Shape-based Virtual Screening Research Articles

Computational discovery of AKT serine/threonine kinase 1 inhibitors through shape screening for rheumatoid arthritis intervention.

Rheumatoid Arthritis (RA) is a chronic, symmetrical inflammatory autoimmune disorder characterized by painful, swollen synovitis and joint erosions, which can cause damage to bone and cartilage and be associated with progressive disability. Despite expanded treatment options, some patients still experience inadequate response or intolerable adverse effects. Consequently, the treatment options for RA remain quite limited. The enzyme AKT1 is crucial in designing drugs for various human diseases, supporting cellular functions like proliferation, survival, metabolism, and angiogenesis in both normal and malignant cells. Therefore, AKT serine/threonine kinase 1 is considered crucial for targeting therapeutic strategies aimed at mitigating RA mechanisms. In this context, directing efforts toward AKT1 represents an innovative approach to developing new anti-arthritis medications. The primary objective of this research is to prioritize AKT1 inhibitors using computational techniques such as molecular modeling and dynamics simulation (MDS) and shape-based virtual screening (SBVS). A combined SBVS approach was employed to predict potent inhibitors against AKT1 by screening a pool of compounds sourced from the ChemDiv and IMPPAT databases. From the SBVS results, only the top three compounds, ChemDiv_7266, ChemDiv_2796, and ChemDiv_9468, were subjected to stability analysis based on their high binding affinity and favorable ADME/Tox properties. The SBVS findings have revealed that critical residues, including Glu17, Gly37, Glu85, and Arg273, significantly contribute to the successful binding of the highest-ranked lead compounds at the active site of AKT1. This insight helps to understand the specific binding mechanism of these leads in inhibiting RA, facilitating the rational design of more effective therapeutic agents.

SpaceGrow: efficient shape-based virtual screening of billion-sized combinatorial fragment spaces

The growing size of make-on-demand chemical libraries is posing new challenges to cheminformatics. These ultra-large chemical libraries became too large for exhaustive enumeration. Using a combinatorial approach instead, the resource requirement scales approximately with the number of synthons instead of the number of molecules. This gives access to billions or trillions of compounds as so-called chemical spaces with moderate hardware and in a reasonable time frame. While extremely performant ligand-based 2D methods exist in this context, 3D methods still largely rely on exhaustive enumeration and therefore fail to apply. Here, we present SpaceGrow: a novel shape-based 3D approach for ligand-based virtual screening of billions of compounds within hours on a single CPU. Compared to a conventional superposition tool, SpaceGrow shows comparable pose reproduction capacity based on RMSD and superior ranking performance while being orders of magnitude faster. Result assessment of two differently sized subsets of the eXplore space reveals a higher probability of finding superior results in larger spaces highlighting the potential of searching in ultra-large spaces. Furthermore, the application of SpaceGrow in a drug discovery workflow was investigated in four examples involving G protein-coupled receptors (GPCRs) with the aim to identify compounds with similar binding capabilities and molecular novelty.Graphical abstractSpaceGrow descriptor comparison for an example cut in the molecule of interest. Scoring scheme is implied for one fragment of this cut.

Shape-Aware Synthon Search (SASS) for Virtual Screening of Synthon-Based Chemical Spaces.

Virtual screening of large-scale chemical libraries has become increasingly useful for identifying high-quality candidates for drug discovery. While it is possible to exhaustively screen chemical spaces that number on the order of billions, indirect combinatorial approaches are needed to efficiently navigate larger, synthon-based virtual spaces. We describe Shape-Aware Synthon Search (SASS), a synthon-based virtual screening method that carries out shape similarity searches in the synthon space instead of the enumerated product space. SASS can replicate results from exhaustive searches in ultralarge, combinatorial spaces with high recall on a variety of query molecules while only scoring a small subspace of possible enumerated products, thereby significantly accelerating large-scale, shape-based virtual screening.

Distribution of Bound Conformations in Conformational Ensembles for X-ray Ligands Predicted by the ANI-2X Machine Learning Potential.

