Consensus Virtual Screening Research Articles

Discovery of novel IDO1/TDO2 dual inhibitors: a consensus Virtual screening approach with molecular dynamics simulations, and binding free energy analysis

The pursuit of effective cancer immunotherapy drugs remains challenging, with overexpression of indoleamine 2,3-dioxygenase 1 (IDO1) and tryptophan 2,3-dioxygenase 2 (TDO2) allowing cancer cells to evade immune attacks. While several IDO1 inhibitors have undergone clinical testing, only three dual IDO1/TDO2 inhibitors have reached human trials. Hence, this study focuses on identifying novel IDO1/TDO2 dual inhibitors through consensus structure-based virtual screening (SBVS). ZINC15 natural products library was refined based on molecular descriptors, and the selected compounds were docked to the holo form IDO1 and TDO2 using two different software programs and ranked according to their consensus docking scores. The top-scoring compounds underwent in silico evaluations for pharmacokinetics, toxicity, CYP3A4 affinity, molecular dynamics (MD) simulations, and MM-GBSA binding free energy calculations. Five compounds (ZINC00000079405/10, ZINC00004028612/11, ZINC00013380497/12, ZINC00014613023/13, and ZINC00103579819/14) were identified as potential IDO1/TDO2 dual inhibitors due to their high consensus docking scores, key residue interactions with the enzymes, favorable pharmacokinetics, and avoidance of CYP3A4 binding. MD simulations of the top three hits with IDO1 indicated conformational changes and compactness, while MM-GBSA analysis revealed strong binding free energy for compounds 10 (ΔG: −20.13 kcal/mol) and 11 (ΔG: −16.22 kcal/mol). These virtual hits signify a promising initial step in identifying candidates as supplementary therapeutics to immune checkpoint inhibitors in cancer treatment. Their potential to deliver potent dual inhibition of IDO1/TDO2, along with safety and favorable pharmacokinetics, makes them compelling. Validation through in vitro and in vivo assays should be conducted to confirm their activity, selectivity, and preclinical potential as holo IDO1/TDO2 dual inhibitors. Communicated by Ramaswamy H. Sarma

Aminopyrimidine Derivatives as Multiflavivirus Antiviral Compounds Identified from a Consensus Virtual Screening Approach.

Around three billion people are at risk of infection by the dengue virus (DENV) and potentially other flaviviruses. Worldwide outbreaks of DENV, Zika virus (ZIKV), and yellow fever virus (YFV), the lack of antiviral drugs, and limitations on vaccine usage emphasize the need for novel antiviral research. Here, we propose a consensus virtual screening approach to discover potential protease inhibitors (NS3pro) against different flavivirus. We employed an in silico combination of a hologram quantitative structure-activity relationship (HQSAR) model and molecular docking on characterized binding sites followed by molecular dynamics (MD) simulations, which filtered a data set of 7.6 million compounds to 2,775 hits. Lastly, docking and MD simulations selected six final potential NS3pro inhibitors with stable interactions along the simulations. Five compounds had their antiviral activity confirmed against ZIKV, YFV, DENV-2, and DENV-3 (ranging from 4.21 ± 0.14 to 37.51 ± 0.8 μM), displaying aggregator characteristics for enzymatic inhibition against ZIKV NS3pro (ranging from 28 ± 7 to 70 ± 7 μM). Taken together, the compounds identified in this approach may contribute to the design of promising candidates to treat different flavivirus infections.

Consensus Virtual Screening Protocol Towards the Identification of Small Molecules Interacting with the Colchicine Binding Site of the Tubulin-microtubule System.

