The scaffold hopping potential of pharmacophores

Ligand-based pharmacophore modeling and machine learning for the discovery of potent aurora A kinase inhibitory leads of novel chemotypes

Aurora-A (AURKA) is serine/threonine protein kinase involved in the regulation of numerous processes of cell division. Numerous studies have demonstrated strong association between AURKA and cancer. AURKA is overexpressed in many cancers, such as colon, breast and prostate cancers. Consequently, AURKA has emerged as promising target for therapeutic intervention in cancer management. Herein, we describe a computational workflow for the discovery of novel anti-AURKA inhibitory leads starting with ligand-based assessment of the pharmacophoric space of six diverse sets of inhibitors. Subsequently, machine learning/QSAR modeling was coupled with genetic function algorithm to search for the best possible combination of machine learner, ligand-based pharmacophore(s) and molecular descriptors capable of explaining variation in anti-AURKA bioactivities within a collected list of inhibitors. Two learners succeeded in achieving acceptable structure/activity correlations, namely, random forests and extreme gradient boosting (XGBoost). Three pharmacophores emerged in the successful ML models. These were then used as 3D search queries to mine the National Cancer Institute database for novel anti-AURKA leads. Top-ranking 38 hits were assessed in vitro for their anti-AURKA bioactivities. Among them, three compounds exhibited promising dose-response curves, demonstrating experimental IC50 values ranging from sub-micromolar to low micromolar values. Remarkably, two of these compounds are of novel chemotypes.

Discovery and Evaluation of C6-Substituted Pyrazolopyrimidine-Based Bisphosphonate Inhibitors of the Human Geranylgeranyl Pyrophosphate Synthase and Evaluation of Their Antitumor Efficacy in Multiple Myeloma, Pancreatic Ductal Adenocarcinoma, and Colorectal Cancer

Novel C6-substituted pyrazolo[3,4-d]pyrimidine- and C2-substituted purine-based bisphosphonate (C6-PyraP-BP and C2-Pur-BP, respectively) inhibitors of the human geranylgeranyl pyrophosphate synthase (hGGPPS) were designed and evaluated for their ability to block the proliferation of multiple myeloma (MM), pancreatic ductal adenocarcinoma (PDAC), and colorectal cancer (CRC) cells. Pyrazolo[3,4-d]pyrimidine analogs were identified that induce selective intracellular target engagement leading to apoptosis and downregulate the prenylation of Rap-1A in MM, PDAC, and CRC cells. The C6-PyraP-BP inhibitor RB-07-16 was found to exhibit antitumor efficacy in xenograft mouse models of MM and PDAC, significantly reducing tumor growth without substantially increasing liver enzymes or causing significant histopathologic damage, usually associated with hepatotoxicity. RB-07-16 is a metabolically stable compound in cross-species liver microsomes, does not inhibit key CYP 450 enzymes, and exhibits good systemic circulation in rat. Collectively, the current studies provide encouraging support for further optimization of the pyrazolo[3,4-d]pyrimidine-based GGPPS inhibitors as potential human therapeutics for various cancers.

Diastereoselective Construction of Tetrahydro-Dispiro[indolinone-3,2'-pyran-5',4″-pyrazolone] Scaffolds via an Oxa-Michael Cascade [4 + 2] Annulation Reaction

A straightforward metal-free oxa-Michael cascade [4 + 2] annulation reaction was established between isatin-derived Morita-Baylis-Hillman (Is-MBH) alcohols with alkylidene pyrazolones to access structural diverse tetrahydro-dispiro[indolinone-3,2'-pyran-5',4″-pyrazolone] scaffolds bearing two tertiary and two quaternary stereocenters. The Is-MBH alcohol was utilized as an oxa-Michael donor for the first time as a new approach in highly atom-economical transformations. This method offered a wide range of bioinspired novel tetrahydro-dispirooxindole-pyran-pyrazolone derivatives in excellent yields (up to 96%) and diastereoselectivities (up to >20:1) in a shorter reaction time (15 min).

