3D Molecular Similarity Research Articles

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.

Building 2D classification models and 3D CoMSIA models on small-molecule inhibitors of both wild-type and T790M/L858R double-mutant EGFR.

Epidermal growth factor receptor (EGFR) has received widespread attention because it is an important target for anticancer drug design. Mutations in the EGFR, especially the T790M/L858R double mutation, have made cancer treatment more difficult. We herein built the structure-activity relationship models of small-molecule inhibitors on wild-type and T790M/L858R double-mutant EGFR with a whole dataset of 379 compounds. For 2D classification models, we used ECFP4 fingerprints to build support vector machine and random forest models and used SMILES to build self-attention recurrent neural network models. Each of all six models resulted in an accuracy of above 0.87 and the Matthews correlation coefficient value of above 0.76 on the test set, respectively. We concluded that inhibitors containing anilinoquinoline and methoxy or fluoro phenyl are highly active against wild EGFR. Substructures such as anilinopyrimidine, acrylamide, amino phenyl, methoxy phenyl, and thienopyrimidinyl amide appeared more in highly active inhibitors against double-mutant EGFR. We also used self-organizing map to cluster the inhibitors into six subsets based on ECFP4 fingerprints and analyzed the activity characteristics of different scaffolds in each subset. Among them, three datasets, which are based on pteridin, anilinopyrimidine, and anilinoquinoline scaffold, were selected to build 3D comparative molecular similarity analysis models individually. Models with the leave-one-out coefficient of determination (q2) above 0.65 were selected, and five descriptor types (steric, electrostatic, hydrophobic, donor, and acceptor) were used to study the effects of side chains of inhibitors on the activity against wild-type and mutant-type EGFR.

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.

Diosgenin Derivatives as Potential Antitumor Agents: Synthesis, Cytotoxicity, and Mechanism of Action.

Thirty-two new diosgenin derivatives were designed, synthesized, and evaluated for their cytotoxic activities in three human cancer cell lines (A549, MCF-7, and HepG2) and normal human liver cells (L02) using an MTT assay in vitro. Most compounds, especially 8, 18, 26, and 30, were more potent when compared with diosgenin. The structure-activity relationship results suggested that the presence of a succinic acid or glutaric acid linker, a piperazinyl amide terminus, and lipophilic cations are all beneficial for promoting cytotoxic activity. Notably, compound 8 displayed excellent cytotoxic activity against HepG2 cells (IC50 = 1.9 μM) and showed relatively low toxicity against L02 cells (IC50 = 18.6 μM), showing some selectivity between normal and tumor cells. Studies on its cellular mechanism of action showed that compound 8 induces G0/G1 cell cycle arrest and apoptosis in HepG2 cells. Predictive studies indicated that p38α mitogen-activated protein kinase (MAPK) is the optimum target of 8 based on its 3D molecular similarity, and docking studies showed that compound 8 fits well into the active site of p38α-MAPK and forms relatively strong interactions with the surrounding amino acid residues. Accordingly, compound 8 may be used as a promising lead compound for the development of new antitumor agents.

Electrostatic-field and surface-shape similarity for virtual screening and pose prediction

We introduce a new method for rapid computation of 3D molecular similarity that combines electrostatic field comparison with comparison of molecular surface-shape and directional hydrogen-bonding preferences (called “eSim”). Rather than employing heuristic “colors” or user-defined molecular feature types to represent conformation-dependent molecular electrostatics, eSim calculates the similarity of the electrostatic fields of two molecules (in addition to shape and hydrogen-bonding). We present detailed virtual screening performance data on the standard 102 target DUD-E set. In its moderately fast screening mode, eSim running on a single computing core is capable of processing over 60 molecules per second. In this mode, eSim performed significantly better than all alternate methods for which full DUD-E data were available (mean ROC area of 0.74, p < 10^{-9}, by paired t-test, compared with the best performing alternate method). In addition, for 92 targets of the DUD-E set where multiple ligand-bound crystal structures were available, screening performance was assessed using alternate ligands or sets thereof (in their bound poses) as similarity targets. Using the joint alignment of five ligands for each protein target, mean ROC area exceeded 0.82 for the 92 targets. Design-focused application of ligand similarity methods depends on accurate predictions of geometric molecular relationships. We comprehensively assessed pose prediction accuracy by curating nearly 400,000 bound ligand pose pairs across the DUD-E targets. Overall, beginning from agnostic initial poses, we observed an 80% success rate for RMSD le 2.0 Å among the top 20 predicted eSim poses. These examples were split roughly 50/50 into cases with high direct atomic overlap (where a shared scaffold exists between a pair) and low direct atomic overlap (where where a ligand pair has dissimilar scaffolds but largely occupies the same space). Within the high direct atomic overlap subset, the pose prediction success rate was 93%. For the more challenging subset (where dissimilar scaffolds are to be aligned), the success rate was 70%. The eSim approach enables both large-scale screening and rational design of ligands and is rooted in physically meaningful, non-heuristic, molecular comparisons.

