Scaffold Hopping Research Articles

2H-Thiazolo[4,5-d][1,2,3]triazole: synthesis, functionalization, and application in scaffold-hopping.

This manuscript unveils the synthesis of 2H-thiazolo[4,5-d][1,2,3]triazole (ThTz), an unprecedented [5-5]-fused heteroaromatic system, and established a scalable synthetic procedure for producing large quantities of the ThTz ring bearing a sulfone group on the thiazole ring. The sulfone moiety proves to be a versatile reactive tag, facilitating diverse transformations such as SNAr reactions, metal-catalyzed couplings, and radical-based alkylations. Furthermore, functionalization of the triazole ring highlights the potential of this newly developed heteroaromatic compound as a valuable heteroaryl building block, promoting scaffold hopping strategies in medicinal chemistry.

Synthesis and evaluation of novel thiophene-dhpms designed having anti-breast cancer potential

The global rise of life-threatening diseases, particularly breast cancer, has emerged as a grave public health challenge. Recognizing the distinct structural demands of anti-breast cancer targets, we have synthesized Thiophene-DHPMs analogs by scaffold hopping approach to exhibit versatility having the ability to target proteins targets of cancer and their interactions contingent upon the various substitutions on them. We have harnessed MCRto synthesize novel Thiophene-DHPMs (4a-4d) derivatives introducing an efficient Biginelli protocol approach. All derivatives are characterized through techniques including FTIR and 1H NMR and evaluated by in vitro SRB assay method on MCF-7 cell line, compared against positive control ADR. Molecular docking studies against kinesin spindle protein Eg5 (1Q0B) revealed superior binding interactions and docking scores (> 8 Kcal) compared to the prototype Eg5 inhibitor Monastrol. Compound 4a binds via Hydrogen bond interaction to target Eg5 with ALA-133, PRO-137, TYR-211 In vitro evaluation of results indicates that compound 4a found to be moderately active (GI50 = -4.41) compared to positive control ADR (GI50 = -7.76) against MCF-7 cells. Compound 4a demonstrates significant activity due to the presence of R=C2H5, X= S, and thiophene ring.

Synthesis of Azepino[4,5-b]indole via Ring Expansion of Tetrahydro-β-carbolines Ammonium Ylide.

The formal cyclization strategy was generally used to construct azepino[4,5-b]indole. Herein, we reported a novel and expeditious protocol for the synthesis of quaternary carbon azepino[4,5-b]indole via ring expansion of ammonium ylide, which was formed by the reaction of tetrahydro-β-carbolines with the diazo compound. The easily available substrates, mild reaction conditions, and broad functional tolerance rendered this method a practical strategy that may significantly afford an efficient method of scaffold hopping in drug discovery.

Performance and Analysis of the Alchemical Transfer Method for Binding-Free-Energy Predictions of Diverse Ligands.

The Alchemical Transfer Method (ATM) is herein validated against the relative binding-free energies (RBFEs) of a diverse set of protein-ligand complexes. We employed a streamlined setup workflow, a bespoke force field, and AToM-OpenMM software to compute the RBFEs of the benchmark set prepared by Schindler and collaborators at Merck KGaA. This benchmark set includes examples of standard small R-group ligand modifications as well as more challenging scenarios, such as large R-group changes, scaffold hopping, formal charge changes, and charge-shifting transformations. The novel coordinate perturbation scheme and a dual-topology approach of ATM address some of the challenges of single-topology alchemical RBFE methods. Specifically, ATM eliminates the need for splitting electrostatic and Lennard-Jones interactions, atom mapping, defining ligand regions, and postcorrections for charge-changing perturbations. Thus, ATM is simpler and more broadly applicable than conventional alchemical methods, especially for scaffold-hopping and charge-changing transformations. Here, we performed well over 500 RBFE calculations for eight protein targets and found that ATM achieves accuracy comparable to that of existing state-of-the-art methods, albeit with larger statistical fluctuations. We discuss insights into the specific strengths and weaknesses of the ATM method that will inform future deployments. This study confirms that ATM can be applied as a production tool for RBFE predictions across a wide range of perturbation types within a unified, open-source framework.

