identifying new lead compounds for inhibiting aldose reductase 2

Aldose reductase 2 (ALR2), which catalyzes the reduction of glucose to sorbitol using NADP as a cofactor, has been implicated in the etiology of secondary complications of diabetes. A pharmacophore model, Hypo1, was built based on 26 compounds with known ALR2-inhibiting activity values. Hypo1 contains important chemical features required for an ALR2 inhibitor, and demonstrates good predictive ability by having a high correlation coefficient (0.95) as well as the highest cost difference (128.44) and the lowest RMS deviation (1.02) among the ten pharmacophore models examined. Hypo1 was further validated by Fisher's randomization method (95%), test set (r = 0.91), and the decoy set shows the goodness of fit (0.70). Furthermore, during virtual screening, Hypo1 was used as a 3D query to screen the NCI database, and the hit leads were sorted by applying Lipinski's rule of five and ADME properties. The best-fitting leads were subjected to docking to identify a suitable orientation at the ALR2 active site. The molecule that showed the strongest interactions with the critical amino acids was used in molecular dynamics simulations to calculate its binding affinity to the candidate molecules. Thus, Hypo1 describes the key structure-activity relationship along with the estimated activities of ALR2 inhibitors. The hit molecules were searched against PubChem to find similar molecules with new scaffolds. Finally, four molecules were found to satisfy all of the chemical features and the geometric constraints of Hypo1, as well as to show good dock scores, PLPs and PMFs. Thus, we believe that Hypo1 facilitates the selection of novel scaffolds for ALR2, allowing new classes of ALR2 inhibitors to be designed.     

Pharmacophore modeling and hybrid virtual screening for the discovery of novel IκB kinase 2 (IKK2) inhibitors

IKK2 (IκB kinase 2) inhibitors have been identified as potential drug candidates in the treatment of various immune/inflammatory disorders as well as cancer. So far more than one hundred small molecule inhibitors against IKK2 have been reported publicly. In this investigation, pharmacophore modeling was carried out to clarify the essential structure-activity relationship for the known IKK2 inhibitors. One of the established pharmacophore hypotheses, namely Hypo8, which has the best prediction ability to an external test data set, was suggested as a template for virtual screening. Evaluation of the performances of Hypo8 and a hybrid method (Hypo81docking) in virtual screening indicated that the use of the hybrid virtual screening considerably increased the hit rate and enrichment factor. The hybrid method was therefore adopted for screening several commercially available chemical databases, including Specs, NCI, Maybridge and Chinese Nature Product Database (CNPD), for novel potent IKK2 inhibitors. The hit compounds were subsequently subjected to filtering by Lipinski's rule of five. Finally some of the final hit compounds were selected and suggested for further experimental investigations.     

Pharmacophore modeling and virtual screening for designing potential PLK1 inhibitors

Pharmacophore models of Polo-like kinase-1 (PLK1) inhibitors have been established by using the HipHop and HypoGen algorithms implemented in the Catalyst software package. The best quantitative pharmacophore model, Hypo1, which has the highest correlation coefficient (0.9895), consists of one hydrogen bond acceptor, one hydrogen bond donor, one hydrophobic feature, and one hydrophobic aliphatic feature. Hypo1 was further validated by test set and cross validation method. Then Hypo1 was used as a 3D query to screen several databases including Specs, NCI, Maybridge, and Chinese Nature Product Database (CNPD). The hit compounds were subsequently subjected to filtering by Lipinski's rule of five and docking study to refine the retrieved hits and as a result to reduce the rate of false positive. Finally, a total of 20 compounds were selected and have been shifted to in vitro and in vivo studies. As far as we know, this is the first report on the pharmacophore modeling even the first publicly reported virtual screening study of PLK1 inhibitors.     

