Dual-channel hypergraph convolutional network for predicting herb-disease associations.

Herbs applicability in disease treatment has been verified through experiences over thousands of years. The understanding of herb-disease associations (HDAs) is yet far from complete due to the complicated mechanism inherent in multi-target and multi-component (MTMC) botanical therapeutics. Most of the existing prediction models fail to incorporate the MTMC mechanism. To overcome this problem, we propose a novel dual-channel hypergraph convolutional network, namely HGHDA, for HDA prediction. Technically, HGHDA first adopts an autoencoder to project components and target protein onto a low-dimensional latent space so as to obtain their embeddings by preserving similarity characteristics in their original feature spaces. To model the high-order relations between herbs and their components, we design a channel in HGHDA to encode a hypergraph that describes the high-order patterns of herb-component relations via hypergraph convolution. The other channel in HGHDA is also established in the same way to model the high-order relations between diseases and target proteins. The embeddings of drugs and diseases are then aggregated through our dual-channel network to obtain the prediction results with a scoring function. To evaluate the performance of HGHDA, a series of extensive experiments have been conducted on two benchmark datasets, and the results demonstrate the superiority of HGHDA over the state-of-the-art algorithms proposed for HDA prediction. Besides, our case study on Chuan Xiong and Astragalus membranaceus is a strong indicator to verify the effectiveness of HGHDA, as seven and eight out of the top 10 diseases predicted by HGHDA for Chuan-Xiong and Astragalus-membranaceus, respectively, have been reported in literature.

Integrated multi-omics unveil the impact of H-phosphinic analogs of glutamate and α-ketoglutarate on Escherichia coli metabolism.

Desmethylphosphinothricin (L-Glu-γ-PH) is the H-phosphinic analog of glutamate with carbon-phosphorus-hydrogen (C-P-H) bonds. In L-Glu-γ-PH the phosphinic group acts as a bioisostere of the glutamate γ-carboxyl group allowing the molecule to be a substrate of Escherichia coli glutamate decarboxylase, a pyridoxal 5'-phosphate-dependent α-decarboxylase. In addition, the L-Glu-γ-PH decarboxylation product, GABA-PH, is further metabolized by bacterial GABA-transaminase, another pyridoxal 5'-phosphate-dependent enzyme, and succinic semialdehyde dehydrogenase, a NADP+-dependent enzyme. The product of these consecutive reactions, the so-called GABA shunt, is succinate-PH, the H-phosphinic analog of succinate, a tricarboxylic acid cycle intermediate. Notably, L-Glu-γ-PH displays antibacterial activity in the same concentration range of well-established antibiotics in E. coli. The dipeptide L-Leu-Glu-γ-PH was shown to display an even higher efficacy, likely as a consequence of an improved penetration into the bacteria. Herein, to further understand the intracellular effects of L-Glu-γ-PH, 1H NMR-based metabolomics, and LC-MS-based shotgun proteomics were used. This study included also the keto-derivative of L-Glu-γ-PH, α-ketoglutarate-γ-PH (α-KG-γ-PH), which also exhibits antimicrobial activity. L-Glu-γ-PH and α-KG-γ-PH are found to similarly impact bacterial metabolism, although the overall effect of α-KG-γ-PH is more pervasive. Notably, α-KG-γ-PH is converted intracellularly into L-Glu-γ-PH, but the opposite was not found. In general, both molecules impact the pathways where aspartate, glutamate, and glutamine are used as precursors for the biosynthesis of related metabolites, activate the acid stress response, and deprive cells of nitrogen. This work highlights the multi-target drug potential of L-Glu-γ-PH and α-KG-γ-PH and paves the way for their exploitation as antimicrobials.

MAK: a machine learning framework improved genomic prediction via multi-target ensemble regressor chains and automatic selection of assistant traits.

Incorporating the genotypic and phenotypic of the correlated traits into the multi-trait model can significantly improve the prediction accuracy of the target trait in animal and plant breeding, as well as human genetics. However, in most cases, the phenotypic information of the correlated and target trait of the individual to be evaluated was null simultaneously, particularly for the newborn. Therefore, we propose a machine learning framework, MAK, to improve the prediction accuracy of the target trait by constructing the multi-target ensemble regression chains and selecting the assistant trait automatically, which predicted the genomic estimated breeding values of the target trait using genotypic information only. The prediction ability of MAK was significantly more robust than the genomic best linear unbiased prediction, BayesB, BayesRR and the multi trait Bayesian method in the four real animal and plant datasets, and the computational efficiency of MAK was roughly 100 times faster than BayesB and BayesRR.