A pharmacophore-guided deep learning approach for bioactive molecular generation

The rational design of novel molecules with the desired bioactivity is a critical but challenging task in drug discovery, especially when treating a novel target family or understudied targets. We propose a Pharmacophore-Guided deep learning approach for bioactive Molecule Generation (PGMG). Through the guidance of pharmacophore, PGMG provides a flexible strategy for generating bioactive molecules. PGMG uses a graph neural network to encode spatially distributed chemical features and a transformer decoder to generate molecules. A latent variable is introduced to solve the many-to-many mapping between pharmacophores and molecules to improve the diversity of the generated molecules. Compared to existing methods, PGMG generates molecules with strong docking affinities and high scores of validity, uniqueness, and novelty. In the case studies, we use PGMG in a ligand-based and structure-based drug de novo design. Overall, the flexibility and effectiveness make PGMG a useful tool to accelerate the drug discovery process.

Discovery of Novel Hydroxyimine-Tethered Benzenesulfonamides as Potential Human Carbonic Anhydrase IX/XII Inhibitors

To discover novel carbonic anhydrase (CA, EC 4.2.1.1) inhibitors for cancer treatment, a series of 4-{4-[(hydroxyimino)methyl]piperazin-1-yl}benzenesulfonamides were designed and synthesized using SLC-0111 as the lead molecule. The developed novel compounds 27-34 were investigated for the inhibition of human (h) isoforms hCA I, hCA II, hCA IX, and hCA XII. The hCA I was inhibited by compound 29 with a K_i value of 3.0 nM, whereas hCA II was inhibited by compound 32 with a K_i value of 4.4 nM. The tumor-associated hCA IX isoform was inhibited by compound 30 effectively with an K_i value of 43 nM, whereas the activity of another cancer-related isoform, hCA XII, was significantly inhibited by 29 and 31 with a K_i value of 5 nM. Molecular modeling showed that drug molecule 30 participates in significant hydrophobic and hydrogen bond interactions with the active site of the investigated hCAs and binds to zinc through the deprotonated sulfonamide group.

FSDscore: An Effective Target-focused Scoring Criterion for Virtual Screening

Improving screening efficiency is one of the most challenging tasks of virtual screening (VS). In this work, we propose an effective target-focused scoring criterion for VS and apply it to the screening of a specific target scaffold replacement library constructed by enumeration of suitable substitution fragments and R-groups of known ligands. This criterion is based on both ligand- and structure-based scoring methods, which includes feature maps, 3D shape similarity, and the pairwise distance information between proteins and ligands (FSDscore). It is precisely due to the hybrid advantages of ligand- and structure-based approaches that FSDscore performs far better on the validation dataset than other scoring methods. We apply FSDscore to the VS of different kinase targets, MERTK (Mer tyrosine kinase) and ABL1 (tyrosine-protein kinase ABL1) in order to avoid occasionality. Finally, a VS case study shows the potential and effectiveness of our scoring criterion in drug discovery and molecular dynamics simulation further verifies its powerful ability.

Exploring isofunctional molecules: Design of a benchmark and evaluation of prediction performance

Identification of novel chemotypes with biological activity similar to a known active molecule is an important challenge in drug discovery called 'scaffold hopping'. Small-, medium-, and large-step scaffold hopping efforts may lead to increasing degrees of chemical structure novelty with respect to the parent compound. In the present paper, we focus on the problem of large-step scaffold hopping. We assembled a high quality and well characterized dataset of scaffold hopping examples comprising pairs of active molecules and including a variety of protein targets. This dataset was used to build a benchmark corresponding to the setting of real-life applications: one active molecule is known, and the second active is searched among a set of decoys chosen in a way to avoid statistical bias. This allowed us to evaluate the performance of computational methods for solving large-step scaffold hopping problems. In particular, we assessed how difficult these problems are, particularly for classical 2D and 3D ligand-based methods. We also showed that a machine-learning chemogenomic algorithm outperforms classical methods and we provided some useful hints for future improvements.

