Scaffold hopping

Pyrimidine-fused Dinitrogenous Penta-heterocycles as a Privileged Scaffold for Anti-Cancer Drug Discovery

Pyrimidine-fused derivatives that are the inextricable part of DNA and RNA play a key role in the normal life cycle of cells. Pyrimidine-fused dinitrogenous penta-heterocycles including pyrazolopyrimidines and imidazopyrimidines is a special class of pyrimidine-fused compounds contributing to an important portion in anti-cancer drug discovery, which have been discovered as core structure for promising anti-cancer agents used in clinic or clinical evaluations. Pyrimidine-fused dinitrogenous penta-heterocycles have become one privileged scaffold for anti-cancer drug discovery. This review consists of the recent progress of pyrimidine-fused dinitrogenous penta-heterocycles as anti-cancer agents and their synthetic strategies. In addition, this review also summarizes some key structure-activity relationships (SARs) of pyrimidine-fused dinitrogenous penta-heterocycle derivatives as anti-cancer agents.

De Novo Molecular Design with Chemical Language Models

Artificial intelligence (AI) offers new possibilities for hit and lead finding in medicinal chemistry. Several instances of AI have been used for prospective de novo drug design. Among these, chemical language models have been shown to perform well in various experimental scenarios. In this study, we provide a hands-on introduction to chemical language modeling. A technique based on recurrent neural networks is discussed in detail, together with a step-by-step guide to applying this AI method for focused compound library design. The program code is freely available at URL: github.com/ETHmodlab/de_novo_design_RNN .

Lead Optimization of Influenza Virus RNA Polymerase Inhibitors Targeting PA-PB1 Interaction

Influenza viruses are responsible for contagious respiratory illnesses in humans and cause seasonal epidemics and occasional pandemics worldwide. Previously, we identified a quinolinone derivative PA-49, which inhibited the influenza virus RNA-dependent RNA polymerase (RdRp) by targeting PA-PB1 interaction. This paper reports the structure optimization of PA-49, which resulted in the identification of 3-((dibenzylamino)methyl)quinolinone derivatives with more potent anti-influenza virus activity. During the optimization, the hit compound 89, which was more active than PA-49, was identified. Further optimization and scaffold hopping of 89 led to the most potent compounds 100 and a 1,8-naphthyridinone derivative 118, respectively. We conclusively determined that compounds 100 and 118 suppressed the replication of influenza virus and exhibited anti-influenza virus activity against both influenza virus types A and B in the range of 50% effective concentration (EC₅₀) = 0.061-0.226 μM with low toxicity (50% cytotoxic concentration (CC₅₀) >10 μM).

Synthesis and In Silico Docking Studies of Ethyl 2-(2-Arylidene-1-Alkylhydrazinyl)Thiazole-4-Carboxylates as Antiglycating agents

Ethyl 2-(2-arylidene-1-alkylhydrazinyl)thiazole-4-carboxylates ( 1a-k ) were synthesized by alkylation on HN- of ethyl 2-(2-arylidenehydrazinyl)thiazole-4-carboxylates. The proposed structures ( 1a-k ) are corroborated by spectro-analytical techniques like UV, FT-IR, 1 H-, 13 C-NMR and HRMS. The compounds ( 1a-k ) were screened for their antiglycation and antioxidant assays. The in vitro antiglycation results revealed promising activity of compounds 1a , 1b , 1d , 1e , 1f , 1g , 1j and 1k with IC 50 values 0.0004 ± 1.097-17.22 ± 0.538 µM/mL when compared to standard, aminoguanidine (IC 50 = 25.50 ± 0.337 µM/mL). Among all tested compounds 1j and 1k are the best antiglycating agents with IC 50 values 1.848 ± 0.646 and 0.0004 ± 1.097 µM/mL, respectively. The in-silico studies also agree with these results where binding energy for 1j and 1k was found to be -9.25 and -8.42 kcal/mol with calculated dissociation constants of 0.16 and 0.67 µM. The antiglycation results demonstrate the application of these compounds in reducing diabetic complications.

