Discovery of VEGFR2 inhibitors by integrating naïve Bayesian classification, molecular docking and drug screening approaches

Comprehensive applications of the artificial intelligence technology in new drug research and development

Purpose

Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field.

Methods

Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [("Artificial Intelligence" OR "Knowledge Graph" OR "Machine Learning") AND ("Drug Target Identification" OR "New Drug Development")].

Results

In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery.

Conclusion

Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry.

Computer-Aided Design of VEGFR-2 Inhibitors as Anticancer Agents: A Review

Due to its intricate molecular and structural characteristics, vascular endothelial growth factor receptor 2 (VEGFR-2) is essential for the development of new blood vessels in various pathological processes and conditions, especially in cancers. VEGFR-2 inhibitors have demonstrated significant anticancer effects by blocking many signaling pathways linked to tumor growth, metastasis, and angiogenesis. Several small compounds, including the well-tolerated sunitinib and sorafenib, have been approved as VEGFR-2 inhibitors. However, the widespread side effects linked to these VEGFR-2 inhibitors-hypertension, epistaxis, proteinuria, and upper respiratory infection-motivate researchers to search for new VEGFR-2 inhibitors with better pharmacokinetic profiles. The key molecular interactions required for the interaction of the small molecules with the protein target to produce the desired pharmacological effects are identified using computer-aided drug design (CADD) methods such as pharmacophore and QSAR modeling, structure-based virtual screening, molecular docking, molecular dynamics (MD) simulation coupled with MM/PB(GB)SA, and other computational strategies. This review discusses the applications of these methods for VEGFR-2 inhibitor design. Future VEGFR-2 inhibitor designs may be influenced by this review, which focuses on the current trends of using multiple screening layers to design better inhibitors.

From Deep Learning to the Discovery of Promising VEGFR-2 Inhibitors

Vascular endothelial growth factor receptor 2 (VEGFR-2) stands as a prominent therapeutic target in oncology, playing a critical role in angiogenesis, tumor growth, and metastasis. FDA-approved VEGFR-2 inhibitors are associated with diverse side effects. Thus, finding novel and more effective inhibitors is of utmost importance. In this study, a deep learning (DL) classification model was first developed and then employed to select putative active VEGFR-2 inhibitors from an in-house chemical library including 187 druglike compounds. A pool of 18 promising candidates was shortlisted and screened against VEGFR-2 by using molecular docking. Finally, two compounds, RHE-334 and EA-11, were prioritized as promising VEGFR-2 inhibitors by employing PLATO, our target fishing and bioactivity prediction platform. Based on this rationale, we prepared RHE-334 and EA-11 and successfully tested their anti-proliferative potential against MCF-7 human breast cancer cells with IC50 values of 26.78±4.02 and 38.73±3.84 μM, respectively. Their toxicities were instead challenged against the WI-38. Interestingly, expression studies indicated that, in the presence of RHE-334, VEGFR-2 was equal to 0.52±0.03, thus comparable to imatinib equal to 0.63±0.03. In conclusion, this workflow based on theoretical and experimental approaches demonstrates effective in identifying VEGFR-2 inhibitors and can be easily adapted to other medicinal chemistry goals.

An efficient and simple approach for synthesizing indazole compounds using palladium-catalyzed Suzuki-Miyaura cross-coupling

A series of indazole derivatives (6a-6i and 7a-7i) has been synthesized using Suzuki Miyaura cross-coupling with a palladium catalyst from readily available starting materials. An efficient and reliable methodology was employed for the synthesis, and the compounds were thoroughly characterized using 1H NMR, 13C NMR, FT-IR, and HRMS analysis to confirm their structural integrity and purity. Density function theory (DFT) computation identified four compounds (6g, 6h, 7g, and 7h) with significant energy band gaps. Additionally, the molecular electrostatic potential study highlighted the distinct electrical characteristics of these indazole molecules. Subsequent molecular docking investigations were carried out using the AUTODOCK method with two separate protein data bank (PDB) structures (6FEW, 4WA9) involved in renal cancer pathways. The results showed that eight substances PDB: 6FEW (6g, 6h, 7g, and 7h) and PDB: 4WA9 (6a, 6c, and 7c, 7g) had the highest binding energies, indicating their potential as therapeutic agents for treating kidney cancer.