Self Cycloaddition of o-Naphthoquinone Nitrosomethide to (±) Spiro{naphthalene(naphthopyranofurazan)}-one Oxide: An Insight into its Formation

2-Hydroxy-1-naphthaldehyde oxime was oxidized by AgO (or Ag2O), in presence of N-methyl morpholine N-oxide (NMMO), to the title spiro adduct-dimer (±)-Spiro{naphthalene-1(2H),4'-(naphtho[2',1':2,3]pyrano[4,5-c]furazan)}-2-one-11'-oxide by a Diels-Alder(D-A) type self-cycloaddition, through the agency of an o-naphthoquinone nitrosomethide (o-NQM). Moreover, 2-hydroxy-8-methoxy-1-naphthaldehyde oxime was prepared and subjected to the same oxidation conditions. Its sterically guided result, 9-methoxynaphtho[1,2-d]isoxazole, was isolated, instead of the expected spiro adduct. The peri intramolecular H bonding in the oxime is considered to have a key contribution to the outcome. Geometry and energy features of the oxidant- and stereo-guided selectivity of both oxidation outcomes have been explored by DFT, perturbation theory and coupled cluster calculations. The reaction free energy of the D-A intermolecular cycloaddition is calculated at -82.0 kcal/mol, indicating its predominance over the intramolecular cyclization of ca. -37.6 kcal/mol. The cycloaddition is facilitated by NMMO through dipolar interactions and hydrogen bonding with both metal complexes and o-NQM. The 8(peri)-OMe substitution of the reactant oxime sterically impedes formation of the spiro adduct, instead it undergoes a more facile cyclodehydration to the isoxazole structure by ca. 4.9 kcal/mol.

Recent Scaffold Hopping Applications in Central Nervous System Drug Discovery

The concept of bioisosterism and the implementation of bioisosteric replacement is fundamental to medicinal chemistry. The exploration of bioisosteres is often used to probe key structural features of candidate pharmacophores and enhance pharmacokinetic properties. As the understanding of bioisosterism has evolved, capabilities to undertake more ambitious bioisosteric replacements have emerged. Scaffold hopping is a broadly used term in the literature referring to a variety of different bioisosteric replacement strategies, ranging from simple heterocyclic replacements to topological structural overhauls. In this work, we have highlighted recent applications of scaffold hopping in the central nervous system drug discovery space. While we have highlighted the benefits of using scaffold hopping approaches in central nervous system drug discovery, these are also widely applicable to other medicinal chemistry fields. We also recommend a shift toward the use of more refined and meaningful terminology within the realm of scaffold hopping.

Novel covalent and non-covalent complex-based pharmacophore models of SARS-CoV-2 main protease (Mpro) elucidated by microsecond MD simulations

As the world enters its second year of the pandemic caused by SARS-CoV-2, intense efforts have been directed to develop an effective diagnosis, prevention, and treatment strategies. One promising drug target to design COVID-19 treatments is the SARS-CoV-2 M^pro. To date, a comparative understanding of M^pro dynamic stereoelectronic interactions with either covalent or non-covalent inhibitors (depending on their interaction with a pocket called S1' or oxyanion hole) has not been still achieved. In this study, we seek to fill this knowledge gap using a cascade in silico protocol of docking, molecular dynamics simulations, and MM/PBSA in order to elucidate pharmacophore models for both types of inhibitors. After docking and MD analysis, a set of complex-based pharmacophore models was elucidated for covalent and non-covalent categories making use of the residue bonding point feature. The highest ranked models exhibited ROC-AUC values of 0.93 and 0.73, respectively for each category. Interestingly, we observed that the active site region of M^pro protein-ligand complex undergoes large conformational changes, especially within the S2 and S4 subsites. The results reported in this article may be helpful in virtual screening (VS) campaigns to guide the design and discovery of novel small-molecule therapeutic agents against SARS-CoV-2 M^pro protein.

Drug Chemical Space as a Guide for New Herbicide Development: A Cheminformatic Analysis

Herbicides are critical resources for meeting agricultural demand. While similar in structure and function to pharmaceuticals, the development of new herbicidal mechanisms of action and new scaffolds against known mechanisms of action has been much slower than in pharmaceutical sciences. We hypothesized that this may be due in part to a relative undersampling of possible herbicidal chemistries and set out to test whether this difference in sampling existed and whether increasing the diversity of possible herbicidal chemistries would be likely to result in more efficacious herbicides. To conduct this work, we first identified databases of commercially available herbicides and clinically approved pharmaceuticals. Using these databases, we created a two-dimensional embedding of the chemical, which provides a qualitative visualization of the degree to which each chemotype is distributed within the combined chemical space and shows a moderate degree of overlap between the two sets. Next, we trained several machine learning models to classify herbicides versus drugs based on physicochemical characteristics. The most accurate of these models has an accuracy of 93% with the key differentiating characteristics being the number of polar hydrogens, number of amide bonds, LogP, and polar surface area. We then used several types of scaffold decomposition to quantitatively evaluate the chemical diversity of each molecular family and showed herbicides to have considerably fewer unique structural fragments. Finally, we used molecular docking as an in silico evaluation of further structural diversification in herbicide development. To this end, we identified herbicides with well-characterized binding sites and modified those scaffolds based on similar structural subunits from the drug dataset not present in any commercial herbicide while using the machine-learned model to ensure that required herbicide properties were maintained. Redocking the original and modified scaffolds of several herbicides showed that even this simple design strategy is capable of yielding new molecules with higher predicted affinity for the target enzymes. Overall, we show that herbicides are distinct from drugs based on physicochemical properties but less diverse in their chemistry in a way not governed by these properties. We also demonstrate in silico that increasing the diversity of herbicide scaffolds has the potential to increase potency, potentially reducing the amount needed in agricultural practice.

