The severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection drove the global coronavirus disease 2019 (COVID-19) pandemic, causing a huge loss of human life and a negative impact on economic development. It is an urgent necessity to explore potential drugs against viruses, such as SARS-CoV-2. Silymarin, a mixture of herb-derived polyphenolic flavonoids extracted from the milk thistle, possesses potent antioxidative, anti-apoptotic, and anti-inflammatory properties. Accumulating research studies have demonstrated the killing activity of silymarin against viruses, such as dengue virus, chikungunya virus, and hepatitis C virus. However, the anti-COVID-19 mechanisms of silymarin remain unclear. In this study, multiple disciplinary approaches and methodologies were applied to evaluate the potential mechanisms of silymarin as an anti-viral agent against SARS-CoV-2 infection. In silico approaches such as molecular docking, network pharmacology, and bioinformatic methods were incorporated to assess the ligand-protein binding properties and analyze the protein-protein interaction network. The DAVID database was used to analyze gene functions, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment. TCMSP and GeneCards were used to identify drug target genes and COVID-19-related genes. Our results revealed that silymarin compounds, such as silybin A/B and silymonin, displayed triplicate functions against SARS-CoV-2 infection, including directly binding with human angiotensin-converting enzyme 2 (ACE2) to inhibit SARS-CoV-2 entry into the host cells, directly binding with viral proteins RdRp and helicase to inhibit viral replication and proliferation, and regulating host immune response to indirectly inhibit viral infection. Specifically, the targets of silymarin molecules in immune regulation were screened out, such as proinflammatory cytokines TNF and IL-6 and cell growth factors VEGFA and EGF. In addition, the molecular mechanism of drug-target protein interaction was investigated, including the binding pockets of drug molecules in human ACE2 and viral proteins, the formation of hydrogen bonds, hydrophobic interactions, and other drug-protein ligand interactions. Finally, the drug-likeness results of candidate molecules passed the criteria for drug screening. Overall, this study demonstrates the molecular mechanism of silymarin molecules against SARS-CoV-2 infection.

Recent studies suggest RNAs act as promising drug targets. However, limited development has been achieved in detecting RNA-ligand interactions. To guide the discovery of RNA-binding ligands, it is necessary to characterize them comprehensively, especially in the binding specificity, binding affinity and drug-like properties. We established a database, RNALID (http://biomed.nscc-gz.cn/RNALID/html/index.html#/database), which collects RNA-ligand interactions validated by low-throughput experiment. RNALID contains 358 RNA-ligand interactions. Comparing to the fellow database, 94.5% of ligands in RNALID are completely or partially novel collections, and 51.78% have novel two-dimensional (2D) structures. Through the analysis of ligand structure, binding affinity and cheminformatic parameters we found that multivalent (MV) ligands mainly binding to RNA repeats are more structurally conserved in both 2D and 3D structures than other ligand types, exhibit higher binding specificity and binding affinity than ligands binding to non-repeat RNAs, but deviate far from the Lipinski\'s rule of five. In contrary, small molecule (SM) ligands binding to virus RNA exhibit higher affinity and more resemble protein-ligands, but potentially possess low binding specificity. Further analysis on 28 detailed drug-likeness properties indicated that RNA-ligands\' development need to balance between the binding affinity and the drug-likeness because of the significant linear co-relationship between the two. Comparing RNALID ligands to FDA-approved drugs and ligands without bioactivity indicated that RNA-binding ligands are different from them in chemical properties, structural properties and drug-likeness. Thus, characterizing the RNA-ligand interactions in RNALID in multiple respects provides new insights into discovering and designing druggable ligands binding with RNA.

Alzheimer\'s disease (AD) is a chronic, progressive brain degenerative disease that is common in the elderly. So far, there is no effective treatment. The multi-target-directed ligands (MTDLs) strategy has been recognised as the most promising approach due to the complexity of the pathogenesis of AD. Herein, novel salicylic acid-donepezil-rivastigmine hybrids were designed and synthesised. The bioactivity results exhibited that 5a was a reversible and selective eqBChE inhibitor (IC50 = 0.53 μM), and the docking provided the possible mechanism. Compound 5a also displayed potential anti-inflammatory effects and significant neuroprotective effect. Moreover, 5a exhibited favourable stabilities in artificial gastrointestinal solution and plasma. Finally, 5a demonstrated potential cognitive improvement in scopolamine-induced cognitive dysfunction. Hence, 5a was a potential multifunctional lead compound against AD.