Discover potential inhibitors for PFKFB3 using 3D-QSAR,virtual screening,molecular docking and molecular dynamics simulation

Abstract

The 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase-3 (PFKFB3) is a master regulator of glycolysis in cancer cells by synthesizing fructose-2,6-bisphosphate (F-2,6-BP), a potent allosteric activator of phosphofructokinase-1 (PFK-1), which is a rate-limiting enzyme of glycolysis. PFKFB3 is an attractive target for cancer treatment. It is valuable to discover promising inhibitors by using 3D-QSAR pharmacophore modeling, virtual screening, molecular docking and molecular dynamics simulation. Twenty molecules with known activity were used to build 3D-QSAR pharmacophore models. The best pharmacophore model was ADHR called Hypo1, which had the highest correlation value of 0.98 and the lowest RMSD of 0.82. Then, the Hypo1 was validated by cost value method, test set method and decoy set validation method. Next, the Hypo1 combined with Lipinski's rule of five and ADMET properties were employed to screen databases including Asinex and Specs, total of 1,048,159 molecules. The hits retrieved from screening were docked into protein by different procedures including HTVS, SP and XP. Finally, nine molecules were picked out as potential PFKFB3 inhibitors. The stability of PFKFB3-lead complexes was verified by 40?ns molecular dynamics simulation. The binding free energy and the energy contribution of per residue to the binding energy were calculated by MM-PBSA based on molecular dynamics simulation.     

Discovery of novel 5α-reductase type II inhibitors by pharmacophore modelling,virtual screening,molecular docking and molecular dynamics simulations

Benign prostatic hyperplasia (BPH) is caused by augmented levels of androgen dihydrotestosterone (DHT) which is involved in the growth of the prostate in humans. 5α-Reductase type II (5αR2) is an intracellular enzyme that catalyses the formation of DHT from testosterone; hence, the inhibition of 5αR2 has emerged as one of the most promising strategies for the treatment of BPH. In this study, a computational approach that integrates ligand-based pharmacophore modelling, virtual screening, molecular docking and molecular dynamics (MD) simulations was adopted to discover novel 5αR2 inhibitors with less side effects. After validating by Fischer's randomisation and Güner–Henry test, the best quantitative pharmacophore model (Hypo1), consisting of two hydrogen-bond acceptors and three hydrophobic features, was subsequently used as a three-dimensional-query in virtual screening to identify potential hits from Maybridge and National Cancer Institute databases. These hits were further filtered by ADMET (absorption, distribution, metabolism, elimination and toxicology) and molecular docking experiments, and their binding stabilities were validated by 10-ns MD simulations. Finally, only one hit was identified as a potential lead based on higher predicted inhibitory activity to 5αR2 compared with the most active inhibitor (finasteride). Our results further suggest that this potential lead could easily be synthesised and has structural novelty, making it a promising candidate for treating BPH.     

Identification of potent virtual leads to design novel PLK1 inhibitors: pharmacophore modelling,virtual screening and molecular docking studies

The aim of this study was to identify novel scaffolds and utilise them in designing potent PLK1 inhibitors. Three-dimensional pharmacophore models on the basis of chemical features were developed for PLK1 on the basis of the known inhibitors. The best pharmacophore model, Hypo 1, which has the highest correlation (0.96), the highest cost difference (75.7494), the lowest total cost and RMSD (75.7494, 0.5458), contains two hydrophobics, one ring aromatic and one hydrogen donor. Hypo 1 was validated by the test set, decoy set and the Fischer's randomisation method. Then it was used for chemical database virtual screening. The hit compounds were filtered by Lipinski's rule of five and absorption, distribution, metabolism, elimination and toxicity properties. Finally, 24 compounds with good estimated activity values were used for docking studies. These results will be used to develop new inhibitors of PLK1 as leads.     

Pharmacophore modeling and virtual screening for designing potential 5-Lipoxygenase inhibitors