Can Pretrained Models Really Learn Better Molecular Representations for AI-Aided Drug Discovery?

Self-supervised pretrained models are gaining increasingly more popularity in AI-aided drug discovery, leading to more and more pretrained models with the promise that they can extract better feature representations for molecules. Yet, the quality of learned representations has not been fully explored. In this work, inspired by the two phenomena of Activity Cliffs (ACs) and Scaffold Hopping (SH) in traditional Quantitative Structure-Activity Relationship analysis, we propose a method named Representation-Property Relationship Analysis (RePRA) to evaluate the quality of the representations extracted by the pretrained model and visualize the relationship between the representations and properties. The concepts of ACs and SH are generalized from the structure-activity context to the representation-property context, and the underlying principles of RePRA are analyzed theoretically. Two scores are designed to measure the generalized ACs and SH detected by RePRA, and therefore, the quality of representations can be evaluated. In experiments, representations of molecules from 10 target tasks generated by 7 pretrained models are analyzed. The results indicate that the state-of-the-art pretrained models can overcome some shortcomings of canonical Extended-Connectivity FingerPrints, while the correlation between the basis of the representation space and specific molecular substructures are not explicit. Thus, some representations could be even worse than the canonical fingerprints. Our method enables researchers to evaluate the quality of molecular representations generated by their proposed self-supervised pretrained models. And our findings can guide the community to develop better pretraining techniques to regularize the occurrence of ACs and SH.

Scaffold Hopping and Structural Modification of NSC 663284: Discovery of Potent (Non)Halogenated Aminobenzoquinones.

The development of new anticancer drugs is still ongoing as a solution to the unsatisfactory results obtained by chemotherapy patients. Our previous studies on natural product-based anticancer agents led us to synthesize a new series of Plastoquinone (PQ) analogs and study their anticancer effects. Four members of PQ analogs (PQ1-4) were designed based on the scaffold hopping strategy; the design was later completed with structural modification. The obtained PQ analogs were synthesized and biologically evaluated against different cancer genotypes according to NCI-60 screening in vitro. According to the NCI results, bromo and iodo-substituted PQ analogs (PQ2 and PQ3) showed remarkable anticancer activities with a wide-spectrum profile. Among the two selected analogs (PQ2 and PQ3), PQ2 showed promising anticancer activity, in particular against leukemia cell lines, at both single- and five-dose NCI screenings. This compound was also detected by MTT assay to reveal significant selectivity between Jurkat cells and PBMC (healthy) compared to imatinib. Further in silico studies indicated that PQ2 was able to occupy the ATP-binding cleft of Abl TK, one of the main targets of leukemia, through key interactions similar to dasatinib and imatinib. PQ2 is also bound to the minor groove of the double helix of DNA. Based on computational pharmacokinetic studies, PQ2 possessed a remarkable drug-like profile, making it a potential anti-leukemia drug candidate for future studies.

Discovery of a 1,6-naphthyridin-4-one-based AXL inhibitor with improved pharmacokinetics and enhanced in vivo antitumor efficacy

The receptor tyrosine kinase AXL has emerged as an attractive target in anticancer drug discovery. Herein, we described the discovery of a new series of 1,6-naphthyridin-4-one derivatives as potent AXL inhibitors. Starting from a low in vivo potency compound 9 which was previously reported by our group, we utilized structure-based drug design and scaffold hopping strategies to discover potent AXL inhibitors. The privileged compound 13c was a highly potent and orally bioavailable AXL inhibitor with an IC50 value of 3.2 ± 0.3 nM. Compound 13c exhibited significantly improved in vivo antitumor efficacy in AXL-driven tumor xenograft mice, causing tumor regression at well-tolerated dose, and demonstrated favorable pharmacokinetic properties (MRT = 16.5 h, AUC0-∞ = 59,815 ng h/mL) in Sprague-Dawley rats. These results suggest that 13c is a promising therapeutic candidate for AXL-targeting cancer treatment.