Identification of novel covalent JAK3 inhibitors through consensus scoring virtual screening: integration of common feature pharmacophore and covalent docking

Accumulated research strongly indicates that Janus kinase 3 (JAK3) is intricately involved in the initiation and advancement of a diverse range of human diseases, underscoring JAK3 as a promising target for therapeutic intervention. However, JAK3 shows significant homology with other JAK family isoforms, posing substantial challenges in the development of JAK3 inhibitors. To address these limitations, one strategy is to design selective covalent JAK3 inhibitors. Therefore, this study introduces a virtual screening approach that combines common feature pharmacophore modeling, covalent docking, and consensus scoring to identify novel inhibitors for JAK3. First, common feature pharmacophore models were constructed based on a selection of representative covalent JAK3 inhibitors. The optimal qualitative pharmacophore model proved highly effective in distinguishing active and inactive compounds. Second, 14 crystal structures of the JAK3-covalent inhibitor complex were chosen for the covalent docking studies. Following validation of the screening performance, 5TTU was identified as the most suitable candidate for screening potential JAK3 inhibitors due to its higher predictive accuracy. Finally, a virtual screening protocol based on consensus scoring was conducted, integrating pharmacophore mapping and covalent docking. This approach resulted in the discovery of multiple compounds with notable potential as effective JAK3 inhibitors. We hope that the developed virtual screening strategy will provide valuable guidance in the discovery of novel covalent JAK3 inhibitors.

Recent advances in the development of DprE1 inhibitors using AI/CADD approaches

Tuberculosis (TB) is a global lethal disease caused by Mycobacterium tuberculosis (Mtb). The flavoenzyme decaprenylphosphoryl-β-d-ribose 2'-oxidase (DprE1) plays a crucial part in the biosynthesis of lipoarabinomannan and arabinogalactan for the cell wall of Mtb and represents a promising target for anti-TB drug development. Therefore, there is an urgent need to discover DprE1 inhibitors with novel scaffolds, improved bioactivity and high drug-likeness. Recent studies have shown that artificial intelligence/computer-aided drug design (AI/CADD) techniques are powerful tools in the discovery of novel DprE1 inhibitors. This review provides an overview of the discovery of DprE1 inhibitors and their underlying mechanism of action and highlights recent advances in the discovery and optimization of DprE1 inhibitors using AI/CADD approaches.

Optimized models and deep learning methods for drug response prediction in cancer treatments: a review

Recent advancements in deep learning (DL) have played a crucial role in aiding experts to develop personalized healthcare services, particularly in drug response prediction (DRP) for cancer patients. The DL's techniques contribution to this field is significant, and they have proven indispensable in the medical field. This review aims to analyze the diverse effectiveness of various DL models in making these predictions, drawing on research published from 2017 to 2023. We utilized the VOS-Viewer 1.6.18 software to create a word cloud from the titles and abstracts of the selected studies. This study offers insights into the focus areas within DL models used for drug response. The word cloud revealed a strong link between certain keywords and grouped themes, highlighting terms such as deep learning, machine learning, precision medicine, precision oncology, drug response prediction, and personalized medicine. In order to achieve an advance in DRP using DL, the researchers need to work on enhancing the models' generalizability and interoperability. It is also crucial to develop models that not only accurately represent various architectures but also simplify these architectures, balancing the complexity with the predictive capabilities. In the future, researchers should try to combine methods that make DL models easier to understand; this will make DRP reviews more open and help doctors trust the decisions made by DL models in cancer DRP.

Flubendazole suppresses VEGF-induced angiogenesis in HUVECs and exerts antitumor effects in PC-3 cells

Flubendazole, an FDA-approved anthelmintic, has been predicted to show strong VEGFR2 inhibitory activity in silico screening combined with in vitro experimental validation, and it has shown anti-cancer effects on some human cancer cell lines, but little is known about the anti-angiogenesis effects and anti-prostate cancer effects. In this study, we analyzed the binding modes and kinetic analysis of flubendazole with VEGFR2 and first demonstrated that flubendazole suppressed VEGF-stimulated cell proliferation, wound-healing migration, cell invasion and tube formation of HUVEC cells, and decreased the phosphorylation of extracellular signal-regulated kinase and serine/threonine kinase Akt, which are the downstream proteins of VEGFR2 that are important for cell growth. What's more, our results showed that flubendazole decreased PC-3 cell viability and proliferation ability, and suppressed PC-3 cell wound healing migration and invasion across a Matrigel-coated Transwell membrane in a concentration-dependent manner. The antiproliferative effects of flubendazole were due to induction of G2-M phase cell cycle arrest in PC-3 cells with decreasing expression of the Cyclin D1 and induction of cell apoptosis with the number of apoptotic cells increased after flubendazole treatment. These results indicated that flubendazole could exert anti-angiogenic and anticancer effects by inhibiting cell cycle and inducing cell apoptosis.