Innovative pathological network-based multitarget approaches for Alzheimer's disease treatment.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease and is a major health threat globally. Its prevalence is forecasted to exponentially increase during the next 30 years due to the global aging population. Currently, approved drugs are merely symptomatic, being ineffective in delaying or blocking the relentless disease advance. Intensive AD research describes this disease as a highly complex multifactorial disease. Disclosure of novel pathological pathways and their interconnections has had a major impact on medicinal chemistry drug development for AD over the last two decades. The complex network of pathological events involved in the onset of the disease has prompted the development of multitarget drugs. These chemical entities combine pharmacological activities toward two or more drug targets of interest. These multitarget-directed ligands are proposed to modify different nodes in the pathological network aiming to delay or even stop disease progression. Here, we review the multitarget drug development strategy for AD during the last decade.

The antisickling agent, 5-hydroxymethyl-2-furfural: Other potential pharmacological applications.

For the last two decades, the aromatic aldehyde 5-hydroxymethyl-furfural (5-HMF) has been the subject of several investigations for its pharmacologic potential. In 2004, the Safo group reported that 5-HMF has potent antisickling activity by targeting and ameliorating the primary pathophysiology of hypoxia-induced sickling of erythrocytes (red blood cells [RBC]). Following the encouraging outcome of the preclinical and phase I/II clinical studies of 5-HMF for the treatment of sickle cell disease (SCD), there have been multiple studies suggesting 5-HMF has several other biological or pharmacologic activities, including anti-allergic, antioxidant, anti-hypoxic, anti-ischemic, cognitive improvement, anti-tyrosinase, anti-proliferation, cytoprotective, and anti-inflammatory activities. The wide range of its effects makes 5-HMF a potential candidate for treating a variety of diseases including cognitive disorders, gout, allergic disorders, anemia, hypoxia, cancers, ischemia, hemorrhagic shock, liver fibrosis, and oxidative injury. Several of these therapeutic claims are currently under investigation and, while promising, vary in terms of the strength of their evidence. This review presents the research regarding the therapeutic potential of 5-HMF in addition to its sources, physicochemical properties, safety, absorption, distribution, metabolism, and excretion (ADME) profiles.

Advances in drug design and therapeutic potential of selective or multitarget 5-HT1A receptor ligands.

5-HT1A receptor (5-HT1A-R) is a serotoninergic G-protein coupled receptor subtype which contributes to several physiological processes in both central nervous system and periphery. Despite being the first 5-HT-R identified, cloned and studied, it still represents a very attractive target in drug discovery and continues to be the focus of a myriad of drug discovery campaigns due to its involvement in numerous neuropsychiatric disorders. The structure-activity relationship studies (SAR) performed over the last years have been devoted to three main goals: (i) design and synthesis of 5-HT1A-R selective/preferential ligands; (ii) identification of 5-HT1A-R biased agonists, differentiating pre- versus post-synaptic agonism and signaling cellular mechanisms; (iii) development of multitarget compounds endowed with well-defined poly-pharmacological profiles targeting 5-HT1A-R along with other serotonin receptors, serotonin transporter (SERT), D2-like receptors and/or enzymes, such as acetylcholinesterase and phosphodiesterase, as a promising strategy for the management of complex psychiatric and neurodegenerative disorders. In this review, medicinal chemistry aspects of ligands acting as selective/preferential or multitarget 5-HT1A-R agonists and antagonists belonging to different chemotypes and developed in the last 7 years (2017-2023) have been discussed. The development of chemical and pharmacological 5-HT1A-R tools for molecular imaging have also been described. Finally, the pharmacological interest of 5-HT1A-R and the therapeutic potential of ligands targeting this receptor have been considered.

GMOIT: a tool for effective screening of genetically modified crops.

