“Lead Hopping”. Validation of Topomer Similarity as a Superior Predictor of Similar Biological Activities

Targeting cancer using scaffold-hopping approaches: illuminating SAR to improve drug design

Scaffold hopping is a design approach involving alterations to the core structure of an already bioactive scaffold to generate novel molecules to discover bioactive hit compounds with innovative core structures. Scaffold hopping enhances selectivity and potency while maintaining physicochemical, pharmacodynamic (PD), and pharmacokinetic (PK) properties, including toxicity parameters. Numerous molecules have been designed based on a scaffold-hopping strategy that showed potent inhibition activity against multiple targets for the diverse types of malignancy. In this review, we critically discuss recent applications of scaffold hopping along with essential components of medicinal chemistry, such as structure-activity relationship (SAR) profiles. Moreover, we shed light on the limitations and challenges associated with scaffold hopping-based anticancer drug discovery.

[3 + 2] Annulation of Vinyl Azides with Aldehydes for the Synthesis of 3-Oxazolines via the [CO + CCN] Strategy

Despite the widespread utilizable value of 3-oxazolines, mild and efficient access to such a class of unique structures still remains, to date, a challenge. Herein, we present a [3 + 2] annulation strategy, guided by the retrosynthetic principle of [CO + CCN], that utilizes vinyl azides as the CCN module and aldehydes as the CO module. This approach enables the efficient construction of the 3-oxazoline framework with remarkable features, including operational simplicity, environmental friendliness, and high efficiency. Notably, it solely requires the addition of inexpensive and readily available N-hydroxyphthalimide (NHPI) and air oxygen to obtain the desired product. It also provides a new way to generate the hydroxyl radical, which is produced by the homolysis of peroxycarboxylic acid. In addition, control experiments, X-ray crystallographic analysis, high-resolution mass spectrometry (HRMS), and density functional theory (DFT) calculations afford evidence for the key intermediates (hydroxyl radical, carboxyl radical, imine radical, hydroxyl substituted amide derivatives), further confirming the path for realization of 3-oxazolines.

Molecular medicinal insights into scaffold hopping-based drug discovery success

In both academia and the pharmaceutical industry, innovative hypotheses, methodologies and technologies that can shorten the drug research and development, leading to higher success rates, are vital. In this review, we demonstrate how innovative variations of the scaffold-hopping strategy have been used to create new druggable molecular spaces, drugs, clinical candidates, preclinical candidates, and bioactive agents. We also analyze molecular modulations that enabled improvements of the pharmacodynamic (PD), physiochemical, and pharmacokinetic (PK) properties (P3 properties) of the drugs resulting from these scaffold-hopping strategies.

Green synthesis, characterization, and biochemical impacts of new bioactive isoxazoline-sulfonamides as potential insecticidal agents against the Sphodroxia maroccana Ley

BACKGROUND

Sphodroxia maroccana Ley is a pest of cork oak crops that damages the roots of seedlings and can severely impair cork oak regeneration. Since the banning of carbosulfan and chlorpyriphos, which were widely used against the larvae of Sphodroxia maroccana because of their harmful impact on the environment, until now there has been no pesticide against these pests. Therefore, it is particularly urgent to develop highly effective insecticidal molecules with novel scaffolds. Isoxazolines are a class of insecticides that act on γ-aminobutyric acid (GABA)-gated chloride channel allosteric modulators. In this work, a green synthesis of novel 3,5-disubstituted isoxazoline-sulfonamide derivatives was achieved in water via ultrasound-assisted four-component reactions, and their insecticidal activities against fourth-instar larvae of S. maroccana were evaluated.

RESULTS

Most of the tested compounds showed insecticidal activity compared to fluralaner as positive control and commercially available insecticide. Especially, the isoxazoline-secondary sulfonamides containing halogens (Br and Cl) on the phenyl group attached to the isoxazoline, 6g (LC50  = 0.31 mg/mL), 6j (LC50  = 0.38 mg/mL), 6k (LC50  = 0.18 mg/mL), 6L (LC50  = 0.49 mg/mL), 6m (LC50  = 0.24 mg/mL), 6q (LC50  = 0.46 mg/mL), exhibited much higher larvicidal activity than fluralaner (LC50  = 0.99 mg/mL).

CONCLUSION

Novel isoxazolines containing sulfonamide moieties were designed, synthesized and confirmed by two single-crystal structures of 4e and 6q. Their bioassay results showed significant larvicidal activity with significant morphological changes in vivo. These results will lay the foundation for the further discovery and development of isoxazoline-sulfonamide derivatives as novel crop protection larvicides of cork oak. © 2023 Society of Chemical Industry.

