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In this study, we aimed to develop a pharmacophore-based three-dimensional quantitative structure activity relationship (3D-QSAR) for a set including sixty-two cytotoxic quinolines (1-62) as anticancer agents with tubulin inhibitory activity. A total of 279 pharmacophore hypotheses were generated based on the survival score to build QSAR models. A six-point pharmacophore model (AAARRR.1061) was identified as the best model which consisted of three hydrogen bond acceptors (A) and three aromatic ring (R) features. The model showed a high correlation coefficient (R2=0.865), cross-validation coefficient (Q2=0.718), and F value (72.3). The best pharmacophore model was then validated by the Y-Randomization test and ROC-AUC analysis. The generated 3D contour maps were used to reveal the structure activity relationship of the compounds. The IBScreen database was screened against AAARRR.1061, and after calculating ADMET properties, 10 compounds were selected for further docking study. Molecular docking analysis showed that compound STOCK2S-23597 with the highest docking score (-10.948 kcal/mol) had hydrophobic interactions and can form four hydrogen bonds with active site residues.

Anshen Dingzhi prescription (ADP) is a classic prescription of traditional Chinese medicine, which has been used in the treatment of neuropsychiatric diseases. However, its treatment of breast cancer-related post-traumatic stress disorder (BC-PTSD) lacks clinical research evidence and its mechanism is not clear. The present study investigated the efficacy and action mechanism of ADP against BC-PTSD. The results of the clinical trial showed that after 4 weeks of treatment, both groups showed reduced post-traumatic stress disorder checklist-civilian version (PCL-C), Pittsburgh sleep quality index (PSQI), self-rating depression scale (SDS) and self-rating anxiety scale (SAS) scores, and increased functional assessment of cancer therapy-breast (FACT-B) scores. The serum cortisol (CORT), tumor necrosis factor-alpha (TNF-α) and interleukin-1 beta (IL-1β) levels were decreased and brain-derived neurotrophic factor (BDNF) level were increased, and the improvement of serum TNF-α, IL-1β, and BDNF in treatment group was better than that of the control group. The overall treatment efficacy in the treatment group (43.90%) was superior to that in the control group (23.81%), and the overall incidence of adverse effects was lower than that in the control group. The results of network analysis and molecular docking showed that ADP blood components could act on IL1B, TNF, and BDNF. ADP contributes to the treatment of BC-PTSD symptoms, with a mechanism possibly related to its regulatory effect on TNF-α, IL-1β, and BDNF levels. Trial registration: Chinese Clinical Trial Registry, http://www.chictr.org.cn,ChiCTR2300077801

The rapid advances of 3D techniques for the structural determination of proteins and the development of numerous computational methods and strategies have led to identifying highly active compounds in computer drug design. Molecular docking is a method widely used in high-throughput virtual screening campaigns to filter potential ligands targeted to proteins. A great variety of docking programs are currently available, which differ in the algorithms and approaches used to predict the binding mode and the affinity of the ligand. All programs heavily rely on scoring functions to accurately predict ligand binding affinity, and despite differences in performance, none of these docking programs is preferable to the others. To overcome this problem, consensus scoring methods improve the outcome of virtual screening by averaging the rank or score of individual molecules obtained from different docking programs. The successful application of consensus docking in high-throughput virtual screening highlights the need to optimize the predictive power of molecular docking methods.