In this study, we systematically studied the energy distribution of bioactive conformations of small molecular ligands in their conformational ensembles using ANI-2X, a machine learning potential, in conjunction with one of our recently developed geometry optimization algorithms, known as a conjugate gradient with backtracking line search (CG-BS). We first evaluated the combination of these methods (ANI-2X/CG-BS) using two molecule sets. For the 231-molecule set, ab initio calculations were performed at both the ωB97X/6-31G(d) and B3LYP-D3BJ/DZVP levels for accuracy comparison, while for the 8,992-molecule set, ab initio calculations were carried out at the B3LYP-D3BJ/DZVP level. For each molecule in the two molecular sets, up to 10 conformations were generated, which diminish the influence of individual outliers on the performance evaluation. Encouraged by the performance of ANI-2x/CG-BS in these evaluations, we calculated the energy distributions using ANI-2x/CG-BS for more than 27,000 ligands in the protein data bank (PDB). Each ligand has at least one conformation bound to a biological molecule, and this ligand conformation is labeled as a bound conformation. Besides the bound conformations, up to 200 conformations were generated using OpenEye's Omega2 software (https://docs.eyesopen.com/applications/ omega/) for each conformation. We performed a statistical analysis of how the bound conformation energies are distributed in the ensembles for 17,197 PDB ligands that have their bound conformation energies within the energy ranges of the Omega2-generated conformation ensembles. We found that half of the ligands have their relative conformation energy lower than 2.91 kcal/mol for the bound conformations in comparison with the global conformations, and about 90% of the bound conformations are within 10 kcal/mol above the global conformation energies. This information is useful to guide the construction of libraries for shape-based virtual screening and to improve the docking algorithm to efficiently sample bound conformations.

Shape-Based Virtual Screening of a Billion-Compound Library Identifies Mycobacterial Lipoamide Dehydrogenase Inhibitors.

Lpd (lipoamide dehydrogenase) in Mycobacterium tuberculosis (Mtb) is required for virulence and is a genetically validated tuberculosis (TB) target. Numerous screens have been performed over the last decade, yet only two inhibitor series have been identified. Recent advances in large-scale virtual screening methods combined with make-on-demand compound libraries have shown the potential for finding novel hits. In this study, the Enamine REAL library consisting of ∼1.12 billion compounds was efficiently screened using the GPU Shape screen method against Mtb Lpd to find additional chemical matter that would expand on the known sulfonamide inhibitor series. We identified six new inhibitors with IC50 in the range of 5-100 μM. While these compounds remained chemically close to the already known sulfonamide series inhibitors, some diversity was found in the cores of the hits. The two most potent hits were further validated by one-step potency optimization to submicromolar levels. The co-crystal structure of optimized analogue TDI-13537 provided new insights into the potency determinants of the series.

VSFlow: an open-source ligand-based virtual screening tool

Ligand-based virtual screening is a widespread method in modern drug design. It allows for a rapid screening of large compound databases in order to identify similar structures. Here we report an open-source command line tool which includes a substructure-, fingerprint- and shape-based virtual screening. Most of the implemented features fully rely on the RDKit cheminformatics framework. VSFlow accepts a wide range of input file formats and is highly customizable. Additionally, a quick visualization of the screening results as pdf and/or pymol file is supported.Graphical

Design, synthesis and evaluation of 1-(1,5-bis(4-substituted phenyl)-2-methyl-1H-pyrrol-3-yl)-N-methylmethanamines as SERT inhibitors with potential antidepressant action.

BM212 is a potent anti-TB agent with pharmacophoric features similar to the antidepressant drug sertraline. The shape-based virtual screening of the DrugBank database on BM212 resulted in the identification of several CNS drugs with appreciable Tanimoto scores. The docking simulations also ascertained the selectivity of BM212 towards the serotonin reuptake transporter protein (SERT) with a docking score of -6.51 kcal mol-1. Based on the SAR data available for sertraline and other antidepressant drugs, we designed, synthesized and screened twelve 1-(1,5-bis(4-substituted phenyl)-2-methyl-1H-pyrrol-3-yl)-N-methylmethanamines (SA-1 to SA-12) for in vitro SERT inhibition and in vivo antidepressant activity. The compounds were screened for in vitro 5HT reuptake inhibition using the platelet model. Among the screened compounds, (1-(1,5-bis(4-chlorophenyl)-2-methyl-1H-pyrrol-3-yl)-N-methylmethanamine) showed the same serotonin uptake inhibition (absorbance 0.22) as that of the standard drug sertraline (absorbance 0.22). BM212 had an effect on 5-HT uptake, albeit a weaker one compared to the standard (absorbance 0.671). Further, SA-5 was screened for in vivo antidepressant activity using the unpredictable chronic mild stress (UCMS) protocol to induce depression in mice. The effect of BM212 and SA-5 on the behaviour of the animals was assessed and compared against the standard drug sertraline. SA-5 at 20 mg per kg body weight was found to have a statistically significant impact on the behaviour of depressed animals.