Oxetane-, Azetidine-, and Bicyclopentane-Bearing N-Heterocycles from Ynones: Scaffold Diversification via Ruthenium-Catalyzed Oxidative Alkynylation

A process for 3-fold scaffold diversification is achieved via ruthenium-catalyzed oxidative alkynylation of commercially available oxetanols, azetidinols and bicyclopentanols to form α,β-acetylenic ketones (ynones), which are subsequently converted to oxetane-, azetidine- and bicyclopentane-bearing pyrazoles, isoxazoles and pyrimidines. A one-pot oxidative alkynylation-condensation protocol that directly converts azetidinols to azetidine-substituted pyrazoles or pyrimidines is demonstrated.

Computational Drug Repurposing Resources and Approaches for Discovering Novel Antifungal Drugs against Candida albicans N-Myristoyl Transferase

Candida albicans is a yeast that is an opportunistic fungal pathogen and also identified as ubiquitous polymorphic species that is mainly linked with major fungal infections in humans, particularly in the immunocompromised patients including transplant recipients, chemotherapy patients, HIV-infected patients as well as in low-birth-weight infants. Systemic Candida infections have a high mortality rate of around 29 to 76%. For reducing its infection, limited drugs are existing such as caspofungin, fluconazole, terbinafine, and amphotericin B, etc. which contain unlikable side effects and also toxic. This review intends to utilize advanced bioinformatics technologies such as Molecular docking, Scaffold hopping, Virtual screening, Pharmacophore modeling, Molecular dynamics (MD) simulation for the development of potentially new drug candidates with a drug-repurpose approach against Candida albicans within a limited time frame and also cost reductive.

Design, synthesis, and insecticidal activities of novel 5-substituted 4,5-dihydropyrazolo[1,5-a]quinazoline derivatives

BACKGROUND

Chemical pesticides are the main measures for pest control, but have caused growing resistance of pests and brought a series of environmental problems. Development of high-efficient insecticidal molecules with novel scaffolds is therefore particularly urgent.

RESULTS

Based on a [5 + 1] annulation reaction with 5-amino-1H-phenylpyrazole and dialkyl bromomalonate, 27 novel five-substituted 4,5-dihydropyrazolo[1,5-a]quinazolines were designed following the intermediate derivatization method and synthesized. Bioassay results indicated that most of the test compounds displayed good insecticidal activities against Plutella xylostella, Spodoptera frugiperda, and Solenopsis invicta. In particular, the insecticidal activities of compounds 4a, 4f, and 4m against P. xylostella [median lethal concentration (LC₅₀ ) values ranged from 3.87 to 5.10 mg L^-1 ] were comparable to that of indoxacarb (LC₅₀ = 4.82 mg L^-1 ). In addition, compounds 4a and 9e showed similar high insecticidal activities against Spodoptera frugiperda (mortality rate = 79.63% and 72.12%) at 100 mg L^-1 , comparable to that of fipronil (mortality rate: 68.44%); compound 9a showed possible delayed toxicity against Solenopsis invicta (mortality rate: 95.66%) after 5 days of treatment at 1.0 mg L^-1 .

CONCLUSION

Due to their high insecticidal activities against P. xylostella, compound 4m, 4a, and 4f could be considered as qualified candidates for novel insecticide. Several other 4,5-dihydropyrazolo[1,5-a]quinazolines with relatively high bioactivity, such as compounds 9a and 9e, are also worth further optimization as potential insecticide or anticide candidates.

Design, synthesis and structure-activity relationship study of novel urea compounds as FGFR1 inhibitors to treat metastatic triple-negative breast cancer

Triple-negative breast cancer (TNBC) is an aggressive type of cancer characterized by higher metastatic and reoccurrence rates, where approximately one-third of TNBC patients suffer from the metastasis in the brain. At the same time, TNBC shows good responses to chemotherapy, a feature that fuels the search for novel compounds with therapeutic potential in this area. Recently, we have identified novel urea-based compounds with cytotoxicity against selected cell lines and with the ability to cross the blood-brain barrier in vivo. We have synthesized and analyzed a library of more than 40 compounds to elucidate the key features responsible for the observed activity. We have also identified FGFR1 as a molecular target that is affected by the presence of these compounds, confirming our data using in silico model. Overall, we envision that these compounds can be further developed for the potential treatment of metastatic breast cancer.

