Discovery of novel and selective farnesoid X receptor antagonists through structure-based virtual screening, preliminary structure-activity relationship study, and biological evaluation.

Farnesoid X receptor (FXR) is a bile acids receptor and plays a crucial role in regulating bile acids, lipids, and glucose metabolism. Previous research suggests that inhibiting FXR activation can be beneficial in reducing cholesterol and low-density lipoprotein cholesterol (LDL-C) levels, offering potential treatment options for metabolic syndrome with lipid disorders. Herein, we report p-acetylaminobenzene sulfonate derivatives as a novel scaffold of FXR antagonists by multistage screening. Among these derivatives, compound F44-A13 exhibited a half-maximal inhibitory concentration of 1.1 µM. Furthermore, compound F44-A13 demonstrated effective inhibition of FXR activation in cellular assays and exhibited high selectivity over eleven other nuclear receptors. Besides, compound F44-A13 significantly suppressed the regulation of FXR target genes Shp, Besp, and Cyp7a1, while reducing cholesterol levels in human hepatoma HepG2 cells. Pharmacological studies conducted on C57BL/6 mice further confirmed that compound F44-A13 had beneficial effects in reducing cholesterol, triglycerides, and LDL-C levels. These findings highlight that F44-A13 is a highly selective FXR antagonist that might serve as a useful molecule for further FXR studies as well as the development of FXR antagonists for the potential treatment of metabolic diseases with lipid disorders.

Exploring Chemical Reaction Space with Machine Learning Models: Representation and Feature Perspective.

Chemical reactions serve as foundational building blocks for organic chemistry and drug design. In the era of large AI models, data-driven approaches have emerged to innovate the design of novel reactions, optimize existing ones for higher yields, and discover new pathways for synthesizing chemical structures comprehensively. To effectively address these challenges with machine learning models, it is imperative to derive robust and informative representations or engage in feature engineering using extensive data sets of reactions. This work aims to provide a comprehensive review of established reaction featurization approaches, offering insights into the selection of representations and the design of features for a wide array of tasks. The advantages and limitations of employing SMILES, molecular fingerprints, molecular graphs, and physics-based properties are meticulously elaborated. Solutions to bridge the gap between different representations will also be critically evaluated. Additionally, we introduce a new frontier in chemical reaction pretraining, holding promise as an innovative yet unexplored avenue.

Allosteric Activation of α7 Nicotinic Acetylcholine Receptors by Novel 2-Arylamino-thiazole-5-carboxylic Acid Amide Derivatives for the Improvement of Cognitive Deficits in Mice.

Enhancing α7 nAChR function serves as a therapeutic strategy for cognitive disorders. Here, we report the synthesis and evaluation of 2-arylamino-thiazole-5-carboxylic acid amide derivatives 6-9 that as positive allosteric modulators (PAMs) activate human α7 nAChR current expressed in Xenopus ooctyes. Among the 4-amino derivatives, a representative atypical type I PAM 6p exhibits potent activation of α7 current with an EC50 of 1.3 µM and the maximum activation effect on the current over 48-fold in the presence of acetylcholine (100 µM). The structure-activity relationship (SAR) analysis reveals that the 4-amino group is crucial for the allosteric activation of α7 currents by compound 6p as the substitution of 4-methyl group results in its conversion to compound 7b (EC50 = 2.1 µM; max effect: 58-fold) characterized as a typical type I PAM. Furthermore, both 6p and 7b are able to rescue auditory gating deficits in mouse schizophrenia-like model of acoustic startle prepulse inhibition.

Deep reinforcement learning enables better bias control in benchmark for virtual screening.