Molecular photoswitches in aqueous environments

Molecular photoswitches enable dynamic control of processes with high spatiotemporal precision, using light as external stimulus, and hence are ideal tools for different research areas spanning from chemical biology to smart materials. Photoswitches are typically organic molecules that feature extended aromatic systems to make them responsive to (visible) light. However, this renders them inherently lipophilic, while water-solubility is of crucial importance to apply photoswitchable organic molecules in biological systems, like in the rapidly emerging field of photopharmacology. Several strategies for solubilizing organic molecules in water are known, but there are not yet clear rules for applying them to photoswitchable molecules. Importantly, rendering photoswitches water-soluble has a serious impact on both their photophysical and biological properties, which must be taken into consideration when designing new systems. Altogether, these aspects pose considerable challenges for successfully applying molecular photoswitches in aqueous systems, and in particular in biologically relevant media. In this review, we focus on fully water-soluble photoswitches, such as those used in biological environments, in both in vitro and in vivo studies. We discuss the design principles and prospects for water-soluble photoswitches to inspire and enable their future applications.

Deep generative design with 3D pharmacophoric constraints

Generative models have increasingly been proposed as a solution to the molecular design problem. However, it has proved challenging to control the design process or incorporate prior knowledge, limiting their practical use in drug discovery. In particular, generative methods have made limited use of three-dimensional (3D) structural information even though this is critical to binding. This work describes a method to incorporate such information and demonstrates the benefit of doing so. We combine an existing graph-based deep generative model, DeLinker, with a convolutional neural network to utilise physically-meaningful 3D representations of molecules and target pharmacophores. We apply our model, DEVELOP, to both linker and R-group design, demonstrating its suitability for both hit-to-lead and lead optimisation. The 3D pharmacophoric information results in improved generation and allows greater control of the design process. In multiple large-scale evaluations, we show that including 3D pharmacophoric constraints results in substantial improvements in the quality of generated molecules. On a challenging test set derived from PDBbind, our model improves the proportion of generated molecules with high 3D similarity to the original molecule by over 300%. In addition, DEVELOP recovers 10× more of the original molecules compared to the baseline DeLinker method. Our approach is general-purpose, readily modifiable to alternate 3D representations, and can be incorporated into other generative frameworks. Code is available at https://github.com/oxpig/DEVELOP.

Iron-Catalyzed C-C Single-Bond Cleavage of Alcohols

An iron-catalyzed deconstruction/hydrogenation reaction of alcohols through C-C bond cleavage is developed through photocatalysis, to produce ketones or aldehydes as the products. Tertiary, secondary, and primary alcohols bearing a wide range of substituents are suitable substrates. Complex natural alcohols can also perform the transformation selectively. A investigation of the mechanism reveals a procedure that involves chlorine radical improved O-H homolysis, with the assistance of 2,4,6-collidine.

Guided rational design with scaffold hopping leading to novel histamine H3 receptor ligands

During the past decades, histamine H₃ receptors have received widespread attention in pharmaceutical research due to their involvement in pathophysiology of several diseases such as neurodegenerative disorders. In this context, blocking of these receptors is of paramount importance in progression of such diseases. In the current investigation, novel histamine H₃ receptor ligands were designed by exploiting scaffold-hopping drug-design strategy. We inspected the designed molecules in terms of ADME properties, drug-likeness, as well as toxicity profiles. Additionally molecular docking and dynamics simulation studies were performed to predict binding mode and binding free energy calculations, respectively. Among the designed structures, we selected compound d2 and its demethylated derivative as examples for synthesis and affinity measurement. In vitro binding assays of the synthesized molecules demonstrated that d2 has lower binding affinity (K_i = 2.61 μM) in radioligand displacement assay to hH₃R than that of demethylated form (K_i = 12.53 μM). The newly designed compounds avoid of any toxicity predictors resulted from extended in silico and experimental studies, can offer another scaffold for histamine H₃R antagonists for further structure-activity relationship studies.

1- and 2-Azetines via Visible Light-Mediated [2 + 2]-Cycloadditions of Alkynes and Oximes

Azetines, four-membered unsaturated nitrogen-containing heterocycles, hold great potential for drug design and development but remain underexplored due to challenges associated with their synthesis. We report an efficient, visible light-mediated approach toward 1- and 2-azetines relying on alkynes and the unique triplet state reactivity of oximes, specifically 2-isoxazolines. While 2-azetine products are accessible upon intermolecular [2 + 2]-cycloaddition via triplet energy transfer from a commercially available iridium photocatalyst, the selective formation of 1-azetines proceeds upon a second, consecutive, energy transfer process. Mechanistic studies are consistent with a stepwise reaction mechanism via N-O bond homolysis following the second energy transfer event to result in the formation of 1-azetine products. Characteristic for this method is its operational simplicity, mild conditions, and modular approach that allow for the synthesis of functionalized azetines and tetrahydrofurans (via in situ hydrolysis) from readily available precursors.