ESHAFTS: Integrated and graphical drug design software based on 3D molecular similarity.

With the development of computer technology, computer-aided drug design (CADD) has become an important means for drug research and development, and its representative method is virtual screening. Various virtual screening platforms have emerged in an endless stream and play great roles in the field of drug discovery. With the increasing number of compound molecules, virtual screening platforms face two challenges: low fluency and low visibility of software operations. In this article, we present an integrated and graphical drug design software eSHAFTS based on three-dimensional (3D) molecular similarity to overcome these shortcomings. Compared with other software, eSHAFTS has four main advantages, which are integrated molecular editing and drawing, interactive loading and analysis of proteins, multithread and multimode 3D molecular similarity calculation, and multidimensional information visualization. Experiments have verified its convenience, usability, and reliability. By using eSHAFTS, various tasks can be submitted and visualized in one desktop software without locally installing any dependent plug-ins or software. The software installation package can be downloaded for free at http://lilab.ecust.edu.cn/home/resource.html. © 2019 Wiley Periodicals, Inc.

Design, synthesis and biological evaluation of 3-piperazinecarboxylate sarsasapogenin derivatives as potential multifunctional anti-Alzheimer agents

A series of multifunctional 3-piperazinecarboxylate sarsasapogenin derivatives were designed and synthesized against Alzheimer's disease (AD). The protection against H2O2-triggered oxidative stress in PC12 cells, and inhibition on LPS-induced NO production in RAW264.7 cell lines in vitro by these derivatives were firstly evaluated. Most of the compounds showed better antioxidant and antiinflammatory activities compared with sarsasapogenin, especially AA34 and AA36. Structure-activity relationships revealed that benzyl group, electron-donating group and intramolecular hydrogen bond might be beneficial to enhancing their neuroprotective activities. Moreover, Aβ42 was the optimum predicted target based on the high 3D molecular similarity between compound AA36 and caprospinol. In the following experiments, AA36 significantly protected PC12 cells from Aβ-induced damage and improved learning and memory impairments in Aβ-injected mice. Thus AA36 is regarded as a potent anti-AD agent and N-substituted piperazinecarboxylate can be served as a promising structural unit for anti-AD drug design.

Discovery and Rational Design of Natural-Product-Derived 2-Phenyl-3,4-dihydro-2H-benzo[f]chromen-3-amine Analogs as Novel and Potent Dipeptidyl Peptidase 4 (DPP-4) Inhibitors for the Treatment of Type 2 Diabetes.

Starting from the lead isodaphnetin, a natural product inhibitor of DPP-4 discovered through a target fishing docking based approach, a series of novel 2-phenyl-3,4-dihydro-2H-benzo[f]chromen-3-amine derivatives as potent DPP-4 inhibitors are rationally designed utilizing highly efficient 3D molecular similarity based scaffold hopping as well as electrostatic complementary methods. Those ingenious drug design strategies bring us approximate 7400-fold boost in potency. Compounds 22a and 24a are the most potent ones (IC50 ≈ 2.0 nM) with good pharmacokinetic profiles. Compound 22a demonstrated stable pharmacological effect. A 3 mg/kg oral dose provided >80% inhibition of DPP-4 activity within 24 h, which is comparable to the performance of the long-acting control omarigliptin. Moreover, the efficacy of 22a in improving the glucose tolerance is also comparable with omarigliptin. In this study, not only promising DPP-4 inhibitors as long acting antidiabetic that are clinically on demand are identified, but the target fish docking and medicinal chemistry strategies were successfully implemented.