BACKGROUND: Advancement in agricultural biotechnology has resulted in increasing numbers of commercial varieties of genetically modified (GM) crops worldwide. Though several databases on GM crops are available, these databases generally focus on collecting and providing information on transgenic crops rather than on screening strategies. To overcome this, we constructed a novel tool named, Genetically Modified Organisms Identification Tool (GMOIT), designed to integrate basic and genetic information on genetic modification events and detection methods. RESULTS: At present, data for each element from 118 independent genetic modification events in soybean, maize, canola, and rice were included in the database. Particularly, GMOIT allows users to customize assay ranges and thus obtain the corresponding optimized screening strategies using common elements or specific locations as the detection targets with high flexibility. Using the 118 genetic modification events currently included in GMOIT as the range and algorithm selection results, a "6 + 4" protocol (six exogenous elements and four endogenous reference genes as the detection targets) covering 108 events for the four crops was established. Plasmids pGMOIT-1 and pGMOIT-2 were constructed as positive controls or calibrators in qualitative and quantitative transgene detection. CONCLUSIONS: Our study provides a simple, practical tool for selecting, detecting, and screening strategies for a sustainable and efficient application of genetic modification.

A chronotherapeutics-applicable multi-target therapeutics based on AI: Example of therapeutic hypothermia.

Nowadays, the complexity of disease mechanisms and the inadequacy of single-target therapies in restoring the biological system have inevitably instigated the strategy of multi-target therapeutics with the analysis of each target individually. However, it is not suitable for dealing with the conflicts between targets or between drugs. With the release of high-precision protein structure prediction artificial intelligence, large-scale high-precision protein structure prediction and docking have become possible. In this article, we propose a multi-target drug discovery method by the example of therapeutic hypothermia (TH). First, we performed protein structure prediction for all protein targets of each group by AlphaFold2 and RoseTTAFold. Then, QuickVina 2 is used for molecular docking between the proteins and drugs. After docking, we use PageRank to rank single drugs and drug combinations of each group. The ePharmaLib was used for predicting the side effect targets. Given the differences in the weights of different targets, the method can effectively avoid inhibiting beneficial proteins while inhibiting harmful proteins. So it could minimize the conflicts between different doses and be friendly to chronotherapeutics. Besides, this method also has potential in precision medicine for its high compatibility with bioinformatics and promotes the development of pharmacogenomics and bioinfo-pharmacology.

Anlotinib alone or in combination with bevacizumab in the treatment of recurrent high-grade glioma: a prospective single-arm, open-label phase II trial.

BACKGROUND: Anlotinib is a multi-target tyrosine kinase inhibitor (TKI) targeting the vascular endothelial growth factor receptor (VEGFR), platelet-derived growth factor receptor (PDGFR), fibroblast growth factor receptor (FGFR), and c-Kit. This phase II study aimed to assess the efficacy and safety of anlotinib, either alone or in combination with bevacizumab (Bev) for recurrent high-grade glioma (rHGG) (NCT04822805, 30/03/2021). METHODS: Eligible patients had a histological diagnosis of rHGG with first or subsequent recurrences. All patients received oral anlotinib 12 mg or 10 mg on days 1-14 (repeated every 21 days). In cases where brain magnetic resonance imaging examination revealed an increase in peritumoral edema without worsening of symptoms, patients received a temporary treatment of intravenous bevacizumab 10 mg/kg to alleviate edema. The primary endpoint was the median progression-free survival (mPFS), and the secondary endpoints included median overall survival (mOS), objective response rate (ORR), disease control rate (DCR), and safety. RESULTS: Twenty-five patients with rHGG were included in the efficacy and safety assessments. Eighteen patients received anlotinib alone, and seven patients received anlotinib in combination with Bev. For all patients, the mPFS and mOS were 5.0 months and 13.6 months, respectively. The ORR was 32%, and the DCR was 96%. It is noteworthy that the survival and response data of recurrent glioblastoma (rGBM) exhibit similarities to those of rHGG. For rGBM patients, there were no significant differences in mPFS, mOS, ORR, or DCR between the anlotinib alone and anlotinib + Bev groups. However, the incidence of treatment-related adverse events of any grade was higher in the anlotinib + Bev group compared to the anlotinib alone group (100% vs. 78%, p = 0.041). CONCLUSIONS: Both anlotinib alone and its combination with Bev demonstrated good efficacy and safety in the treatment of rHGG.

Potentials and future perspectives of multi-target drugs in cancer treatment: the next generation anti-cancer agents.