Synthesis of spirocyclic Δ4-isoxazolines via [3 + 2] cycloaddition of indanone-derived ketonitrones with alkynes

A [3 + 2] cycloaddition of indanone-derived nitrones and alkynes under mild conditions is developed, allowing facile synthesis of spirocyclicindenyl isoxazolines with structural diversity. The sequential protocol of generated in situ ketonitrone from unsaturated ketones and N-alkylhydroxylamines is also achieved successfully, affording the desired products in considerable yield with moderate to good diastereoselectivity. Moreover, the spirocyclic product can be conveniently transformed into indenyl-based allylic alcohol and enamide.

A [3 + 2] cycloaddition of indanone-derived nitrones with alkynes under mild conditions has been developed. It is a highly efficient and straightforward method for the synthesis of diverse spirocyclicindenyl isoxazolines.

Discovery of potential Toxoplasma gondii CDPK1 inhibitors with new scaffolds based on the combination of QSAR and scaffold-hopping method with in vitro validation

To discover drugs for toxoplasmosis with less side-effects and less probability to get drug resistance is eagerly appealed for pregnant women, infant or immunocompromised patients. In this work, using TgCDPK1 as drug target, we design a method to discover new inhibitors for CDPK1 as potential drug lead for toxoplasmosis with novel scaffolds based on the combination of 2D/3D-QSAR and scaffold-hopping methods. All the binding sites of the potential inhibitors were checked by docking method, and only the ones that docked to the most conserved sites of TgCDPK1, which make them have less probability to get drug resistance, were remained. As a result, 10 potential inhibitors within two new scaffolds were discovered for TgCDPK1 with experimentally verified inhibitory activities in micromole level. The discovery of these inhibitors may contribute to the drug development for toxoplasmosis. Besides, the pipeline which is composed in this work as the combination of QSAR and scaffold-hopping is simple, easy to repeat for researchers without need of in-depth knowledge of pharmacology to get inhibitors with novel scaffolds, which will accelerate the procedure of drug discovery and contribute to the drug repurposing study.

Computationally exploring novel xanthine oxidase inhibitors using docking-based 3D-QSAR, molecular dynamics, and virtual screening

Computationally exploring novel potential xanthine oxidase inhibitors using a systematic modeling study.

Design of dipicolinic acid derivatives as New Delhi metallo-β-lactamase-1 inhibitors using a combined computational approach

New Delhi metallo-β-lactamase (NDM-1) is the most recent addition to the class of metallo-β-lactamases (MBLs). This enzyme leads to antibiotic resistance in clinical treatments owing to its exertion of hydrolysis activity in almost all clinically available β-lactam antibiotics. Consequently, inhibitors targeting NDM-1 have attracted considerable research attention. However, progress has been slow regarding the study of the quantitative structure-activity relationship (QSAR) of NDM-1 inhibitors. In this study, a three-dimensional QSAR (3 D-QSAR) for NDM-1 inhibitors was established using Topomer CoMFA. The multiple correlation coefficients of the fitting model, leave-one-out cross validation, and external validation were found to be 0.761, 0.976, and 0.972, respectively. Topomer Search was used to design 16 new molecules that inhibit NDM-1 using R-group search from ZINC databases, 10 of which had comparatively high activities against NDM-1. The results indicate that Topomer CoMFA and Topomer Search can be used to design new NDM-1 inhibitors and guide the design of new NDM-1 drugs with good predictive capability. Furthermore, from molecular modeling and binding free-energy calculation, it was found that the newly designed molecules can bind to the catalytic region of NDM-1. Additionally, the newly designed inhibitors formed strong interactions with Ile35, Met67, Phe70, Trp93, His122, His189, Cys208, and His250 around the Zn²⁺-centered active region of NDM-1. These findings will facilitate the development of more effective NDM-1 inhibitors for use as potential antibacterial agents.Communicated by Ramaswamy H. Sarma.

Molecular Modeling and Design Studies of Purine Derivatives as Novel CDK2 Inhibitors