Ligand-based discovery of a novel GATA2 inhibitor targeting acute myeloid leukemia cells

Despite major therapeutic advances leading to improved patient outcomes for other haematological malignancies, development of new therapeutics to improve prognosis for acute myeloid leukemia (AML) patients remains an area of unmet clinical need. Overexpression of GATA2, a member of the GATA family of zinc finger transcription factors, has been implicated in AML. In settings where GATA2 is overexpressed in human AML cells, K7174, a proteasome inhibitor that inhibits GATA2, induces apoptosis and enhances the killing activity of AML chemotherapeutics in vitro yet targeting the proteasome has been associated with high toxicity in the clinic. Using an in silico approach, we embarked on a screen to identify specific GATA2 inhibitors that will target AML cells independently of the proteasome. A shape-based virtual screening of an in-house library of small molecules was performed using a low-energy conformation of K7174. The virtual hit compounds were subsequently filtered according to their potential selectivity for GATA2 over the proteasome. From 15 selected compounds evaluated for their ability to kill AML cells in vitro, one compound, an asymmetrical substituted piperazine with Hepatitis C antiviral activity, exhibited superior ability to induce apoptosis and reduce cell cycling in AML cells without proteasome inhibition. This compound was also able to promote cell death of the relapse propagating leukemic stem cell (LSC) compartment while sparing Gata2 knockout LSCs, crucially demonstrating specificity to inhibit GATA2. We have identified a GATA2 specific inhibitor with promising capability to target AML cells in vitro, including LSCs that underpin poor prognosis in AML.

Cryptosporidiosis, a public health challenge: A combined 3D shape-based virtual screening, docking study, and molecular dynamics simulation approach to identify inhibitors with novel scaffolds for the treatment of cryptosporidiosis

Cryptosporidiosis is a neglected tropical disease caused by the protozoan parasite Cryptosporidium parvum. Limited therapeutic options, limitation in in vitro parasite culture, and lack of a reliable animal model of parasite for replication of in vivo life cycle and drug testing demand alternative methods for drug development. The in silico methods of drug discovery prove a crucial process in such conditions.Recent research reported a limited number of small molecules for drug development. Purine nucleotide biosynthesis in Cryptosporidium species is dependent on the IMPDH (CpIMPDH) enzyme, so distortion of parasite IMPDH has been pursued as a compelling strategy for curbing Cryptosporidium infection due to its different kinetics from the host enzyme. Our study's primary aim was to discover novel ligand molecules with noticeable activity against Cryptosporidium parvum IMPDH. For this purpose, we selected 18 previously discovered ligands to understand the interaction feature between ligand and receptor, and their shape and electronic features are employed as a template for shape-based virtual screening of the ZINC database (drug-like subset) search approach via Schrodinger-2019 (Maestro 11.9). The obtained hits were subsequently subjected to structure-based screening, quantum polarized ligand docking (QPLD), and molecular dynamics simulations to fetch potential small molecules with the highest binding affinity for CpIMPDH protein. Further ligand binding energy and pharmacokinetic analysis were also taken into consideration as filtering criteria for selecting the most promising drug-like compounds. On this experimentation analysis, three top-ranked (ZINC24855054, ZINC58171263, and ZINC08000072) molecules were found to have appropriate pharmacokinetic properties along with surpassing in silico inhibitory potential towards the CpIMPDH compared to known inhibitors. The molecular docking and molecular dynamics simulation analysis results satisfactorily confirmed the inhibitory action. Therefore, these new scaffolds deduced by the presented computational methodology could recommend lead molecules for designing promising anti-cryptosporidial drugs targeting CpIMPDH protein.

Identification of a Chlorogenic Ester as a Monoamine Oxidase (MAO-B) Inhibitor by Integrating "Traditional and Machine Learning" Virtual Screening and In Vitro as well as In Vivo Validation: A Lead against Neurodegenerative Disorders?