Virtual screening (VS) has been incorporated into the paradigm of modern drug discovery. This field is now undergoing a new wave of revolution driven by artificial intelligence and more specifically, machine learning (ML). In terms of those out-of-the-box datasets for model training or benchmarking, their data volume and applicability domain are limited. They are suffering from the biases constantly reported in the ML application. To address these issues, we present a novel benchmark named MUBDsyn. The utilization of synthetic decoys (i.e., presumed inactives) is the main feature of MUBDsyn, where deep reinforcement learning was leveraged for bias control during decoy generation. Then, we carried out extensive validations on this new benchmark. First, we confirmed that MUBDsyn was superior to the classical benchmarks in control of domain bias, artificial enrichment bias and analogue bias. Moreover, we found that the assessment of ML models based on MUBDsyn was less biased as revealed by the analysis of asymmetric validation embedding bias. In addition, MUBDsyn showed better setting of benchmarking challenge for deep learning models compared with NRLiSt-BDB. Overall, we have proven that MUBDsyn is the close-to-ideal benchmark for VS. The computational tool is publicly available for the easy extension of MUBDsyn.

Ancient chicken remains reveal the origins of virulence in Marek's disease virus.

The pronounced growth in livestock populations since the 1950s has altered the epidemiological and evolutionary trajectory of their associated pathogens. For example, Marek's disease virus (MDV), which causes lymphoid tumors in chickens, has experienced a marked increase in virulence over the past century. Today, MDV infections kill >90% of unvaccinated birds, and controlling it costs more than US$1 billion annually. By sequencing MDV genomes derived from archeological chickens, we demonstrate that it has been circulating for at least 1000 years. We functionally tested the Meq oncogene, one of 49 viral genes positively selected in modern strains, demonstrating that ancient MDV was likely incapable of driving tumor formation. Our results demonstrate the power of ancient DNA approaches to trace the molecular basis of virulence in economically relevant pathogens.

Carbazole and tetrahydro-carboline derivatives as dopamine D3 receptor antagonists with the multiple antipsychotic-like properties.

Dopamine D3 receptor (D3R) is implicated in multiple psychotic symptoms. Increasing the D3R selectivity over dopamine D2 receptor (D2R) would facilitate the antipsychotic treatments. Herein, novel carbazole and tetrahydro-carboline derivatives were reported as D3R selective ligands. Through a structure-based virtual screen, ZLG-25 (D3R Ki = 685 nmol/L; D2R Ki > 10,000 nmol/L) was identified as a novel D3R selective bitopic ligand with a carbazole scaffold. Scaffolds hopping led to the discovery of novel D3R-selective analogs with tetrahydro-ß-carboline or tetrahydro-γ-carboline core. Further functional studies showed that most derivatives acted as hD3R-selective antagonists. Several lead compounds could dose-dependently inhibit the MK-801-induced hyperactivity. Additional investigation revealed that 23j and 36b could decrease the apomorphine-induced climbing without cataleptic reaction. Furthermore, 36b demonstrated unusual antidepressant-like activity in the forced swimming tests and the tail suspension tests, and alleviated the MK-801-induced disruption of novel object recognition in mice. Additionally, preliminary studies confirmed the favorable PK/PD profiles, no weight gain and limited serum prolactin levels in mice. These results revealed that 36b provided potential opportunities to new antipsychotic drugs with the multiple antipsychotic-like properties.

Discovery of NLRP3 inhibitors using machine learning: Identification of a hit compound to treat NLRP3 activation-driven diseases.

NLRP3 is vital in developing many human diseases as one of the most critical inflammasomes. Developing related inhibitors has been instrumental in advancing the development of therapies for associated diseases. To date, there are no NLRP3 inhibitors on the market. This study identified a series of NLRP3 inhibitors using the self-developed machine learning model. Among them, CSC-6 was validated as the hit molecule with optimal activity and significantly inhibited IL-1ß secreted by PMA-THP-1 cells (IC50 = 2.3 ± 0.38 µM). The results show that CSC-6 specifically binds NLRP3 and inhibits NLRP3 activation by blocking ASC oligomerization during NLRP3 assembly. In vivo experiments have demonstrated that CSC-6 effectively reduces the symptoms of NLRP3 overactivation-mediated sepsis and Gout in mouse models. Importantly, CSC-6 has lower cytotoxicity and exhibits better stability in human-derived liver microsomes, which is more favorable for the drug to maintain its efficacy in vivo for longer. The discovery of CSC-6 may contribute to the design and discovery of related NLRP3 inhibitors.