Organocatalyzed umpolung addition for synthesis of heterocyclic-fused arylidene-imidazolones as anticancer agents

A strategy of "Nature-to-new" with iterative scaffold-hopping was considered for investigation of privileged ring/functional motif-elaborated analogs of natural aurones. An organocatalyzed umpolung chemistry based method was established for molecular-diversity feasible synthesis of title class of chemotypes i.e. (Z)-2-Arylideneimidazo[1,2-a]pyridinones and (Z)-2-Arylidenebenzo[d]imidazo[2,1-b]thiazol-3-ones. Various biophysical experiments indicated their important biological properties. The analogs showed characteristic anticancer activities with efficiency more than an anticancer drug. The compounds induced apoptosis with arrest in the S phase of the cell cycle regulation. The compounds' significant effect in up/down-regulation of various apoptotic proteins, an apoptosis cascade, and the inhibition of topoisomerases-mediated DNA relaxation process was identified. The analysis of the structure-activity relationship, interference with biological events and the drug-likeness physicochemical properties of the compounds in the acceptable window indicated distinctive medicinal molecule-to-properties of the investigated chemotypes.

3D-QSAR and scaffold hopping based designing of benzo[d]ox-azol-2(3H)-one and 2-oxazolo[4,5-b]pyridin-2(3H)-one derivatives as selective aldehyde dehydrogenase 1A1 inhibitors: Synthesis and biological evaluation

Aldehyde dehydrogenase 1 (ALDH1A1), an oxidoreductase class of enzymes, is overexpressed in various types of cancer cell lines and is the major cause of resistance to the Food and Drug Administration (FDA)-approved drug, cyclophosphamide (CP). In cancer conditions, CP undergoes a sequence of biotransformations to form an active metabolite, aldophosphamide, which further biotransforms to its putative cytotoxic metabolite, phosphoramide mustard. However, in resistant cancer conditions, aldophosphamide is converted into its inactive metabolite, carboxyphosphamide, via oxidation with ALDH1A1. Herein, to address the issue of ALDH1A1 mediated CP resistance, we report a series of benzo[d]oxazol-2(3H)-one and 2-oxazolo[4,5-b]pyridin-2(3H)-one derivatives as selective ALDH1A1 inhibitors. These inhibitors were designed using a validated 3D-quantitative structure activity relationship (3D-QSAR) model coupled with scaffold hopping. The 3D-QSAR model was developed using reported indole-2,3-diones based ALDH1A1 inhibitors, which provided field points in terms of electrostatic, van der Waals and hydrophobic potentials required for selectively inhibiting ALDH1A1. The most selective indole-2,3-diones-based compound, that is, cmp 3, was further considered for scaffold hopping. Two top-ranked bioisosteres, that is, benzo[d]oxazol-2(3H)-one and 2-oxazolo[4,5-b]pyridin-2(3H)-one, were selected for designing new inhibitors by considering the field pattern of 3D-QSAR. All designed molecules were mapped perfectly on the 3D-QSAR model and found to be predictive with good inhibitory potency (pIC₅₀ range: 7.5-6.8). Molecular docking was carried out for each designed molecule to identify key interactions that are required for ALDH1A1 inhibition and to authenticate the 3D-QSAR result. The top five inhibitor-ALDH1A1 complexes were also submitted for molecular dynamics simulations to access their stability. In vitro enzyme assays of 21 compounds suggested that these compounds are selective toward ALDH1A1 over the other two isoforms, that is, ALDH2 and ALDH3A1. All the compounds were found to be at least three and two times more selective toward ALDH1A1 over ALDH2 and ALDH3A1, respectively. All the compounds showed an IC₅₀ value in the range of 0.02-0.80 μM, which indicates the potential for these to be developed as adjuvant therapy for CP resistance.