α-Pinene: A never-ending story

α-Pinene represents a member of the monoterpene class and is highly distributed in higher plants like conifers, Juniper ssp. and Cannabis ssp. α-Pinene has been used to treat respiratory tract infections for centuries. Furthermore, it plays a crucial role in the fragrance and flavor industry. In vitro assays have shown an enantioselective profile of (+)- and (-)-α-pinene for antibacterial and insecticidal activity, respectively. Recent research has used pre-validated biological structures to synthesize new chemical entities with pharmacological and herbicidal activities. In summary, this review focuses on recent literature covering synthetic pathways of flavor compounds and scaffold hopping based on the α-pinene core domaine, as well as the (enantioselective) activities of α-pinene. Recent approaches for authenticity control of essential oils based on their enantiomeric profile are also presented.

TRIOMPHE: Transcriptome-Based Inference and Generation of Molecules with Desired Phenotypes by Machine Learning

One of the most challenging tasks in the drug-discovery process is the efficient identification of small molecules with desired phenotypes. In this study, we propose a novel computational method for omics-based de novo drug design, which we call TRIOMPHE (transcriptome-based inference and generation of molecules with desired phenotypes). We investigated the correlation between chemically induced transcriptome profiles (reflecting cellular responses to compound treatment) and genetically perturbed transcriptome profiles (reflecting cellular responses to gene knock-down or gene overexpression of target proteins) in terms of ligand-target interactions. Subsequently, we developed novel machine learning methods to generate the chemical structures of new molecules with desired transcriptome profiles in the framework of a variational autoencoder. The use of desired transcriptome profiles enables the automatic design of molecules that are likely to have bioactivities for target proteins of interest. We showed that our methods can generate chemically valid molecules that are likely to have biological activities on 10 target proteins; moreover, they can outperform previous methods that had the same objective. Our omics-based structure generator is expected to be useful for the de novo design of drugs for a variety of target proteins.

SARS-COV-2 Mpro conformational changes induced by covalently bound ligands

SARS-CoV-2's main protease (Mpro) interaction with ligands has been explored with a myriad of crystal structures, most of the monomers. Nonetheless, Mpro is known to be active as a dimer but the relevance of the dimerization in the ligand-induced conformational changes has not been fully elucidated. We systematically simulated different Mpro-ligand complexes aiming to study their conformational changes and interactions, through molecular dynamics (MD). We focused on covalently bound ligands (N1 and N3, ∼9 μs per system both monomers and dimers) and compared these trajectories against the apostructure. Our results suggest that the monomeric simulations led to an unrealistically flexible active site. In contrast, the Mpro dimer displayed a stable oxyanion-loop conformation along the trajectory. Also, ligand interactions with residues His41, Gly143, His163, Glu166 and Gln189 are postulated to impact the ligands' inhibitory activity significantly. In dimeric simulations, especially Gly143 and His163 have increased interaction frequencies. In conclusion, long-timescale MD is a more suitable tool for exploring in silico the activity of bioactive compounds that potentially inhibit the dimeric form of SARS-CoV-2 Mpro.Communicated by Ramaswamy H. Sarma.

SimilarityLab: Molecular Similarity for SAR Exploration and Target Prediction on the Web

Exploration of chemical space around hit, experimental, and known active compounds is an important step in the early stages of drug discovery. In academia, where access to chemical synthesis efforts is restricted in comparison to the pharma-industry, hits from primary screens are typically followed up through purchase and testing of similar compounds, before further funding is sought to begin medicinal chemistry efforts. Rapid exploration of druglike similars and structure–activity relationship profiles can be achieved through our new webservice SimilarityLab. In addition to searching for commercially available molecules similar to a query compound, SimilarityLab also enables the search of compounds with recorded activities, generating consensus counts of activities, which enables target and off-target prediction. In contrast to other online offerings utilizing the USRCAT similarity measure, SimilarityLab’s set of commercially available small molecules is consistently updated, currently containing over 12.7 million unique small molecules, and not relying on published databases which may be many years out of date. This ensures researchers have access to up-to-date chemistries and synthetic processes enabling greater diversity and access to a wider area of commercial chemical space. All source code is available in the SimilarityLab source repository.