Chemosensitization of non-small cell lung cancer to sorafenib via non-hydroxamate s-triazinedione-based MMP-9/10 inhibitors

Non-small cell lung cancer (NSCLC) continues to be a leading cause of cancer death. Its fatality is associated with angiogenesis and metastasis. While VEGFR inhibitors are expected to be the central pillar for halting lung cancer, several clinical reports declared their subpar activities as monotherapy. These results directed combination studies of VEGFR inhibitors, especially sorafenib (Nexavar®), with various chemotherapeutic agents. Matrix metalloproteinase (MMP) inhibitors are seldom utilized in such combinations despite the expected complementary therapeutic outcome. This could be attributed to the clinical unsuitability of MMP inhibitors from the hydroxamate family. Herein, we report new non-hydroxamate s-triazinedione-based inhibitors of MMP-9 (6b; IC50 = 0.112 μM), and MMP-10 (6e; IC50 = 0.076 μM) surpassing the hydroxamate inhibitor NNGH for chemosensitization of NSCLC to sorafenib. MMPs inhibition profiling of the hits revealed MMP-9 over -2 and MMP-10 over -13 selectivity. 6b and 6e were potent (IC50 = 0.139 and 0.136 µM), safe (SI up to 6.77) and superior to sorafenib (IC50 = 0.506 µM, SI = 6.27) against A549 cells. When combined with sorafenib, the studied MMP inhibitors enhanced its cytotoxic efficacy up to 26 folds as confirmed by CI and DRI values for 6b (CI = 0.160 and DRI = 22.175) and 6e (CI = 0.096 and DRI = 29.060). 6b and 6e exerted anti-invasive activities in A549 cells as single agents (22.66 and 39.67 %) and in sorafenib combinations (29.96 and 91.83 %) compared to untreated control. Both compounds downregulated VEGF in A549 cells by approximately 70 % when combined with sorafenib, highlighting enhanced anti-angiogenic activities. Collectively, combinations of 6b and 6e with sorafenib demonstrated synergistic NSCLC cytotoxicity with pronounced anti-invasive and anti-angiogenic activities introducing a promising start point for preclinical studies.

Drug Repurposing to Circumvent Immune Checkpoint Inhibitor Resistance in Cancer Immunotherapy

Immune checkpoint inhibitors (ICI) have achieved unprecedented clinical success in cancer treatment. However, drug resistance to ICI therapy is a major hurdle that prevents cancer patients from responding to the treatment or having durable disease control. Drug repurposing refers to the application of clinically approved drugs, with characterized pharmacological properties and known adverse effect profiles, to new indications. It has also emerged as a promising strategy to overcome drug resistance. In this review, we summarized the latest research about drug repurposing to overcome ICI resistance. Repurposed drugs work by either exerting immunostimulatory activities or abolishing the immunosuppressive tumor microenvironment (TME). Compared to the de novo drug design strategy, they provide novel and affordable treatment options to enhance cancer immunotherapy that can be readily evaluated in the clinic. Biomarkers are exploited to identify the right patient population to benefit from the repurposed drugs and drug combinations. Phenotypic screening of chemical libraries has been conducted to search for T-cell-modifying drugs. Genomics and integrated bioinformatics analysis, artificial intelligence, machine and deep learning approaches are employed to identify novel modulators of the immunosuppressive TME.

Roxadustat and its failure: A comparative dynamic study

Roxadustat, a small-molecule inhibitor of hypoxia-inducible factor prolyl hydroxylase domain 2 (HIF-PHD2) has been recently overruled by the American Food and Drug Administration (FDA) in Phase 3 clinical trials. This study provides insights into the dynamics of Roxadustat with PHD2 and proposes two FDA-approved drugs; Pemetrexed and Valrubicin to treat chronic kidney disease (CKD). Role of chemical scaffolds such as synthetic pyrimidine-based antifolate is found critical for PHD2 inhibitory activity, which is concurrent with the experimental findings for stimulating Endogenous erythropoietin (EPO) gene expression. Furthermore, Fe⁺² and Mn⁺² in solution are essential for imparting structural stability to the screened carboxylic and non-carboxylic acid drugs. Comparative analysis of FDA-approved drugs namely, Roxadustat, two-hit carboxylic, and non-carboxylic-acid type compounds (Pemetrexed and Valrubicin), as well as the control ligands (KU1 and 4JR), unveil structural dynamics of Roxadustat and its failure. However, the proposed FDA compounds, Pemetrexed and Valrubicin, used to treat mesothelioma, non-small cell lung cancer, and bladder cancer should be subjected to in vitro analysis for renal anemia.