Inhibitors of the 5-Lipoxygenase (5-LOX) pathway have a therapeutic potential in a variety of inflammatory disorders such as asthma. In this study, chemical feature based pharmacophore models of inhibitors of 5-LOX have been developed with the aid of HipHop and HypoGen modules within Catalyst program package. The best quantitative pharmacophore model, Hypo1, which has the highest correlation coefficient (0.97), consists of two hydrogen-bond acceptors, one hydrophobic feature and one ring aromatic feature. Hypo1 was further validated by test set and cross validation method. The application of the model shows great success in predicting the activities of 65 known 5-LOX inhibitors in our test set with a correlation coefficient of 0.85 with a cross validation of 95% confidence level, proving that the model is reliable in identifying structurally diverse compounds for inhibitory activity against 5-LOX. Furthermore, Hypo1 was used as a 3D query for screening Maybridge and NCI databases within catalyst and also drug like compounds obtained from Enamine Ltd, which follow Lipinski’s rule of five. The hit compounds were subsequently subjected to filtering by docking and visualization, to identify the potential lead molecules. Finally 5 potential lead compounds, identified in the above process, were evaluated for their inhibitory activities. These studies resulted in the identification of two compounds with potent inhibition of 5-LOX activity with IC₅₀ of 14 μM and 35 μM, respectively. These studies thus validate the pharmacophore model generated and suggest the usefulness of the model in screening of various small molecule libraries and identification of potential lead compounds for 5-LOX inhibition.     

Exploration of Novel Inhibitors for Bruton’s Tyrosine Kinase by 3D QSAR Modeling and Molecular Dynamics Simulation

Bruton’s tyrosine kinase (BTK) is a cytoplasmic, non-receptor tyrosine kinase which is expressed in most of the hematopoietic cells and plays an important role in many cellular signaling pathways. B cell malignancies are dependent on BCR signaling, thus making BTK an efficient therapeutic target. Over the last few years, significant efforts have been made in order to develop BTK inhibitors to treat B-cell malignancies, and autoimmunity or allergy/hypersensitivity but limited success has been achieved. Here in this study, 3D QSAR pharmacophore models were generated for Btk based on known IC₅₀ values and experimental energy scores with extensive validations. The five features pharmacophore model, Hypo1, includes one hydrogen bond acceptor lipid, one hydrogen bond donor, and three hydrophobic features, which has the highest correlation coefficient (0.98), cost difference (112.87), and low RMS (1.68). It was further validated by the Fisher’s randomization method and test set. The well validated Hypo1 was used as a 3D query to search novel Btk inhibitors with different chemical scaffold using high throughput virtual screening technique. The screened compounds were further sorted by applying ADMET properties, Lipinski’s rule of five and molecular docking studies to refine the retrieved hits. Furthermore, molecular dynamic simulation was employed to study the stability of docked conformation and to investigate the binding interactions in detail. Several important hydrogen bonds with Btk were revealed, which includes the gatekeeper residues Glu475 and Met 477 at the hinge region. Overall, this study suggests that the proposed hits may be more effective inhibitors for cancer and autoimmune therapy.     

Molecular modelling study on human histamine H1 receptor and its applications in virtual lead identification for designing novel inverse agonists

Human histamine H1 receptor (HHR1) is one of the G protein-coupled receptors (GPCRs) known for their constitutive activation in the absence of agonist binding. Inverse agonists are the compounds that inhibit this constitutive activity of GPCRs. HHR1 is involved in allergic reactions and is also known to be constitutively active. An updated quantitative pharmacophore model, Hypo1, has been developed using a diverse set of known HHR1 inverse agonists employing the HypoGen algorithm as implemented in Accelrys Discovery Studio 2.1. Hypo1 comprised four pharmacophore features (each one of hydrogen bond acceptor, hydrophobic, ring aromatic and positive ionisable group) along with a high correlation value of 0.944. This pharmacophore model was validated using an external test set containing 25 diverse inverse agonists and CatScramble method. Three chemical databases were screened for novel chemical scaffolds using Hypo1 as a query, to be utilised in drug design. The 3D structure of HHR1 has been constructed using human β2 adrenergic receptor. Molecular docking studies were performed with the database hit compounds using GOLD 4.1 program. The combination of all results led us to identify novel compounds to be deployed in designing new generation HHR1 inverse agonists.     