Sparse Topological Pharmacophore Graphs for Interpretable Scaffold Hopping

The aim of scaffold hopping (SH) is to find compounds consisting of different scaffolds from those in already known active compounds, giving an opportunity for unexplored regions of chemical space. We previously demonstrated the usefulness of pharmacophore graphs (PhGs) for this purpose through proof-of-concept virtual screening experiments. PhGs consist of nodes and edges corresponding to pharmacophoric features (PFs) and their topological distances. Although PhGs were effective in SH, they are hard to interpret as they are complete graphs. Herein, we introduce an intuitive representation of a molecule, termed as sparse pharmacophore graphs (SPhG) by keeping the topological distances among PFs as much as possible while reducing the number of edges in the graphs. Several benchmark calculations quantitatively confirmed the sparseness of the graphs and the preservation of topological distances among pharmacophoric points. As proof-of-concept applications, virtual screening (VS) trials for SH were conducted using active and inactive compounds from ChEMBL and PubChem databases for three biological targets: thrombin, tyrosine kinase ABL1, and κ-opioid receptor. The performances of VS were comparable with using fully connected PhGs. Furthermore, highly ranked SPhGs were interpretable for the three biological targets, in particular for thrombin, for which selected SPhGs were in agreement with the structure-based interpretation.

De novo design with deep generative models based on 3D similarity scoring

We have demonstrated the utility of a 3D shape and pharmacophore similarity scoring component in molecular design with a deep generative model trained with reinforcement learning. Using Dopamine receptor type 2 (DRD2) as an example and its antagonist haloperidol 1 as a starting point in a ligand based design context, we have shown in a retrospective study that a 3D similarity enabled generative model can discover new leads in the absence of any other information. It can be efficiently used for scaffold hopping and generation of novel series. 3D similarity based models were compared against 2D QSAR based, indicating a significant degree of orthogonality of the generated outputs and with the former having a more diverse output. In addition, when the two scoring components are combined together for training of the generative model, it results in more efficient exploration of desirable chemical space compared to the individual components.

Emerging Therapeutic Potential of SIRT6 Modulators

Sirtuin 6 (SIRT6) is an NAD⁺-dependent protein deacylase and mono-ADP-ribosyltransferase of the sirtuin family with a wide substrate specificity. In vitro and in vivo studies have indicated that SIRT6 overexpression or activation has beneficial effects for cellular processes such as DNA repair, metabolic regulation, and aging. On the other hand, SIRT6 has contrasting roles in cancer, acting either as a tumor suppressor or promoter in a context-specific manner. Given its central role in cellular homeostasis, SIRT6 has emerged as a promising target for the development of small-molecule activators and inhibitors possessing a therapeutic potential in diseases ranging from cancer to age-related disorders. Moreover, specific modulators allow the molecular details of SIRT6 activity to be scrutinized and further validate the enzyme as a pharmacological target. In this Perspective, we summarize the current knowledge about SIRT6 pharmacology and medicinal chemistry and describe the features of the activators and inhibitors identified so far.

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.

Structure-Activity-Relationship-Aided Design and Synthesis of xCT Antiporter Inhibitors

The xCT antiporter is a cell membrane protein involved in active counter‐transportation of glutamate (outflux) with cystine (influx) over the human cell membrane. This feature makes the xCT antiporter a crucial element of the biosynthesis of the vital free radical scavenger glutathione. The prodrug sulfasalazine, a medication for the treatment of ulcerative colitis, was previously proven to inhibit the xCT antiporter. Starting from sulfasalazine, a molecular scaffold jumping followed by SAR‐assisted design and synthesis provided a series of styryl hydroxy‐benzoic acid analogues that were biologically tested in vitro for their ability to decrease intracellular glutathione levels using four different cancer cell lines: A172 (glioma), A375 (melanoma), U87 (glioma) and MCF7 (breast carcinoma). Depletion of glutathione levels varied among the compounds as well as among the cell lines. Flow cytometry using propidium iodide and the annexin V marker demonstrated minimal toxicity in normal human astrocytes for a promising candidate molecule (E)‐5‐(2‐([1,1′‐biphenyl]‐4‐yl)vinyl)‐2‐hydroxybenzoic acid.

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.