Free energy perturbation (FEP)-guided scaffold hopping

Scaffold hopping refers to computer-aided screening for active compounds with different structures against the same receptor to enrich privileged scaffolds, which is a topic of high interest in organic and medicinal chemistry. However, most approaches cannot efficiently predict the potency level of candidates after scaffold hopping. Herein, we identified potent PDE5 inhibitors with a novel scaffold via a free energy perturbation (FEP)-guided scaffold-hopping strategy, and FEP shows great advantages to precisely predict the theoretical binding potencies ΔG FEP between ligands and their target, which were more consistent with the experimental binding potencies ΔG EXP (the mean absolute deviations| Δ G FEP - Δ G EXP |  < 2 kcal/mol) than those ΔG MM-PBSA or ΔG MM-GBSA predicted by the MM-PBSA or MM-GBSA method. Lead L12 had an IC50 of 8.7 nmol/L and exhibited a different binding pattern in its crystal structure with PDE5 from the famous starting drug tadalafil. Our work provides the first report via the FEP-guided scaffold hopping strategy for potent inhibitor discovery with a novel scaffold, implying that it will have a variety of future applications in rational molecular design and drug discovery.

Virtual Combinatorial Chemistry and Pharmacological Screening: A Short Guide to Drug Design

Traditionally, drug development involved the individual synthesis and biological evaluation of hundreds to thousands of compounds with the intention of highlighting their biological activity, selectivity, and bioavailability, as well as their low toxicity. On average, this process of new drug development involved, in addition to high economic costs, a period of several years before hopefully finding a drug with suitable characteristics to drive its commercialization. Therefore, the chemical synthesis of new compounds became the limiting step in the process of searching for or optimizing leads for new drug development. This need for large chemical libraries led to the birth of high-throughput synthesis methods and combinatorial chemistry. Virtual combinatorial chemistry is based on the same principle as real chemistry—many different compounds can be generated from a few building blocks at once. The difference lies in its speed, as millions of compounds can be produced in a few seconds. On the other hand, many virtual screening methods, such as QSAR (Quantitative Sturcture-Activity Relationship), pharmacophore models, and molecular docking, have been developed to study these libraries. These models allow for the selection of molecules to be synthesized and tested with a high probability of success. The virtual combinatorial chemistry–virtual screening tandem has become a fundamental tool in the process of searching for and developing a drug, as it allows the process to be accelerated with extraordinary economic savings.

Deep learning-driven scaffold hopping in the discovery of Akt kinase inhibitors

Scaffold hopping has been widely used in drug discovery and is a topic of high interest. Here a deep conditional transformer neural network, SyntaLinker, was applied for the scaffold hopping of a phase III clinical Akt inhibitor, AZD5363. A number of novel scaffolds were generated and compound 1a as a proof-of-concept was synthesized and validated by biochemical assay. Further structure-based optimization of 1a led to a novel Akt inhibitor with high potency (Akt1 IC₅₀ = 88 nM) and in vitro antitumor activities.

Photodegradation of organic water pollutants under visible light using anatase F, N co-doped TiO2/SiO2 nanocomposite: Semi-pilot plant experiment and density functional theory calculations

Visible-light driven photocatalysts are of great importance in wastewater treatment. In this work, fluorine and nitrogen co-doped titanium dioxide/silica nanocomposite (F-N-TiO₂/SiO₂) was synthetized using a sol-gel approach. The as-developed nanocomposite was well characterized using different techniques. In particular, an anatase structure with high surface area (345.69 m²/g) and a band gap of 2.97 eV were observed for the as-synthesized nanocomposite, which makes it a potential candidate for photocatalytic applications under visible light. A systematic density functional theory calculation was performed to get more insight into the effect of dopant atoms on the band gap of TiO₂ nanoparticles. To enhance the reusability of the photocatalyst in semi-pilot scale, the as-developed nanocomposite was immobilized onto the glass beads by coupling dip-coating and heat attachment methods. A semi-pilot scale custom-designed fixed-bed photoreactor was used to evaluate the photocatalytic performance of the as-developed nanocomposite under both visible and solar irradiations. A mixture of three azo dyes (i.e., basic red 29, basic blue 41 and basic yellow 51) was used as the model industrial wastewater. The analysis of the wastewater showed that the complete removal of the pollutants under visible light and sunlight can occurred at pH of 3 and flow rate of 280 mL/min. The durability results demonstrated the successful degradation of the pollutants for five cycles. The results of this study show how careful controlling the operational parameters as well as using a highly photocatalytic nanomaterial can lead to successful decontamination of organic water pollutants. This approach might open up new windows to the future applications of photocatalytic nanomaterials for wastewater treatment.