Cancer is a major public health problem worldwide with more than an estimated 19.3 million new cases in 2020. The occurrence rises dramatically with age, and the overall risk accumulation is combined with the tendency for cellular repair mechanisms to be less effective in older individuals. Conventional cancer treatments, such as radiotherapy, surgery, and chemotherapy, have been used for decades to combat cancer. However, the emergence of novel fields of cancer research has led to the exploration of innovative treatment approaches focused on immunotherapy, epigenetic therapy, targeted therapy, multi-omics, and also multi-target therapy. The hypothesis was based on that drugs designed to act against individual targets cannot usually battle multigenic diseases like cancer. Multi-target therapies, either in combination or sequential order, have been recommended to combat acquired and intrinsic resistance to anti-cancer treatments. Several studies focused on multi-targeting treatments due to their advantages include; overcoming clonal heterogeneity, lower risk of multi-drug resistance (MDR), decreased drug toxicity, and thereby lower side effects. In this study, we'll discuss about multi-target drugs, their benefits in improving cancer treatments, and recent advances in the field of multi-targeted drugs. Also, we will study the research that performed clinical trials using multi-target therapeutic agents for cancer treatment.

Multitarget ligands that comprise opioid/nonopioid pharmacophores for pain management: Current state of the science.

Chronic pain, which affects more than one-third of the world's population, represents one of the greatest medical challenges of the 21st century, yet its effective management remains sub-optimal. The 'gold standard' for the treatment of moderate to severe pain consists of opioid ligands, such as morphine and fentanyl, that target the µ-opioid receptor (MOP). Paradoxically, these opioids also cause serious side effects, including constipation, respiratory depression, tolerance, and addiction. In addition, the development of opioid-use disorders, such as opioid diversion, misuse, and abuse, has led to the current opioid crisis, with dramatic increases in addiction, overdoses, and ultimately deaths. As pain is a complex, multidimensional experience involving a variety of pathways and mediators, dual or multitarget ligands that can bind to more than one receptor and exert complementary analgesic effects, represent a promising avenue for pain relief. Indeed, unlike monomodal therapeutic approaches, the modulation of several endogenous nociceptive systems can often result in an additive or even synergistic effect, thereby improving the analgesic-to-side-effect ratio. Here, we provide a comprehensive overview of research efforts towards the development of dual- or multi-targeting opioid/nonopioid hybrid ligands for effective and safer pain management. We reflect on the underpinning discovery rationale by discussing the design, medicinal chemistry, and in vivo pharmacological effects of multitarget antinociceptive compounds.

Medicinal Polypharmacology in the Clinic - Translating the Polypharmacolome into Therapeutic Benefit.

Drugs with multiple targets, often annotated as 'unselective', 'promiscuous', 'multitarget', or 'polypharmacological', are widely considered in both academic and industrial research as a high risk due to the likelihood of adverse effects. However, retrospective analyses have shown that particularly approved drugs bear rich polypharmacological profiles. This raises the question whether our perception of the specificity paradigm ('one drug-one target concept') is correct - and if specifically multitarget drugs should be developed instead of being rejected. These questions provoke a paradigm shift - regarding the development of polypharmacological drugs not as a 'waste of investment', but acknowledging the existence of a 'lack of investment'. This perspective provides an insight into modern drug development highlighting latest drug candidates that have not been assessed in a broader polypharmacology-based context elsewhere embedded in a historic framework of classical and modern approved multitarget drugs. The article shall be an inspiration to the scientific community to re-consider current standards, and more, to evolve to a better understanding of polypharmacology from a challenge to an opportunity.

Synthesis, biosimulation and pharmacological evaluation of benzimidazole derivatives with antihypertensive multitarget effect.

In this study, we synthesized a series of seven benzimidazole derivatives incorporating the structural acidic framework of angiotensin II (Ang II) type 1 receptor (AT1R) antagonists (ARA-II) employing a three-step reaction sequence. The chemical structures were confirmed by 1H NMR, 13C NMR and mass spectral data. Through biosimulation, compounds 1-7 were identified as computational safe hits, thus, best candidates underwent ex vivo testing against two distinct mechanisms implicated in hypertension: antagonism of the Ang II type 1 receptor and the blockade of calcium channel. Molecular docking studies helped to understand at the molecular level the dual vasorelaxant effects with the recognition sites of the AT1R and the L-type calcium channel. In an in vivo spontaneously hypertensive rat model (SHR), intraperitoneally administration of compound 1 at 20 mg/kg resulted in a 25 % reduction in systolic blood pressure, demonstrating both ex vivo vasorelaxant action and in vivo antihypertensive multitarget efficacy. ©2024 Elsevier.