Modern approaches to the development of synthetic cannabinoid receptor probes

The endocannabinoid system, which spans the central and peripheral nervous systems and regulates many biologic processes, is an important target for probe discovery and medications development. Whereas the earliest endocannabinoid receptor probes were derivatives of the non-selective phytocannabinoids isolated from Cannabis species, modern drug discovery techniques have expanded the definitions of what constitutes a CB1R or CB2R cannabinoid receptor ligand. This review highlights recent advances in synthetic cannabinoid receptor chemistry and pharmacology. We provide examples of new CB1R- and CB2R-selective probes, and discuss rational approaches to the design of peripherally-restricted agents. We also describe structural classes of positive- and negative allosteric modulators (PAMs and NAMs) of CB1R and CB2R. Finally, we introduce new opportunities for cannabinoid receptor probe development that have emerged in recent years, including biased agonists that may lead to medications lacking adverse effects.

C-C Bond Acylation of Oxime Ethers via NHC Catalysis

An N-heterocyclic carbene (NHC)-catalyzed strategy has been developed to address the issue of using toxic transitional metals in the field of C-C bond activation. The novel reaction mode enables an efficient docking between the cyanoalkyl from the cycloketone oxime derivative and the acyl group from the aldehyde, affording ketonitrile in moderate to good yields, which is one kind of useful building block for synthesizing nitrogen-containing pharmacophores.

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 .

Design, Synthesis, and Insecticidal Activity of 5,5-Disubstituted 4,5-Dihydropyrazolo[1,5-a]quinazolines as Novel Antagonists of GABA Receptors

To control the development of resistance to conventional insecticides acting as γ-aminobutyric acid (GABA) receptor antagonists (e.g., fipronil), new GABAergic 5,5-disubstituted 4,5-dihydropyrazolo[1,5-a]quinazolines were designed via a scaffold-hopping strategy and synthesized with a facile method. Among the 50 target compounds obtained, compounds 5a, 5b, 7a, and 7g showed excellent insecticidal activities against a susceptible strain of Plutella xylostella (LC₅₀ values ranging from 1.03 to 1.44 μg/mL), which were superior to that of fipronil (LC₅₀ = 3.02 μg/mL). Remarkably, the insecticidal activity of compound 5a was 64-fold better than that of fipronil against the field population of fipronil-resistant P. xylostella. Electrophysiological studies against the housefly GABA receptor heterologously expressed in Xenopus oocytes indicated that compound 5a could act as a potent GABA receptor antagonist, and IC₅₀ was calculated to be 32.5 nM. Molecular docking showed that the binding poses of compound 5a with the housefly GABA receptor can be different compared to fipronil, which explains the effectiveness of compound 5a against fipronil-resistant insects. These findings have suggested compound 5a as a lead compound for a novel GABA receptor antagonist controlling field-resistant insects and provided a basis for further design, structural modification, and development of 4,5-dihydropyrazolo[1,5-a]quinazoline motifs as new insecticidal GABA receptor antagonists.

Scaffold-Constrained Molecular Generation

One of the major applications of generative models for drug discovery targets the lead-optimization phase. During the optimization of a lead series, it is common to have scaffold constraints imposed on the structure of the molecules designed. Without enforcing such constraints, the probability of generating molecules with the required scaffold is extremely low and hinders the practicality of generative models for de novo drug design. To tackle this issue, we introduce a new algorithm, named SAMOA (Scaffold Constrained Molecular Generation), to perform scaffold-constrained in silico molecular design. We build on the well-known SMILES-based Recurrent Neural Network (RNN) generative model, with a modified sampling procedure to achieve scaffold-constrained generation. We directly benefit from the associated reinforcement learning methods, allowing to design molecules optimized for different properties while exploring only the relevant chemical space. We showcase the method's ability to perform scaffold-constrained generation on various tasks: designing novel molecules around scaffolds extracted from SureChEMBL chemical series, generating novel active molecules on the Dopamine Receptor D2 (DRD2) target, and finally, designing predicted actives on the MMP-12 series, an industrial lead-optimization project.

Discovery and optimizing polycyclic pyridone compounds as anti-HBV agents

INTRODUCTION

Hepatitis B disease is caused by the hepatitis B virus (HBV), which is a DNA virus that belongs to the Hepadnaviridae family. It is a considerable health burden, with 257 million active cases globally. Long-standing infection may create a fundamental cause of liver disease and chronic infections, including cirrhosis, hepatocellular, and carcinoma liver failure. There is an urgent need to develop novel, safe, and effective drug candidates with a novel mechanism of action, improved activity, efficacy, and cure rate.