Computational Bioactivity Fingerprint Similarities To Navigate the Discovery of Novel Scaffolds

As one of the central tasks of modern medicinal chemistry, scaffold hopping is expected to lead to the discovery of structural novel biological active compounds and broaden the chemical space of known active compounds. Here, we report the computational bioactivity fingerprint (CBFP) for easier scaffold hopping, where the predicted activities in multiple quantitative structure-activity relationship models are integrated to characterize the biological space of a molecule. In retrospective benchmarks, the CBFP representation shows outstanding scaffold hopping potential relative to other chemical descriptors. In the prospective validation for the discovery of novel inhibitors of poly [ADP-ribose] polymerase 1, 35 predicted compounds with diverse structures are tested, 25 of which show detectable growth-inhibitory activity; beyond this, the most potent (compound 6) has an IC₅₀ of 0.263 nM. These results support the use of CBFP representation as the bioactivity proxy of molecules to explore uncharted chemical space and discover novel compounds.

Synthesis of 2-ethoxycarbonylthieno[2,3-b]quinolines in biomass-derived solvent γ-valerolactone and their biological evaluation against protein tyrosine phosphatase 1B

A series of 2-ethoxycarbonylthieno[2,3-b]quinolines were synthesized in the bio-derived "green" solvent γ-valerolactone (GVL) and evaluated for their inhibitory activities against PTP1B, the representative compound 6a displayed an IC50 value of 8.04 ± 0.71 μM with 4.34-fold preference over TCPTP. These results provided novel lead compounds for the design of inhibitors of PTP1B as well as other PTPs.

Molecular Scaffold Hopping via Holistic Molecular Representation

Molecular descriptors encode a variety of molecular representations for computer-assisted drug discovery. Here, we focus on the Weighted Holistic Atom Localization and Entity Shape (WHALES) descriptors, which were originally designed for scaffold hopping from natural products to synthetic molecules. WHALES descriptors capture molecular shape and partial charges simultaneously. We introduce the key aspects of the WHALES concept and provide a step-by-step guide on how to use these descriptors for virtual compound screening and scaffold hopping. The results presented can be reproduced by using the code freely available from URL: github.com/ETHmodlab/scaffold_hopping_whales .

Exploiting Water Dynamics for Pharmacophore Screening

Three-dimensional pharmacophore models have been proven extremely valuable in exploring novel chemical space through virtual screening. However, traditional pharmacophore-based approaches need ligand information and rely on static snapshots of highly dynamic systems. In this chapter, we describe PyRod, a novel tool to generate three-dimensional pharmacophore models based on water traces of a molecular dynamics simulation of an apo-protein.The protocol described herein was successfully applied for the discovery of novel drug-like inhibitors of West Nile virus NS2B-NS3 protease. By using this recent example, we highlight the key steps of the generation and validation of PyRod-derived pharmacophore models and their application for virtual screening.

In Silico Strategies in Tuberculosis Drug Discovery

Tuberculosis (TB) remains a serious threat to global public health, responsible for an estimated 1.5 million mortalities in 2018. While there are available therapeutics for this infection, slow-acting drugs, poor patient compliance, drug toxicity, and drug resistance require the discovery of novel TB drugs. Discovering new and more potent antibiotics that target novel TB protein targets is an attractive strategy towards controlling the global TB epidemic. In silico strategies can be applied at multiple stages of the drug discovery paradigm to expedite the identification of novel anti-TB therapeutics. In this paper, we discuss the current TB treatment, emergence of drug resistance, and the effective application of computational tools to the different stages of TB drug discovery when combined with traditional biochemical methods. We will also highlight the strengths and points of improvement in in silico TB drug discovery research, as well as possible future perspectives in this field.

Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Applications Exemplified on Hydroxysteroid Dehydrogenases

Computational methods are well-established tools in the drug discovery process and can be employed for a variety of tasks. Common applications include lead identification and scaffold hopping, as well as lead optimization by structure-activity relationship analysis and selectivity profiling. In addition, compound-target interactions associated with potentially harmful effects can be identified and investigated. This review focuses on pharmacophore-based virtual screening campaigns specifically addressing the target class of hydroxysteroid dehydrogenases. Many members of this enzyme family are associated with specific pathological conditions, and pharmacological modulation of their activity may represent promising therapeutic strategies. On the other hand, unintended interference with their biological functions, e.g., upon inhibition by xenobiotics, can disrupt steroid hormone-mediated effects, thereby contributing to the development and progression of major diseases. Besides a general introduction to pharmacophore modeling and pharmacophore-based virtual screening, exemplary case studies from the field of short-chain dehydrogenase/reductase (SDR) research are presented. These success stories highlight the suitability of pharmacophore modeling for the various application fields and suggest its application also in futures studies.

Combinatorial Consensus Scoring for Ligand-Based Virtual Fragment Screening: A Comparative Case Study for Serotonin 5-HT(3)A, Histamine H(1), and Histamine H(4) Receptors

In the current study we have evaluated the applicability of ligand-based virtual screening (LBVS) methods for the identification of small fragment-like biologically active molecules using different similarity descriptors and different consensus scoring approaches. For this purpose, we have evaluated the performance of 14 chemical similarity descriptors in retrospective virtual screening studies to discriminate fragment-like ligands of three membrane-bound receptors from fragments that are experimentally determined to have no affinity for these proteins (true inactives). We used a complete fragment affinity data set of experimentally determined ligands and inactives for two G protein-coupled receptors (GPCRs), the histamine H1 receptor (H1R) and the histamine H4 receptor (H4R), and one ligand-gated ion channel (LGIC), the serotonin receptor (5-HT3AR), to validate our retrospective virtual screening studies. We have exhaustively tested consensus scoring strategies that combine the results of multiple actives (group fusion) or combine different similarity descriptors (similarity fusion), and for the first time systematically evaluated different combinations of group fusion and similarity fusion approaches. Our studies show that for these three case study protein targets both consensus scoring approaches can increase virtual screening enrichments compared to single chemical similarity search methods. Our cheminformatics analyses recommend to use a combination of both group fusion and similarity fusion for prospective ligand-based virtual fragment screening.

Prospective performance evaluation of selected common virtual screening tools. Case study: Cyclooxygenase (COX) 1 and 2

Computational methods can be applied in drug development for the identification of novel lead candidates, but also for the prediction of pharmacokinetic properties and potential adverse effects, thereby aiding to prioritize and identify the most promising compounds. In principle, several techniques are available for this purpose, however, which one is the most suitable for a specific research objective still requires further investigation. Within this study, the performance of several programs, representing common virtual screening methods, was compared in a prospective manner. First, we selected top-ranked virtual screening hits from the three methods pharmacophore modeling, shape-based modeling, and docking. For comparison, these hits were then additionally predicted by external pharmacophore- and 2D similarity-based bioactivity profiling tools. Subsequently, the biological activities of the selected hits were assessed in vitro, which allowed for evaluating and comparing the prospective performance of the applied tools. Although all methods performed well, considerable differences were observed concerning hit rates, true positive and true negative hits, and hitlist composition. Our results suggest that a rational selection of the applied method represents a powerful strategy to maximize the success of a research project, tightly linked to its aims. We employed cyclooxygenase as application example, however, the focus of this study lied on highlighting the differences in the virtual screening tool performances and not in the identification of novel COX-inhibitors.

Structure-based virtual screening for fragment-like ligands of the G protein-coupled histamine H4 receptor

Structure-based virtual screening using H₁R- and β₂R-based histamine H₄R homology models identified 9 fragments with an affinity ranging from 0.14 to 6.3 μm for H₄R.