Identification of a small chemical as a lysosomal calcium mobilizer and characterization of its ability to inhibit autophagy and viral infection.

We previously identified glyceraldehyde 3-phosphate dehydrogenase (GAPDH) as one of the cyclic adenosine diphosphoribose (cADPR)'s binding proteins and found that GAPDH participates in cADPR-mediated Ca2+ release from endoplasmic reticulum via ryanodine receptors (RyRs). Here, we aimed to chemically synthesise and pharmacologically characterise novel cADPR analogues. Based on the simulated cADPR-GAPDH complex structure, we performed the structure-based drug screening, identified several small chemicals with high docking scores to cADPR's binding pocket in GAPDH and showed that two of these compounds, C244 and C346, are potential cADPR antagonists. We further synthesised several analogues of C346 and found that its analogue, G42, also mobilised Ca2+ release from lysosomes. G42 alkalised lysosomal pH and inhibited autophagosome-lysosome fusion. Moreover, G42 markedly inhibited Zika virus (ZIKV, a flavivirus) or murine hepatitis virus (MHV, a ß-coronavirus) infections of host cells. These results suggest that G42 inhibits virus infection, likely by triggering lysosomal Ca2+ mobilisation and inhibiting autophagy.

NSD3: Advances in cancer therapeutic potential and inhibitors research.

Nuclear receptor-binding SET domain 3, otherwise known as NSD3, is a member of the group of lysine methyltransferases and is involved in a variety of cellular processes, including transcriptional regulation, DNA damage repair, non-histone related functions and several others. NSD3 gene is mutated or loss of function in a variety of cancers, including breast, lung, pancreatic, and osteosarcoma. These mutations produce dysfunction of the corresponding tumor tissue proteins, leading to tumorigenesis, progression, chemoresistance, and unfavorable prognosis, which suggests that the development of NSD3 probe molecules is important for understanding the specific role of NSD3 in disease and drug discovery. In recent years, NSD3 has been increasingly reported, demonstrating that this target is a very hot epigenetic target. However, the number of NSD3 inhibitors available for cancer therapy is limited and none of the drugs that target NSD3 are currently available on the market. In addition, there are very few reviews describing NSD3. Within this review, we highlight the role of NSD3 in tumorigenesis and the development of NSD3 targeted small-molecule inhibitors over the last decade. We hope that this publication can serve as a guide for the development of potential drug candidates for various diseases in the field of epigenetics, especially for the NSD3 target.

Machine learning- and structure-based discovery of a novel chemotype as FXR agonists for potential treatment of nonalcoholic fatty liver disease.

Farnesoid X receptor (FXR) is a promising target for drug discovery against nonalcoholic fatty liver disease (NAFLD). However, no FXR agonist has been approved for NAFLD so far. The R & D of FXR agonists are somewhat hindered by the lack of effective and safe chemotypes. To this end, we developed a multi-stage computational workflow to screen the Specs and ChemDiv chemical library for FXR agonists, which consisted of machine learning (ML)-based classifiers, shape-based and electrostatic-based models, a FRED-based molecular docking protocol, an ADMET prediction protocol and substructure search. As a result, we identified a novel chemotype that has never been reported before, with compound XJ02862 (ChemDiv ID: Y020-6413) as the representative. By designing an asymmetric synthesis strategy, we were able to prepare four isomers of compound XJ02862. Interestingly, one of the isomers, 2-((S)-1-((2S,4R)-2-methyl-4-(phenylamino)-3,4-dihydroquinolin-1(2H)-yl)-1-oxopropan-2-yl)hexahydro-1H-isoindole-1,3(2H)-dione (XJ02862-S2), showed potent FXR agonistic activity in HEK293T cells. The molecular docking, molecular dynamics simulations and site-directed mutagenesis suggested the hydrogen bond between compound XJ02862-S2 and HIS294 of FXR is essential for ligand binding. We further demonstrated that compound XJ02862-S2 had no agonistic effect on TGR5. Further biological experiments have shown that compound XJ02862-S2 could ameliorate hypercholesterolemia, hepatic steatosis, hyperglycemia, insulin resistance (IR) in high-fat-diet induced obese (DIO) mice. In term of molecular mechanism, compound XJ02862-S2 regulates the expression of FXR downstream genes involved in lipogenesis, cholesterol transport and bile acid biosynthesis and transport. Taken together, we have discovered a novel chemotype as potent FXR agonists for NAFLD by computational modeling, chemical synthesis and biological evaluation.