Toll-Like Receptor 7 Agonists: Chemical Feature Based Pharmacophore Identification and Molecular Docking Studies

Chemical feature based pharmacophore models were generated for Toll-like receptors 7 (TLR7) agonists using HypoGen algorithm, which is implemented in the Discovery Studio software. Several methods tools used in validation of pharmacophore model were presented. The first hypothesis Hypo1 was considered to be the best pharmacophore model, which consists of four features: one hydrogen bond acceptor, one hydrogen bond donor, and two hydrophobic features. In addition, homology modeling and molecular docking studies were employed to probe the intermolecular interactions between TLR7 and its agonists. The results further confirmed the reliability of the pharmacophore model. The obtained pharmacophore model (Hypo1) was then employed as a query to screen the Traditional Chinese Medicine Database (TCMD) for other potential lead compounds. One hit was identified as a potent TLR7 agonist, which has antiviral activity against hepatitis virus in vitro. Therefore, our current work provides confidence for the utility of the selected chemical feature based pharmacophore model to design novel TLR7 agonists with desired biological activity.     

Novel GSK-3beta inhibitors from sequential virtual screening

Glycogen synthase kinase-3 (GSK-3beta) has been emerging as a key therapeutic target for type-2 diabetics, Alzheimer's disease, cancer, and chronic inflammation. For the purpose of finding biologically active and novel compounds and providing new idea for drug-design, we performed virtual screening using commercially available database. Three-dimensional common feature pharmacophore model was developed by using HipHop program provided in Catalyst software and it was used as a query for screening database. Recursive partitioning (RP) model was developed as a filtering system, which was able to classify active and inactive compounds. Eventually, a sequential virtual screening procedure (SQSP) was conducted by applying the common feature pharmacophore and RP model in succession to discover novel potent GSK-3beta inhibitors. The final 56 hit compounds were carefully selected considering predicted docking mode in crystal structures. Subsequent enzyme assay for human GSK-3beta protein confirmed that three compounds of these hit compounds exhibit micromolar inhibitory activity. Here, we report novel hit compounds and their binding mode in the active site of GSK-3beta crystal structure.     

Discovery of potential pancreatic cholesterol esterase inhibitors using pharmacophore modelling, virtual screening, and optimization studies

Pancreatic cholesterol esterase (CEase) is a serine hydrolase involved in the hydrolysis of variety of lipids and transport of free cholesterol. In this study, pharmacophore hypotheses based on known inhibitors were generated using common feature pharmacophore generation protocol available in Discovery Studio program. The best pharmacophore model containing two hydrogen bond acceptor and three hydrophobic features was selected and validated. It was further used in screening three diverse chemical databases. Hit compounds were subjected to drug-likeness and molecular docking studies. Four hits, namely SEW00846, NCI0040784, GK03167, and CD10645, were selected based on the GOLD fitness score and interaction with active site amino acids. All hit compounds were further optimized to improve their binding in the active site. The optimized compounds were found to have improved binding at the active site. Strongly binding optimized hits at the active site can act as virtual leads in potent CEase inhibitor designing.     

Homology modeling,docking and structure-based virtual screening for new inhibitor identification of Klebsiella pneumoniae heptosyltransferase-III

Abstract

Klebsiella pneumoniae (K. pneumoniae) is a Gram-negative opportunistic pathogen commonly associated with hospital-acquired infections that are often resistant even to antibiotics. Heptosyltransferase (HEP) belongs to the family of glycosyltransferase-B (GT-B) and plays an important in the synthesis of lipopolysaccharides (LPS) essential for the formation of bacterial cell membrane. HEP-III participates in the transfer of heptose sugar to the outer surface of bacteria to synthesize LPS. LPS truncation increases the bacterial sensitivity to hydrophobic antibiotics and detergents, making the HEP as a novel drug target. In the present study, we report the 3D homology model of K. pneumoniae HEP-III and its structure validation. Active site was identified based on similarities with known structures using Dali server, and structure-based pharmacophore model was developed for the active site substrate ADP. The generated pharmacophore model was used as a 3D search query for virtual screening of the ASINEX database. The hit compounds were further filtered based on fit value, molecular docking, docking scores, molecular dynamics (MD) simulations of HEP-III complexed with hit molecules, followed by binding free energy calculations using Molecular Mechanics-Poisson–Boltzmann Surface Area (MM-PBSA). The insights obtained in this work provide the rationale for design of novel inhibitors targeting K. pneumoniae HEP-III and the mechanistic aspects of their binding.