Modification of the tubulin-microtubule (Tub-Mts) system has generated effective strategies for developing different treatments for cancer. A huge amount of clinical data about inhibitors of the tubulin-microtubule system have supported and validated the studies on this pharmacological target. However, many tubulin-microtubule inhibitors have been developed from representative and common scaffolds that cover a small region of the chemical space with limited structural innovation. The main goal of this study is to develop the first consensus virtual screening protocol for natural products (ligand- and structure-based drug design methods) tuned for the identification of new potential inhibitors of the Tub-Mts system. A combined strategy that involves molecular similarity, molecular docking, pharmacophore modeling, and in silico ADMET prediction has been employed to prioritize the selections of potential inhibitors of the Tub-Mts system. Five compounds were selected and further studied using molecular dynamics and binding energy predictions to characterize their possible binding mechanisms. Their structures correspond to 5-[2-(4-hydroxy-3-methoxyphenyl) ethyl]-2,3-dimethoxyphenol (1), 9,10-dihydro-3,4-dimethoxy-2,7-phenanthrenediol (2), 2-(3,4-dimethoxyphenyl)-5,7-dihydroxy-6-methoxy-4H-1-benzopyran-4-one (3), 13,14-epoxyparvifoline-4',5',6'-trimethoxybenzoate (4), and phenylmethyl 6-hydroxy-2,3-dimethoxybenzoate (5). Compounds 1-3 have been associated with literature reports that confirm their activity against several cancer cell lines, thus supporting the utility of this protocol.

Identifying Novel Inhibitors for Hepatic Organic Anion Transporting Polypeptides by Machine Learning-Based Virtual Screening.

Integration of statistical learning methods with structure-based modeling approaches is a contemporary strategy to identify novel lead compounds in drug discovery. Hepatic organic anion transporting polypeptides (OATP1B1, OATP1B3, and OATP2B1) are classical off-targets, and it is well recognized that their ability to interfere with a wide range of chemically unrelated drugs, environmental chemicals, or food additives can lead to unwanted adverse effects like liver toxicity and drug-drug or drug-food interactions. Therefore, the identification of novel (tool) compounds for hepatic OATPs by virtual screening approaches and subsequent experimental validation is a major asset for elucidating structure-function relationships of (related) transporters: they enhance our understanding about molecular determinants and structural aspects of hepatic OATPs driving ligand binding and selectivity. In the present study, we performed a consensus virtual screening approach by using different types of machine learning models (proteochemometric models, conformal prediction models, and XGBoost models for hepatic OATPs), followed by molecular docking of preselected hits using previously established structural models for hepatic OATPs. Screening the diverse REAL drug-like set (Enamine) shows a comparable hit rate for OATP1B1 (36% actives) and OATP1B3 (32% actives), while the hit rate for OATP2B1 was even higher (66% actives). Percentage inhibition values for 44 selected compounds were determined using dedicated in vitro assays and guided the prioritization of several highly potent novel hepatic OATP inhibitors: six (strong) OATP2B1 inhibitors (IC50 values ranging from 0.04 to 6 μM), three OATP1B1 inhibitors (2.69 to 10 μM), and five OATP1B3 inhibitors (1.53 to 10 μM) were identified. Strikingly, two novel OATP2B1 inhibitors were uncovered (C7 and H5) which show high affinity (IC50 values: 40 nM and 390 nM) comparable to the recently described estrone-based inhibitor (IC50 = 41 nM). A molecularly detailed explanation for the observed differences in ligand binding to the three transporters is given by means of structural comparison of the detected binding sites and docking poses.

Potential Cancer- and Alzheimer's Disease-Targeting Phosphodiesterase Inhibitors from Uvaria alba: Insights from In Vitro and Consensus Virtual Screening.