AREAS COVERED

Herein, the authors provide a concise report focusing on a general and cutting-edge overview of the current state of polycyclic pyridone-related anti-HBV agent patents from 2016 to 2018 and some future perspectives.

EXPERT OPINION

In medicinal chemistry, high-throughput screening (HTS), hit-to-lead optimization (H2L), bioisosteric replacement, and scaffold hopping approaches are playing a major role in the discovery and development of HBV inhibitors. Developing polycyclic pyridone-related anti-HBV agents that could target host factors has attracted significant interest and attention in recent years.

Diversity-oriented Functionalization of Cyclodienes Through Selective Cycloaddition/Ring-opening/Cross-metathesis Protocols; Transformation of a "Flatland" into Three-dimensional Scaffolds With Stereo- and Regiocontrol

This article presents selective transformations of some readily available cyclodienes through simple chemical procedures into novel functionalized small-molecular entities. The syntheses hereby described involved selective cycloadditions, followed by ring-opening metathesis of the resulting β-lactam or isoxazoline derivatives and selective cross-metathesis by differentiation of the olefin bonds on the alkenylated heterocycles. The cross-metathesis transformations have been detailed, which were performed under various experimental conditions with the aim of exploring chemodiscrimination of the olefin bonds and delivering the corresponding functionalized β-lactam or isoxazoline derivatives.

PaccMann: a web service for interpretable anticancer compound sensitivity prediction

The identification of new targeted and personalized therapies for cancer requires the fast and accurate assessment of the drug efficacy of potential compounds against a particular biomolecular sample. It has been suggested that the integration of complementary sources of information might strengthen the accuracy of a drug efficacy prediction model. Here, we present a web-based platform for the Prediction of AntiCancer Compound sensitivity with Multimodal Attention-based Neural Networks (PaccMann). PaccMann is trained on public transcriptomic cell line profiles, compound structure information and drug sensitivity screenings, and outperforms state-of-the-art methods on anticancer drug sensitivity prediction. On the open-access web service (https://ibm.biz/paccmann-aas), users can select a known drug compound or design their own compound structure in an interactive editor, perform in-silico drug testing and investigate compound efficacy on publicly available or user-provided transcriptomic profiles. PaccMann leverages methods for model interpretability and outputs confidence scores as well as attention heatmaps that highlight the genes and chemical sub-structures that were more important to make a prediction, hence facilitating the understanding of the model's decision making and the involved biochemical processes. We hope to serve the community with a toolbox for fast and efficient validation in drug repositioning or lead compound identification regimes.

Scaffold hopping enables direct access to more potent PROTACs with in vivo activity

Herein we employ a scaffold hopping approach to enhance the activity of a previously reported BCR-Abl PROTAC. This represents a significant advance in the PROTAC field since it can abrogate the need to optimize the linker to access a more potent degrader. The new PROTAC demonstrates a >10 fold increase in ability to induce degradation and demonstrates in vivo activity.

Deep Generative Models for 3D Linker Design

Rational compound design remains a challenging problem for both computational methods and medicinal chemists. Computational generative methods have begun to show promising results for the design problem. However, they have not yet used the power of three-dimensional (3D) structural information. We have developed a novel graph-based deep generative model that combines state-of-the-art machine learning techniques with structural knowledge. Our method ("DeLinker") takes two fragments or partial structures and designs a molecule incorporating both. The generation process is protein-context-dependent, utilizing the relative distance and orientation between the partial structures. This 3D information is vital to successful compound design, and we demonstrate its impact on the generation process and the limitations of omitting such information. In a large-scale evaluation, DeLinker designed 60% more molecules with high 3D similarity to the original molecule than a database baseline. When considering the more relevant problem of longer linkers with at least five atoms, the outperformance increased to 200%. We demonstrate the effectiveness and applicability of this approach on a diverse range of design problems: fragment linking, scaffold hopping, and proteolysis targeting chimera (PROTAC) design. As far as we are aware, this is the first molecular generative model to incorporate 3D structural information directly in the design process. The code is available at https://github.com/oxpig/DeLinker.