Evolution of Antiretroviral Drug Rilpivirine and Approach to Oncology

Rilpivirine is an antiretroviral drug used to treat AIDS worldwide. The drug is a non-nucleoside reverse transcriptase inhibitor that halts the cDNA elongation process and, thus, the capacity of the HIV-1 virus to replicate. With the new wave of drug repurposing in recent years, rilpivirine has been studied in this regard. This drug is useful in Zika virus treatment, with in vivo results indicating regression in neuronal effects often associated with this infection. Several cancer types have also been researched, from breast to leukemia and pancreatic cancer, and rilpivirine has proved to have inhibitory effects in various cell lines with low concentrations, causing cellular death, apoptosis, and cell cycle arrest. The pathways are not yet established, but some works have hypothesized and demonstrated that rilpivirine causes inhibition of Aurora A kinase and has effects on the Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway and the vascular endothelial growth factors-receptors (VEGFs-VEGFRs) pathway, which are known to be altered in cancer and tumors and can be targeted for cancer treatment. Further testing and clinical trials are needed, but this review demonstrates the potential of rilpivirine's repurposing for cancer treatment.

Synthesis of Palladium-Catalysed C-C Bond Forming 5-Chloro Quinolines via Suzuki-Miyaura Coupling; Anti-Pancreatic Cancer Screening on PANC-1 Cell Lines

Pancreatic cancer is the most severe among other cancers due to its late detection and less chance of survivability. Heterocycles are proven ring systems in the treatment of various cancers and this is due to the presence of two biodynamic molecules combined, which have a greater synergistic efficacy in many anticancer drugs. Quinoline and pyridine ring systems are brought together to obtain greater potency and this is achieved by coupling both using Pd-catalyst, and in the present investigation, Suzuki-Miyaura coupling (SMC) reactions are adopted to generate potent molecular entities. Pancreatic cancer is difficult to treat due to overexpression of the VEGFR2 protein. VEGFR2 is targeted to design the molecules of quinoline-coupled pyridine moieties and is docked to evaluate the protein-ligand interaction at the binding site. The binding affinity of conjugates revealed the potency and capability of ligands to inhibit the VEGFR2 pathway. The in-silico ADMET properties determined their inherent pharmacokinetic feasibility. The synthesized conjugates have been evaluated by MTT assay against the human pancreatic cancer cell lines (PANC-1). Among the series, compounds 5d, 5e, and 5h exhibited a greater inhibitory activity against the cell lines with an IC₅₀ value of 82.32±1.38, 54.74±1.18 and 80.35±1.68 μM. In the present exploration, 5e exhibited greater inhibitory activity and it could be a promising lead for the development of new chemotherapeutics against pancreatic cancer.

Benzoxazole derivatives as new VEGFR-2 inhibitors and apoptosis inducers: design, synthesis, in silico studies, and antiproliferative evaluation

In this study, a set of novel benzoxazole derivatives were designed, synthesised, and biologically evaluated as potential VEGFR-2 inhibitors. Five compounds (12d, 12f, 12i, 12l, and 13a) displayed high growth inhibitory activities against HepG2 and MCF-7 cell lines and were further investigated for their VEGFR-2 inhibitory activities. The most potent anti-proliferative member 12 l (IC₅₀ = 10.50 μM and 15.21 μM against HepG2 and MCF-7, respectively) had the most promising VEGFR-2 inhibitory activity (IC₅₀ = 97.38 nM). A further biological evaluation revealed that compound 12l could arrest the HepG2 cell growth mainly at the Pre-G1 and G1 phases. Furthermore, compound 12l could induce apoptosis in HepG2 cells by 35.13%. likely, compound 12l exhibited a significant elevation in caspase-3 level (2.98-fold) and BAX (3.40-fold), and a significant reduction in Bcl-2 level (2.12-fold). Finally, docking studies indicated that 12l exhibited interactions with the key amino acids in a similar way to sorafenib.

New benzoxazole derivatives as potential VEGFR-2 inhibitors and apoptosis inducers: design, synthesis, anti-proliferative evaluation, flowcytometric analysis, and in silico studies

A new series of benzoxazole derivatives were designed and synthesised to have the main essential pharmacophoric features of VEGFR-2 inhibitors. Cytotoxic activities were evaluated for all derivatives against two human cancer cell lines, MCF-7 and HepG2. Also, the effect of the most cytotoxic derivatives on VEGFR-2 protein concentration was assessed by ELISA. Compounds 14o, 14l, and 14b showed the highest activities with VEGFR-2 protein concentrations of 586.3, 636.2, and 705.7 pg/ml, respectively. Additionally, the anti-angiogenic property of compound 14b against human umbilical vascular endothelial cell (HUVEC) was performed using a wound healing migration assay. Compound 14b reduced proliferation and migratory potential of HUVEC cells. Furthermore, compound 14b was subjected to further biological investigations including cell cycle and apoptosis analyses. Compound 14b arrested the HepG2 cell growth at the Pre-G1 phase and induced apoptosis by 16.52%, compared to 0.67% in the control (HepG2) cells. The effect of apoptosis was buttressed by a 4.8-fold increase in caspase-3 level compared to the control cells. Besides, different in silico docking studies were also performed to get better insights into the possible binding mode of the target compounds with VEGFR-2 active sites.