Inhibition of the major cyclic adenosine monophosphate-metabolizing enzyme PDE4 has shown potential for the discovery of drugs for cancer, inflammation, and neurodegenerative disorders such as Alzheimer’s disease. As a springboard to explore new anti-cancer and anti-Alzheimer’s chemical prototypes from rare Annonaceae species, the present study evaluated anti-PDE4B along with antiproliferative and anti-cholinesterase activities of the extracts of the Philippine endemic species Uvaria alba using in vitro assays and framed the resulting biological significance through computational binding and reactivity-based experiments. Thus, the PDE4 B2B-inhibiting dichloromethane sub-extract (UaD) of U. alba elicited antiproliferative activity against chronic myelogenous leukemia (K-562) and cytostatic effects against human cervical cancer (HeLa). The extract also profoundly inhibited acetylcholinesterase (AChE), an enzyme involved in the progression of neurodegenerative diseases. Chemical profiling analysis of the bioactive extract identified 18 putative secondary metabolites. Molecular docking and molecular dynamics simulations showed strong free energy binding mechanisms and dynamic stability at 50-ns simulations in the catalytic domains of PDE4 B2B, ubiquitin-specific peptidase 14, and Kelch-like ECH-associated protein 1 (KEAP-1 Kelch domain) for the benzylated dihydroflavone dichamanetin (16), and of an AChE and KEAP-1 BTB domain for 3-(3,4-dihydroxybenzyl)-3′,4′,6-trihydroxy-2,4-dimethoxychalcone (8) and grandifloracin (15), respectively. Density functional theory calculations to demonstrate Michael addition reaction of the most electrophilic metabolite and kinetically stable grandifloracin (15) with Cys151 of the KEAP-1 BTB domain illustrated favorable formation of a β-addition adduct. The top-ranked compounds also conferred favorable in silico pharmacokinetic properties.

Identification of 14 Known Drugs as Inhibitors of the Main Protease of SARS-CoV-2

A consensus virtual screening protocol has been applied to ca. 2000 approved drugs to seek inhibitors of the main protease (Mpro) of SARS-CoV-2, the virus responsible for COVID-19. 42 drugs emerged as top candidates, and after visual analyses of the predicted structures of their complexes with Mpro, 17 were chosen for evaluation in a kinetic assay for Mpro inhibition. Remarkably 14 of the compounds at 100-μM concentration were found to reduce the enzymatic activity and 5 provided IC50 values below 40 μM: manidipine (4.8 μM), boceprevir (5.4 μM), lercanidipine (16.2 μM), bedaquiline (18.7 μM), and efonidipine (38.5 μM). Structural analyses reveal a common cloverleaf pattern for the binding of the active compounds to the P1, P1′, and P2 pockets of Mpro. Further study of the most active compounds in the context of COVID-19 therapy is warranted, while all of the active compounds may provide a foundation for lead optimization to deliver valuable chemotherapeutics to combat the pandemic.

Structure-Based Virtual Screening: From Classical to Artificial Intelligence.

The drug development process is a major challenge in the pharmaceutical industry since it takes a substantial amount of time and money to move through all the phases of developing of a new drug. One extensively used method to minimize the cost and time for the drug development process is computer-aided drug design (CADD). CADD allows better focusing on experiments, which can reduce the time and cost involved in researching new drugs. In this context, structure-based virtual screening (SBVS) is robust and useful and is one of the most promising in silico techniques for drug design. SBVS attempts to predict the best interaction mode between two molecules to form a stable complex, and it uses scoring functions to estimate the force of non-covalent interactions between a ligand and molecular target. Thus, scoring functions are the main reason for the success or failure of SBVS software. Many software programs are used to perform SBVS, and since they use different algorithms, it is possible to obtain different results from different software using the same input. In the last decade, a new technique of SBVS called consensus virtual screening (CVS) has been used in some studies to increase the accuracy of SBVS and to reduce the false positives obtained in these experiments. An indispensable condition to be able to utilize SBVS is the availability of a 3D structure of the target protein. Some virtual databases, such as the Protein Data Bank, have been created to store the 3D structures of molecules. However, sometimes it is not possible to experimentally obtain the 3D structure. In this situation, the homology modeling methodology allows the prediction of the 3D structure of a protein from its amino acid sequence. This review presents an overview of the challenges involved in the use of CADD to perform SBVS, the areas where CADD tools support SBVS, a comparison between the most commonly used tools, and the techniques currently used in an attempt to reduce the time and cost in the drug development process. Finally, the final considerations demonstrate the importance of using SBVS in the drug development process.