Parkinson's disease (PD) is the furthermost motor disorder of adult-onset dementia connected to memory and other cognitive abilities. Monoamine oxidases (MAOs) have gained significant attention in recent years owing to their possible therapeutic use against PD. Expression of MAO-B has been found to be elevated in PD patients for increased uptake of dopamine, producing hydrogen peroxide and finally causing neuronal injury. In this work, two new compounds have been identified as leads against MAO-B, and one of those compounds has been validated in vitro and in vivo. From the Protein Data Bank, MAO-B protein structures complexed with selegiline, 6-hydroxy-N-propargyl-1(R)-aminoindan, or a chromen derivative have been selected as templates for shape-based virtual screening (SB-VS) against the Traditional Chinese Medicinal (TCM) natural database. In parallel, using machine learning, a molecular-descriptor-based support vector model (SVM) was prepared and screened. For this purpose, naïve Bayesian, logistic regression, and random forest strategies were employed with the best specific molecular descriptor, which yielded a model with an overall accuracy (Q) of 0.81. Two common hit compounds lead-1 and lead-2 resulting from both shape and SVM screenings were analyzed through molecular docking and molecular dynamics (MD) simulation (200 ns). Also, from trajectory analysis such as molecular mechanics generalized Born surface area (MMGB/SA) and the residual interaction network (RIN) analyzer, both leads were found to bind at the active site with a favorable correlated motion, including domain movements. Lead-2, which is a chlorogenic ester, was synthesized and found to have no cytotoxic effect up to 50 μg/mL on Neuro-2A cells. The significant reactive oxygen species (ROS) scavenging activity by lead-2 could be correlated to its neuroprotective efficacy. Its capacity to inhibit human MAO-B through a competitive mode could be observed. An experimental zebra fish model confirms the neuroprotection by lead-2 by assessing the locomotor activities under malathion influence and treatment of lead-2. Also, histopathology analysis revealed that lead-2 could slow down degeneration in the brain. The present study emphasizes that integrating machine learning in parallel with traditional virtual screening may be useful to identify effective lead compounds for a given target.

Discovery of 4-arylthiophene-3-carboxylic acid as inhibitor of ANO1 and its effect as analgesic agent

Anoctamin 1 (ANO1) is a kind of calcium-activated chloride channel involved in nerve depolarization. ANO1 inhibitors display significant analgesic activity by the local peripheral and intrathecal administration. In this study, several thiophenecarboxylic acid and benzoic acid derivatives were identified as novel ANO1 inhibitors through the shape-based virtual screening, among which the 4-arylthiophene-3-carboxylic acid analogues with the best ANO1 inhibitory activity were designed, synthesized and compound 42 (IC50 = 0.79 μmol/L) was finally obtained. Compound 42 selectively inhibited ANO1 without affecting ANO2 and intracellular Ca2+ concentration. Subsequently, the analgesic effect was investigated by intragastric administration in pain models. Compound 42 significantly attenuated allodynia which was induced by formalin and chronic constriction injury. Through homology modeling and molecular dynamics, the binding site was predicted to be located near the calcium-binding region between α6 and α8. Our study validates ANO1 inhibitors having a significant analgesic effect by intragastric administration and also provides selective molecular tools for ANO1-related research.

Drug screening using shape-based virtual screening and in vitro experimental models of cutaneous Leishmaniasis.

Cutaneous leishmaniasis (CL) is one of the most disregarded tropical neglected disease with the occurrence of self-limiting ulcers and triggering mucosal damage and stigmatizing scars, leading to huge public health problems and social negative impacts. Pentavalent antimonials are the first-line drug for CL treatment for over 70 years and present several drawbacks in terms of safety and efficacy. Thus, there is an urgent need to search for non-invasive, non-toxic and potent drug candidates for CL. In this sense, we have implemented a shape-based virtual screening approach and identified a set of 32 hit compounds. In vitro phenotypic screenings were conducted using these hit compounds to check their potential leishmanicidal effect towards Leishmania amazonensis (L. amazonensis). Two (Cp1 and Cp2) out of the 32 compounds revealed promising antiparasitic activities, exhibiting considerable potency against intracellular amastigotes present in peritoneal macrophages (IC50 values of 9.35 and 7.25 μm, respectively). Also, a sterile cidality profile was reached at 20 μm after 48 h of incubation, besides a reasonable selectivity (≈8), quite similarly to pentamidine, a diamidine still in use clinically for leishmaniasis. Cp1 with an oxazolo[4,5-b]pyridine scaffold and Cp2 with benzimidazole scaffold could be developed by lead optimization studies to enhance their leishmanicidal potency.

Identification of Selective Inhibitors of LdDHFR Enzyme Using Pharmacoinformatic Methods.