Synthesis and biological evaluation of novel aminoquinolines with an n-octyl linker: Impact of halogen substituents on C(7) or a terminal amino group on anticholinesterase and BACE1 activity.

Alzheimer's disease is age-related multifactorial neurodegenerative disease manifested by gradual loss of memory, cognitive decline and changes in personality. Due to rapid and continuous growth of its prevalence, the treatment of Alzheimer's disease calls for development of new and efficacies drugs, especially those that could be able to simultaneously act on more than one of possible targets of action. Aminoquinolines have proven to be a highly promising structural scaffold in the design of such a drug as cholinesterases and ß-secretase 1 inhibitors. In this study, we synthesised twenty-two new 4-aminoquinolines with different halogen atom and its position in the terminal N-benzyl group or with a trifluoromethyl or a chlorine as C(7)-substituents on the quinoline moiety. All compounds were evaluated as multi-target-directedligands by determining their inhibition potency towards human acetylcholinesterase, butyrylcholinesterase and ß-secretase 1. All of the tested derivatives were very potent inhibitors of human acetylcholinesterase and butyrylcholinesterase with inhibition constants (Ki) in the nM to low µM range. Most were estimated to be able to cross the blood-brain barrier by passive transport and were nontoxic toward cells that represented the main models of individual organs.

Design, synthesis and biological evaluation of arylpropylamine derivatives as potential multi-target antidepressants.

In this study, a series of novel arylpropylamine derivatives were designed, synthesized and evaluated as potential multi-target antidepressants. Among them, compound (R)-13j displayed unique pharmacological features, exhibiting excellent inhibitory potency against serotonin and noradrenaline transporters (SERT/NET) and high affinity for 5-HT2A/2C receptor, and showing low affinity for histamine H1, adrenergic α1 receptors and hERG channels (to reduce QT interval prolongation). Molecular docking studies provided a rational binding model of (R)-13j in complex with SERT and 5-HT2A/2C receptor. In animal models, compound (R)-13j dose-dependently reduced the immobility time in the tail suspension test (TST) and the forced swimming test (FST) in mice, with higher efficacy when compared to duloxetine, and showed no stimulatory effect on the locomotor activity. Moreover, compound (R)-13j significantly shortened the immobility time in the ACTH-induced rat model of treatment-resistant depression (TRD). Furthermore, compound (R)-13j also exhibited a higher threshold for acute toxicity than duloxetine. In addition, compound (R)-13j possessed a favorable pharmacokinetic profile in mice. Taken together, compound (R)-13j may constitute a novel class of drugs for the treatment of depression.

Disclosure of cinnamic acid/4,9-diaminoacridine conjugates as multi-stage antiplasmodial hits.

4,9-diaminoacridines with reported antiplasmodial activity were coupled to different trans-cinnamic acids, delivering a new series of conjugates inspired by the covalent bitherapy concept. The new compounds were more potent than primaquine against hepatic stages of Plasmodium berghei, although this was accompanied by cytotoxic effects on Huh-7 hepatocytes. Relevantly, the conjugates displayed nanomolar activities against blood stage P. falciparum parasites, with no evidence of hemolytic effects below 100 µM. Moreover, the new compounds were at least 25-fold more potent than primaquine against P. falciparum gametocytes. Thus, the new antiplasmodial hits disclosed herein emerge as valuable templates for the development of multi-stage antiplasmodial drug candidates.

Research progress of multi-target HDAC inhibitors blocking the BRD4-LIFR-JAK1-STAT3 signaling pathway in the treatment of cancer.

Histone deacetylase inhibitors (HDACis) show beneficial effects on different hematological malignancy subtypes. However, their impacts on treating solid tumors are still limited due to diverse resistance mechanisms. Recent studies have found that the feedback activation of BRD4-LIFR-JAK1-STAT3 pathway after HDACi incubation is a vital mechanism inducing resistance of specific solid tumor cells to HDACis. This review summarizes the recent development of multi-target HDACis that can concurrently block BRD4-LIFR-JAK1-STAT3 pathway. Moreover, our findings hope to shed novel lights on developing novel multi-target HDACis with reduced BRD4-LIFR-JAK1-STAT3-mediated drug resistance in some tumors.