Communicated by Ramaswamy H. Sarma     

Exploration of Virtual Candidates for Human HMG-CoA Reductase Inhibitors Using Pharmacophore Modeling and Molecular Dynamics Simulations

3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) is a rate-controlling enzyme in the mevalonate pathway which involved in biosynthesis of cholesterol and other isoprenoids. This enzyme catalyzes the conversion of HMG-CoA to mevalonate and is regarded as a drug target to treat hypercholesterolemia. In this study, ten qualitative pharmacophore models were generated based on chemical features in active inhibitors of HMGR. The generated models were validated using a test set. In a validation process, the best hypothesis was selected based on the statistical parameters and used for virtual screening of chemical databases to find novel lead candidates. The screened compounds were sorted by applying drug-like properties. The compounds that satisfied all drug-like properties were used for molecular docking study to identify their binding conformations at active site of HMGR. The final hit compounds were selected based on docking score and binding orientation. The HMGR structures in complex with the hit compounds were subjected to 10 ns molecular dynamics simulations to refine the binding orientation as well as to check the stability of the hits. After simulation, binding modes including hydrogen bonding patterns and molecular interactions with the active site residues were analyzed. In conclusion, four hit compounds with new structural scaffold were suggested as novel and potent HMGR inhibitors.     

In silico approaches to identify novel myeloid cell leukemia-1 (Mcl-1) inhibitors for treatment of cancer

Myeloid cell leukemia-1 (Mcl-1) has been a validated and attractive target for cancer therapy. Over-expression of Mcl-1 in many cancers allows cancer cells to evade apoptosis and contributes to the resistance to current chemotherapeutics. Here, we identified new Mcl-1 inhibitors using a multi-step virtual screening approach. First, based on two different ligand-receptor complexes, 20 pharmacophore models were established by simultaneously using ‘Receptor-Ligand Pharmacophore Generation’ method and manual build feature method, and then carefully validated by a test database. Then, pharmacophore-based virtual screening (PB-VS) could be performed by using the 20 pharmacophore models. In addition, docking study was used to predict the possible binding poses of compounds, and the docking parameters were optimized before performing docking-based virtual screening (DB-VS). Moreover, a 3D QSAR model was established by applying the 55 aligned Mcl-1 inhibitors. The 55 inhibitors sharing the same scaffold were docked into the Mcl-1 active site before alignment, then the inhibitors with possible binding conformations were aligned. For the training set, the 3D QSAR model gave a correlation coefficient r² of 0.996; for the test set, the correlation coefficient r² was 0.812. Therefore, the developed 3D QSAR model was a good model, which could be applied for carrying out 3D QSAR-based virtual screening (QSARD-VS). After the above three virtual screening methods orderly filtering, 23 potential inhibitors with novel scaffolds were identified. Furthermore, we have discussed in detail the mapping results of two potent compounds onto pharmacophore models, 3D QSAR model, and the interactions between the compounds and active site residues.     

Pharmacophore mapping of diverse classes of farnesyltransferase inhibitors

Protein farnesyltransferase (FTase) is a zinc-dependent enzyme that catalyzes the attachment of a farnesyl lipid group to the sulfur atom of a cysteine residue of numerous proteins involved in cell signaling including the oncogenic H-Ras protein. Pharmacophore models were developed by using Catalyst HypoGen program with a training set of 22 farnesyltransferase inhibitors (FTIs), which were carefully selected with great diversity in both molecular structure and bioactivity for discovering new potent FTIs. The best pharmacophore hypothesis (Hypo 1), consisting of four features, namely, one hydrogen-bond acceptor (HBA), one hydrophobic point (HY), and two ring aromatics (RA), has a correlation coefficient of 0.961, a root mean square deviation (RMSD) of 0.885, and a cost difference of 62.436, suggesting that a highly predictive pharmacophore model was successfully obtained. For the test series, a classification scheme was used to distinguish highly active from moderately active and inactive compounds on the basis of activity ranges. Hypo 1 was validated with 181 test set compounds, which has a correlation coefficient of 0.713 between estimated activity and experimentally measured activity. The model was further validated by screening a database spiked with 25 known inhibitors. The model picked up all 25 known inhibitors giving an enrichment factor of 10.892. The results demonstrate that the hypothesis derived in this study can be considered to be a useful and reliable tool in identifying structurally diverse compounds with desired biological activity.