On average, an approved drug currently costs US$2–3 billion and takes more than 10 years to develop 1 . In part, this is due to expensive and time-consuming wet-laboratory experiments, poor initial hit compounds and the high attrition rates in the (pre-)clinical phases. Structure-based virtual screening has the potential to mitigate these problems. With structure-based virtual screening, the quality of the hits improves with the number of compounds screened 2 . However, despite the fact that large databases of compounds exist, the ability to carry out large-scale structure-based virtual screening on computer clusters in an accessible, efficient and flexible manner has remained difficult. Here we describe VirtualFlow, a highly automated and versatile open-source platform with perfect scaling behaviour that is able to prepare and efficiently screen ultra-large libraries of compounds. VirtualFlow is able to use a variety of the most powerful docking programs. Using VirtualFlow, we prepared one of the largest and freely available ready-to-dock ligand libraries, with more than 1.4 billion commercially available molecules. To demonstrate the power of VirtualFlow, we screened more than 1 billion compounds and identified a set of structurally diverse molecules that bind to KEAP1 with submicromolar affinity. One of the lead inhibitors (iKeap1) engages KEAP1 with nanomolar affinity (dissociation constant ( K d ) = 114 nM) and disrupts the interaction between KEAP1 and the transcription factor NRF2. This illustrates the potential of VirtualFlow to access vast regions of the chemical space and identify molecules that bind with high affinity to target proteins. VirtualFlow, an open-source drug discovery platform, enables the efficient preparation and virtual screening of ultra-large ligand libraries to identify molecules that bind with high affinity to target proteins.

Virtual screening is a useful in silico approach to identify potential leads against various targets. It is known that carbapenems (doripenem and faropenem) do not show any reasonable inhibitory activities against L,D-transpeptidase 5 (Ldt Mt5 ) and also an adduct of meropenem exhibited slow acylation. Since these drugs are active against L,D-transpeptidase 2 (Ldt Mt2 ), understanding the differences between these two enzymes is essential. In this study, a ligand-based virtual screening of 12,766 compounds followed by molecular dynamics (MD) simulations was applied to identify potential leads against Ldt Mt5 . To further validate the obtained virtual screening ranking for Ldt Mt5 , we screened the same libraries of compounds against Ldt Mt2 which had more experimetal and calculated binding energies reported. The observed consistency between the binding affinities of Ldt Mt2 validates the obtained virtual screening binding scores for Ldt Mt5 . We subjected 37 compounds with docking scores ranging from − 7.2 to − 9.9 kcal mol −1 obtained from virtual screening for further MD analysis. A set of compounds ( n  = 12) from four antibiotic classes with ≤ − 30 kcal mol −1 molecular mechanics/generalized born surface area ( MM-GBSA) binding free energies (ΔG bind ) was characterized. A final set of that, all β -lactams ( n  = 4), was considered. The outcome of this study provides insight into the design of potential novel leads for Ldt Mt5 . Graphical abstract

NMDA-type glutamate receptors (NMDAR) are ligand-gated ion channels that contribute to excitatory neurotransmission in the central nervous system. NMDAR dysfunction has been found to be involved in various neurological disorders. Recent crystallographic and EM studies have shown the static structure of different states of the non-human NMDARs. Here we describe a model of a human NMDA receptor (hNMDAR) and its molecular dynamics (MD) before and after the binding of agonist ligands, glutamate and glycine. It is shown that the binding of ligands promotes a global reduction in molecular flexibility that produces a more tightly packed conformation than the unbound hNMDAR, and a higher cooperative regularity of moving. The ligand-induced synchronization of motion, identified on all structural levels of the modular hNMDA receptor is apparently a fundamental factor in channel gating. Although the time scale of the MD simulations (300 ns) was not sufficient to observe the complete gating event, the obtained data has shown the ligand-induced stabilization of hNMDAR that conforms the \"going to be open state\". We propose a mechanistic dynamic model of the ligand-dependent gating mechanism in the hNMDA receptor. At the binding of the ligands, the differently twisted conformations of the highly flexible receptor are stabilized in unique conformation with a linear molecular axis, which is a condition that is optimal for pore development. By searching the receptor surface, we have identified three new pockets, which are different from the pockets described in the literature as the potential and known positive allosteric modulator binding sites. A successful docking of two NMDAR modulators to their binding sites validates the model of a human NMDA receptor as a biological relevant target.