Cyclin-dependent kinase 2 (CDK2) is a potential target for treating cancer. Purine heterocycles have attracted particular attention as the scaffolds for the development of CDK2 inhibitors. To explore the interaction mechanism and the structure–activity relationship (SAR) and to design novel candidate compounds as potential CDK2 inhibitors, a systematic molecular modeling study was conducted on 35 purine derivatives as CDK2 inhibitors by combining three-dimensional quantitative SAR (3D-QSAR), virtual screening, molecular docking, and molecular dynamics (MD) simulations. The predictive CoMFA model (q² = 0.743, rpred2 = 0.991), the CoMSIA model (q² = 0.808, rpred2 = 0.990), and the Topomer CoMFA model (q² = 0.779, rpred2 = 0.962) were obtained. Contour maps revealed that the electrostatic, hydrophobic, hydrogen bond donor and steric fields played key roles in the QSAR models. Thirty-one novel candidate compounds with suitable predicted activity (predicted pIC₅₀ > 8) were designed by using the results of virtual screening. Molecular docking indicated that residues Asp86, Glu81, Leu83, Lys89, Lys33, and Gln131 formed hydrogen bonds with the ligand, which affected activity of the ligand. Based on the QSAR model prediction and molecular docking, two candidate compounds, I13 and I60 (predicted pIC₅₀ > 8, docking score > 10), with the most potential research value were further screened out. MD simulations of the corresponding complexes of these two candidate compounds further verified their stability. This study provided valuable information for the development of new potential CDK2 inhibitors.

Database and Bioinformatics Studies of Probiotics

Probiotics have been widely explored for health benefits, animal cares, and agricultural applications. Recent advances in microbiome, microbiota, and microbial dark matter research have fueled greater interests in and paved ways for the study of the mechanisms of probiotics and the discovery of new probiotics from uncharacterized microbial sources. A probiotics database named PROBIO was developed to facilitate these efforts and the need for the information on the known probiotics, which provides the comprehensive information about the probiotic functions of 448 marketed, 167 clinical trial/field trial, and 382 research probiotics for use or being studied for use in humans, animals, and plants. The potential applications of the probiotics data are illustrated by several literature-reported investigations, which have used the relevant information for probing the function and mechanism of the probiotics and for discovering new probiotics. PROBIO can be accessed free of charge at http://bidd2.nus.edu.sg/probio/homepage.htm .

Pharmacological relationships and ligand discovery of G protein-coupled receptors revealed by simultaneous ligand and receptor clustering

Conventional ligand and receptor similarity methods have been extensively used for exposing pharmacological relationships and drug lead discovery. They may in some cases neglect minor relationships useful for target hopping particularly against the remote family members. To complement the conventional methods for capturing these minor relationships, we developed a new method that uses a SLARC (Simultaneous Ligand And Receptor Clustering) 2D map to simultaneously characterize the ligand structural and receptor binding-site sequence relationships of a receptor family. The SLARC maps of the rhodopsin-like GPCR family comprehensively revealed scaffold hopping, target hopping, and multi-target relationships for the ligands of both homologous and remote family members. Their usefulness in new ligand discovery was validated by guiding the prospective discovery of novel indole piperazinylpyrimidine dual-targeting adenosine A2A receptor antagonist and dopamine D2 agonist compounds. The SLARC approach is useful for revealing pharmacological relationships and discovering new ligands at target family levels.

Rule of five in 2015 and beyond: Target and ligand structural limitations, ligand chemistry structure and drug discovery project decisions

The rule of five (Ro5), based on physicochemical profiles of phase II drugs, is consistent with structural limitations in protein targets and the drug target ligands. Three of four parameters in Ro5 are fundamental to the structure of both target and drug binding sites. The chemical structure of the drug ligand depends on the ligand chemistry and design philosophy. Two extremes of chemical structure and design philosophy exist; ligands constructed in the medicinal chemistry synthesis laboratory without input from natural selection and natural product (NP) metabolites biosynthesized based on evolutionary selection. Exceptions to Ro5 are found mostly among NPs. Chemistry chameleon-like behavior of some NPs due to intra-molecular hydrogen bonding as exemplified by cyclosporine A is a strong contributor to NP Ro5 outliers. The fragment derived, drug Navitoclax is an example of the extensive expertise, resources, time and key decisions required for the rare discovery of a non-NP Ro5 outlier.

Computer-Aided Drug Discovery Approaches against the Tropical Infectious Diseases Malaria, Tuberculosis, Trypanosomiasis, and Leishmaniasis

Despite the tremendous improvement in overall global health heralded by the adoption of the Millennium Declaration in the year 2000, tropical infections remain a major health problem in the developing world. Recent estimates indicate that the major tropical infectious diseases, namely, malaria, tuberculosis, trypanosomiasis, and leishmaniasis, account for more than 2.2 million deaths and a loss of approximately 85 million disability-adjusted life years annually. The crucial role of chemotherapy in curtailing the deleterious health and economic impacts of these infections has invigorated the search for new drugs against tropical infectious diseases. The research efforts have involved increased application of computational technologies in mainstream drug discovery programs at the hit identification, hit-to-lead, and lead optimization stages. This review highlights various computer-aided drug discovery approaches that have been utilized in efforts to identify novel antimalarial, antitubercular, antitrypanosomal, and antileishmanial agents. The focus is largely on developments over the past 5 years (2010-2014).