Three-dimensional visualization of thyroid ultrasound images based on multi-scale features fusion and hierarchical attention.

BACKGROUND: Ultrasound three-dimensional visualization, a cutting-edge technology in medical imaging, enhances diagnostic accuracy by providing a more comprehensive and readable portrayal of anatomical structures compared to traditional two-dimensional ultrasound. Crucial to this visualization is the segmentation of multiple targets. However, challenges like noise interference, inaccurate boundaries, and difficulties in segmenting small structures exist in the multi-target segmentation of ultrasound images. This study, using neck ultrasound images, concentrates on researching multi-target segmentation methods for the thyroid and surrounding tissues. METHOD: We improved the Unet++ to propose PA-Unet++ to enhance the multi-target segmentation accuracy of the thyroid and its surrounding tissues by addressing ultrasound noise interference. This involves integrating multi-scale feature information using a pyramid pooling module to facilitate segmentation of structures of various sizes. Additionally, an attention gate mechanism is applied to each decoding layer to progressively highlight target tissues and suppress the impact of background pixels. RESULTS: Video data obtained from 2D ultrasound thyroid serial scans served as the dataset for this paper.4600 images containing 23,000 annotated regions were divided into training and test sets at a ratio of 9:1, the results showed that: compared with the results of U-net++, the Dice of our model increased from 78.78% to 81.88% (+ 3.10%), the mIOU increased from 73.44% to 80.35% (+ 6.91%), and the PA index increased from 92.95% to 94.79% (+ 1.84%). CONCLUSIONS: Accurate segmentation is fundamental for various clinical applications, including disease diagnosis, treatment planning, and monitoring. This study will have a positive impact on the improvement of 3D visualization capabilities and clinical decision-making and research in the context of ultrasound image.

Design of Multi-Target drugs of HDACs and other Anti-Alzheimer related Targets: Current strategies and future prospects in Alzheimer's diseases therapy.

Alzheimer disease (AD) is the most prevalent form of dementia that develops spontaneously in the elderly. It's worth mentioning that as people age, the epigenetic profile of the central nervous system cells changes, which may speed up the development of various neurodegenerative disorders including AD. Histone deacetylases (HDACs) are a class of epigenetic enzymes that can control gene expression without altering the gene sequence. Moreover, a promising strategy for multi-target hybrid design was proposed to potentially improve drug efficacy and reduce side effects. These hybrids are monocular drugs that contain various pharmacophore components and have the ability to bind to different targets at the same time. The HDACs ability to synergistically boost the performance of other anti-AD drugs, as well as the ease with which HDACs inhibitor cap group, can be modified. This has prompted numerous medicinal chemists to design a novel generation of HDACs multi-target inhibitors. Different HDACs inhibitors and other ones such as acetylcholinesterase, butyryl-cholinesterase, phosphodiesterase 9, phosphodiesterase 5 or glycogen synthase kinase 3ß inhibitors were merged into hybrids for treatment of AD. This review goes over the scientific rationale for targeting HDACs along with several other crucial targets in AD therapy. This review presents the latest hybrids of HDACs and other AD target pharmacophores.

Novel multi-target angiogenesis inhibitors as potential anticancer agents: Design, synthesis and preliminary activity evaluation.

Based on the crucial role of histone deacetylase (HDAC) and receptor tyrosine kinase in angiogenesis, in situ assembly, skeletal transition, molecular hybridization, and pharmacophore fusion were employed to yield seventy-six multi-target angiogenesis inhibitors. Biological evaluation indicated that most of the compounds exhibited potent proliferation inhibitory activity on MCF-7 cells, with the TH series having the highest inhibitory activity on MCF-7 cells. In addition, the IC50 values of TA11 and TH3 against HT-29 cellswere 0.078 µmol/L and 0.068 µmol/L, respectively. The cytotoxicity evaluation indicated that TC9, TA11, TM4, and TH3 displayed good safety against HEK293T cells. TH2 and TH3 could induce apoptosis of MCF-7 cells. Molecular modeling and ADMET prediction results indicated that most of target compounds showed promising medicinal properties, which was consistent with the experimental results. Our findings provided new lead compounds for the structural optimization of multi-target angiogenesis inhibitors.