Targeting the TGF-β signaling pathway: an updated patent review (2021-present).

Introduction: The TGF-β signaling pathway is a complex network that plays a crucial role in regulating essential biological functions and is implicated in the onset and progression of multiple diseases. This review highlights the recent advancements in developing inhibitors targeting the TGF-β signaling pathway and their potential therapeutic applications in various diseases.

Area Covered: The review discusses patents on active molecules related to the TGF-β signaling pathway, focusing on three strategies: TGF-β activity inhibition, blocking TGF-β receptor binding, and disruption of the signaling pathway using small molecule inhibitors.

Design, synthesis and biological evaluation of novel 3C-like protease inhibitors as lead compounds against SARS-CoV-2.

The epidemic caused by SARS-CoV-2 swept the world in 2019. The 3C-like protease (3CLpro) of SARS-CoV-2 plays a key role in viral replication, and its inhibition could inhibit viral replication. The virtual screen based on receptor-ligand pharmacophore models and molecular docking were conducted to obtain the novel scaffolds of the 3CLpro.

Discovery of Covalent Lead Compounds Targeting 3CL Protease with a Lateral Interactions Spiking Neural Network.

Covalent drugs exhibit advantages in that noncovalent drugs cannot match, and covalent docking is an important method for screening covalent lead compounds. However, it is difficult for covalent docking to screen covalent compounds on a large scale because covalent docking requires determination of the covalent reaction type of the compound. Here, we propose to use deep learning of a lateral interactions spiking neural network to construct a covalent lead compound screening model to quickly screen covalent lead compounds.

GcForest-based compound-protein interaction prediction model and its application in discovering small-molecule drugs targeting CD47.

Identifying compound-protein interaction plays a vital role in drug discovery. Artificial intelligence (AI), especially machine learning (ML) and deep learning (DL) algorithms, are playing increasingly important roles in compound-protein interaction (CPI) prediction. However, ML relies on learning from large sample data.

Targeting the LPA1 signaling pathway for fibrosis therapy: a patent review (2010-present).

Introduction: Fibrosis is a disease that damages organs and even causes death. Because of the complicated pathogenesis, the development of drugs for fibrosis is challenging. In the lysophosphatidic acid receptor type 1 (LPA1) signaling pathway, LPA1 and its downstream Rho-associated coiled-coil forming protein kinase (ROCK) are related to the process of fibrosis.

Structural analysis and binding sites of inhibitors targeting the CD47/SIRPα interaction in anticancer therapy.

Cluster of differentiation 47 (CD47)/signal regulatory protein alpha (SIRPα) is a negative innate immune checkpoint signaling pathway that restrains immunosurveillance and immune clearance, and thus has aroused wide interest in cancer immunotherapy. Blockade of the CD47/SIRPα signaling pathway shows remarkable antitumor effects in clinical trials. Currently, all inhibitors targeting CD47/SIRPα in clinical trials are biomacromolecules.

Targeting the TGF-β signaling pathway for fibrosis therapy: a patent review (2015-2020).

Introduction: Fibrosis is a serious disease that occurs in many organs, such as kidney, liver and lung. The deterioration of these organs ultimately leads to death. Due to the complex mechanisms of fibrosis, research and development of antifibrotic drugs is difficult.

Chemical space exploration based on recurrent neural networks: applications in discovering kinase inhibitors.

With the rise of artificial intelligence (AI) in drug discovery, de novo molecular generation provides new ways to explore chemical space. However, because de novo molecular generation methods rely on abundant known molecules, generated molecules may have a problem of novelty. Novelty is important in highly competitive areas of medicinal chemistry, such as the discovery of kinase inhibitors.

Convolutional Neural Network-based Virtual Screening.

Virtual screening is an important means for lead compound discovery. The scoring function is the key to selecting hit compounds. Many scoring functions are currently available; however, there are no all-purpose scoring functions because different scoring functions tend to have conflicting results.

NMR-based metabolomic analysis for the effects of Huiyang Shengji extract on rat diabetic skin ulcers.

Ethnopharmacological Relevance: Huiyang Shengji formula (HSF) is a compound Chinese herbal medicine prescription, and has long been used for treating chronic non-healing wounds.

Aim Of The Study: The purpose of this study was to provide new insight into molecular mechanisms of healing effects of the HSF treatments.

Materials And Methods: We established a rat diabetic skin ulcer (DSU) model, and assessed healing effects of four HSF treatments on DSUs by calculating wound healing rates and immunohistochemical detection of the expressions of angiogenesis-related factors in the model rats (Mod) relative to normal rats (Nor), including Huiyang extract (HE), Shengji extract (SE), Huiyang Shengji extract (HSE) and HSE associated with acupuncture (Ac-HSE).

Discovery and evaluation of new compounds targeting ribosomal protein S1 in antibiotic-resistant Mycobacterium Tuberculosis.

The emergence of antibiotic-resistant Mycobacterium Tuberculosis (Mtb) infections compels new treatment strategies, of which targeting trans-translation is promising. During the trans-translation process, the ribosomal protein S1 (RpsA) plays a key role, and the Ala438 mutant is related to pyrazinamide (PZA) resistance, which shows its effects after being hydrolysed to pyrazinoic acid (POA). In this study, based on the structure of the RpsA C-terminal domain (RpsA-CTD) and POA complex, new compounds were designed.

NMR-based metabonomic analysis of HUVEC cells during replicative senescence.