Searching for Dual Inhibitors of the MDM2-p53 and MDMX-p53 Protein-Protein Interaction by a Scaffold-Hopping Approach

Two libraries of substituted benzimidazoles were designed using a 'scaffold-hopping' approach based on reported MDM2-p53 inhibitors. Substituents were chosen following library enumeration and docking into an MDM2 X-ray structure. Benzimidazole libraries were prepared using an efficient solution-phase approach and screened for inhibition of the MDM2-p53 and MDMX-p53 protein-protein interactions. Key examples showed inhibitory activity against both targets.

Fragment-based similarity searching with infinite color space

Fragment-based searching and abstract representation of molecular features through reduced graphs have separately been used for virtual screening. Here, we combine these two approaches and apply the algorithm RedFrag to virtual screens retrospectively and prospectively. It uses a new type of reduced graph that does not suffer from information loss during its construction and bypasses the necessity of feature definitions. Built upon chemical epitopes resulting from molecule fragmentation, the reduced graph embodies physico-chemical and 2D-structural properties of a molecule. Reduced graphs are compared with a continuous-similarity-distance-driven maximal common subgraph algorithm, which calculates similarity at the fragmental and topological levels. The performance of the algorithm is evaluated by retrieval experiments utilizing precompiled validation sets. By predicting and experimentally testing ligands for endothiapepsin, a challenging model protease, the method is assessed in a prospective setting. Here, we identified five novel ligands with affinities as low as 2.08 μM.

A structural hierarchy matching approach for molecular similarity/substructure searching

An approach for molecular similarity/substructure searching based on structural hierarchy matching is proposed. In this approach, small molecules are divided into two categories, acyclic and cyclic forms. The latter are further divided into three structural hierarchies, namely, framework, complicated-, and mono-rings. During searching, the similarity coefficients of a structural query and each retrieved molecule are calculated using the hierarchy of the query as the reference. A total of 13,911 chemicals were involved in this work, from which the minimal cyclic and acyclic substructures are extracted, and further processed into fuzzy structural fingerprints. Subsequently, the fingerprints are used as the searching indices for molecular similarity or substructure searching. The tests show that this approach can give user options to choose between one-substructure and multi-substructure searching with sorted results. Moreover, this algorithm has the potential to be developed for molecular similarity searching and substructure analysis.

CFam: a chemical families database based on iterative selection of functional seeds and seed-directed compound clustering

Similarity-based clustering and classification of compounds enable the search of drug leads and the structural and chemogenomic studies for facilitating chemical, biomedical, agricultural, material and other industrial applications. A database that organizes compounds into similarity-based as well as scaffold-based and property-based families is useful for facilitating these tasks. CFam Chemical Family database http://bidd2.cse.nus.edu.sg/cfam was developed to hierarchically cluster drugs, bioactive molecules, human metabolites, natural products, patented agents and other molecules into functional families, superfamilies and classes of structurally similar compounds based on the literature-reported high, intermediate and remote similarity measures. The compounds were represented by molecular fingerprint and molecular similarity was measured by Tanimoto coefficient. The functional seeds of CFam families were from hierarchically clustered drugs, bioactive molecules, human metabolites, natural products, patented agents, respectively, which were used to characterize families and cluster compounds into families, superfamilies and classes. CFam currently contains 11 643 classes, 34 880 superfamilies and 87 136 families of 490 279 compounds (1691 approved drugs, 1228 clinical trial drugs, 12 386 investigative drugs, 262 881 highly active molecules, 15 055 human metabolites, 80 255 ZINC-processed natural products and 116 783 patented agents). Efforts will be made to further expand CFam database and add more functional categories and families based on other types of molecular representations.

QSAR modeling: where have you been? Where are you going to?

Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.

Template CoMFA: the 3D-QSAR Grail?

