Ligand-based Virtual Screening Methods Research Articles

AutoDock-SS: AutoDock for Multiconformational Ligand-Based Virtual Screening.

Ligand-based virtual screening (LBVS) can be pivotal for identifying potential drug leads, especially when the target protein's structure is unknown. However, current LBVS methods are limited in their ability to consider the ligand conformational flexibility. This study presents AutoDock-SS (Similarity Searching), which adapts protein-ligand docking for use in LBVS. AutoDock-SS integrates novel ligand-based grid maps and AutoDock-GPU into a novel three-dimensional LBVS workflow. Unlike other approaches based on pregenerated conformer libraries, AutoDock-SS's built-in conformational search optimizes conformations dynamically based on the reference ligand, thus providing a more accurate representation of relevant ligand conformations. AutoDock-SS supports two modes: single and multiple ligand queries, allowing for the seamless consideration of multiple reference ligands. When tested on the Directory of Useful Decoys─Enhanced (DUD-E) data set, AutoDock-SS surpassed alternative 3D LBVS methods, achieving a mean AUROC of 0.775 and an EF1% of 25.72 in single-reference mode. The multireference mode, evaluated on the augmented DUD-E+ data set, demonstrated superior accuracy with a mean AUROC of 0.843 and an EF1% of 34.59. This enhanced performance underscores AutoDock-SS's ability to treat compounds as conformationally flexible while considering the ligand's shape, pharmacophore, and electrostatic potential, expanding the potential of LBVS methods.

On the relevance of query definition in the performance of 3D ligand-based virtual screening

Ligand-based virtual screening (LBVS) methods are widely used to explore the vast chemical space in the search of novel compounds resorting to a variety of properties encoded in 1D, 2D or 3D descriptors. The success of 3D-LBVS is affected by the overlay of molecular pairs, thus making selection of the template compound, search of accessible conformational space and choice of the query conformation to be potential factors that modulate the successful retrieval of actives. This study examines the impact of adopting different choices for the query conformation of the template, paying also attention to the influence exerted by the structural similarity between templates and actives. The analysis is performed using PharmScreen, a 3D LBVS tool that relies on similarity measurements of the hydrophobic/philic pattern of molecules, and Phase Shape, which is based on the alignment of atom triplets followed by refinement of the volume overlap. The study is performed for the original DUD-E+ database and a Morgan Fingerprint filtered version (denoted DUD-E+-Diverse; available in https://github.com/Pharmacelera/Query-models-to-3DLBVS), which was prepared to minimize the 2D resemblance between template and actives. Although in most cases the query conformation exhibits a mild influence on the overall performance, a critical analysis is made to disclose factors, such as the content of structural features between template and actives and the induction of conformational strain in the template, that underlie the drastic impact of the query definition in the recovery of actives for certain targets. The findings of this research also provide valuable guidance for assisting the selection of the query definition in 3D LBVS campaigns.Graphical

Ligand and structure based hierarchical virtual screening cascade for finding novel epidermal growth factor receptor inhibitors.

The epidermal growth factor receptor (EGFR) tyrosine kinase plays an important role in tumor formation and growth by mediating cell growth and other physiological processes. Therefore, EGFR is a promising target for the treatment of cancer. In this work, we combined ligand-based and structure-based virtual screening methods to identify novel EGFR inhibitors from a library of more than 103 thousand compounds. We first obtained hundreds of compounds with similar physiochemical properties through 3D molecular shape and electrostatic similarity screening with potent inhibitors AEE788 and Afatinib as queries. Next, we identified compounds with strong binding affinities to the EGFR pocket through molecular docking, which makes good use of the structure information of the receptor. After molecular scaffold analysis, our bioassay confirmed 13 compounds with EGFR inhibitory activity and three compounds had IC50 values below 1000 nM. In addition, we collected 5371 EGFR inhibitors from online databases, and clustered them into 7 groups by K-means method using their ECFP4 fingerprints as input. Each cluster had typical molecular fragments and corresponding activity characteristics, which could guide the design of EGFR inhibitors, and we concluded that the fragments from some of the hits are indicated in the highly active scaffolds.