The use of machine learning modeling, virtual screening, molecular docking, and molecular dynamics simulations to identify potential VEGFR2 kinase inhibitors

Targeting the signaling pathway of the Vascular endothelial growth factor receptor-2 is a promising approach that has drawn attention in the quest to develop novel anti-cancer drugs and cardiovascular disease treatments. We construct a screening pipeline using machine learning classification integrated with similarity checks of approved drugs to find new inhibitors. The statistical metrics reveal that the random forest approach has slightly better performance. By further similarity screening against several approved drugs, two candidates are selected. Analysis of absorption, distribution, metabolism, excretion, and toxicity, along with molecular docking and dynamics are performed for the two candidates with regorafenib as a reference. The binding energies of molecule1, molecule2, and regorafenib are - 89.1, - 95.3, and - 87.4 (kJ/mol), respectively which suggest candidate compounds have strong binding to the target. Meanwhile, the median lethal dose and maximum tolerated dose for regorafenib, molecule1, and molecule2 are predicted to be 800, 1600, and 393 mg/kg, and 0.257, 0.527, and 0.428 log mg/kg/day, respectively. Also, the inhibitory activity of these compounds is predicted to be 7.23 and 7.31, which is comparable with the activity of pazopanib and sorafenib drugs. In light of these findings, the two compounds could be further investigated as potential candidates for anti-angiogenesis therapy.

Discovery of new nicotinamides as apoptotic VEGFR-2 inhibitors: virtual screening, synthesis, anti-proliferative, immunomodulatory, ADMET, toxicity, and molecular dynamic simulation studies

A library of modified VEGFR-2 inhibitors was designed as VEGFR-2 inhibitors. Virtual screening was conducted for the hypothetical library using in silico docking, ADMET, and toxicity studies. Four compounds exhibited high in silico affinity against VEGFR-2 and an acceptable range of the drug-likeness. These compounds were synthesised and subjected to in vitro cytotoxicity assay against two cancer cell lines besides VEGFR-2 inhibitory determination. Compound D-1 showed cytotoxic activity against HCT-116 cells almost double that of sorafenib. Compounds A-1, C-6, and D-1 showed good IC₅₀ values against VEGFR-2. Compound D-1 markedly increased the levels of caspase-8 and BAX expression and decreased the anti-apoptotic Bcl-2 level. Additionally, compound D-1 caused cell cycle arrest at pre-G1 and G2-M phases in HCT-116 cells and induced apoptosis at both early and late apoptotic stages. Compound D-1 decreased the level of TNF-α and IL6 and inhibited TNF-α and IL6. MD simulations studies were performed over 100 ns.

Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD)

There have been numerous advances in the development of computational and statistical methods and applications of big data and artificial intelligence (AI) techniques for computer-aided drug design (CADD). Drug design is a costly and laborious process considering the biological complexity of diseases. To effectively and efficiently design and develop a new drug, CADD can be used to apply cutting-edge techniques to various limitations in the drug design field. Data pre-processing approaches, which clean the raw data for consistent and reproducible applications of big data and AI methods are introduced. We include the current status of the applicability of big data and AI methods to drug design areas such as the identification of binding sites in target proteins, structure-based virtual screening (SBVS), and absorption, distribution, metabolism, excretion and toxicity (ADMET) property prediction. Data pre-processing and applications of big data and AI methods enable the accurate and comprehensive analysis of massive biomedical data and the development of predictive models in the field of drug design. Understanding and analyzing biological, chemical, or pharmaceutical architectures of biomedical entities related to drug design will provide beneficial information in the biomedical big data era.

Cyclodextrin Dispersion of Mebendazole and Flubendazole Improves In Vitro Antiproliferative Activity

Mebendazole and flubendazole are antihelmintic drugs that have re-entered the research spotlight due to their exhibited anticancer effects, thus making them strong candidates as repurposed drugs. However, these benzimidazole derivatives exhibit poor solubility in water and various organic solvents, which limits their bioavailability. With the aim of obtaining an improved drug solubility and increased biological effect, mebendazole and flubendazole were complexed with 2-hydroxypropyl-β-cyclodextrin (HPBCD). The binary 1:1 conjugates were physicochemically evaluated by X-ray diffraction, thermal analysis, and FTIR spectroscopy, revealing the formation of physical mixtures. The increased aqueous solubility of the binary 1:1 conjugates vs. pure benzimidazole compounds was demonstrated by performing dissolution tests. The in vitro antiproliferative activity of mebendazole and flubendazole, as well as their combination with HPBCD, was tested on two cancer cell lines, human melanoma—A375 and pulmonary adenocarcinoma—A549 by the MTT assay. The cytotoxic activity manifested in a dose-dependent manner while the presence of HPBCD increased the antiproliferative activity against the targeted cells. Treatment of A375 and A549 cell lines with the binary conjugates induced a significant inhibition of mitochondrial respiration, as revealed by high-resolution respirometry studies. Molecular docking analysis showed that one of the mechanisms related to MEB and FLU cytotoxic activity may be due to the inhibition of MEK/ERK proteins.