Template CoMFA, a novel alignment methodology for training or test set structures in 3D-QSAR, is introduced. Its two most significant advantages are its complete automation and its ability to derive a single combined model from multiple structural series affecting a biological target. Its only two inputs are one or more "template" structures having 3D coordinates that share some Cartesian space, as may result from X-ray crystallography or pharmacophoric hypothesis, and one or more connectivity-only SAR tables associated with a common target. Template CoMFA also overcomes the major disadvantages of both existing 3D-QSAR alignment methodologies, specifically the tedium and subjectivity of familiar ad hoc approaches, and the awkwardness, occasional physicochemical heresies, and structural scope limitations of the purely topomer approach. The template CoMFA algorithms are described, and two of its application classes are presented. The first class, general models of binding to factor Xa and P38 map kinase, uses crystallographic structures as templates, with the encouraging result that the statistical qualities of each of these two combined models are equivalent to those of their constituent individual series models. The second, 15 data sets originally collected for validation of topomer CoMFA, with arbitrary structures as templates, confirms that the modeling power of template CoMFA resembles that of its predecessors.

Comprehensive 3D-QSAR and binding mode of BACE-1 inhibitors using R-group search and molecular docking

The β-enzyme (BACE), which takes an active part in the processing of amyloid precursor protein, thereby leads to the production of amyloid-β (Aβ) in the brain, is a major therapeutic target against Alzheimer's disease. The present study is aimed at studying 3D-QSAR of BACE-1 inhibitors and their binding mode. We build a 3D-QSAR model involving 99 training BACE-1 inhibitors based on Topomer CoMFA, and 26 molecules are employed to validate the external predictive power of the model obtained. The multiple correlation coefficients of fitting modeling, leave one out cross validation, and external validation are 0.966, 0.767 and 0.784, respectively. Topomer search is used as a tool for virtual screening in lead-like compounds of ZINC databases (2012); as a result, we successfully design 30 new molecules with higher activity than that of all training and test inhibitors. Besides, Surflex-dock is employed to explore binding mode of the inhibitors studied when acting with BACE-1 enzyme. The result shows that the inhibitors closely interact with the key sites related to ASP93, THR133, GLN134, ASP289, GLY291, THR292, THR293, ASN294, ARG296 and SER386 of BACE-1.

Identification of novel anthrax toxin countermeasures using in silico methods

Anthrax is an acute infectious disease caused by the spore-forming, gram-positive, rod-shaped bacterium Bacillus anthracis. The anthrax toxin lethal factor (LF) is the primary anthrax toxin component responsible for cytotoxicity and host death and has been a heavily researched target for design of postexposure therapeutics in the event of a bioterror attack. Various computer-aided drug design methodologies have proven useful for pinpointing new antianthrax drug scaffolds, optimizing existing leads and probes, and elucidating key mechanisms of action. We present a selection of in silico virtual screening protocols incorporating docking and scoring, shape-based searching, and pharmacophore mapping techniques to identify and prioritize small molecules with potential biological activity against LF. We also recommend screening parameters that have been shown to increase the accuracy and reliability of these computational results.

QSAR studies of sulfamate and sulfamide inhibitors targeting human carbonic anhydrase isozymes I, II, IX and XII

The last version of the PRECLAV algorithm was used to investigate a series of sulfamate/sulfamide carbonic anhydrase (CA, EC 4.2.1.1) inhibitors. PRECLAV allows identification of outliers for lead hopping, significant molecular fragments and similarity computation of a calibration set vs. a prediction set of compounds, from the viewpoint of computed QSAR. In the current study the database included 65 sulfamates and sulfamides as calibration set and 51 not yet synthesized sulfamates and sulfamides as prediction set. The dependent property was inhibitory activity for human (h) CA isozymes I, II, IX and XII. There were no outliers for lead hopping in the calibration set. In some cases, replacing of the O-SO2-NH2 by the NH-SO2-NH2 moiety, led to modifications in the structure of molecular fragments in another regions of the molecule. According to the descriptors in the obtained models, the inhibition of all CA isozymes was strongly influenced by the molecular shape and size of the sulfamates/sulfamides (which incorporate a substituted aryl-ureido scaffold). The lipophilicity of the inhibitors seemed to be a minor factor influencing CA inhibitory action. The presence of specific PRECLAV molecular fragments such as a C6 (totally substituted benzene), F, O and NO2 increases the activity of inhibitors against certain isoforms. The presence of molecular fragments such as C, CH, CxHy (substituted benzene/naphthalene) and NH decreased the activity of inhibitors against certain isoforms. In the prediction set, we evidenced seven compounds estimated to be highly active inhibitors for at least three of the investigated isozymes and eight molecules estimated to possess low activity against at least three CAs. The paper suggests the structure of some possible interesting inhibitors incorporating F, O and NO2 fragments that may have affinity for certain human CAs.