Employing Molecular Conformations for Ligand-Based Virtual Screening with Equivariant Graph Neural Network and Deep Multiple Instance Learning.

Ligand-based virtual screening (LBVS) is a promising approach for rapid and low-cost screening of potentially bioactive molecules in the early stage of drug discovery. Compared with traditional similarity-based machine learning methods, deep learning frameworks for LBVS can more effectively extract high-order molecule structure representations from molecular fingerprints or structures. However, the 3D conformation of a molecule largely influences its bioactivity and physical properties, and has rarely been considered in previous deep learning-based LBVS methods. Moreover, the relative bioactivity benchmark dataset is still lacking. To address these issues, we introduce a novel end-to-end deep learning architecture trained from molecular conformers for LBVS. We first extracted molecule conformers from multiple public molecular bioactivity data and consolidated them into a large-scale bioactivity benchmark dataset, which totally includes millions of endpoints and molecules corresponding to 954 targets. Then, we devised a deep learning-based LBVS called EquiVS to learn molecule representations from conformers for bioactivity prediction. Specifically, graph convolutional network (GCN) and equivariant graph neural network (EGNN) are sequentially stacked to learn high-order molecule-level and conformer-level representations, followed with attention-based deep multiple-instance learning (MIL) to aggregate these representations and then predict the potential bioactivity for the query molecule on a given target. We conducted various experiments to validate the data quality of our benchmark dataset, and confirmed EquiVS achieved better performance compared with 10 traditional machine learning or deep learning-based LBVS methods. Further ablation studies demonstrate the significant contribution of molecular conformation for bioactivity prediction, as well as the reasonability and non-redundancy of deep learning architecture in EquiVS. Finally, a model interpretation case study on CDK2 shows the potential of EquiVS in optimal conformer discovery. The overall study shows that our proposed benchmark dataset and EquiVS method have promising prospects in virtual screening applications.

Discovery of novel TRPV1 modulators through machine learning-based molecular docking and molecular similarity searching.

The transient receptor potential vanilloid 1 (TRPV1) channel belongs to the transient receptor potential channel superfamily and participates in many physiological processes. TRPV1 modulators (both agonists and antagonists) can effectively inhibit pain caused by various factors and have curative effects in various diseases, such as itch, cancer, and cardiovascular diseases. Therefore, the development of TRPV1 channel modulators is of great importance. In this study, the structure-based virtual screening and ligand-based virtual screening methods were used to screen compound databases respectively. In the structure-based virtual screening route, a full-length human TRPV1 protein was first constructed, three molecular docking methods with different precisions were performed based on the hTRPV1 structure, and a machine learning-based rescoring model by the XGBoost algorithm was constructed to enrich active compounds. In the ligand-based virtual screening route, the ROCS program was used for 3D shape similarity searching and the EON program was used for electrostatic similarity searching. Final 77 compounds were selected from two routes for in vitro assays. The results showed that 8 of them were identified as active compounds, including three hits with IC50 values close to capsazepine. In addition, one hit is a partial agonist with both agonistic and antagonistic activity. The mechanisms of some active compounds were investigated by molecular dynamics simulation, which explained their agonism or antagonism.

Virtual Screening Based on Ligand and Structure with in vitro Assessment of Neolignans against Trypanosoma cruzi

Chagas disease, caused by the parasite Trypanosoma cruzi, occurs most commonly in Latin America. As the treatment is highly toxic and ineffective in the chronic phase of the disease, alternative treatments are needed. Through quantitative structure-activity relationship modeling (QSAR) analysis using ligand-based and structure-based virtual screening methods, we predicted the trypanocidal potential of 47 neolignans against three targets, the enzymes cruzain, trypanothione reductase, and sterol 14-alpha demethylase. A combined analysis allowed for the selection of potent inhibitors against Trypanosoma cruzi. Of these compounds, two were isolated and shown to inhibit the growth of epimastigotes at concentrations of 9.64 and 8.72 μM, and trypomastigote forms at 4.88 and 2.73 μM. Therefore, the compounds (2R, 3R)-2,3-dihydro-2‑(4‑methoxyphenyl)- 3-methyl-5-(E)-propenylbenzofuran (46) and ottomentosa (47) may be a good option of growth inhibitors for the parasite stages and warrant additional study.