Virtual screening in small molecule discovery for epigenetic targets

Epigenetic modifications are critical mechanisms that regulate many biological processes and establish normal cellular phenotypes. Aberrant epigenetic modifications are frequently linked to the development and maintenance of several diseases including cancer, inflammation and metabolic diseases and so on. The key proteins that mediate epigenetic modifications have been thus recognized as potential therapeutic targets for these diseases. Consequently, discovery of small molecule inhibitors for epigenetic targets has received considerable attention in recent years. Here, virtual screening methods and their applications in the discovery of epigenetic target inhibitors are the focus of this review. Newly emerging approaches or strategies including rescoring methods, docking pose filtering methods, machine learning methods and 3D molecular similarity methods were also underlined. They are expected to be employed for identifying novel inhibitors targeting epigenetic regulation more efficiently.

IDrug: a web-accessible and interactive drug discovery and design platform.

BackgroundThe progress in computer-aided drug design (CADD) approaches over the past decades accelerated the early-stage pharmaceutical research. Many powerful standalone tools for CADD have been developed in academia. As programs are developed by various research groups, a consistent user-friendly online graphical working environment, combining computational techniques such as pharmacophore mapping, similarity calculation, scoring, and target identification is needed.ResultsWe presented a versatile, user-friendly, and efficient online tool for computer-aided drug design based on pharmacophore and 3D molecular similarity searching. The web interface enables binding sites detection, virtual screening hits identification, and drug targets prediction in an interactive manner through a seamless interface to all adapted packages (e.g., Cavity, PocketV.2, PharmMapper, SHAFTS). Several commercially available compound databases for hit identification and a well-annotated pharmacophore database for drug targets prediction were integrated in iDrug as well. The web interface provides tools for real-time molecular building/editing, converting, displaying, and analyzing. All the customized configurations of the functional modules can be accessed through featured session files provided, which can be saved to the local disk and uploaded to resume or update the history work.ConclusionsiDrug is easy to use, and provides a novel, fast and reliable tool for conducting drug design experiments. By using iDrug, various molecular design processing tasks can be submitted and visualized simply in one browser without installing locally any standalone modeling softwares. iDrug is accessible free of charge at http://lilab.ecust.edu.cn/idrug.

COSMOsar3D: Molecular Field Analysis Based on Local COSMO σ-Profiles

The COSMO surface polarization charge density σ resulting from quantum chemical calculations combined with a virtual conductor embedding has been widely proven to be a very suitable descriptor for the quantification of interactions of molecules in liquids. In a preceding paper, grid-based local histograms of σ have been introduced in the COSMOsim3D method, resulting in a novel 3D-molecular similarity measure and going along with a novel property-based molecular alignment method. In this paper, we introduce under the name COSMOsar3D the usage of the resulting array of local σ-profiles as a novel set of molecular interaction fields for 3D-QSAR, containing all information required for quantifying the virtual ligand-receptor interactions, including desolvation. In contrast to currently used molecular interaction fields, we provide a theoretical rationale that the logarithmic binding constants of ligands should be a linear function of the array of local σ-profiles. This makes them especially suitable for linear regression analysis methods such as PLS. We demonstrate that the usage of local σ-profiles in molecular field analysis inverts the role of ligands and receptor; while conventional 3D-QSAR considers the virtual receptor in potential energy fields provided by the ligands, our COSMOsar3D approach corresponds to the calculation of the free energy of the ligands in a virtual free energy field provided by the receptor. First applications of the COSMOsar3D method are presented, which demonstrate its ability to yield robust and predictive models that seem to be superior to the models generated on the basis of conventionally used molecular fields.

Surflex-Dock: Docking benchmarks and real-world application

Benchmarks for molecular docking have historically focused on re-docking the cognate ligand of a well-determined protein-ligand complex to measure geometric pose prediction accuracy, and measurement of virtual screening performance has been focused on increasingly large and diverse sets of target protein structures, cognate ligands, and various types of decoy sets. Here, pose prediction is reported on the Astex Diverse set of 85 protein ligand complexes, and virtual screening performance is reported on the DUD set of 40 protein targets. In both cases, prepared structures of targets and ligands were provided by symposium organizers. The re-prepared data sets yielded results not significantly different than previous reports of Surflex-Dock on the two benchmarks. Minor changes to protein coordinates resulting from complex pre-optimization had large effects on observed performance, highlighting the limitations of cognate ligand re-docking for pose prediction assessment. Docking protocols developed for cross-docking, which address protein flexibility and produce discrete families of predicted poses, produced substantially better performance for pose prediction. Performance on virtual screening performance was shown to benefit by employing and combining multiple screening methods: docking, 2D molecular similarity, and 3D molecular similarity. In addition, use of multiple protein conformations significantly improved screening enrichment.