Deconstructive Oxygenation of Unstrained Cycloalkanamines

A deconstructive oxygenation of unstrained primary cycloalkanamines has been developed for the first time using an auto-oxidative aromatization promoted C(sp³ )-C(sp³ ) bond cleavage strategy. This metal-free method involves the substitution reaction of cycloalkanamines with hydrazonyl chlorides and subsequent auto-oxidative annulation to in situ generate pre-aromatics, followed by N-radical-promoted ring-opening and further oxygenation by 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO) and m-cholorperoxybenzoic acid (mCPBA). Consequently, a series of 1,2,4-triazole-containing acyclic carbonyl compounds were efficiently produced. This protocol features a one-pot operation, mild reaction conditions, high regioselectivity and ring-opening efficiency, broad substrate scope, and is compatible with alkaloids, osamines, and peptides, as well as steroids.

Design, synthesis, biological evaluation, common feature pharmacophore model and molecular dynamics simulation studies of ethyl 4-(phenoxymethyl)-2-phenylthiazole-5-carboxylate as Src homology-2 domain containing protein tyrosine phosphatase-2 (SHP2) inhibitors

SHP2 is a non-receptor protein tyrosine phosphatase (PTP) encoded by the PTPN11 gene involved in cell death pathway (PD-1/PD-L1) and cell growth and differentiation pathway (MAPK). Moreover, mutations in SHP2 have been implicated in Leopard syndrome (LS), Noonan syndrome (NS), juvenile myelomonocytic leukemia (JMML) and several types of cancer and solid tumors. Thus, SHP2 inhibitors are much needed reagents for evaluation of SHP2 as a therapeutic target. A series of novel ethyl 4-(phenoxymethyl)-2-phenylthiazole-5-carboxylate derivatives were designed and synthesized, and their SHP2 inhibitory activities (IC₅₀) were determined. Among the desired compounds, 1d shares the highest inhibitory activity (IC₅₀ = 0.99 μM) against SHP2. Additionally, a common feature pharmacophore model was established to explain the structure activity relationship of the desired compounds. Finally, molecular dynamics simulation was carried out to explore the most likely binding mode of compound 1d with SHP2. In brief, the findings reported here may at least provide a new strategy or useful insights in discovering novel effective SHP2 inhibitors.Communicated by Ramaswamy H. Sarma.

Quantitative Predictions for Molecular Initiating Events Using Three-Dimensional Quantitative Structure-Activity Relationships

The aim of human toxicity risk assessment is to determine a safe dose or exposure to a chemical for humans. This requires an understanding of the exposure of a person to a chemical and how much of the chemical is required to cause an adverse effect. To do this computationally, we need to understand how much of a chemical is required to perturb normal biological function in an adverse outcome pathway (AOP). The molecular initiating event (MIE) is the first step in an adverse outcome pathway and can be considered as a chemical interaction between a chemical toxicant and a biological molecule. Key chemical characteristics can be identified and used to model the chemistry of these MIEs. In this study, we do just this by using chemical substructures to categorize chemicals and 3D quantitative structure-activity relationships (QSARs) based on comparative molecular field analysis (CoMFA) to calculate molecular activity. Models have been constructed across a variety of human biological targets, the glucocorticoid receptor, mu opioid receptor, cyclooxygenase-2 enzyme, human ether-à-go-go related gene channel, and dopamine transporter. These models tend to provide molecular activity estimation well within one log unit and electronic and steric fields that can be visualized to better understand the MIE and biological target of interest. The outputs of these fields can be used to identify key aspects of a chemical's chemistry which can be changed to reduce its ability to activate a given MIE. With this methodology, the quantitative chemical activity can be predicted for a wide variety of MIEs, which can feed into AOP-based chemical risk assessments, and understanding of the chemistry behind the MIE can be gained.

Scaffold-hopping as a strategy to address metabolic liabilities of aromatic compounds

Understanding and minimizing oxidative metabolism of aromatic compounds is a key hurdle in lead optimization. Metabolic processes not only clear compounds from the body, but they can also transform parent compounds into reactive metabolites. One particularly useful strategy when addressing metabolically labile or oxidation-prone structures is scaffold-hopping. Replacement of an aromatic system with a more electron-deficient ring system can often increase robustness towards cytochrome P450-mediated oxidation while conserving the structural requirements of the pharmacophore. The most common example of this substitution strategy, replacement of a phenyl ring with a pyridyl substituent, is prevalent throughout the literature; however scaffold-hopping encompasses a much wider scope of heterocycle replacement. This review will showcase recent examples where different scaffold-hopping approaches were used to reduce metabolic clearance or block the formation of reactive metabolites. Additionally, we will highlight considerations that should be made to garner the most benefit from a scaffold-hopping strategy for lead optimization.