Molecular Architect: A User-Friendly Workflow for Virtual Screening.

Computer-assisted drug design (CADD) methods have greatly contributed to the development of new drugs. Among CADD methodologies, virtual screening (VS) can enrich the compound collection with molecules that have the desired physicochemical and pharmacophoric characteristics that are needed to become drugs. Many free tools are available for this purpose, but they are difficult to use and do not have a graphical user interface. Furthermore, several free tools must be used to carry out the entire VS process, requiring the user to process the results of one software program so that they can be used in another program, adding a potential source of human error. Moreover, some software programs require knowledge of advanced computational skills, such as programming languages. This context has motivated us to develop Molecular Architect (MolAr). MolAr is a workflow with a simple and intuitive interface that acts in an integrated and automated form to perform the entire VS process, from protein preparation (homology modeling and protonation state) to virtual screening. MolAr carries out VS through AutoDock Vina, DOCK 6, or a consensus of the two. Two case studies were conducted to demonstrate the performance of MolAr. In the first study, the feasibility of using MolAr for DNA–ligand systems was assessed. Both AutoDock Vina and DOCK 6 showed good results in performing VS in DNA–ligand systems. However, the use of consensus virtual screening was able to enrich the results. According to the area under the ROC curve and the enrichment factors, consensus VS was better able to predict the positions of the active ligands. The second case study was performed on 8 targets from the DUD-E database and 10 active ligands for each target. The results demonstrated that using the final ligand conformation provided by AutoDock Vina as an input for DOCK 6 improved the DOCK 6 ROC curves by up to 42% in VS. These case studies demonstrated that MolAr is capable conducting the VS process and is an easy-to-use and effective tool. MolAr is available for download free of charge at http: //www.drugdiscovery.com.br/software/.

Consensus virtual screening of dark chemical matter and food chemicals uncover potential inhibitors of SARS-CoV-2 main protease.

The pandemic caused by SARS-CoV-2 (COVID-19 disease) has claimed more than 500 000 lives worldwide, and more than nine million people are infected. Unfortunately, an effective drug or vaccine for its treatment is yet to be found. The increasing information available on critical molecular targets of SARS-CoV-2 and active compounds against related coronaviruses facilitates the proposal (or repurposing) of drug candidates for the treatment of COVID-19, with the aid of in silico methods. As part of a global effort to fight the COVID-19 pandemic, herein we report a consensus virtual screening of extensive collections of food chemicals and compounds known as dark chemical matter. The rationale is to contribute to global efforts with a description of currently underexplored chemical space regions. The consensus approach included combining similarity searching with various queries and fingerprints, molecular docking with two docking protocols, and ADMETox profiling. We propose compounds commercially available for experimental testing. The full list of virtual screening hits is disclosed.

SEARCHING BY METOD OF NEURAL NETWORK MODELING OF CONDENSED BENZIMIDAZOLE DERIVATIVES WITH MULTI-TARGET RAGE-INHIBITORY ACTIVITY

Using a multitarget neural network model of RAGE-inhibitory activity, a consensus virtual screening of a library of new condensed benzimidazole derivatives was performed. Compounds with a essential RAGE-inhibitory effect have been found.

Virtual Screening with Generative Topographic Maps: How Many Maps Are Required?