Synthesis and Rational Design of New Appended 1,2,3-Triazole-uracil Ensembles as Promising Anti-Tumor Agents via In Silico VEGFR-2 Transferase Inhibition

Angiogenesis inhibition is a key step towards the designing of new chemotherapeutic agents. In a view to preparing new molecular entities for cancer treatment, eighteen 1,2,3-triazole-uracil ensembles 5a-r were designed and synthesized via the click reaction. The ligands were well characterized using 1H-, 13C-NMR, elemental analysis and ESI-mass spectrometry. The in silico binding propinquities of the ligands were studied sequentially in the active region of VEGFR-2 using the Molegro virtual docker. All the compounds produced remarkable interactions and potentially inhibitory ligands against VEGFR-2 were obtained with high negative binding energies. Drug-likeness was assessed from the ADME properties. Cytotoxicity of the test compounds was measured against HeLa and HUH-7 tumor cells and NIH/3T3 normal cells by MTT assay. Compound 5h showed higher growth inhibition activity than the positive control, 5-fluorouracil (5-FU), against both HeLa and HUH-7 cells with IC50 values of 4.5 and 7.7 μM respectively. Interestingly, the compounds 5a-r did not show any cytotoxicity towards the normal cell lines. The results advance the position of substituted triazoles in the area of drug design with no ambiguity.

ADMET profile and virtual screening of plant and microbial natural metabolites as SARS-CoV-2 S1 glycoprotein receptor binding domain and main protease inhibitors

In an attempt to search for selective inhibitors against the SARS-CoV-2 which caused devastating of lives and livelihoods across the globe, 415 natural metabolites isolated from several plants, fungi and bacteria, belonging to different classes, were investigated. The drug metabolism and safety profiles were computed in silico and the results showed seven compounds namely fusaric acid, jasmonic acid, jasmonic acid methyl ester, putaminoxin, putaminoxin B and D, and stagonolide K were predicted to having considerable absorption, metabolism, distribution and excretion parameters (ADME) and safety indices. Molecular docking against the receptor binding domain (RBD) of spike glycoprotein (S1) and the main protease (Mpro) exposed the compounds having better binding affinity to main protease as compared to the S1 receptor binding domain. The docking results were compared to an antiviral drug penciclovir reportedly of clinical significance in treating the SARS-CoV-2 infected patients. The results demonstrated the test compounds jasmonic acid, putaminoxins B and D bound to the HIS-CYS catalytic dyad as well as to other residues within the MPro active site with much greater affinity than penciclovir. The findings of the study suggest that these compounds could be explored as potential SARS-CoV-2 inhibitors, and could further be combined with the experimental investigations to develop effective therapeutics to deal with the present pandemic.

Machine and deep learning approaches for cancer drug repurposing

Knowledge of the underpinnings of cancer initiation, progression and metastasis has increased exponentially in recent years. Advanced "omics" coupled with machine learning and artificial intelligence (deep learning) methods have helped elucidate targets and pathways critical to those processes that may be amenable to pharmacologic modulation. However, the current anti-cancer therapeutic armamentarium continues to lag behind. As the cost of developing a new drug remains prohibitively expensive, repurposing of existing approved and investigational drugs is sought after given known safety profiles and reduction in the cost barrier. Notably, successes in oncologic drug repurposing have been infrequent. Computational in-silico strategies have been developed to aid in modeling biological processes to find new disease-relevant targets and discovering novel drug-target and drug-phenotype associations. Machine and deep learning methods have especially enabled leaps in those successes. This review will discuss these methods as they pertain to cancer biology as well as immunomodulation for drug repurposing opportunities in oncologic diseases.

Design, synthesis, and anti-proliferative evaluation of new quinazolin-4(3H)-ones as potential VEGFR-2 inhibitors

Inhibiting VEGFR-2 has been set up as a therapeutic strategy for treatment of cancer. Thus, nineteen new quinazoline-4(3H)-one derivatives were designed and synthesized. Preliminary cytotoxicity studies of the synthesized compounds were evaluated against three human cancer cell lines (HepG-2, MCF-7 and HCT-116) using MTT assay method. Doxorubicin and sorafenib were used as positive controls. Five compounds were found to have promising cytotoxic activities against all cell lines. Compound 16_f, containing a 2-chloro-5-nitrophenyl group, has emerged as the most active member. It was approximately 4.39-, 5.73- and 1.96-fold more active than doxorubicin and 3.88-, 5.59- and 1.84-fold more active than sorafenib against HepG2, HCT-116 and MCF-7 cells, respectively. The most active cytotoxic agents were further evaluated in vitro for their VEGFR-2 inhibitory activities. The results of in vitro VEGFR-2 inhibition were consistent with that of the cytotoxicity data. Molecular docking of these compounds into the kinase domain, moreover, supported the results.