Quantitative structure–retention relationship of curcumin and its analogues

The natural product curcumin is widely used in Asian countries for the treatment of several diseases. However, the clinical potential of curcumins remains limited due to their relatively poor bioavailability and no experimental data about their lipophilicity for bioavailability prediction. To evaluate the retention and lipophilicity of curcumin and its 31 newly synthesized analogues, they were subjected to 3D quantitative structure-retention relationship studies by RP-HPLC. Superior than the classical four-variant quantitative structure-retention relationship model (conventional r(2) =0.734), the 3D comparative molecular similarity index analysis model with combined steric, electrostatic, and H-bond donor fields, resulted in a robust structure-retention correlation (cross-validated q(2) =0.613 and r(2) =0.979). The statistical analyses indicate that the electrostatic and H-bond donor fields have a primary influence on the chromatographic retention of analytes. The predictive power and robustness of the derived comparative molecular similarity index analysis model was further confirmed by the test-set validation (q(2) =0.702, r(2) =0.905, and the slope K=1.016) and Y-randomization examination. Statistically significant and physically meaningful 3D-quantitative structure-retention relationship provided better insight into understanding the retention behaviors of curcumin and its analogues, and their separation mechanism in a given RP-HPLC system.

3D-Quantitative structure–activity relationship and docking studies of the tachykinin NK3 receptor

The tachykinin NK(3) receptor (NK(3)R) is a novel drug target for schizophrenia and drug abuse. Since few non-peptide antagonists of this G protein-coupled receptor are available, we have initiated this study to gain a better understanding of the structure-activity relationships of NK(3) antagonist compounds. We developed a 3D comparative molecular similarity index analysis (CoMSIA) model that gave cross-validated PLS values with q(2) >0.5 which were validated using a test set. We also describe the development of a homology model of the NK(3)R. The model was then used to develop a pharmacophore for docked ligands. This pharmacophore showed two aromatic, two hydrogen donor and one acceptor/aromatic points. These data will be useful for future structure-based drug discovery of ligands for the NK(3)R.

SHAFTS: A Hybrid Approach for 3D Molecular Similarity Calculation. 1. Method and Assessment of Virtual Screening

We developed a novel approach called SHAFTS (SHApe-FeaTure Similarity) for 3D molecular similarity calculation and ligand-based virtual screening. SHAFTS adopts a hybrid similarity metric combined with molecular shape and colored (labeled) chemistry groups annotated by pharmacophore features for 3D similarity calculation and ranking, which is designed to integrate the strength of pharmacophore matching and volumetric overlay approaches. A feature triplet hashing method is used for fast molecular alignment poses enumeration, and the optimal superposition between the target and the query molecules can be prioritized by calculating corresponding "hybrid similarities". SHAFTS is suitable for large-scale virtual screening with single or multiple bioactive compounds as the query "templates" regardless of whether corresponding experimentally determined conformations are available. Two public test sets (DUD and Jain's sets) including active and decoy molecules from a panel of useful drug targets were adopted to evaluate the virtual screening performance. SHAFTS outperformed several other widely used virtual screening methods in terms of enrichment of known active compounds as well as novel chemotypes, thereby indicating its robustness in hit compounds identification and potential of scaffold hopping in virtual screening.

