Journal of Computer-Aided Molecular Design最新文献

The burgeoning field of RNA-targeted drug discovery necessitates robust and accurate computational methods for predicting RNA-small molecule binding affinity. This review synthesizes recent advancements in deep learning and machine learning approaches, highlighting their methodologies, performance, and impact on accelerating drug design. We delve into methods that leverage diverse data representations, including sequence-based features, 3D structural information (voxel grids and molecular surfaces), and sophisticated graph-based networks. Key innovations such as contrastive learning, multi-scale feature extraction, and cross-fusion mechanisms are discussed, alongside their contributions to model robustness, generalization, and interpretability. We also consider the relative computational demands of these advanced models. Despite tremendous advancements, problems still exist, especially with regard to the lack of data because of the intrinsic flexibility of RNA structures and the inherent experimental difficulty in determining their structure and dynamic nature. The present state of computational RNA-small molecule affinity prediction is thoroughly reviewed in this article, along with important limits and future directions.

Alzheimer's Disease (AD) is a degenerative disorder of the brain that causes a gradual loss of cognitive function. The cholinergic hypothesis suggests that acetylcholine deficiency is the main cause of AD, which explains why blocking acetylcholinesterase (AChE) is the most effective way to treat AD. Nevertheless, there are some drawbacks to the currently available AChE inhibitors; thus, new molecules with better therapeutic effects and fewer side effects are needed. In this study, the anti-Alzheimer activity of diosmetin, a natural flavonoid, was investigated via an integrated computational and experimental approach. Pharmacophore mapping revealed that the essential chemical features of diosmetin are responsible for AChE inhibition, and density functional theory calculations were employed to investigate its electronic properties and chemical behavior. Molecular docking experiments indicated that diosmetin could bind firmly to AChE with a binding energy of -9.49 kcal/mol. Molecular dynamics simulations strengthened this hypothesis by showing that the diosmetin-AChE complex remained stable over time. In vivo verification using a scopolamine-induced zebrafish model of Alzheimer’s disease revealed that diosmetin administration notably enhanced learning and memory abilities in zebrafish. Various behavioral paradigms, including the light/dark preference test, novel tank diving test, T-maze test, and novel object recognition test, have been used to assess cognitive function. Biochemistry revealed that diosmetin counteracted the scopolamine-induced increase in AChE activity, increased oxidative stress, increased myeloperoxidase inflammatory markers, decreased antioxidant activity, and restored normal histology in the brains of the zebrafish. Most importantly, high-dose diosmetin demonstrated comparable neuroprotective efficacy to donepezil in behavioral and biochemical assays while exhibiting weaker molecular binding affinity toward AChE, as indicated by MM-PBSA analysis, underscoring that similar in vivo outcomes do not necessarily imply molecular equivalence at the binding level.

Among the cancers that pose the greatest threat to life worldwide is lung cancer. According to estimates from the World Cancer Research Fund International, there will be 1.8 million new instances of this disease diagnosed in 2022. When medical personnel diagnose and classify patients’ conditions proactively, they may treat them safely and efficiently. The advent of the microarray method has made it possible to examine the connections between genes and various diseases, including lung malignancies. Numerous methods have been developed to forecast gene-based diseases, but they still have problems with high computational cost, time consumption, complex data, and inaccurate prediction. Therefore, create an efficient lung cancer detection system in this research by designing an Improved Convolutional Neural Network with Honey Bee Mating Optimization (ICNN-HBMO). First, the system is trained using Omix data, and the dataset is normalized using min–max normalization. Then Kernel Principal Component Analysis (KPCA) technique is employed for feature reduction. Furthermore, an enhanced CNN is employed to classify lung cancer using HBMO. The HBMO algorithm optimizes the weight and bias parameters of the ICNN to improve prediction performance. The developed method is implemented in the Matlab tool, and the improved performance is compared to other existing methods. The developed technique attains high accuracy and high precision of 99.2% and 99%.

Molecular docking, a relatively well-established research instrument, is extensively applied in protein research and drug development. Recently, the technique has also exerted a vital role in nanomedicine research, especially in nanotechnology-based drug design and delivery system research. However, so far no one has sorted out the current situation of this interdisciplinary field. In this review, firstly, the basic principles and methods of molecular docking were introduced; then several specific applications were highlighted in the context of nanomedicine re-search, including nanomaterial modification, screening of nanomedicine carriers, screening (validation) of potential ligands (targets) of nanomaterials, and assessment of the toxicity or biosafety of nanomaterials were summarized; finally, the current challenges of molecular docking in nanomedicine re-search were discussed, and an outlook on its development and optimization was also given. This work is expected to make it easier for researchers to comprehend molecular docking and its applications in nanomedicine.

A combined computational workflow featuring response surface methodology, a hybrid teaching-learning-based optimization (TLBO)-ANN model, and Support Vector Regression (SVR) was used to design and optimize novel Schiff bases 1,2-bis(furan-2-ylmethylene)hydrazine (A₁), 1,2-bis(furan-2-ylethylene)hydrazine (A₂), 1,2-bis(thiophen-2-ylmethylene)hydrazine (A₃), and 1,2-bis(thiophen-2-ylethylene)hydrazine (A₄). The TLBO-ANN model achieved high predictive accuracy (R² = 0.98) for synthesis yield. However, the ANN model produced the best yield prediction accuracy, as confirmed by experiments (yield: 91%). The compounds were characterized by NMR, IR, UV-visible spectroscopy, mass spectrometry, and cyclic voltammetry, which reveal their structure, optical, and electrochemical properties with wide applications. Density Functional Theory (DFT) and molecular docking simulations elucidated molecular properties and binding affinities to antibacterial targets (– 7.4 to − 8.2 kcal/mol). ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) predictions were also conducted to evaluate the compounds’ pharmacokinetic behavior. This integrated computational approach yielded compounds with potent antibacterial activity, with minimum inhibitory concentrations as low as 0.070 mg/mL, validating the models’ utility in rational drug design.