Predicting coated-nanoparticle drug release systems with perturbation-theory machine learning (PTML) models

Nanoparticles (NPs) decorated with coating agents (polymers, gels, proteins, etc.) form Nanoparticle Drug Delivery Systems (DDNS), which are of high interest in nanotechnology and biomaterials science. There have been increasing reports of experimental data sets of biological activity, toxicity, and delivery properties of DDNS. However, these data sets are still dispersed and not as large as the datasets of DDNS components (NP and drugs). This has prompted researchers to train Machine Learning (ML) algorithms that are able to design new DDNS based on the properties of their components. However, most ML models reported up to date predictions of the specific activities of NP or drugs over a determined target or cell line. In this paper, we combine Perturbation Theory and Machine Learning (PTML algorithm) to train a model that is able to predict the best components (NP, coating agent, and drug) for DDNS design. In so doing, we downloaded a dataset of >30 000 preclinical assays of drugs from ChEMBL. We also downloaded an NP data set formed by preclinical assays of coated Metal Oxide Nanoparticles (MONPs) from public sources. Both the drugs and NP datasets of preclinical assays cover multiple conditions of assays that can be listed as two arrays, namely, cjdrug and cjNP. The cjdrug array includes >504 biological activity parameters (c0drug), >340 target proteins (c1drug), >650 types of cells (c2drug), >120 assay organisms (c3drug), and >60 assay strains (c4drug). On the other hand, the cjNP array includes 3 biological activity parameters (c0NP), 40 types of proteins (c1NP), 10 shapes of nanoparticles (c2NP), 6 assay media (c3NP), and 12 coating agents (c4NP). After downloading, we pre-processed both the data sets by separate calculation PT operators that are able to account for changes (perturbations) in the drug, coating agents, and NP chemical structure and/or physicochemical properties as well as for the assay conditions. Next, we carry out an information fusion process to form a final dataset of above 500 000 DDNS (drug + MONP pairs). We also trained other linear and non-linear PTML models using R studio scripts for comparative purposes. To the best of our knowledge, this is the first multi-label PTML model that is useful for the selection of drugs, coating agents, and metal or metal-oxide nanoparticles to be assembled in order to design new DDNS with optimal activity/toxicity profiles.

Cell Growth Stimulation, Cell Cycle Alternation, and Anti-Apoptosis Effects of Bovine Bone Collagen Hydrolysates Derived Peptides on MC3T3-E1 Cells Ex Vivo

Bovine bone collagen hydrolysates promote bone formation through regulating bone growth. However, the peptide sequences within these isolates have not been characterized. In this study, twenty-nine peptides from bovine bone collagen hydrolysates were purified and identified using nano-HPLC-MS-MS and Peak Studio analysis. HHGDQGAPGAVGPAGPRGPAGPSGPAGKDGR (Deamidation) and GPAGANGDRGEAGPAGPAGPAGPR (Deamidation) enhanced cell viability, inhibited apoptosis, and significantly altered the cell cycle of MC3T3-E1 osteoblast cells. These peptides were selected to perform molecular docking analysis to examine the mechanism underlying these bioactivities. Molecular docking analysis showed that these two peptides formed hydrophobic interactions and hydrogen bonds with epidermal growth factor receptor (EGFR) to activate the EGFR-signaling pathway, which may explain their bioactivity. These findings indicate that these and other similar peptides might be candidates for the treatment of osteoporosis.

Protein kinase inhibitors' classification using K-Nearest neighbor algorithm

Protein kinases are enzymes acting as a source of phosphate through ATP to regulate protein biological activities by phosphorylating groups of specific amino acids. For that reason, inhibiting protein kinases with an active small molecule plays a significant role in cancer treatment. To achieve this aim, computational drug design, especially QSAR model, is one of the best economical approaches to reduce time and save in costs. In this respect, active inhibitors are attempted to be distinguished from inactive ones using hybrid QSAR model. Therefore, genetic algorithm and K-Nearest Neighbor method were suggested as a dimensional reduction and classification model, respectively. Finally, to evaluate the proposed model's performance, support vector machine and Naïve Bayesian algorithm were examined. The outputs of the proposed model demonstrated significant superiority to other QSAR models.