Amongst the most prevalent malignancies worldwide, head and neck squamous cell carcinoma (HNSCC) is characterized by high morbidity and mortality. The failure of standard treatment modalities, such as surgery, radiotherapy, and chemotherapy, demands the need for in-depth understanding of the complex signaling networks involved in the development of treatment resistance. A tumor\'s invasive growth and high levels of intrinsic or acquired treatment resistance are the primary causes of treatment failure. This may be a result of the presence of HNSCC\'s cancer stem cells, which are known to have self-renewing capabilities that result in therapeutic resistance. Using bioinformatics methods, we discovered that elevated expressions of MET, STAT3, and AKT were associated with poor overall survival in HNSCC patients. We then evaluated the therapeutic potential of our newly synthesized small molecule HNC018 towards its potential as a novel anticancer drug. Our computer-aided structure characterization and target identification study predicted that HNC018 could target these oncogenic markers implicated in HNSCC. Subsequently, the HNC018 has demonstrated its anti-proliferative and anticancer activities towards the head and neck squamous cell carcinoma cell lines, along with displaying the stronger binding affinities towards the MET, STAT3, and AKT than the standard drug cisplatin. Reduction in the clonogenic and tumor-sphere-forming ability displays HNC018\'s role in decreasing the tumorigenicity. Importantly, an vivo study has shown a significant delay in tumor growth in HNC018 alone or in combination with cisplatin-treated xenograft mice model. Collectively with our findings, HNC018 highlights the desirable properties of a drug-like candidate and could be considered as a novel small molecule for treating head and neck squamous cell carcinoma.

Glycogen synthase kinase-3 (GSK3β), a serine/threonine protein kinase, has been discovered as a novel target for anticancer drugs. Although GSK3β is involved in multiple pathways linked to the etiology of various cancers, no specific GSK3β inhibitor has been authorized for cancer therapy. Most of its inhibitors have toxicity effects therefore, there is a need to develop safe and more potent inhibitors. In this study, a library of 4,222 anti-cancer compounds underwent rigorous computational screening to identify potential candidates for targeting the binding pocket of GSK3β. The screening process involved various stages, including docking-based virtual screening, physicochemical and ADMET analysis, and molecular dynamics simulations. Ultimately, two hit compounds, BMS-754807 and GSK429286A, were identified as having high binding affinities to GSK3β. BMS-754807 and GSK429286A exhibited binding affinities of -11.9, and -9.8 kcal/mol, respectively, which were greater than that of the positive control (-7.6 kcal/mol). Further, molecular dynamics simulations for 100 ns were employed to optimize the interaction between the compounds and GSK3β, and the simulations demonstrated that the interaction was stable and consistent throughout the study. These hits were also anticipated to have good drug-like properties. Finally, this study suggests that BMS-754807 and GSK429286A may undergo experimental validation to evaluate their potential as cancer treatments in clinical settings.

Senkyunolide I (SI) is a natural phthalide that has drawn increasing interest for its potential as a cardio-cerebral vascular drug candidate. In this paper, the botanical sources, phytochemical characteristics, chemical and biological transformations, pharmacological and pharmacokinetic properties, and drug-likeness of SI are reviewed through a comprehensive literature survey, in order to provide support for its further research and applications. In general, SI is mainly distributed in Umbelliferae plants, and it is relatively stable to heat, acid, and oxygen, with good blood-brain barrier (BBB) permeability. Substantial studies have established reliable methods for the isolation, purification, and content determination of SI. Its pharmacological effects include analgesic, anti-inflammatory, antioxidant, anti-thrombotic, anti-tumor effects, alleviating ischemia-reperfusion injury, etc. Pharmacokinetic parameters indicate that its metabolic pathway is mainly phase Ⅱ metabolism, and it is rapidly absorbed in vivo and widely distributed in the kidneys, liver, and lungs.