Universal generative topographic maps (GTMs) provide two-dimensional representations of chemical space selected for their "polypharmacological competence", that is, the ability to simultaneously represent meaningful activity and property landscapes, associated with many distinct targets and properties. Several such GTMs can be generated, each based on a different initial descriptor vector, encoding distinct structural features. While their average polypharmacological competence may indeed be equivalent, they nevertheless significantly diverge with respect to the quality of each property-specific landscape. In this work, we show that distinct universal maps represent complementary and strongly synergistic views of biologically relevant chemical space. Eight universal GTMs were employed as support for predictive classification landscapes, using more than 600 active/inactive ligand series associated with as many targets from the ChEMBL database (v.23). For nine of these targets, it was possible to extract, from the Directory of Useful Decoys (DUD), truly external sets featuring sufficient "actives" and "decoys" not present in the landscape-defining ChEMBL ligand sets. For each such molecule, projected on every class landscape of a particular universal map, a probability of activity was estimated, in analogy to a virtual screening (VS) experiment. Cross-validated (CV) balanced accuracy on landscape-defining ChEMBL data was unable to predict the success of that landscape in VS. Thus, the universal map with best CV results for a given property should not be prioritized as the implicitly best predictor. For a given map, predictions for many DUD compounds are not trustworthy, according to applicability domain considerations. By contrast, simultaneous application of all universal maps, and rating of the likelihood of activity as the mean returned by all applicable maps, significantly improved prediction results. Performance measures in consensus VS using multiple maps were always superior or similar to those of the best individual map.

Identification of human flap endonuclease 1 (FEN1) inhibitors using a machine learning based consensus virtual screening.

Human Flap endonuclease1 (FEN1) is an enzyme that is indispensable for DNA replication and repair processes and inhibition of its Flap cleavage activity results in increased cellular sensitivity to DNA damaging agents (cisplatin, temozolomide, MMS, etc.), with the potential to improve cancer prognosis. Reports of the high expression levels of FEN1 in several cancer cells support the idea that FEN1 inhibitors may target cancer cells with minimum side effects to normal cells. In this study, we used large publicly available, high-throughput screening data of small molecule compounds targeted against FEN1. Two machine learning algorithms, Support Vector Machine (SVM) and Random Forest (RF), were utilized to generate four classification models from huge PubChem bioassay data containing probable FEN1 inhibitors and non-inhibitors. We also investigated the influence of randomly selected Zinc-database compounds as negative data on the outcome of classification modelling. The results show that the SVM model with inactive compounds was superior to RF with Matthews's correlation coefficient (MCC) of 0.67 for the test set. A Maybridge database containing approximately 53 000 compounds was screened and top ranking 5 compounds were selected for enzyme and cell-based in vitro screening. The compound JFD00950 was identified as a novel FEN1 inhibitor with in vitro inhibition of flap cleavage activity as well as cytotoxic activity against a colon cancer cell line, DLD-1.

Discovery and Optimization of a Selective Ligand for the Switch/Sucrose Nonfermenting-Related Bromodomains of Polybromo Protein-1 by the Use of Virtual Screening and Hydration Analysis.

Bromodomains (BRDs) are epigenetic interaction domains currently recognized as emerging drug targets for development of anticancer or anti-inflammatory agents. In this study, development of a selective ligand of the fifth BRD of polybromo protein-1 (PB1(5)) related to switch/sucrose nonfermenting (SWI/SNF) chromatin remodeling complexes is presented. A compound collection was evaluated by consensus virtual screening and a hit was identified. The biophysical study of protein–ligand interactions was performed using X-ray crystallography and isothermal titration calorimetry. Collective data supported the hypothesis that affinity improvement could be achieved by enhancing interactions of the complex with the solvent. The derived SAR along with free energy calculations and a consensus hydration analysis using WaterMap and SZmap algorithms guided rational design of a set of novel analogues. The most potent analogue demonstrated high affinity of 3.3 μM and an excellent selectivity profile, thus comprising a promising lead for the development of chemical probes targeting PB1(5).

Structure-Based Consensus Scoring Scheme for Selecting Class A Aminergic GPCR Fragments.