Unconventional 2D Shape Similarity Method Affords Comparable Enrichment as a 3D Shape Method in Virtual Screening Experiments

3D molecular shape similarity search has recently become an attractive method for virtual screening and scaffold hopping in drug discovery and chemical genomics research. Among these 3D similarity methods is ROCS (Rapid Overlay of Chemical Structures), a popular tool because of its efficiency and effectiveness. However, searching a large multiconformer molecular database remains a very challenging task because of the nature of such calculations. To simplify shape similarity calculations and potentially increase the efficiency for large scale virtual screening, we have explored an alternative shape similarity approach that does not depend on multiconformers of molecules. The hypothesis underlying this approach is that similar chemical structures tend to have similar 2D chemical depictions and that shape comparison techniques can be utilized to effectively compare the shapes between chemical depictions. We use a 2D depiction program to generate 2-D chemical drawings for both the query molecule and database molecules. We have built a 2D shape comparison program based on the OESHAPE Toolkit (OE Scientific, NM) that compares the molecular depictions and quantifies the shape similarity between the molecules. We demonstrate that this unconventional 2D shape similarity method performs fairly well in virtual screening experiments compared to the 3D Shape method ROCS, with an added advantage of its computational efficiency.

WwLigCSRre: a 3D ligand-based server for hit identification and optimization

The wwLigCSRre web server performs ligand-based screening using a 3D molecular similarity engine. Its aim is to provide an online versatile facility to assist the exploration of the chemical similarity of families of compounds, or to propose some scaffold hopping from a query compound. The service allows the user to screen several chemically diversified focused banks, such as Kinase-, CNS-, GPCR-, Ion-channel-, Antibacterial-, Anticancer- and Analgesic-focused libraries. The server also provides the possibility to screen the DrugBank and DSSTOX/Carcinogenic compounds databases. User banks can also been downloaded. The 3D similarity search combines both geometrical (3D) and physicochemical information. Starting from one 3D ligand molecule as query, the screening of such databases can lead to unraveled compound scaffold as hits or help to optimize previously identified hit molecules in a SAR (Structure activity relationship) project. wwLigCSRre can be accessed at http://bioserv.rpbs.univ-paris-diderot.fr/wwLigCSRre.html.

CLONING AND PHARMACOLOGICAL PROFILE OF THE GERBIL (MERIONESUNGUICULATUS) NK3 RECEPTOR: COMPARISON WITH HUMAN, RAT ANDMOUSE NK3 RECEPTORS

The tachykinin NK3 receptor (NK3R) is a novel drug target for schizophrenia and drug abuse. Since few non-peptide antagonists of this G protein-coupled receptor are available, we have initiated this study to gain a better understanding of the structure–activity relationships of NK3 antagonist compounds. We developed a 3D comparative molecular similarity index analysis (CoMSIA) model that gave cross-validated PLS values with q2 >0.5 which were validated using a test set. We also describe the development of a homology model of the NK3R. The model was then used to develop a pharmacophore for docked ligands. This pharmacophore showed two aromatic, two hydrogen donor and one acceptor/aromatic points. These data will be useful for future structure-based drug discovery of ligands for the NK3R.

3D Molecular Similarity: Method and Algorithms

This study presents a method and algorithms for calculation of 3D similarity between pairs of chemical structures represented as 3D molecular graphs. Similarity searching in chemical databases is widely used for virtual screening, lead discovery and optimization, and most recently protein amino-acid sequences studies to discover and determine the functionality of a new isolated protein. This method has obvious advantages over other known methods due to the following: (i) the superposition method does not depend on the preliminary alignments of the chemical structures; (ii) entire conformational space is searched without generation of each conformer; (iii) excellent discrimination between geometrical isomers. Although it is a computationally demanding method, recent implementation of maximum clique algorithm and bound smoothing algorithm made possible the optimization of this method and application to similarity searching in chemical databases of non trivial size.

The combination of principal component analysis, genetic algorithm and tabu search in 3d molecular similarity

Recently, molecular similarity, as an important tool of computer-aided drug design has developed rapidly. Its calculation has also developed from planar, rigid, 2D molecules to steric, flexible, 3D molecules. However, 3D molecular similarity calculation is easy to fall into local optima and the calculation is always time-consuming. In this paper, a method of flexible 3D molecular similarity calculation through the evaluation of molecular electrostatic potentials (MEP) with principal component analysis (PCA), genetic algorithm (GA) and Tabu search (TS) was presented. PCA was used to preprocess, GA was used to align two molecules and TS was used to decrease the probability of falling into local optima. The authors calculated the molecular similarities of benzene and its derivatives, a group of insecticides and a series of acetylcholinesterase inhibitors. To further evaluate the method, the authors calculated the similarities of HIV-1 protease inhibitors TIBO derivatives and predicted the pIC50 values, in which the linear relationship between similarities and logP values was also discussed.