Discovery of ML210-Based glutathione peroxidase 4 (GPX4) degrader inducing ferroptosis of human cancer cells.

Ferroptosis is an iron-dependent cell death caused by the accumulation of lipid peroxidation. The glutathione peroxidase 4 (GPX4) is an antioxidative enzyme and a major regulator of ferroptosis. Targeting GPX4 has become a promising strategy for cancer therapy. Here in this article, we designed and synthesized a series of GPX4 degraders using ML210 as a warhead. DC-2 among them has been found to have the best degradation activity with the DC50 value of 0.03 µM in HT1080 cells. It also showed an obvious cell growth inhibition effect with the IC50 value of 0.1 µM in HT1080 cells. Mechanism research showed that DC-2 induced GPX4 degradation via the ubiquitin-proteasome pathway and autophagy-lysosome pathway. GPX4 degradation induced by DC-2 could result in the accumulation of ROS and subsequent ferroptosis. The pharmacodynamics study showed that DC-2 could reduce the GPX4 level in HT1080 tumor tissue in mice and has a good safety profile. Above all, a potent and safe compound DC-2 has been found to induce GPX4 degradation and subsequent ferroptosis. This study may lay the foundation for a highly efficient and safe drug with a new mechanism for cancer therapy.

Structure-activity relationship study of RSL3-based GPX4 degraders and its potential noncovalent optimization.

Ferroptosis is an iron-dependent, non-apoptotic form of cell death involving in various disease processes. Mechanistically, glutathione peroxidase 4 (GPX4) which belongs to the redox enzyme can convert lipid hydroperoxides into innocuous lipid alcohol to protect cells from ferroptosis. Therefore, targeting manipulation of GPX4 may represent a promising strategy for regulating cell redox homeostasis and ferroptosis. In this work, we designed, synthesized and evaluated a series of RSL3-based GPX4 degraders using PROTAC strategy. The structure-activity relationship of these compounds with different E3 ligase ligands, linker lengths and chemical compositions was systematically studied. Compound R17 with carbon chain linker and lenalidomide E3 ligand was selected as the most potent GPX4 degrader for degrading GPX4 protein in nanomolar level either in wild tumor cells or in drug-resistant tumor cells. We also optimized the POI ligand of R17 with chloracetylamine replaced to propionamide to construct noncovalent GPX4 degrader NC-R17. Such noncovalent modification led to a moderate GPX4 degradation activity and represents a promising strategy for the development of noncovalent GPX4 PROTACs. In general, we screened a set of GPX4 degraders to give the compound R17 with excellent protein degradation activity, and further optimization gave the noncovalent degrader NC-R17 with moderate efficacy. These results lay a firm foundation for the discovery of novel anti-tumor drugs targeting GPX4 and offer the proof of concept for the design of noncovalent GPX4 PROTACs.

The structure-based optimization of 3-substituted indolin-2-one derivatives as potent and isoform-selective c-Jun N-terminal kinase 3 (JNK3) inhibitors and biological evaluation.

An indolin-2-(4-thiazolidinone) scaffold was previously shown to be a novel chemotype for JNK3 inhibition. However, more in vivo applications were limited due to the unconfirmed configuration and poor physicochemical properties. Here, the indolin-2-(4-thiazolidinone) scaffold validated the absolute configuration; substituents on the scaffold were optimized. Extensive structure activity relationship (SAR) studies were performed using kinase activity assays, thus leading to potent and highly selective JNK3 inhibitors with neuroprotective activity and good oral bioavailability. One lead compound, A53, was a potent and selective JNK3 inhibitor (IC50 = 78 nM) that had significant inhibition (>80% at 1 µM) to only JNK3 in a 398-kinase panel. A53 had low inhibition against JNK3 and high stability (t1/2(α) = 0.98 h, t1/2(ß) = 2.74 h) during oral administration. A modeling study of A53 in human JNK3 showed that the indolin-2-(4-thiazolidinone)-based JNK3 inhibitor with a 5-position-substituted hydrophilic group offered improved kinase inhibition.