Blood diseases, such as leukemia, and particularly acute myeloid leukemia (AML), bring a severe burden on patients, while conventional therapies are often limited by poor target specificity, toxicity and drug resistance. This underscores the urgent need for innovative strategies in drug discovery. Computer-aided drug design (CADD) integrates computational biology, quantum chemistry, and systems pharmacology, has potential to meet this need. CADD employs advanced computational techniques such as molecular docking, molecular dynamics and virtual screening to accelerate drug design and screening through the more accurate prediction of ligands-targets binding affinities and high-throughput screening. The integrate of artificial intelligence (AI) with CADD has further improve the efficiency, speed and accuracy of drug design and screening through improved drugs-targets binding prediction, better structures optimization, and faster screening in AML drug development. This review delineates the mechanistic principles underlying major CADD methods and highlights their latest applications for AML-targeted therapeutics such as developing the next generation highly selective FMS-like tyrosine kinase 3 (FLT3) inhibitors, de novo design of inhibitors for novel targets like methyltransferase-like 3 (METTL3), and overcoming acquired drug resistance. Finally, we propose a future direction of personalized precision treatment assisted by CADD and AI driven models for drug response prediction and drugs combination recommendation. This review aims to serve as a key reference and inspiration for scientists working at the intersection of AI, CADD and AML drug discovery.

The application of single-stranded RNA (ssRNA) aptamers may be limited by their chemical instability and susceptibility to enzymatic degradation, despite their high structural specificity. Thus, this study presents a computational workflow for the rational conversion of ssRNA aptamers (A6 and A11), previously selected for prostate cancer cells, into structurally preserved single-stranded DNA (ssDNA) analogues, termed LN-A6 and LN-A11. The workflow integrates three-dimensional structural modeling, targeted chemical modifications, and molecular dynamics simulations conducted for 200 ns at 300 K and 310 K, aiming to assess conformational preservation under different thermal conditions. Structural comparisons between ssRNA and ssDNA were performed using widely adopted molecular dynamics descriptors, including root-mean-square deviation and the number of intramolecular hydrogen bonds throughout the simulation trajectories. The results indicate that the ssDNA variants retained key structural features of their ssRNA precursors, exhibiting consistent conformational behavior at both analyzed temperatures. Although the ssRNA aptamers displayed more conformationally restricted architectures, the ssDNA analogues preserved sufficient structural integrity to support the feasibility of the conversion from a conformational standpoint. Overall, this study describes a reproducible computational workflow for evaluating structural preservation during the conversion of ssRNA to ssDNA aptamers, providing a methodological foundation for future experimental investigations.

Resistance mutations in the epidermal growth factor receptor (EGFR), particularly the gatekeeper T790M mutation, pose a major challenge for tyrosine kinase inhibitors in the treatment of non-small cell lung cancer. Previous experimental studies reported that the inhibitor BLU-945 selectively targets EGFR variants (EGFR^L858R, EGFR^L858R/T790M, and EGFR^{L858R/T790M/C797S}) while sparing wild-type EGFR. Here, we dissect the binding mechanisms of BLU-945 with wild-type and mutant EGFR by integrating structural dynamics and conformational landscape analysis. Our results reveal that the T790M mutation introduces a bulky, hydrophobic MET-790, which remodels the ATP-binding pocket and forms a hotspot for strong interactions with BLU-945’s aromatic groups. A compact pocket configuration highly compatible with BLU-945 is stabilized by conformational shifts of the P-loop and αC-helix, which modulate essential residue networks, while MET-790 restricts P-loop flexibility. The resulting pocket conformation creates an ideal environment that enhances BLU-945’s binding affinity to EGFR mutants, consistent with the experimental results. Our findings reveal the molecular basis of BLU-945’s efficacy to provide inhibitor designing strategies for overcoming EGFR-driven drug resistance.

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The study explores the formulation of triptorelin, a therapeutic peptide used in prostate cancer treatment, within an emulsion-based delivery system to address challenges associated with rapid clearance and limited stability in aqueous environments. Molecular dynamics (MD) simulations were integrated with formulation design to provide molecular-level insight into the encapsulation process, interfacial organization, and hydration behavior of triptorelin in systems comprising palmitic acid, Tween 80, and polyethylene glycol 400 (PEG 400). Four distinct systems were analyzed: triptorelin–water, triptorelin–palmitic acid–water, triptorelin–palmitic acid–Tween 80–water, and triptorelin–palmitic acid–Tween 80–PEG 400–water. The MD results revealed a substantial reduction in water exposure for triptorelin within the excipient matrix, particularly in the fully developed system containing PEG 400. Palmitic acid promotes the encapsulation of hydrophobic regions of triptorelin within the emulsion droplet, while Tween 80 contributes to interfacial stabilization through interactions with lipid components. Notably, spontaneous droplet formation around the peptide was observed across all simulated systems, with the final formulation exhibiting enhanced structural stability. Collectively, the MD simulations establish a direct link between excipient composition and formulation performance, underscoring the synergistic roles of palmitic acid, Tween 80, and PEG 400 in generating a stable and well-defined emulsion-based delivery system. These findings demonstrate how MD simulations can be used as a rational method to design and optimize Triptorelin formulation strategies at the molecular level.