Dihydrofolate reductase (DHFR) is a well-known enzyme of the folate metabolic pathway and it is a validated drug target for leishmaniasis. However, only a few leads are reported against Leishmania donovani DHFR (LdDHFR), and thus, there is a need to identify new inhibitors. In this article, pharmacoinformatic tools such as molecular docking, virtual screening, absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling, and molecular dynamics (MD) simulations were utilized to identify potential LdDHFR inhibitors. Initially, a natural DHFR substrate (dihydrofolate), a classical DHFR inhibitor (methotrexate), and a potent LdDHFR inhibitor, that is, "5-(3-(octyloxy)benzyl)pyrimidine-2,4-diamine" (LEAD) were docked in the active site of the LdDHFR and MD simulated to understand the binding mode characteristics of the substrates/inhibitors in the LdDHFR. The shape of the LEAD molecule was used as a query for shape-based virtual screening, while the three-dimensional structure of LdDHFR was utilized for docking-based virtual screening. In silico ADMET factors were also considered during virtual screening. These two screening processes yielded 25 suitable hits, which were further validated for their selectivity toward LdDHFR using molecular docking and prime molecular mechanics/generalized born surface area analysis in the human DHFR (HsDHFR). Best six hits, which were selective and energetically favorable for the LdDHFR, were chosen for MD simulations. The MD analysis showed that four of the hits exhibited very good binding affinity for LdDHFR with respect to HsDHFR, and two hits were found to be more selective than the reported potent LdDHFR inhibitor. The present study thus identifies hits that can be further designed and modified as potent LdDHFR inhibitors.

Novel natural non-nucleoside inhibitors of HIV-1 reverse transcriptase identified by shape- and structure-based virtual screening techniques

In this work we report a parallel application of both docking- and shape-based virtual screening (VS) methods, followed by Molecular Dynamics simulations (MDs), for discovering new compounds able to inhibit the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT) RNA-dependent DNA polymerase activity. Specifically, we screened more than 143000 natural compounds commercially available in the ZINC database against the best five RT crystallographic models, taking into account the five approved NNRTIs as query compounds. As a result, 20 hit molecules were selected and tested on biochemical assays for the inhibition of the RNA dependent DNA polymerase RT function and, among them, an indoline pyrrolidine (hit1), an indonyl piperazine (hit2) and an indolyl indolinone (hit3) derivatives were identified as novel non-nucleoside RT inhibitors in the low micromolar range.

Computer-Aided Identification of Novel Anti-Paracoccidioidomycosis Compounds

The shape-based virtual screening was used for the identification of new compounds anti-paracoccidioidomycosis (PCM). The study was performed according to the following steps: collection and curation of a dataset of quinolinyl N-oxide chalcones with anti-PCM activity, development and validation of shape-based models, application of the best model for virtual screening, and experimental validation. Among 31 computational hits, eight compounds showed potent antifungal activity and low cytotoxicity for mammalian cells. The checkerboard assay showed that most promising hit (compound 3) displayed additive effects with the antifungal cotrimoxazole and amphotericin B. Therefore, the shape-based virtual screening allowed us to discover promising compounds in prospective hit-to-lead optimization studies for tackling PCM.

Enhanced Sampling of LNCRNA Conformational Space for Defining Ensembles of Structures Used in Ensemble Docking and Virtual Screening of RNA-Focused Small Molecules

RNA remains an underexplored class of drug targets owing to the limited number of RNA-focused experimental and computational screening assays. Importantly, the highly charged phosphate backbone and inherent flexibility unique to RNA macromolecules present computational challenges to virtual screening techniques involving conformational sampling and molecular docking. We have developed a computational methodology to examine docking of small molecules to an ensemble of RNA conformations. A pipeline is developed to target a critical triple helix in the oncogenic MALAT1, a long noncoding RNA that promotes metastasis in a large number of cancer types. We use a conformational FRET assay to show that the MALAT1 triple helix (M1TH) samples a large conformational space consisting of both native states at physiological conditions and inhibitory states when solutions conditions are altered. Preliminary Small Angle X-ray Scattering (SAXS) experiments show a change in radius of gyration (Rg) consistent with conformational FRET data. Replica Exchange Molecular Dynamics are being used to calculate two ensemble sets (i) native-like conformations and (ii) inhibitory conformations) of M1TH structures. Structural states are clustered based on Rg consistent with SAXS data. The relatively small size of the 76 nucleotide M1TH simulation structure allows rigorous sampling of conformational space. We hypothesize that small molecule induced disruption of this RNA triple helix is a novel approach for therapeutic intervention to inhibit progression of multiple cancer types. M1TH ensembles will be used in ensemble docking of compounds identified through a shape-based virtual screening methodology to identify novel M1TH-specific lead compounds that reduce cancer proliferation.