Aminergic G-protein coupled receptors (GPRCs) represent well-known targets of central nervous-system related diseases. In this study a structure-based consensus virtual screening scheme was developed for designing targeted fragment libraries against class A aminergic GPCRs. Nine representative aminergic GPCR structures were selected by first clustering available X-ray structures and then choosing the one in each cluster that performs best in self-docking calculations. A consensus scoring protocol was developed using known promiscuous aminergic ligands and decoys as a training set. The consensus score (FrACS-fragment aminergic consensus score) calculated for the optimized protein ensemble showed improved enrichments in most cases as compared to stand-alone structures. Retrospective validation was carried out on public screening data for aminergic targets (5-HT1 serotonin receptor, TA1 trace-amine receptor) showing 8-17-fold enrichments using an ensemble of aminergic receptor structures. The performance of the structure based FrACS in combination with our ligand-based prefilter (FrAGS) was investigated both in a retrospective validation on the ChEMBL database and in a prospective validation on an in-house fragment library. In prospective validation virtual fragment hits were tested on 5-HT6 serotonin receptors not involved in the development of FrACS. Six out of the 36 experimentally tested fragments exhibited remarkable antagonist efficacies, and 4 showed IC50 values in the low micromolar or submicromolar range in a cell-based assay. Both retrospective and prospective validations revealed that the methodology is suitable for designing focused class A GPCR fragment libraries from large screening decks, commercial compound collections, or virtual databases.

Fingerprint-based consensus virtual screening towards structurally new 5-HT(6)R ligands.

Virtual screening towards the search of new 5-HT6R ligands was carried out with three different fingerprints used for molecules representation. Two structurally new compounds were found to be characterized by a significant 5-HT6R activity (Ki of 119 and 670 nM). The compounds do not possess a positive ionizable group in their structures and therefore they belong to the group of atypical, non-basic 5-HT6R ligands. The obtained hits were proved to fit well in the 5-HT6R binding cavity by docking and molecular dynamic simulation experiments. Moreover, an in silico evaluation of the ADMET properties of these compounds predicted their drug-like character.

Consensus scoring approach to identify the inhibitors of AMP-activated protein kinase α2 with virtual screening.

Due to the involvement in the ischemic damage in the brain, 5'-adenosine monophosphate-activated protein kinase subunit α2 (AMPK2) serves as a promising target for the development of new medicines for stroke. Despite such a pharmaceutical importance, only a few small-molecule inhibitors have been reported so far. We aim in this study to identify a new class of AMPK2 inhibitors based on the structure-based virtual screening with docking simulations. To take advantage of and supplement the deficiencies of force field-based and empirical scoring functions, a consensus scoring method is employed to select the putative inhibitors by the combined use of AutoDock and FlexX programs. Prior to the virtual screening with docking simulations, both scoring functions are modified by implementing the molecular solvation free energy term to enhance the accuracy in estimating the protein-ligand binding affinity. As a consequence of the consensus virtual screening with the two modified scoring functions, we find seven structurally diverse AMPK2 inhibitors with micromolar inhibitory activity. Detailed binding mode analyses indicate that all these inhibitors can be stabilized in the ATP-binding pocket through the simultaneous establishment of the multiple hydrogen bonds and hydrophobic interactions. It is also found that a high inhibitory activity can be achieved by the reduction of desolvation cost for the inhibitor as well as by the strengthening of the enzyme-inhibitor interactions. Thus, the results of the present study demonstrate the outperformance of consensus scoring with the force field-based and empirical scoring functions that are modified to include the effects of ligand solvation on protein-ligand docking.

Consensus virtual screening approaches to predict protein ligands

AbstractIn order to exploit the advantages of receptor-based virtual screening, namely time/cost saving and specificity, it is important to rely on algorithms that predict a high number of active ligands at the top ranks of a small molecule database. Towards that goal consensus methods combining the results of several docking algorithms were developed and compared against the individual algorithms. Furthermore, a recently proposed rescoring method based on drug efficiency indices was evaluated. Among AutoDock Vina 1.0, AutoDock 4.2 and GemDock, AutoDock Vina was the best performing single method in predicting high affinity ligands from a database of known ligands and decoys. The rescoring of predicted binding energies with the water/butanol partition coeffcient did not lead to an improvement averaged over all receptor targets. Various consensus algorithms were investigated and a simple combination of AutoDock and AutoDock Vina results gave the most consistent performance that showed early enrichment of known ligands for all receptor targets investigated. In case a number ligands is known for a specific target, every method proposed in this study should be evaluated.