A two-layer mono-objective algorithm based on guided optimization to reduce the computational cost in virtual screening

Virtual screening methods focus on searching molecules with similar properties to a given compound. Molecule databases are made up of large numbers of compounds and are constantly increasing. Therefore, fast and efficient methodologies and tools have to be designed to explore them quickly. In this context, ligand-based virtual screening methods are a well-known and helpful tool. These methods focus on searching for the most similar molecules in a database to a reference one. In this work, we propose a new tool called 2L-GO-Pharm, which requires less computational effort than OptiPharm, an efficient and robust piece of software recently proposed in the literature. The new-implemented tool maintains or improves the quality of the solutions found by OptiPharm, and achieves it by considerably reducing the number of evaluations needed. Some of the strengths that help 2L-GO-Pharm enhance searchability are the reduction of the search space dimension and the introduction of some circular limits for the angular variables. Furthermore, to ensure a trade-off between exploration and exploitation of the search space, it implements a two-layer strategy and a guided search procedure combined with a convergence test on the rotation axis. The performance of 2L-GO-Pharm has been tested by considering two different descriptors, i.e. shape similarity and electrostatic potential. The results show that it saves up to 87.5 million evaluations per query molecule.

Discovering inhibitors of TEAD palmitate binding pocket through virtual screening and molecular dynamics simulation

Transcriptional enhanced associate domain (TEAD) proteins bind to YAP/TAZ and mediate YAP/TAZ-induced gene expression. TEADs are not only the key transcription factors and final effector of the Hippo signaling pathway, but also the proteins that regulate cell proliferation and apoptosis. Disorders of Hippo signaling pathway occur in liver cancer, breast cancer, colon cancer and other cancers. S-palmitylation can stabilize the structure of TEADs and is also a necessary condition for the binding of TEADs to YAP/TAZ. The absence of TEAD palmitoylation prevents TEADs from binding to chromatin, thereby inhibiting the transcription and expression of downstream target genes in the Hippo pathway through a dominant-negative mechanism. Therefore, disrupting the S-palmitylation of TEADs has become an attractive and very feasible method in cancer treatment. The palmitate binding pockets of TEADs are conservative, and the crystal structures of TEAD2-palmitoylation inhibitor complexes and the potential TEAD2 inhibitors are more than other TEADs, TEAD2 can be selected to be the target receptor. In this study, structure-based and ligand-based virtual screening, molecular dynamics simulations, Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) calculations, residue decomposition binding energy calculations, and ADME predictions have been performed to discover 11 potential TEAD2 S-palmitylation inhibitors. ChEBML196567 and ZINC000013942794 are the most recommended, because they formed strong binding energies and stable hydrogen bonds with TEAD2 and have good drugbility and high human oral absorption. We found that it was easier to find the targeting small molecules using a combination of structure-based and ligand-based virtual screening methods. Besides, a new core structure has been found in the selected small molecules. In addition, we analyzed the binding modes of these small molecules to TEAD2, and confirmed the hot spot residues Cys380, Ser345, Tyr426, Phe428, Ile408, and Met379. Availability of data and materialSupplementary materials are available online.

Ligand Based Virtual Screening Using Self-organizing Maps.

Conventional drug discovery methods rely primarily on in-vitro experiments with a target molecule and an extensive set of small molecules to choose the suitable ligand. The exploration space for the selected ligand being huge; this approach is highly time-consuming and requires high capital for facilitation. Virtual screening, a computational technique used to reduce this search space and identify lead molecules, can speed up the drug discovery process. This paper proposes a ligand-based virtual screening method using an artificial neural network called self-organizing map (SOM). The proposed work uses two SOMs to predict the active and inactive molecules separately. This SOM based technique can uniquely label a small molecule as active, inactive, and undefined as well. This can reduce the number of false positives in the screening process and improve recall; compared to support vector machine and random forest based models. Additionally, by exploiting the parallelism present in the learning and classification phases of a SOM, a graphics processing unit (GPU) based model yields much better execution time. The proposed GPU-based SOM tool can successfully evaluate a large number of molecules in training and screening phases. The source code of the implementation and related files are available at https://github.com/jayarajpbalakrishnan/2_SOM_SCREEN.