Effects of multiple conformers per compound upon 3-D similarity search and bioassay data analysis

Background

To improve the utility of PubChem, a public repository containing biological activities of small molecules, the PubChem3D project adds computationally-derived three-dimensional (3-D) descriptions to the small-molecule records contained in the PubChem Compound database and provides various search and analysis tools that exploit 3-D molecular similarity. Therefore, the efficient use of PubChem3D resources requires an understanding of the statistical and biological meaning of computed 3-D molecular similarity scores between molecules.

Results

The present study investigated effects of employing multiple conformers per compound upon the 3-D similarity scores between ten thousand randomly selected biologically-tested compounds (10-K set) and between non-inactive compounds in a given biological assay (156-K set). When the “best-conformer-pair” approach, in which a 3-D similarity score between two compounds is represented by the greatest similarity score among all possible conformer pairs arising from a compound pair, was employed with ten diverse conformers per compound, the average 3-D similarity scores for the 10-K set increased by 0.11, 0.09, 0.15, 0.16, 0.07, and 0.18 for ST^ST-opt, CT^ST-opt, ComboT^ST-opt, ST^CT-opt, CT^CT-opt, and ComboT^CT-opt, respectively, relative to the corresponding averages computed using a single conformer per compound. Interestingly, the best-conformer-pair approach also increased the average 3-D similarity scores for the non-inactive–non-inactive (NN) pairs for a given assay, by comparable amounts to those for the random compound pairs, although some assays showed a pronounced increase in the per-assay NN-pair 3-D similarity scores, compared to the average increase for the random compound pairs.

Conclusion

These results suggest that the use of ten diverse conformers per compound in PubChem bioassay data analysis using 3-D molecular similarity is not expected to increase the separation of non-inactive from random and inactive spaces “on average”, although some assays show a noticeable separation between the non-inactive and random spaces when multiple conformers are used for each compound. The present study is a critical next step to understand effects of conformational diversity of the molecules upon the 3-D molecular similarity and its application to biological activity data analysis in PubChem. The results of this study may be helpful to build search and analysis tools that exploit 3-D molecular similarity between compounds archived in PubChem and other molecular libraries in a more efficient way.

R-group template CoMFA combines benefits of "ad hoc" and topomer alignments using 3D-QSAR for lead optimization

Template CoMFA methodologies extend topomer CoMFA by allowing user-designated templates, for example the experimental receptor-bound conformation of a prototypical ligand, to help determine the alignment of training and test set structures for 3D-QSAR. The algorithms that generate its new structural modality, template-constrained topomers, are described. Template CoMFA's resolution of certain topomer CoMFA concerns, by providing user control of topological consistency and structural acceptability, is demonstrated for sixteen 3D-QSAR training sets, in particular the Selwood dataset.

Classification of scaffold-hopping approaches

The general goal of drug discovery is to identify novel compounds that are active against a preselected biological target with acceptable pharmacological properties defined by marketed drugs. Scaffold hopping has been widely applied by medicinal chemists to discover equipotent compounds with novel backbones that have improved properties. In this article we classify scaffold hopping into four major categories, namely heterocycle replacements, ring opening or closure, peptidomimetics and topology-based hopping. We review the structural diversity of original and final scaffolds with respect to each category. We discuss the advantages and limitations of small, medium and large-step scaffold hopping. Finally, we summarize software that is frequently used to facilitate different kinds of scaffold-hopping methods.

Linear and Nonlinear Support Vector Machine for the Classification of Human 5-HT1A Ligand Functionality

Upon binding to a receptor, agonists and antagonists can induce distinct biological functions and thus lead to significantly different pharmacological responses. Thus, in silico prediction or in vitro characterization of ligand agonistic or antagonistic functionalities is an important step toward identifying specific pharmacological therapeutics. In this study, we investigated the molecular properties of agonists and antagonists of human 5-hydroxytryptamine receptor subtype 1A (5-HT1A ). Subsequently, intrinsic functions of these ligands (agonists/antagonists) were modeled by support vector machine (SVM), using five 2D molecular fingerprints and the 3D Topomer distance. Five kernel functions, including linear, polynomial, RBF, Tanimoto and a novel Topomer kernel based on Topomer 3D similarity were used to develop linear and nonlinear classifiers. These classifiers were validated through cross-validation, yielding a classification accuracy ranging from 80.4 % to 92.3 %. The performance of different kernels and fingerprints was analyzed and discussed. Linear and nonlinear models were further interpreted through the illustration of underlying classification mechanism. The computation protocol has been automated and demonstrated through our online service. This study expands the scope and applicability of similarity-based methods in cheminformatics, which are typically used for the identification of active molecules against a target protein. Our findings provide a good starting point for further systematic classifications of other GPCR ligands and for the data mining of large chemical libraries.