Discovery of Coumarin-Based MEK1/2 PROTAC Effective in Human Cancer Cells.

The RAF/MEK/ERK pathway is a crucial signal path which is closely associated with the proliferation, differentiation, and apoptosis of tumors. MEK1/2 is a key kinase target in the pathway, with ERK1/2 acting as the main substrate of it. Despite the rapid development of MEK1/2 inhibitors, acquired resistance still happens and remains a significant problem. Most of the inhibitors possess a similar diarylamine scaffold. Here we designed and synthesized a series of MEK1/2 degraders based on a coumarin derivative which was a potent non-diarylamine allosteric MEK1/2 inhibitor. P6b among them showed the most potent degradation effect, with DC50 values of 0.3 µM and 0.2 µM in MEK1 and MEK2 degradation, respectively. An antiproliferation assay showed that it more significantly inhibits the growth of A375 cells (IC50= 2.8 µM) compared to A549 cells (IC50 = 27.3 µM). To sum up, we discovered P6b with a non-diarylamine scaffold for the first time as a potent MEK PROTAC effective in human cancer cells.

Dual degradation mechanism of GPX4 degrader in induction of ferroptosis exerting anti-resistant tumor effect.

Targeting Glutathione peroxidase 4 (GPX4) has become a promising strategy for drug-resistant cancer therapy via ferroptosis induction. It was found that the GPX4 inhibitors such as RSL3 have GPX4 degradation ability via not only autophagy-lysosome pathway but also ubiquitin-proteasome system (UPS). Proteolysis targeting chimeras (PROTACs) using small molecule with both inhibition and degradation ability as the ligand of protein of interest (POI) have not been reported. To obtain better compounds with effective disturbance of GPX4 activity, and compare the difference between GPX4 inhibitors with degradation ability and their related PROTACs, we designed and synthesized a series of GPX4 degraders using PROTAC technology in terms of its excellent characteristics such as high efficiency and selectivity and the capacity of overcoming resistance. Hence, 8e was discovered as a potent and highly efficacious GPX4 degrader based upon the inhibitor RSL3. It was 2-3 times more potent than RSL3 in all the in vitro anti-tumor assays, indicating the importance of the PROTAC ternary complex of GPX4, 8e and E3 ligase ligand. 8e revealed better potency in resistant tumor cells than in wide type cells. Furthermore, we discovered for the first time that degrader 8e exhibit GPX4 degradation activity via ubiquitin-proteasome system (UPS) and autophagy-lysosome pathway with UPS plays the major role in the process. Our data also suggested that 8e and RSL3 could potently induce ferroptosis of HT1080 cells via GPX4 inhibition and degradation. In summary, our data revealed that the GPX4 degrader 8e achieves better degradation and anti-tumor effects compared to its related GPX4 inhibitor RSL3. Thus, an efficient strategy to induce GPX4 degradation and subsequent ferroptosis was established in this study for malignant cancer treatment in the future.

Recent advances in gout drugs.

Gout is an autoinflammatory disease caused by the deposition of urate crystals. As the most common inflammatory arthritis, gout has a high incidence and can induce various severe complications. At present, there is no effective cure method in the world. With the deepening of medical research, gout treatment drugs continue to progress. In this review, we provide a landscape view of the current state of the research on gout treatment drugs, including the research progress of anti-inflammatory and analgesic drugs, drugs that promote uric acid excretion, and drugs that inhibit uric acid production. We mainly emphasize the understanding of gout as an auto-inflammatory disease and the discovery strategy of related gout drugs to provide a systematic and theoretical basis for the new exploration of gout drug discovery.