We construct a protein-protein interaction (PPI) targeted drug-likeness dataset and propose a deep molecular generative framework to generate novel drug-likeness molecules from the features of the seed compounds. This framework gains inspiration from published molecular generative models, uses the key features associated with PPI inhibitors as input and develops deep molecular generative models for de novo molecular design of PPI inhibitors. For the first time, quantitative estimation index for compounds targeting PPI was applied to the evaluation of the molecular generation model for de novo design of PPI-targeted compounds. Our results estimated that the generated molecules had better PPI-targeted drug-likeness and drug-likeness. Additionally, our model also exhibits comparable performance to other several state-of-the-art molecule generation models. The generated molecules share chemical space with iPPI-DB inhibitors as demonstrated by chemical space analysis. The peptide characterization-oriented design of PPI inhibitors and the ligand-based design of PPI inhibitors are explored. Finally, we recommend that this framework will be an important step forward for the de novo design of PPI-targeted therapeutics.

Proteolysis targeting chimaeras (PROTACs) is a cutting edge and rapidly growing technique for new drug discovery and development. Currently, the largest challenge in the molecular design and drug development of PROTACs is efficient identification of potent and drug-like degraders. This review aims to comprehensively summarize and analyse state-of-the-art methods and strategies in the design of PROTACs. We provide a detailed illustration of the general principles and tactics for designing potent PROTACs, highlight representative case studies, and discuss the advantages and limitations of these strategies. Particularly, structure-based rational PROTAC design and emerging new types of PROTACs (e.g., homo-PROTACs, multitargeting PROTACs, photo-control PROTACs and PROTAC-based conjugates) will be focused on.

Inflammation is an active defense response of the body against external stimuli. Long term low-grade inflammation has been considered as a deteriorated factor for aging, cancer, neurodegeneration and metabolic disorders. The clinically used glucocorticoids and non-steroidal anti-inflammatory drugs are not suitable for chronic inflammation. Therefore, it\'s urgent to discover and develop new effective and safe drugs to attenuate inflammation. Clerodane diterpenoids, a class of bicyclic diterpenoids, are widely distributed in plants of the Labiatae, Euphorbiaceae and Verbenaceae families, as well as fungi, bacteria, and marine sponges. Dozens of anti-inflammatory clerodane diterpenoids have been identified on different assays, both in vitro and in vivo. In the current review, the up-to-date research progresses of anti-inflammatory clerodane diterpenoids were summarized, and their druglikeness was analyzed, which provided the possibility for further development of anti-inflammatory drugs.

High-throughput screening (HTS) and virtual screening (VS) have been widely used to identify potential hits from large chemical libraries. However, the frequent occurrence of \'noisy compounds\' in the screened libraries, such as compounds with poor drug-likeness, poor selectivity or potential toxicity, has greatly weakened the enrichment capability of HTS and VS campaigns. Therefore, the development of comprehensive and credible tools to detect noisy compounds from chemical libraries is urgently needed in early stages of drug discovery.
In this study, we developed a freely available integrated python library for negative design, called Scopy, which supports the functions of data preparation, calculation of descriptors, scaffolds and screening filters, and data visualization. The current version of Scopy can calculate 39 basic molecular properties, 3 comprehensive molecular evaluation scores, 2 types of molecular scaffolds, 6 types of substructure descriptors and 2 types of fingerprints. A number of important screening rules are also provided by Scopy, including 15 drug-likeness rules (13 drug-likeness rules and 2 building block rules), 8 frequent hitter rules (four assay interference substructure filters and four promiscuous compound substructure filters), and 11 toxicophore filters (five human-related toxicity substructure filters, three environment-related toxicity substructure filters and three comprehensive toxicity substructure filters). Moreover, this library supports four different visualization functions to help users to gain a better understanding of the screened data, including basic feature radar chart, feature-feature-related scatter diagram, functional group marker gram and cloud gram.
Scopy provides a comprehensive Python package to filter out compounds with undesirable properties or substructures, which will benefit the design of high-quality chemical libraries for drug design and discovery. It is freely available at https://github.com/kotori-y/Scopy.