Novel human mPGES-1 inhibitors identified through structure-based virtual screening

Microsomal prostaglandin E synthase-1 (mPGES-1) is an inducible prostaglandin E synthase after exposure to pro-inflammatory stimuli and, therefore, represents a novel target for therapeutic treatment of acute and chronic inflammatory disorders. It is essential to identify mPGES-1 inhibitors with novel scaffolds as new leads or hits for the purpose of drug design and discovery that aim to develop the next-generation anti-inflammatory drugs. Herein we report novel mPGES-1 inhibitors identified through a combination of large-scale structure-based virtual screening, flexible docking, molecular dynamics simulations, binding free energy calculations, and in vitro assays on the actual inhibitory activity of the computationally selected compounds. The computational studies are based on our recently developed three-dimensional (3D) structural model of mPGES-1 in its open state. The combined computational and experimental studies have led to identification of new mPGES-1 inhibitors with new scaffolds. In particular, (Z)-5-benzylidene-2-iminothiazolidin-4-one is a promising novel scaffold for the further rational design and discovery of new mPGES-1 inhibitors. To our best knowledge, this is the first time a 3D structural model of the open state mPGES-1 is used in structure-based virtual screening of a large library of available compounds for the mPGES-1 inhibitor identification. The positive experimental results suggest that our recently modeled trimeric structure of mPGES-1 in its open state is ready for the structure-based drug design and discovery.

Enrichment of virtual hits by progressive shape-matching and docking

The main applications of virtual chemical screening include the selection of a minimal receptor-relevant subset of a chemical library with a maximal chemical diversity. We have previously reported that the combination of ligand-centric and receptor-centric virtual screening methods may provide a compromise between computational time and accuracy during the hit enrichment process. In the present work, we propose a "progressive distributed docking" method that improves the virtual screening process using an iterative combination of shape-matching and docking steps. Known ligands with low docking scores were used as initial 3D templates for the shape comparisons with the chemical library. Next, new compounds with good template shape matches and low receptor docking scores were selected for the next round of shape searching and docking. The present iterative virtual screening process was tested for enriching peroxisome proliferator-activated receptor and phosphoinositide 3-kinase relevant compounds from a selected subset of the chemical libraries. It was demonstrated that the iterative combination improved the lead-hopping practice by improving the chemical diversity in the selected list of virtual hits.

Application of QSAR and shape pharmacophore modeling approaches for targeted chemical library design

Optimization of chemical library composition affords more efficient identification of hits from biological screening experiments. The optimization could be achieved through rational selection of reagents used in combinatorial library synthesis. However, with a rapid advent of parallel synthesis methods and availability of millions of compounds synthesized by many vendors, it may be more efficient to design targeted libraries by means of virtual screening of commercial compound collections. This chapter reviews the application of advanced cheminformatics approaches such as quantitative structure-activity relationships (QSAR) and pharmacophore modeling (both ligand and structure based) for virtual screening. Both approaches rely on empirical SAR data to build models; thus, the emphasis is placed on achieving models of the highest rigor and external predictive power. We present several examples of successful applications of both approaches for virtual screening to illustrate their utility. We suggest that the expert use of both QSAR and pharmacophore models, either independently or in combination, enables users to achieve targeted libraries enriched with experimentally confirmed hit compounds.

Computational medicinal chemistry in fragment-based drug discovery: what, how and when

The use of fragment-based drug discovery (FBDD) has increased in the last decade due to the encouraging results obtained to date. In this scenario, computational approaches, together with experimental information, play an important role to guide and speed up the process. By default, FBDD is generally considered as a constructive approach. However, such additive behavior is not always present, therefore, simple fragment maturation will not always deliver the expected results. In this review, computational approaches utilized in FBDD are reported together with real case studies, where applicability domains are exemplified, in order to analyze them, and then, maximize their performance and reliability. Thus, a proper use of these computational tools can minimize misleading conclusions, keeping the credit on FBDD strategy, as well as achieve higher impact in the drug-discovery process. FBDD goes one step beyond a simple constructive approach. A broad set of computational tools: docking, R group quantitative structure–activity relationship, fragmentation tools, fragments management tools, patents analysis and fragment-hopping, for example, can be utilized in FBDD, providing a clear positive impact if they are utilized in the proper scenario – what, how and when. An initial assessment of additive/non-additive behavior is a critical point to define the most convenient approach for fragments elaboration.