Design, synthesis, and lead optimization of piperazinyl-pyrimidine analogues as potent small molecules targeting the viral capping machinery of Chikungunya virus

The worldwide re-emerge of the Chikungunya virus (CHIKV), the high morbidity associated with it, and the lack of an available vaccine or antiviral treatment make the development of a potent CHIKV-inhibitor highly desirable. Therefore, an extensive lead optimization was performed based on the previously reported CHVB compound 1b and the reported synthesis route was optimized — improving the overall yield in remarkably shorter synthesis and work-up time. Hundred analogues were designed, synthesized, and investigated for their antiviral activity, physiochemistry, and toxicological profile. An extensive structure-activity relationship study (SAR) was performed, which focused mainly on the combination of scaffold changes and revealed the key chemical features for potent anti-CHIKV inhibition. Further, a thorough ADMET investigation of the compounds was carried out: the compounds were screened for their aqueous solubility, lipophilicity, their toxicity in CaCo-2 cells, and possible hERG channel interactions. Additionally, 55 analogues were assessed for their metabolic stability in human liver microsomes (HLMs), leading to a structure-metabolism relationship study (SMR). The compounds showed an excellent safety profile, favourable physicochemical characteristics, and the required metabolic stability. A cross-resistance study confirmed the viral capping machinery (nsP1) to be the viral target of these compounds. This study identified 31b and 34 as potent, safe, and stable lead compounds for further development as selective CHIKV inhibitors. Finally, the collected insight led to a successful scaffold hop (64b) for future antiviral research studies.

Tree-Invent: A Novel Multipurpose Molecular Generative Model Constrained with a Topological Tree.

De novo molecular design plays an important role in drug discovery. Here, a novel generative model, Tree-Invent, was proposed to integrate topological constraints in the generation of a molecular graph. In this model, a molecular graph is represented as a topological tree in which a ring system, a nonring atom, and a chemical bond are regarded as the ring node, single node, and edge, respectively. The molecule generation is driven by three independent submodels for carrying out operations of node addition, ring generation, and node connection. One unique feature of the generative model is that the topological tree structure can be specified as a constraint for structure generation, which provides more precise control of structure generation. Combined with reinforcement learning, the Tree-Invent model could efficiently explore targeted chemical space. Moreover, the Tree-Invent model is flexible enough to be used in versatile molecule design settings such as scaffold decoration, scaffold hopping, and linker generation.

Discovery of Novel NLRP3 Inflammasome Inhibitors Composed of an Oxazole Scaffold Bearing an Acylsulfamide.

The NLRP3 inflammasome plays an important role in the defense mechanism of the innate immune system and has recently attracted much attention as a drug target for various inflammatory disorders. Among the strategies for generating the novel chemotype in current drug discovery, scaffold hopping and bioisosteric replacement are known to be attractive approaches. As the results of our medicinal chemistry campaign, which involved exploration of core motifs using a ring closing approach, a five-membered oxazole-based scaffold was identified, and subsequent implementation of bioisosteric replacement led to discovery of a novel chemical class of NLRP3 inflammasome inhibitor bearing the acylsulfamide group. Further optimization of aniline and sulfamide moieties to improve potency in human whole blood assay led to the identification of the orally bioactive compound 32 in the LPS challenge model. Furthermore, compound 32 attenuated kidney injury in adriamycin-induced glomerulonephritis in mice. These investigations indicated that the NLRP3 inhibitor could be a potential therapeutic agent for glomerulonephritis.

Discovery of inhibitors of protein tyrosine phosphatase 1B contained in a natural products library from Mexican medicinal plants and fungi using a combination of enzymatic and in silico methods*.