Consensus virtual screening approaches to predict protein ligands

In order to exploit the advantages of receptor-based virtual screening, namely time/cost saving and specificity, it is important to rely on algorithms that predict a high number of active ligands at the top ranks of a small molecule database. Towards that goal consensus methods combining the results of several docking algorithms were developed and compared against the individual algorithms. Furthermore, a recently proposed rescoring method based on drug efficiency indices was evaluated. Among AutoDock Vina 1.0, AutoDock 4.2 and GemDock, AutoDock Vina was the best performing single method in predicting high affinity ligands from a database of known ligands and decoys. The rescoring of predicted binding energies with the water/octanol partition coefficient did not lead to an improvement averaged over ten receptor targets. Various consensus algorithms were investigated and a simple combination of AutoDock and AutoDock Vina results gave the most consistent performance that showed early enrichment of known ligands for all receptor targets investigated. In case a number of ligands is known for a specific target, every method proposed in this study should be evaluated.

How Similar Are Similarity Searching Methods? A Principal Component Analysis of Molecular Descriptor Space

Different molecular descriptors capture different aspects of molecular structures, but this effect has not yet been quantified systematically on a large scale. In this work, we calculate the similarity of 37 descriptors by repeatedly selecting query compounds and ranking the rest of the database. Euclidean distances between the rank-ordering of different descriptors are calculated to determine descriptor (as opposed to compound) similarity, followed by PCA for visualization. Four broad descriptor classes are identified, which are circular fingerprints; circular fingerprints considering counts; path-based and keyed fingerprints; and pharmacophoric descriptors. Descriptor behavior is much more defined by those four classes than the particular parametrization. Using counts instead of the presence/absence of fingerprints significantly changes descriptor behavior, which is crucial for performance of topological autocorrelation vectors, but not circular fingerprints. Four-point pharmacophores (piDAPH4) surprisingly lead to much higher retrieval rates than three-point pharmacophores (28.21% vs 19.15%) but still similar rank-ordering of compounds (retrieval of similar actives). Looking into individual rankings, circular fingerprints seem more appropriate than path-based fingerprints if complex ring systems or branching patterns are present; count-based fingerprints could be more suitable in databases with a large number of repeated subunits (amide bonds, sugar rings, terpenes). Information-based selection of diverse fingerprints for consensus scoring (ECFP4/TGD fingerprints) led only to marginal improvement over single fingerprint results. While it seems to be nontrivial to exploit orthogonal descriptor behavior to improve retrieval rates in consensus virtual screening, those descriptors still each retrieve different actives which corroborates the strategy of employing diverse descriptors individually in prospective virtual screening settings.

Discovery of Novel Small-Molecule Inhibitors of Human Epidermal Growth Factor Receptor-2: Combined Ligand and Target-Based Approach

Consensus virtual screening models were generated and validated utilizing a set of known human epidermal growth factor receptor-2 (HER2) inhibitors and modeled HER2 active and inactive state structures. The virtual screening models were successfully employed to discover a set of structurally diverse compounds with growth inhibitory activity against HER2-overexpressing SKBR3 breast cancer cell line. A search of a 3D database containing 350000 small-molecules using the consensus models retrieved 531 potential hits. Of the 531 hits, 57 were selected for testing in SKBR3 cells on the basis of structural novelty and desirable drug-like properties. Seven compounds inhibited growth of SKBR3 cells with IC50 values <10 microM. These lead compounds have desirable physicochemical properties and are excellent candidates for further optimization.