Inhibitory potential of plant-derived metabolites against Zika virus: a computational-aided approach

BackgroundThe Zika virus (ZIKV) has caused a heavy concern all over the world because of its high infectivity and mortality rate. Still, there's no specific drug or preventive medication to treat ZIKV infection despite comprehensive analysis by the researchers. PurposeThis study was designed to demonstrate the efficacy of some plant-derived bioactive compounds against ZIKV by using both structure and ligand-based virtual screening methods. MethodsA total of 35 plant metabolites were screened on the basis of their reported pharmacological activities against ZIKV NS2B-NS3 protease (5LC0), envelope protein (5JHM), capsid protein (5YGH) and NS5 RNA-dependent RNA polymerase protein (5U04) employing a molecular docking approach. Furthermore, the ADMET properties along with structural similar drug compounds as well as the probable drug targets were also predicted for our screened four metabolites i.e., chicoric acid, luteone, reserpine and rosmarinic acid. ResultsResults showed that our four screened metabolites i.e., chicoric acid, luteone, reserpine and rosmarinic acid provide the highest binding affinity to target ZIKV proteins. The Crucial binding sites, as well as drug surface hotspots were unraveled for viral targeted protein. The analysis of ADME properties exhibited that none of the compounds had side effects that would reduce their drug-like properties. As compared, the toxicity pattern analysis has unmasked the non-toxic essence of screened drug candidates. The root-mean-square deviation (RMSD) values of ligand-macromolecule complexes were 2 Å apart envelop protein-chicoric acid, although the root-mean-square fluctuation (RMSF) values showed normal atomic fluctuations within the molecular dynamics analysis except envelop protein-chicoric acid. Most of the targets of the tested compounds lie in enzymes such as protease, hydrolase and phosphatase. Besides, the study of drug structural similarity showed several structural analogs from the drugbank such as isoformononetin (DB04202), deserpidine (DB01089) and rescinnamine (DB01180) etc. and these analogs could even be a choice to combat ZIKV. ConclusionThese results may lead to the development of effective anti-ZIKV natural medicines. Further in vivo trials are strongly recommended.

Discovery of novel p90 ribosomal S6 kinase 2 inhibitors for potential cancer treatment through ligand-based and structure-based virtual screening methods

The p90 ribosomal S6 kinase 2 (RSK2) belongs to Ser/Thr kinase family which is involved in a variety of cellular processes, such as cell proliferation, survival and transformation. Recent studies suggest that RSK2 also is a critical proliferative signaling effector of oncogenic tyrosine kinases, which represents targeting RSK2 would be a potential anticancer therapy. Therefore, it would have great clinical significance to discover more novel RSK2 inhibitors by using an in silico strategy that combined ligand-based and structure-based virtual screening (VS). Quantitative structure-activity relationship (QSAR) models were developed based on the structures of known RSK2 bioactivity inhibitors and the verified models were used for screening SPECS database. By molecular docking, the virtual hit compounds were further filtered to obtained compounds with strong binding affinity. Finally, 30 molecules were selected and purchased for in vitro inhibition and SPR binding assays after ADMET evaluation. 5 hit compounds exhibited breast cancer or melanoma cell inhibitory activities with IC50 values from 5.7 to 28.4 ​μM, and the following surface plasmon resonance (SPR) assay suggested that 5 compounds showed strong affinity to RSK2 with the KD values in the range 4.76 ​× ​10−6 to 3.17 ​× ​10−7 ​M, wherein, C2 as the most active compound displayed an IC50 of 5.7 ​μM against melanoma, and with an KD of 856 ​nM against RSK2. Therefore, these compounds can provide us a valuable insight for further optimization and development of more potent RSK2 inhibitors.