This work aimed to discover protein tyrosine phosphatase 1B (PTP1B) inhibitors from a small molecule library of natural products (NPs) derived from selected Mexican medicinal plants and fungi to find new hits for developing antidiabetic drugs. The products showing similar IC50 values to ursolic acid (UA) (positive control, IC50 = 26.5) were considered hits. These compounds were canophyllol (1), 5-O-(β-D-glucopyranosyl)-7-methoxy-3',4'-dihydroxy-4-phenylcoumarin (2), 3,4-dimethoxy-2,5-phenanthrenediol (3), masticadienonic acid (4), 4',5,6-trihydroxy-3',7-dimethoxyflavone (5), E/Z vermelhotin (6), tajixanthone hydrate (7), quercetin-3-O-(6″-benzoyl)-β-D-galactoside (8), lichexanthone (9), melianodiol (10), and confusarin (11). According to the double-reciprocal plots, 1 was a non-competitive inhibitor, 3 a mixed-type, and 6 competitive. The chemical space analysis of the hits (IC50 < 100μM) and compounds possessing activity (IC50 in the range of 100-1,000μM) with the BIOFACQUIM library indicated that the active molecules are chemically diverse, covering most of the known Mexican NPs' chemical space. Finally, a structure-activity similarity (SAS) map was built using the Tanimoto similarity index and PTP1B absolute inhibitory activity, which allows the identification of seven scaffold hops, namely, compounds 3, 5, 6, 7, 8, 9, and 11. Canophyllol (1), on the other hand, is a true analog of UA since it is an SAR continuous zone of the SAS map.

DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning

Established molecular machine learning models process individual molecules as inputs to predict their biological, chemical, or physical properties. However, such algorithms require large datasets and have not been optimized to predict property differences between molecules, limiting their ability to learn from smaller datasets and to directly compare the anticipated properties of two molecules. Many drug and material development tasks would benefit from an algorithm that can directly compare two molecules to guide molecular optimization and prioritization, especially for tasks with limited available data. Here, we develop DeepDelta, a pairwise deep learning approach that processes two molecules simultaneously and learns to predict property differences between two molecules from small datasets. On 10 ADMET benchmark tasks, our DeepDelta approach significantly outperforms two established molecular machine learning algorithms, the directed message passing neural network (D-MPNN) ChemProp and Random Forest using radial fingerprints, for 70% of benchmarks in terms of Pearson’s r, 60% of benchmarks in terms of mean absolute error (MAE), and all external test sets for both Pearson’s r and MAE. We further analyze our performance and find that DeepDelta is particularly outperforming established approaches at predicting large differences in molecular properties and can perform scaffold hopping. Furthermore, we derive mathematically fundamental computational tests of our models based on mathematical invariants and show that compliance to these tests correlates with overall model performance — providing an innovative, unsupervised, and easily computable measure of expected model performance and applicability. Taken together, DeepDelta provides an accurate approach to predict molecular property differences by directly training on molecular pairs and their property differences to further support fidelity and transparency in molecular optimization for drug development and the chemical sciences.

Discovery of novel potent PI3K/mTOR dual-target inhibitors based on scaffold hopping: Design, synthesis, and antiproliferative activity.

The PI3K/AKT/mTOR pathway is one of the most common dysregulated signaling cascade responses in human cancers, playing a crucial role in cell proliferation and angiogenesis. Therefore, the development of anticancer drugs targeting the PI3K and mTOR pathways has become a research hotspot in cancer treatment. In this study, the PI3K selective inhibitor GDC-0941 was selected as a lead compound, and 28 thiophenyl-triazine derivatives with aromatic urea structures were synthesized based on scaffold hopping, serving as a novel class of PI3K/mTOR dual inhibitors. The most promising compound Y-2 was obtained through antiproliferative activity evaluation, kinase inhibition, and toxicity assays. The results showed that Y-2 demonstrated potential inhibitory effects on both PI3K kinase and mTOR kinase, with IC50 values of 171.4 and 10.2 nM, respectively. The inhibitory effect of Y-2 on mTOR kinase was 52 times greater than that of the positive drug GDC-0941. Subsequently, the antitumor activity of Y-2 was verified through pharmacological experiments such as AO staining, cell apoptosis, scratch assays, and cell colony formation. The antitumor mechanism of Y-2 was further investigated through JC-1 experiments, real-time quantitative PCR, and Western blot analysis. Based on the above experiments, Y-2 can be identified as a potent PI3K/mTOR dual inhibitor for cancer treatment.