Cellular senescence is a physiological process reacting to stimuli, in which cells enter a state of irreversible growth arrest in response to adverse consequences associated with metabolic disorders. Molecular mechanisms underlying the progression of cellular senescence remain unclear. Here, we established a replicative senescence model of human umbilical vein endothelial cells (HUVEC) from passage 3 (P3) to 18 (P18), and performed biochemical characterizations and NMR-based metabolomic analyses.

Development and strategies of CDK4/6 inhibitors.

CDK4/6 have critical roles in the early stage of the cell cycle. CDK2 acts later in the cell cycle and has a considerably broader range of protein substrates, some of which are essential for normal cell proliferation. Therefore, increasing the selectivity of cyclin-dependent kinase (CDK) inhibitors is critical.

The Characteristics of PD-L1 Inhibitors, from Peptides to Small Molecules.

The programmed cell death ligand protein 1 (PD-L1) is a member of the B7 protein family and consists of 290 amino acid residues. The blockade of the PD-1/PD-L1 immune checkpoint pathway is effective in tumor treatment. Results: Two pharmacophore models were generated based on peptides and small molecules.

Characteristics of parathyroid hormone-1 receptor agonists and antagonists.

Parathyroid hormone-1 receptor (PTH1R) is a member of B G protein-coupled receptors. The agonistic activation of the PTH1R results in the production and secretion of osteoclast-stimulating cytokines while antagonists may be used to treat bone metastases, hypercalcemia, cachexia and hyperparathyroidism. We built pharmacophore models and investigated the characteristics of PTH1R agonists and antagonists.

AdipoRs- a potential therapeutic target for fibrotic disorders.

: Fibrotic disorders are a leading cause of morbidity and mortality; hence effective treatments are still vigorously sought. AdipoRs (AdipoR1 and Adipo2) are responsible for the antifibrotic effects of adiponectin (APN). APN exerts antifibrotic effects by binding to its receptors.

Discovery of CDK4 inhibitors by convolutional neural networks.

Descriptors of molecules are important in the discovery of lead compounds. Most of these descriptors are used to represent molecular structures, although structural formulas are the most intuitive representation. Convolutional neural networks (ConvNets) are effective for managing intuitive information.

An overview of neural networks for drug discovery and the inputs used.

: Artificial intelligence systems based on neural networks (NNs) find rules for drug discovery according to training molecules, but first, the molecules need to be represented in certain ways. Molecular descriptors and fingerprints have been used as inputs for artificial neural networks (ANNs) for a long time, while other ways for describing molecules are used only for storing and presenting molecules. With the development of deep learning, variants of ANNs are now able to use different kinds of inputs, which provide researchers with more choices for drug discovery.

Quorum sensing inhibitors: a patent review (2014-2018).

: Quorum sensing (QS) is a cell density-dependent phenomenon in which specific pathways are activated after autoinducers (AIs) outside the microorganism reach a threshold concentration. QS creates a positive feedback loop that induces a cascade of gene expression and causes biofilm formation, virulence and sporulation. QS signals are diverse, acyl-homoserine lactone (AHL), AI peptide (AIP) and AI-2 are three major categories of QS signals.

Lead compounds and key residues of ribosomal protein S1 in drug-resistant Mycobacterium tuberculosis.

Ribosomal protein S1 (RpsA) has been identified as a novel target of pyrazinoic acid (POA), which is the active form of pyrazinamide (PZA), in vivo. RpsA plays a crucial role in trans-translation, which is widespread in microbes. In our investigation, we first described the discovery of promising RpsA antagonists for drug-resistant mycobacterium (MtRpsAd438A) and M.

Synthesis and anti-fibrosis activity study of 14-deoxyandrographolide-19-oic acid and 14-deoxydidehydroandrographolide-19-oic acid derivatives.

A series of 14-deoxyandrographolide-19-oic acid and 14-deoxy-11,12 (or 14,15)-didehydroandrographolide-19-oic acid derivatives were designed, synthesized and screened in vitro against the mouse fibroblast cell lines NIH-3T3. Thirteen compounds 8a-f, 14a-c, 14e-f, and 18a-b were found to exhibit better anti-fibrotic activities than andrographolide, with compounds 8b and 14e displaying best activity with IC values of 12.86 and 13.

Virtual screening and biological evaluation of biofilm inhibitors on dual targets in quorum sensing system.

Aim: Resistance to conventional antibiotics has spurred interest in exploring new antimicrobial strategies. Suppressing quorum sensing within biofilm is a promising antimicrobial strategy. LasR in quorum sensing system of the Gram-negative bacteria, Pseudomonas aeruginosa, directly enhances virulence and antibiotic resistance, with QscR as its indirect suppressor, so targeting both of them can synergistically take the effect.

A potent peptide as adiponectin receptor 1 agonist to against fibrosis.

Fibrotic diseases have become a major cause of death in the developed world. AdipoR1 agonists are potent inhibitors of fibrotic responses. Here, we focused on the in silico identification of novel AdipoR1 peptide agonists.

Distinct effects of Cu2+-binding on oligomerization of human and rabbit prion proteins.

The cellular prion protein (PrP(C)) is a kind of cell-surface Cu(2+)-binding glycoprotein. The oligomerization of PrP(C) is highly related to transmissible spongiform encephalopathies (TSEs). Cu(2+) plays a vital role in the oligomerization of PrP(C), and participates in the pathogenic process of TSE diseases.