Discovery of Novel Allosteric Modulators Targeting an Extra-Helical Binding Site of GLP-1R Using Structure- and Ligand-Based Virtual Screening.

Allosteric modulators have emerged with many potential pharmacological advantages as they do not compete the binding of agonist or antagonist to the orthosteric sites but ultimately affect downstream signaling. To identify allosteric modulators targeting an extra-helical binding site of the glucagon-like peptide-1 receptor (GLP-1R) within the membrane environment, the following two computational approaches were applied: structure-based virtual screening with consideration of lipid contacts and ligand-based virtual screening with the maintenance of specific allosteric pocket residue interactions. Verified by radiolabeled ligand binding and cAMP accumulation experiments, two negative allosteric modulators and seven positive allosteric modulators were discovered using structure-based and ligand-based virtual screening methods, respectively. The computational approach presented here could possibly be used to discover allosteric modulators of other G protein-coupled receptors.

A comprehensive comparative assessment of 3D molecular similarity tools in ligand-based virtual screening.

Three-dimensional (3D) molecular similarity, one major ligand-based virtual screening (VS) method, has been widely used in the drug discovery process. A variety of 3D molecular similarity tools have been developed in recent decades. In this study, we assessed a panel of 15 3D molecular similarity programs against the DUD-E and LIT-PCBA datasets, including commercial ROCS and Phase, in terms of screening power and scaffold-hopping power. The results revealed that (1) SHAFTS, LS-align, Phase Shape_Pharm and LIGSIFT showed the best VS capability in terms of screening power. Some 3D similarity tools available to academia can yield relatively better VS performance than commercial ROCS and Phase software. (2) Current 3D similarity VS tools exhibit a considerable ability to capture actives with new chemotypes in terms of scaffold hopping. (3) Multiple conformers relative to single conformations will generally improve VS performance for most 3D similarity tools, with marginal improvement observed in area under the receiving operator characteristic curve values, enrichment factor in the top 1% and hit rate in the top 1% values showed larger improvement. Moreover, redundancy and complementarity analyses of hit lists from different query seeds and different 3D similarity VS tools showed that the combination of different query seeds and/or different 3D similarity tools in VS campaigns retrieved more (and more diverse) active molecules. These findings provide useful information for guiding choices of the optimal 3D molecular similarity tools for VS practices and designing possible combination strategies to discover more diverse active compounds.

Systematic comparison of ligand-based and structure-based virtual screening methods on poly (ADP-ribose) polymerase-1 inhibitors

The poly (ADP-ribose) polymerase-1 (PARP1) has been regarded as a vital target in recent years and PARP1 inhibitors can be used for ovarian and breast cancer therapies. However, it has been realized that most of PARP1 inhibitors have disadvantages of low solubility and permeability. Therefore, by discovering more molecules with novel frameworks, it would have greater opportunities to apply it into broader clinical fields and have a more profound significance. In the present study, multiple virtual screening (VS) methods had been employed to evaluate the screening efficiency of ligand-based, structure-based and data fusion methods on PARP1 target. The VS methods include 2D similarity screening, structure-activity relationship (SAR) models, docking and complex-based pharmacophore screening. Moreover, the sum rank, sum score and reciprocal rank were also adopted for data fusion methods. The evaluation results show that the similarity searching based on Torsion fingerprint, six SAR models, Glide docking and pharmacophore screening using Phase have excellent screening performance. The best data fusion method is the reciprocal rank, but the sum score also performs well in framework enrichment. In general, the ligand-based VS methods show better performance on PARP1 inhibitor screening. These findings confirmed that adding ligand-based methods to the early screening stage will greatly improve the screening efficiency, and be able to enrich more highly active PARP1 inhibitors with diverse structures.