Design, synthesis and anticancer activity evaluation of 4-(3-1H-indazolyl)amino quinazoline derivatives as PAK4 inhibitors

A novel series of 4-(3-1H-indazolyl)amino quinazoline derivatives were developed as PAK4 inhibitors based on a scaffold hopping strategy. Compounds 27e, 27g, 27i and 27j were found to exhibit potent inhibitory activity against PAK4 (IC50 = 10, 13, 11 and 9 nM, respectively). Subsequent cellular assay demonstrated that compound 27e possessed the strongest antiproliferative activity against A549 cells with an IC50 value of 0.61 μM, a little bit better than PF-3758309. Further anticancer mechanistic investigation revealed that compound 27e significantly induced apoptosis of A549 cells in a concentration-dependent manner and blocked the cell cycle at phase G0/G1. A docking model between compound 27e and PAK4 was proposed to elucidate its possible binding modes. As a promising PAK4 inhibitor, compound 27e may serve as a candidate for the development of novel PAK4-targeted anticancer drug.

How Ligands Interact with the Kinase Hinge.

ATP-competitive kinase inhibitors form hydrogen bond interactions with the kinase hinge region at the adenine binding site. Thus, it is crucial to explore hinge-ligand recognition as part of a rational drug design strategy. Here, harnessing known ligand-bound kinase structures and experimental assay resources, we first created a kinase structure-assay database (KSAD) containing 2705 nM ligand-bound kinase complexes. Then, using KSAD, we systematically investigate hinge-ligand binding patterns using interaction fingerprints, thereby delineating 15 different hydrogen-bond interaction modes. We believe these results will be valuable for de novo drug design and/or scaffold hopping of kinase-targeted drugs.

ScaffoldGVAE: scaffold generation and hopping of drug molecules via a variational autoencoder based on multi-view graph neural networks

In recent years, drug design has been revolutionized by the application of deep learning techniques, and molecule generation is a crucial aspect of this transformation. However, most of the current deep learning approaches do not explicitly consider and apply scaffold hopping strategy when performing molecular generation. In this work, we propose ScaffoldGVAE, a variational autoencoder based on multi-view graph neural networks, for scaffold generation and scaffold hopping of drug molecules. The model integrates several important components, such as node-central and edge-central message passing, side-chain embedding, and Gaussian mixture distribution of scaffolds. To assess the efficacy of our model, we conduct a comprehensive evaluation and comparison with baseline models based on seven general generative model evaluation metrics and four scaffold hopping generative model evaluation metrics. The results demonstrate that ScaffoldGVAE can explore the unseen chemical space and generate novel molecules distinct from known compounds. Especially, the scaffold hopped molecules generated by our model are validated by the evaluation of GraphDTA, LeDock, and MM/GBSA. The case study of generating inhibitors of LRRK2 for the treatment of PD further demonstrates the effectiveness of ScaffoldGVAE in generating novel compounds through scaffold hopping. This novel approach can also be applied to other protein targets of various diseases, thereby contributing to the future development of new drugs. Source codes and data are available at https://github.com/ecust-hc/ScaffoldGVAE.

Antifungal Function Oriented Scaffold Hopping for the Discovery of Oxazolyl-oxazoline as a Novel Model against Fusarium graminearum.

Discovery of novel structural models is extremely important in agrochemical innovation. Scaffold hopping was conducted, and 16 kinds of novel models were synthesized and biologically evaluated. Oxazolyl-oxazoline 25 showed a promising in vitro potential against Fusarium graminearum with EC50 value of 18.25 μM, which was 2.4 times more potent than that of carbendazim (EC50 = 43.06 μM). The antifungal structure-activity relationship (SAR) revealed that compound 25am had the most promising antifungal activity against F. graminearum, with an EC50 value of 13.46 μM, which was 3.2 more potent than that of carbendazim. Different from carbendazim, the candidate 25am could form five hydrogen bonds with the amino acid residues in β-tubulin in the molecular docking and could effectively inhibit the carbendazim-resistant F. graminearum strain. Scanning electron microscopy (SEM) revealed that compound 25am induced the mycelia of F. graminearum slight collapse. This work suggests that compound 25am should be prioritized for further evaluation for new antifungal agents.