Forging New Scaffolds from Old: Combining Scaffold Hopping and Hierarchical Virtual Screening for Identifying Novel Bcl-2 Inhibitors

Background:

Though virtual screening methods have proven to be potent in various instances, the technique is practically incomplete to quench the need of drug discovery process. Thus, the quest for novel designing approaches and chemotypes for improved efficacy of lead compounds has been intensified and logistic approaches such as scaffold hopping and hierarchical virtual screening methods were evolved. Till now, in all the previous attempts these two approaches were applied separately.

Objective:

In the current work, we made a novel attempt in terms of blending scaffold hopping and hierarchical virtual screening. The prime objective is to assess the hybrid method for its efficacy in identifying active lead molecules for emerging PPI target Bcl-2 (B-cell Lymphoma 2).

Method:

We designed novel scaffolds from the reported cores and screened a set of 8270 compounds using both scaffold hopping and hierarchical virtual screening for Bcl-2 protein. Also, we enumerated the libraries using clustering, PAINS filtering, physicochemical characterization and SAR matching.

Results:

We generated a focused library of compounds towards Bcl-2 interface, screened the 8270 compounds and identified top hits for seven families upon fine filtering with PAINS algorithm, features, SAR mapping, synthetic accessibility and similarity search. Our approach retrieved a set of 50 lead compounds.

Conclusions:

Finding rational approach meeting the needs of drug discovery process for PPI targets is the need of the hour which can be fulfilled by an extended scaffold hopping approach resulting in focused PPI targeting by providing novel leads with better potency.

Synthetic Access to 3-Substituted Pyroglutamic Acids from Tetramate Derivatives of Serine, Threonine, allo-Threonine, and Cysteine

A general route which provides direct access to pyroglutamates from tetramates, making use of Suzuki coupling on an enol mesylate, followed by reduction, is reported. This work permits direct scaffold hopping from tetramate to substituted pyroglutamates. Some compounds in the library showed modest antibacterial activity against Gram-positive bacteria.

Predicting kinase inhibitors using bioactivity matrix derived informer sets

Prediction of compounds that are active against a desired biological target is a common step in drug discovery efforts. Virtual screening methods seek some active-enriched fraction of a library for experimental testing. Where data are too scarce to train supervised learning models for compound prioritization, initial screening must provide the necessary data. Commonly, such an initial library is selected on the basis of chemical diversity by some pseudo-random process (for example, the first few plates of a larger library) or by selecting an entire smaller library. These approaches may not produce a sufficient number or diversity of actives. An alternative approach is to select an informer set of screening compounds on the basis of chemogenomic information from previous testing of compounds against a large number of targets. We compare different ways of using chemogenomic data to choose a small informer set of compounds based on previously measured bioactivity data. We develop this Informer-Based-Ranking (IBR) approach using the Published Kinase Inhibitor Sets (PKIS) as the chemogenomic data to select the informer sets. We test the informer compounds on a target that is not part of the chemogenomic data, then predict the activity of the remaining compounds based on the experimental informer data and the chemogenomic data. Through new chemical screening experiments, we demonstrate the utility of IBR strategies in a prospective test on three kinase targets not included in the PKIS.

In the early stages of drug discovery efforts, computational models are used to predict activity and prioritize compounds for experimental testing. New targets commonly lack the data necessary to build effective models, and the screening needed to generate that experimental data can be costly. We seek to improve the efficiency of the initial screening phase, and of the process of prioritizing compounds for subsequent screening.

We choose a small informer set of compounds based on publicly available prior screening data on distinct targets. We then collect experimental data on these informer compounds and use that data to predict the activity of other compounds in the set for the target of interest. Computational and statistical tools are needed to identify informer compounds and to prioritize other compounds for subsequent phases of screening. We find that selection of informer compounds on the basis of bioactivity data from previous screening efforts is superior to the traditional approach of selection of a chemically diverse subset of compounds. We demonstrate the success of this approach in retrospective tests on the Published Kinase Inhibitor Sets (PKIS) chemogenomic data and in prospective experimental screens against three additional non-human kinase targets.