Comparative study between deep learning and QSAR classifications for TNBC inhibitors and novel GPCR agonist discovery

Machine learning is a well-known approach for virtual screening. Recently, deep learning, a machine learning algorithm in artificial neural networks, has been applied to the advancement of precision medicine and drug discovery. In this study, we performed comparative studies between deep neural networks (DNN) and other ligand-based virtual screening (LBVS) methods to demonstrate that DNN and random forest (RF) were superior in hit prediction efficiency. By using DNN, several triple-negative breast cancer (TNBC) inhibitors were identified as potent hits from a screening of an in-house database of 165,000 compounds. In broadening the application of this method, we harnessed the predictive properties of trained model in the discovery of G protein-coupled receptor (GPCR) agonist, by which computational structure-based design of molecules could be greatly hindered by lack of structural information. Notably, a potent (~ 500 nM) mu-opioid receptor (MOR) agonist was identified as a hit from a small-size training set of 63 compounds. Our results show that DNN could be an efficient module in hit prediction and provide experimental evidence that machine learning could identify potent hits in silico from a limited training set.

Identification of Novel Phyto-chemicals from Ocimum basilicum for the Treatment of Parkinson's Disease using In Silico Approach.

Parkinson's disease is characterized by decreased level of dopaminergic neurotransmitters and this decrease is due to the degradation of dopamine by protein Monoamine Oxidase B (MAO-B). In order to treat Parkinson's disease, MAO-B should be inhibited. To find out the novel phytochemicals from plant Ocimum basilicum that can inhibit MAO-B by using the in silico methods. The data of chemical constituents from plant Ocimum basilicum was collected and inhibitory activity of these phytochemicals was then predicted by using the Structure-Based (SB) and Ligand-Based Virtual Screening (LBVS) methods. Molecular docking, one of the common Structure-Based Virtual Screening method, has been used during this search. Traditionally, molecular docking is used to predict the orientation and binding affinity of the ligand within the active site of the protein. Molegro Virtual Docker (MVD) software has been used for this purpose. On the other hand, Random Forest Model, one of the LBVS method, has also been used to predict the activity of these chemical constituents of Ocimum basilicum against the MAO-B. During the docking studies, all the 108 compounds found in Ocimum basilicum were docked within the active site of MAO-B (PDB code: 4A79) out of which, 57 compounds successfully formed the hydrogen bond with tyr 435, a crucial amino acid for the biological activity of the enzyme. Rutin (-182.976 Kcal/mol), Luteolin (-163.171 Kcal/mol), Eriodictyol-7-O-glucoside (- 160.13 Kcal/mol), Rosmarinic acid (-133.484 Kcal/mol) and Isoquercitrin (-131.493 Kcal/mol) are among the top hits with the highest MolDock score along with hydrogen interaction with tyr 435. Using the RF model, ten compounds out of 108 chemical constituent of Ocimum basilicum were predicted to be active, Apigenin (1.0), Eriodictyol (1.0), Orientin (0.876), Kaempferol (0.8536), Luteolin (0.813953) and Rosmarinic-Acid (0.7738095) are predicted to be most active with the highest RF score. The comparison of the two screening methods show that the ten compounds that were predicted to be active by the RF model, are also found in top hits of docking studies with the highest score. The top hits obtained during this study are predicted to be the inhibitor of MAO-B, thus, could be used further for the development of drugs for the treatment of Parkinson's disease (PD).

Identification of free fatty acid receptor 2 agonists using virtual screening

Structure- and ligand-based virtual-screening methods (docking, 2D- and 3D-similarity searching) were analyzed for their effectiveness in virtual screening against FFAR2. To evaluate the performance of these methods, retrospective virtual screening was performed. Statistical quality of the methods was evaluated by BEDROC and RIE. The results revealed that electrostatic similarity search protocol using EON (ET combo) outperformed all other protocols with outstanding enrichment of >95% in top 1% and 2% of the dataset with an AUC of 0.958. Interestingly, the hit lists that are obtained from different virtual-screening methods are generally highly complementary to hits found from electrostatic similarity searching. These results suggest that considering electrostatic similarity searching first increases the chance of identifying more (and more diverse) active compounds from a virtual-screening campaign. Accordingly, prospective virtual screening using electrostatic similarity searching was used to identify novel FFAR2 ligands. The discovered compounds provide new chemical matter starting points for the initiation of a medicinal chemistry campaign.