Methods for generating and applying pharmacophore models as virtual screening filters and for bioactivity profiling

Biological effects of small molecules in an organism result from favorable interactions between the molecules and their target proteins. These interactions depend on chemical functionalities, bonds, and their 3D-orientations towards each other. These 3D-arrangements of chemical functionalities that make a small molecule active towards its target can be described by pharmacophore models. In these models, chemical functionalities are represented as so-called features. Commonly, they are obtained either from a set of active compounds or directly from the observed protein-ligand interactions as present in X-ray crystal structures, NMR structures, or docking poses. In this review, we explain the basics of pharmacophore modeling including dataset generation, 3D-representations and conformational analysis of small molecules, pharmacophore model construction, model validation, and its benefits to virtual screening and other applications.

Dual histamine H3R/serotonin 5-HT4R ligands with antiamnesic properties: pharmacophore-based virtual screening and polypharmacology

In recent years, preclinical and clinical studies have generated considerable interest in the development of histamine H3 receptor (H3R) antagonists as novel treatment for degenerative disorders associated with impaired cholinergic function. To identify novel scaffolds for H3R antagonism, a common feature-based pharmacophore model was developed and used to screen the 17,194 compounds of the CERMN (Centre d'Etudes et de Recherche sur le Médicament de Normandie) chemical library. Out of 268 virtual hits which have been gathered in 34 clusters, we were particularly interested in tricyclic derivatives also exhibiting a potent 5HT4R affinity. Benzo[h][1,6]naphthyridine derivatives showed the highest H3R affinity, and compound 17 (H3R Ki = 41.6 nM; 5-HT4R Ki = 208 nM) completely reversed the amnesiant effect of scopolamine at 3 mg/kg in a spatial working memory experiment. For the first time we demonstrated the feasibility to combine H3R and 5-HT4R activities in a single molecule, raising the exciting possibility that dual H3R antagonist/5HT4R agonist have potential for the treatment of neurodegenerative diseases such as Alzheimer's disease.

Development of purely structure-based pharmacophores for the topoisomerase I-DNA-ligand binding pocket

Purely structure-based pharmacophores (SBPs) are an alternative method to ligand-based approaches and have the advantage of describing the entire interaction capability of a binding pocket. Here, we present the development of SBPs for topoisomerase I, an anticancer target with an unusual ligand binding pocket consisting of protein and DNA atoms. Different approaches to cluster and select pharmacophore features are investigated, including hierarchical clustering and energy calculations. In addition, the performance of SBPs is evaluated retrospectively and compared to the performance of ligand- and complex-based pharmacophores. SBPs emerge as a valid method in virtual screening and a complementary approach to ligand-focussed methods. The study further reveals that the choice of pharmacophore feature clustering and selection methods has a large impact on the virtual screening hit lists. A prospective application of the SBPs in virtual screening reveals that they can be used successfully to identify novel topoisomerase inhibitors.

Combination of ligand- and structure-based methods in virtual screening

The combination of ligand- and structure-based molecular modelling methods has become a common approach in virtual screening. This review describes different strategies for integration of ligand- and structure-based methods which can be divided into sequential, parallel or hybrid approaches. Although no thorough performance comparisons between combined approaches are available, examples of successful applications in prospective and retrospective virtual screening are discussed. Most published studies use a sequential approach, utilising well-documented single methods successfully.

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.

Pharmacophore modeling and virtual screening for novel acidic inhibitors of microsomal prostaglandin E₂ synthase-1 (mPGES-1)

Microsomal prostaglandin E₂ synthase-1 (mPGES-1) catalyzes prostaglandin E₂ formation and is considered as a potential anti-inflammatory pharmacological target. To identify novel chemical scaffolds active on this enzyme, two pharmacophore models for acidic mPGES-1 inhibitors were developed and theoretically validated using information on mPGES-1 inhibitors from literature. The models were used to screen chemical databases supplied from the National Cancer Institute (NCI) and the Specs. Out of 29 compounds selected for biological evaluation, nine chemically diverse compounds caused concentration-dependent inhibition of mPGES-1 activity in a cell-free assay with IC₅₀ values between 0.4 and 7.9 μM, respectively. Further pharmacological characterization revealed that also 5-lipoxygenase (5-LO) was inhibited by most of these active compounds in cell-free and cell-based assays with IC₅₀ values in the low micromolar range. Together, nine novel chemical scaffolds inhibiting mPGES-1 are presented that may possess anti-inflammatory properties based on the interference with eicosanoid biosynthesis.