Identification by shape-based virtual screening and evaluation of new tyrosinase inhibitors.

Targeting tyrosinase is considered to be an effective way to control the production of melanin. Tyrosinase inhibitor is anticipated to provide new therapy to prevent skin pigmentation, melanoma and neurodegenerative diseases. Herein, we report our results in identifying new tyrosinase inhibitors. The shape-based virtual screening was performed to discover new tyrosinase inhibitors. Thirteen potential hits derived from virtual screening were tested by biological determinations. Compound 5186-0429 exhibited the most potent inhibitory activity. It dose-dependently inhibited the activity of tyrosinase, with the IC50 values 6.2 ± 2.0 µM and 10.3 ± 5.4 µM on tyrosine and L-Dopa formation, respectively. The kinetic study of 5186-0429 demonstrated that this compound acted as a competitive inhibitor. We believe the discoveries here could serve as a good starting point for further design of potent tyrosinase inhibitor.

SMMDB: a web-accessible database for small molecule modulators and their targets involved in neurological diseases.

High-throughput screening and better understanding of small molecule’s structure–activity relationship (SAR) using computational biology techniques have greatly expanded the face of drug discovery process in better discovery of therapeutics for various disease. Small Molecule Modulators Database (SMMDB) includes >1100 small molecules that have been either approved by US Food and Drug Administration, are under investigation or were rejected in clinical trial for any kind of neurological diseases. The comprehensive information about small molecules includes the details about their molecular targets (such as protein or enzyme, DNA, RNA, antisense RNA etc.), pharmacokinetic and pharmacodynamic properties such as binding affinity to their targets (Kd, Ki, IC50 and EC50 if available), mode of action, log P-value, number of hydrogen bond donor and acceptors, their clinical trial status, their 2D and three-dimensional structures etc. To enrich the basic annotation of every small molecule entry present in SMMDB, it is hyperlinked to their description present in PubChem, DrugBank, PubMed and KEGG database. The annotation about their molecular targets was enriched by linking it with UniProt and GenBank and STRING database that can be utilized to study the interaction and relation between various targets involved in single neurological disease. All molecules present in the SMMDB are made available to download in single file and can be further used in establishing the SAR, structure-based drug designing as well as shape-based virtual screening for developing the novel therapeutics against neurological diseases. The scope of this database majorly covers the interest of scientific community and researchers who are engaged in putting their endeavor toward therapeutic development and investigating the pathogenic mechanism of various neurological diseases. The graphical user interface of the SMMDB is accessible on http://bsbe.iiti.ac.in/bsbe/smmdb.

HybridSim-VS: a web server for large-scale ligand-based virtual screening using hybrid similarity recognition techniques.

Molecular-similarity searches based on two-dimensional (2D) fingerprint and three-dimensional (3D) shape represent two widely used ligand-based virtual screening (VS) methods in computer-aided drug design. 2D fingerprint-based VS utilizes the binary fragment information on a known ligand, whereas 3D shape-based VS takes advantage of geometric information for predefined features from a 3D conformation. Given their different advantages, it would be desirable to hybridize 2D fingerprint and 3D shape molecular-similarity approaches in drug discovery. Here, we presented a general hybrid molecular-similarity protocol, referred to as HybridSim, obtained by combining the 2D fingerprint- and 3D shape-based similarity search methods and evaluated its performance on 595,036 actives and decoys for 40 pharmaceutically relevant targets available in the Directory of Useful Decoys Enhanced (DUD-E). Our results showed that HybridSim significantly improved the overall performance in 40 VS projects as compared with using only 2D fingerprint and 3D shape methods. Furthermore, HybridSim-VS, the first online platform using the proposed HybridSim method coupled with 17,839,945 screenable and purchasable compounds, was developed to provide large-scale and proficient VS capabilities to experts and nonexperts in the field. HybridSim-VS web server is freely available at http://www.rcidm.org/HybridSim-VS/. lingwang@scut.edu.cn. Supplementary data are available at Bioinformatics online.

Shape-based virtual screening, synthesis and evaluation of novel pyrrolone derivatives as antiviral agents against HCV

A ligand-based approach was applied to screen in silico a library of commercially available compounds, with the aim to find novel inhibitors of the HCV replication starting from the study of the viral NS3 helicase. Six structures were selected for evaluation in the HCV subgenomic replicon assay and one hit was found to inhibit the HCV replicon replication in the low micromolar range. A small series of new pyrrolone compounds was designed and synthesised, and novel structures were identified with improved antiviral activity.