Strategies in the Design and Development of Non-Nucleoside Reverse Transcriptase Inhibitors (NNRTIs).

AIDS (acquired immunodeficiency syndrome) is a potentially life-threatening infectious disease caused by human immunodeficiency virus (HIV). To date, thousands of people have lost their lives annually due to HIV infection, and it continues to be a big public health issue globally. Since the discovery of the first drug, Zidovudine (AZT), a nucleoside reverse transcriptase inhibitor (NRTI), to date, 30 drugs have been approved by the FDA, primarily targeting reverse transcriptase, integrase, and/or protease enzymes. The majority of these drugs target the catalytic and allosteric sites of the HIV enzyme reverse transcriptase. Compared to the NRTI family of drugs, the diverse chemical class of non-nucleoside reverse transcriptase inhibitors (NNRTIs) has special anti-HIV activity with high specificity and low toxicity. However, current clinical usage of NRTI and NNRTI drugs has limited therapeutic value due to their adverse drug reactions and the emergence of multidrug-resistant (MDR) strains. To overcome drug resistance and efficacy issues, combination therapy is widely prescribed for HIV patients. Combination antiretroviral therapy (cART) includes more than one antiretroviral agent targeting two or more enzymes in the life cycle of the virus. Medicinal chemistry researchers apply different optimization strategies including structure- and fragment-based drug design, prodrug approach, scaffold hopping, molecular/fragment hybridization, bioisosterism, high-throughput screening, covalent-binding, targeting highly hydrophobic channel, targeting dual site, and multi-target-directed ligand to identify and develop novel NNRTIs with high antiviral activity against wild-type (WT) and mutant strains. The formulation experts design various delivery systems with single or combination therapies and long-acting regimens of NNRTIs to improve pharmacokinetic profiles and provide sustained therapeutic effects.

Structure-Based Discovery of New Succinate Dehydrogenase Inhibitors via Scaffold Hopping Strategy.

Scaffold hopping strategy has become one of the most successful methods in the process of molecular design. Seeking to develop novel succinate dehydrogenase inhibitors (SDHIs), we employed a scaffold hopping strategy to design compounds featuring geminate dichloralkenes (gem-dichloralkenes) fragment. After stepwise modifications, a series of N-cyclopropyl-dichloralkenes-pyrazole-carboxamide derivatives was synthesized. Among them, compounds G28 (IC50 = 26.00 nM) and G40 (IC50 = 27.00 nM) were identified as the best inhibitory activity against porcine SDH, with IC50 values reaching the nanomolar range, outperforming the lead compound pydiflumetofen. Additionally, the greenhouse assay indicated that compounds G37 (EC90 = 0.031 mg/L) and G34 (EC90 = 1.67 mg/L) displayed extremely high activities against wheat powdery mildew (WPM) and cucumber powdery mildew (CPM), respectively. Computational results further revealed that the gem-dichloralkene fragment and fluorine substituted pyrazole form an extra hydrophobic interaction and dipolar-dipolar interaction with SDH. In summary, our study provides a novel gem-dichloralkene scaffold with outstanding fungicidal properties, obtained through scaffold hopping, that holds great potential for future research on PM control.

Design, molecular modelling and synthesis of novel benzothiazole derivatives as BCL-2 inhibitors

Apoptosis plays a crucial role in cancer pathogenesis and drug resistance. BCL-2 family of enzymes is considered as one of the key enzymes which is involved in apoptosis. When there is disruption in the balance between anti-apoptotic and pro-apoptotic members of the BCL-2 family apoptosis is dysregulated in the affected cells. Herein, 33 novel benzothiazole-based molecules 7a-i, 8a-f, 9a-b, 12a-e, 13a-d, 14a,b, and 17a-j were designed, synthesized and tested for their BCL-2 inhibitory activity. Scaffold hopping strategy was applied in designing of the target compounds. Compounds 13c and 13d showed the highest activity with IC50 values equal to 0.471 and 0.363 µM, respectively. Molecular docking studies of the synthesized compounds showed comparable binding interactions with the lead compound. Structure activity relationship study was performed to show the effects of structural modifications on the inhibitory activities on BCL-2.