Structural optimization and biological evaluation of 1-adamantylcarbonyl-4-phenylpiperazine derivatives as FXR agonists for NAFLD.

Farnesoid X receptor (FXR) is an attractive target for drug discovery against non-alcoholic fatty liver disease (NAFLD). We previously reported an orally active, new-chemotype FXR agonist XJ034 by ensemble learning-driven drug discovery. However, its FXR agonistic activity and the efficacy in vivo remain to be improved. In this study, we designed and synthesized 52 derivatives, and preliminarily evaluated their FXR transactivation activity in HEK293T cells at the concentration of 10 µM. 12 FXR agonists were superior or comparable to compound XJ034, two of which showed over 9-fold activity of compound XJ034, and were as potent as OCA. The molecular docking and molecular dynamics simulations implied an additional hydrogen bond with TYR383 is involved in FXR transactivation for both compounds. According to EC50 determined by the confirmatory transactivation assay, we selected adamantan-1-yl(4-(2-amino-5-chlorophenyl)piperazin-1-yl)methanone (10a, EC50: 1.05 µM) as our lead compound. Interestingly, compound 10a had no agonistic effect on TGR5 or PPAR, and no cytotoxicity to HepG2 cells. In vivo bioassays with high-fat-diet induced C57BL/6J obese (DIO) mice have shown that compound 10a (100 mg/kg) is more effective than compound XJ034 (200 mg/kg) in improving hyperlipidemia, hepatic steatosis and insulin resistance. We also observed that compound 10a down-regulated the expression of genes involved in liver inflammation in vivo, implying its potential to treat hepatic inflammation. In summary, the present data have proved that our strategy for structural optimization is effective, and compound 10a is a promising lead compound with improved efficacy for NAFLD.

Current Trends and Challenges in Drug-Likeness Prediction: Are They Generalizable and Interpretable?

Importance: Drug-likeness of a compound is an overall assessment of its potential to succeed in clinical trials, and is essential for economizing research expenditures by filtering compounds with unfavorable properties and poor development potential. To this end, a robust drug-likeness prediction method is indispensable. Various approaches, including discriminative rules, statistical models, and machine learning models, have been developed to predict drug-likeness based on physiochemical properties and structural features. Notably, recent advancements in novel deep learning techniques have significantly advanced drug-likeness prediction, especially in classification performance. Highlights: In this review, we addressed the evolving landscape of drug-likeness prediction, with emphasis on methods employing novel deep learning techniques, and highlighted the current challenges in drug-likeness prediction, specifically regarding the aspects of generalization and interpretability. Moreover, we explored potential remedies and outlined promising avenues for future research. Conclusion: Despite the hurdles of generalization and interpretability, novel deep learning techniques have great potential in drug-likeness prediction and are worthy of further research efforts.

Discovery of 2-(furan-2-ylmethylene)hydrazine-1-carbothioamide derivatives as novel inhibitors of SARS-CoV-2 main protease.

The COVID-19 posed a serious threat to human life and health, and SARS-CoV-2 Mpro has been considered as an attractive drug target for the treatment of COVID-19. Herein, we report 2-(furan-2-ylmethylene)hydrazine-1-carbothioamide derivatives as novel inhibitors of SARS-CoV-2 Mpro developed by in-house library screening and biological evaluation. Similarity search led to the identification of compound F8-S43 with the enzymatic IC50 value of 10.76 µM. Further structure-based drug design and synthetic optimization uncovered compounds F8-B6 and F8-B22 as novel non-peptidomimetic inhibitors of Mpro with IC50 values of 1.57 µM and 1.55 µM, respectively. Moreover, enzymatic kinetic assay and mass spectrometry demonstrated that F8-B6 was a reversible covalent inhibitor of Mpro. Besides, F8-B6 showed low cytotoxicity with CC50 values of more than 100 µM in Vero and MDCK cells. Overall, these novel SARS-CoV-2 Mpro non-peptidomimetic inhibitors provide a useful starting point for further structural optimization.