Identification of Novel TRPC5 Inhibitors by Pharmacophore-Based and Structure-Based Approaches

Canonical transient receptor potential-5 (TRPC5), which belongs to the subfamily of transient receptor potential (TRP) channels, is a non-selective cation channel mainly expressed in the central nervous system and shows more restricted expression in the periphery. TRPC5 plays a crucial role in human physiology and pathology, for instance, anxiety, depression, epilepsy, pain, memory and chronic kidney disease (CKD). However, due to lack of the effective and selective inhibitors, its physiological and pathological mechanism remains so far unknown. It is therefore pivotal to identify potential TRPC5 inhibitors. We have applied ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS) methods. The pharmacophore models of TRPC5 antagonists generated by using the HypoGen and HipHop algorithms were used as a query model for the screening of potential inhibitors against the Specs database. The resultant hits from LBVS were further screened by SBVS. SBVS was carried out based on the homology model generation of human TRPC5, binding site identification, molecular dynamics optimization and molecular docking studies. In our systematic screening approaches, we have identified 7 hits compounds with comparable dock score after Lipinski and Veber rules, ADMET, PAINS analysis, cluster analysis, and similarity analysis. In conclusion, the current research provides novel backbones for the new-generation of TRPC5 inhibitors.

Discovery of Vascular Endothelial Growth Factor Receptor-2 (VEGFR-2) Inhibitors by Ligand-based Virtual High Throughput Screening.

Virtual screening has become one of the important tools in the discovery of novel hits for the given target. The present study reports the successful application of ligand-based virtual screening method for the discovery of novel vascular endothelial growth factor receptor-2 (VEGFR-2) inhibitors. We generated a ligand query model with pharmacophore features from the reported VEGFR-2 inhibitors using vROCS tool and performed virtual screening. Among the 2.4 million lead like molecules of ZINC database screened, nineteen prioritized compounds were purchased from Enamine and ChemBridge and tested for VEGFR-2 inhibitory activity using Promega's ADP-Glo™ kinase assay. Experimental validation led to the discovery of four compounds 3, 7, 10, and 13. Compound 10 exhibited moderate inhibitory activity with the IC50 value of 19.3 μM. Molecular docking was performed for these compounds and the predicted binding modes reported in this paper may further guide to explore the possible structural changes to get more potent VEGFR-2 inhibitors.

Applying Machine Learning to Ultrafast Shape Recognition in Ligand-Based Virtual Screening.

Ultrafast Shape Recognition (USR), along with its derivatives, are Ligand-Based Virtual Screening (LBVS) methods that condense 3-dimensional information about molecular shape, as well as other properties, into a small set of numeric descriptors. These can be used to efficiently compute a measure of similarity between pairs of molecules using a simple inverse Manhattan Distance metric. In this study we explore the use of suitable Machine Learning techniques that can be trained using USR descriptors, so as to improve the similarity detection of potential new leads. We use molecules from the Directory for Useful Decoys-Enhanced to construct machine learning models based on three different algorithms: Gaussian Mixture Models (GMMs), Isolation Forests and Artificial Neural Networks (ANNs). We train models based on full molecule conformer models, as well as the Lowest Energy Conformations (LECs) only. We also investigate the performance of our models when trained on smaller datasets so as to model virtual screening scenarios when only a small number of actives are known a priori. Our results indicate significant performance gains over a state of the art USR-derived method, ElectroShape 5D, with GMMs obtaining a mean performance up to 430% better than that of ElectroShape 5D in terms of Enrichment Factor with a maximum improvement of up to 940%. Additionally, we demonstrate that our models are capable of maintaining their performance, in terms of enrichment factor, within 10% of the mean as the size of the training dataset is successively reduced. Furthermore, we also demonstrate that running times for retrospective screening using the machine learning models we selected are faster than standard USR, on average by a factor of 10, including the time required for training. Our results show that machine learning techniques can significantly improve the virtual screening performance and efficiency of the USR family of methods.