Molecular docking is a computational chemistry method which has become essential for the rational drug design process. In this context, it has had great impact as a successful tool for the study of ligand–receptor interaction modes, and for the exploration of large chemical datasets through virtual screening experiments. Despite their unquestionable merits, docking methods are not reliable for predicting binding energies due to the simple scoring functions they use. However, comparisons between two or three complexes using the predicted binding energies as a criterion are commonly found in the literature. In the present work we tested how wise is it to trust the docking energies when two complexes between a target protein and enantiomer pairs are compared. For this purpose, a ligand library composed by 141 enantiomeric pairs was used, including compounds with biological activities reported against seven protein targets. Docking results using the software Glide (considering extra precision (XP), standard precision (SP), and high-throughput virtual screening (HTVS) modes) and AutoDock Vina were compared with the reported biological activities using a classification scheme. Our test failed for all modes and targets, demonstrating that an accurate prediction when binding energies of enantiomers are compared using docking may be due to chance. We also compared pairs of compounds with different molecular weights and found the same results.

Neurodegenerative diseases (NDs) including Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, and Huntington’s disease are incurable and affect millions of people worldwide. The development of treatments for this unmet clinical need is a major global research challenge. Computer-aided drug design (CADD) methods minimize the huge number of ligands that could be screened in biological assays, reducing the cost, time, and effort required to develop new drugs. In this review, we provide an introduction to CADD and examine the progress in applying CADD and other molecular docking studies to NDs. We provide an updated overview of potential therapeutic targets for various NDs and discuss some of the advantages and disadvantages of these tools.

Background: Cutaneous squamous cell carcinoma (cSCC) is the second most common malignancy of the skin, and its incidence is increasing annually. Once cSCC becomes metastatic, its associated mortality rate is much higher than that of cSCC in situ. However, the current treatments for progressive cSCC have several limitations. The aim of this study was to suggest a potential compound for future research that may benefit patients with cSCC. Methods: In this study, we screened the following differentially expressed genes from the Gene Expression Omnibus database: GSE42677, GSE45164, GSE66359, and GSE98767. Using strategies such as protein-protein interaction network analysis and the CYTOSCAPE plugin MCODE, key modules were identified and then verified by Western blotting. Subsequently, related signalling pathways were constituted in the SIGNOR database. Finally, molecular docking analyses and cell viability assay were used to identify a potential candidate drug and verify its growth inhibition ability to A431 cell line. Results: Fifty-one common differentially expressed genes were screened and two key modules were identified. Among them, three core genes were extracted, constituting two signalling pathways, both of which belong to the module associated with mitotic spindles and cell division. A pathway involving CDK1, the TPX2-KIF11 complex, and spindle organization was validated in a series of analyses, including analyses for overall survival, genetic alteration, and molecular structure. Molecular docking analyses identified the pyridine 2-carbaldehyde thiosemicarbazone (NSC689534), which interacts with TPX2 and KIF11, as a potential candidate for the treatment of cSCC. Conclusions: NSC689534 might be a candidate drug for cSCC targeting TPX2 and KIF11, which are hub genes in cSCC.

Saxitoxin (STX) is one of the potent marine biotoxins that has high rate of lethality. However, there are no effective treatments at present, and the existing detection methods need to be further explored because of ethical problems or technical limitations. In this work, oligonucleotide aptamers toward STX were screened based on immobilizing libraries on Immobilized Metal-Chelate (IMC), such as Ni-NTA Sepharose, and the IMC-SELEX was conducted by the G-quadruplex library and the random library, respectively. Aptamer 45e (from the G-quadruplex library) and aptamer 75a were obtained after optimization, and aptamer 45e turned out to have a higher affinity toward STX. Furthermore, it was found that the hydrogen bonding and the van der Waals forces (VDW) played major roles in the high efficiency and specificity between STX and 45e by means of molecular docking and dynamics simulation. Based on this, aptamer 45e-1 with the Kd value of 19 nM was obtained by further optimization, which was then used to construct a simple, label-free and real-time optical BLI aptasensor for the detection of STX. This aptasensor showed good reproducibility and stability. In summary, with the advantages of screening aptamers of high efficiency and specificity toward the targets, the proposed IMC-SELEX provides a promising screening strategy for discovering aptamers, which could be used as the potential molecular recognition elements in the fields of biomedicine, food safety and environmental monitoring.