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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

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.

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.

The belief that we could always stay ahead of the pathogens was forced upon scientists in the whole world by antimicrobial resistance. According to several reports, there are medications that are yet to be made public in the pipeline and there are little motivations to design novel antimicrobials to combat the worldwide drug resistance issues. Presently, the desire to design and develop efficient novel anti-bacterial agents is very high by researchers; thus, this study focuses on identifying the interactions between the studied ligands and Proplasmepsin IV, as well as examining the relationship between the calculated descriptors and binding affinities. This work shows successful prediction of the reacting and inhibiting efficiency of ten (10) cyclic tetra-peptides using insilico method. The optimization of the studied compound revealed the proficiency of methyl (3S,9S,12S)-12-(1,3-dioxoisoindolin-2-yl)-9-(2-(methylthio)ethyl)-5,8,11-trioxo-4,7,10-triaza-1(1,3)-benzenacyclotridecaphane-3-carboxylate ( F5 ) and 2-((3S,9S,12S)-12-(1,3-dioxoisoindolin-2-yl)-3-(methoxycarbonyl)-5,8,11-trioxo-4,7,10-triaza-1(1,3)-benzenacyclotridecaphane-9-yl)acetic acid (F7) to react more than the remaining molecules in term of HOMO and LUMO energies. In comparison, compound F9 demonstrated a higher inhibitory activity than the reference drug, Chloroquine, based on binding affinity. Molecular dynamics simulations over a 100 ns period further explored the binding affinity between F9 and the reference drug. The results showed that the reference drug (− 21.91 ± 1.16 kcal/mol) had a slightly stronger binding affinity than the F9_complex (− 13.85 ± 0.72 kcal/mol). Additionally, pharmacokinetic studies for F9 were compared with those of the reference compound and presented accordingly.

Molecular docking is a critical computational strategy in drug discovery, but the diversity of biomolecular structures and flexible binding conformations create an enormous search space that challenges conventional computing. Quantum computing holds promise but remains constrained by scalability, hardware limitations, and precision issues. Here, we report a probabilistic computer (p-computer) prototype that solves complex molecular docking. The system is built upon artificial probabilistic bits (p-bits), fabricated in 180 nm CMOS with BEOL HfO₂ RRAM and compatible with compute-in-memory (CIM) schemes. A key innovation is the integration of Gaussian Random Number Generator-based p-bits with CIM, where the sigmoidal response arises from the Gaussian cumulative distribution function with coupling and bias coefficients directly encoded in the RRAM crossbar. This co-design alleviates the memory-to-compute bottleneck of prior CMOS-only and CMOS + X (emerging nanodevices) p-computers. Using this architecture, we experimentally solved a 42-node docking problem of lipoprotein with the LolA–LolCDE complex—a key target in developing antibiotics against Gram-negative bacteria, with results consistent with the Protein-Ligand Interaction Profiler tool. This work represents an early hardware application of p-computing in computational biology and demonstrates its potential to overcome the success rate and efficiency limitations of current technologies for complex bioinformatics problems. Molecular docking is a key tool in computational drug design by searching for numerous poses of ligands bonding to target molecules, which challenges conventional computing. Here, He et al. report a probabilistic computing hardware to accomplish this complex task via a device-architecture co-design.