Evaluation and Identification of the Neuroprotective Compounds of Xiaoxuming Decoction by Machine Learning: A Novel Mode to Explore the Combination Rules in Traditional Chinese Medicine Prescription

Xiaoxuming decoction (XXMD), a classic traditional Chinese medicine (TCM) prescription, has been used as a therapeutic in the treatment of stroke in clinical practice for over 1200 years. However, the pharmacological mechanisms of XXMD have not yet been elucidated. The purpose of this study was to develop neuroprotective models for identifying neuroprotective compounds in XXMD against hypoxia-induced and H2O2-induced brain cell damage. In this study, a phenotype-based classification method was designed by machine learning to identify neuroprotective compounds and to clarify the compatibility of XXMD components. Four different single classifiers (AB, kNN, CT, and RF) and molecular fingerprint descriptors were used to construct stacked naïve Bayesian models. Among them, the RF algorithm had a better performance with an average MCC value of 0.725±0.014 and 0.774±0.042 from 5-fold cross-validation and test set, respectively. The probability values calculated by four models were then integrated into a stacked Bayesian model. In total, two optimal models, s-NB-1-LPFP6 and s-NB-2-LPFP6, were obtained. The two validated optimal models revealed Matthews correlation coefficients (MCC) of 0.968 and 0.993 for 5-fold cross-validation and of 0.874 and 0.959 for the test set, respectively. Furthermore, the two models were used for virtual screening experiments to identify neuroprotective compounds in XXMD. Ten representative compounds with potential therapeutic effects against the two phenotypes were selected for further cell-based assays. Among the selected compounds, two compounds significantly inhibited H2O2-induced and Na2S2O4-induced neurotoxicity simultaneously. Together, our findings suggested that machine learning algorithms such as combination Bayesian models were feasible to predict neuroprotective compounds and to preliminarily demonstrate the pharmacological mechanisms of TCM.

Combined pharmacophore modeling, 3D-QSAR and docking studies to identify novel HDAC inhibitors using drug repurposing

Histone deacetylases (HDACs), a critical family of epigenetic enzymes, has emerged as a promising target for antitumor drugs. Here, we describe our protocol of virtual screening in identification of novel potential HDAC inhibitors through pharmacophore modeling, 3D-QSAR, molecular docking and molecular dynamics (MD) simulation. Considering the limitation of current virtual screening works, drug repurposing strategy was applied to discover druggable HDAC inhibitor. The ligand-based pharmacophore and 3D-QSAR models were established, and their reliability was validated by different methods. Then, the DrugBank database was screened, followed by molecular docking. MD simulation (100 ns) was performed to further study the stability of ligand binding modes. Finally, results indicated the hit DB03889 with high in silico inhibitory potency was suitable for further experimental analysis.Communicated by Ramaswamy H. Sarma.

Design and virtual screening of novel fluoroquinolone analogs as effective mutant DNA GyrA inhibitors against urinary tract infection-causing fluoroquinolone resistant Escherichia coli

Fluoroquinolones (FQs) belong to the class of quinolone drugs that are used to treat Urinary tract infections (UTIs) through inhibition of E. coli DNA gyrase. Resistance to FQs poses a serious problem in the treatment against resistant strains of E. coli which are associated with Ser83 to Leu and Asp87 to Asn mutations at the quinolone resistance determining region (QRDR) of the GyrA subunit of DNA gyrase. Mutant DNA GyrA (mtDNA GyrA) is deemed to be a significant target for the development of novel FQ drugs. Due to resistance to FQ drugs, discovery or development of novel FQs is crucial to inhibit the mtDNA GyrA. Hence, the present study attempts to design and develop novel FQs that are efficient against resistant E. coli strains. A three-dimensional structure of the mtDNA GyrA protein was developed by homology modeling, following which 204 novel FQ analogs were designed using target based SAR. The designed ligands were then screened using molecular docking studies, through which the pattern of interaction between the ligands and the target protein was studied. As expected, the results of the docking study revealed that the molecules FQ-147, FQ-151 and FQ-37 formed hydrogen bonding and Van der Waals interactions with Leu83 and Asn87 (mutated residues), respectively. Further, the wild-type (WT), mtDNA GyrA and docking complex were studied by molecular dynamics (MD) simulations. Subsequently, all the screened compounds were subjected to a structure and ligand based pharmacophore study followed by ADMET and toxicity (TOPKAT) prediction. Finally, eighteen hit FQ analogs which showed good results for the following properties, viz., best binding score, estimated activity (MIC value) and calculated drug-like properties, and least toxicity, were shortlisted and identified as potential leads to treat UTI caused by FQ resistant E. coli. Apart from development of novel drug candidates for inhibition of mtDNA GyrA, the present study also contributes towards a superior comprehension of the interaction pattern of ligands in the target protein. To a more extensive degree, the present work will be useful for the rational design of novel and potent drugs for UTIs.

Design and development of novel fluoroquinolones analogs using target (mutant DNA GyrA), ligand-based SAR and virtual screening techniques.