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Anaplastic lymphoma kinase (ALK) is a key oncogenic driver in cancers such as non-small cell lung cancer (NSCLC), where gene fusions and mutations lead to constitutive kinase activation. Despite the clinical success of current ALK inhibitors, acquired resistance remains a major limitation, underscoring the need for novel and structurally diverse therapeutic agents. In this study, we applied an integrated computational approach combining machine learning (ML), molecular docking, molecular dynamics (MD), and steered molecular dynamics (SMD) to identify potential ALK inhibitors from the Endophytic Microorganism Natural Product Database (EMNPD). CatBoost was selected as the optimal ML model (RMSE = 0.933, MAE = 0.719, and R = 0.808) and used to prioritize candidates with predicted binding free energies below -11 kcal mol(-1). Molecular docking of 369 selected compounds identified five top hits (IDs 100, 248, 254, 277, and 307) with Delta G(dock) < -9.0 kcal mol(-1). MD simulations confirmed stable protein-ligand interactions, with RMSD < 0.25 nm and persistent hydrogen bonding, especially for compounds 248, 254, and 277. SMD simulations indicated that compounds 254 and 277 exhibited the highest rupture forces, pulling work, and Delta G(SMD) values of -9.582 and -9.649 kcal mol(-1), respectively, suggesting strong mechanical stability. Pharmacokinetic and toxicity analyses highlighted compounds 248 and 254 as the most balanced candidates. This study demonstrates that integrating ML with docking and dynamic simulations is an effective strategy for discovering natural product-derived ALK inhibitors, offering promising leads for the development of next-generation targeted therapies.
Piper laetispicum C.DC., a herb in folk medicine belonging to the Piperaceae family, has been widely used for diverse therapeutic properties, including promoting blood circulation, reducing stasis, antiplatelet aggregation, antithrombotic, and antihypertensive properties. Despite its traditional significance, scientific studies on P. laetispicum's pharmacology have not been reported. The volatile oil components and biological activities ofP. laetispicum collected in Quang Tri Province, Vietnam, have been investigated. The hydro-distilled essential oil obtained from the aerial parts of P. laetispicum (PLAE) was analysed by Gas Chromatography-Mass Spectrometry (GC-MS). The GC-MS analysis revealed that the PLAE consists of a mixture of sesquiterpenes (52.22%), monoterpenes (41.57%), and others (2.04%). The principal chemical constituents found were ishwarane (17.30%), (3-pinene (10.27%), gamma-elemene (10.25%), alpha-pinene (9.32%), (3-caryophyllene (7.02%), camphene (6.29%), and sabinene (5.50%). The PLAE showed antibacterial activity against B. cereus only (MIC = 256 mu g/mL). Notably, the PLAE exhibited good anticancer activity against HepG2 (IC50 = 7.96 +/- 0.31 mu g/mL) and HeLa (IC50 = 8.28 +/- 0.48 mu g/mL) cell lines. Among the major components of this PLAE, docking simulations showed that ishwarane (-6.831 kcal/mol) and gamma-elemene (-7.039 kcal/mol) had the highest affinity for the target proteins EGFR and HER2, respectively. In our study, the antimicrobial activity and cytotoxic effect of the PLAE are explored for the first time to provide information for subsequent studies, as well as future therapeutic applications.
This paper investigates the generation of an ultrawide spectrum in the mid-infrared region using a suspended-core fiber (SCF) fabricated from As2Se3 chalcogenide glass, in which carbon disulfide is employed as a liquid filling material for three air-holes instead of conventional air. The inclusion of carbon disulfide in the air pores significantly modifies the dispersion characteristics of the optical fiber, enabling a flattened dispersion profile close to zero over a broad wavelength range. Such dispersion-tuning techniques play a crucial role in enhancing nonlinear spectral broadening. Simulation results demonstrate that an ultrawide spectrum spanning from 0.99 mu m to 3.925 mu m can be generated in the proposed SCF, which is only 10 cm long, using a low peak power of 800 W with a pump pulse centered at 2500 nm. The smooth supercontinuum spectrum obtained in the all-normal dispersion regime is expected to exhibit high temporal coherence, as the spectral broadening is primarily influenced by deterministic nonlinear effects such as self-phase modulation and optical wave breaking.
We propose a polarization-maintaining (PM) photonic crystal fiber (PCF) made of Ge23Sb7S70 chalcogenide for the mid-infrared (MIR) supercontinuum generation (SCG). This fiber features five rings of air holes arranged in a hexagonal lattice, with the air holes in the first ring shaped elliptically to induce birefringence. By tuning the structural parameters of the fiber, the proposed PM-PCF achieves an ultra-flat normal dispersion profile for x-polarization mode, spanning from -2.3 to -4.44 ps nm-1 km-1 with a small variation of 1.07 ps nm-1 km-1 over a broad wavelength range of 2.8 & micro;m-7.9 & micro;m (corresponding to a bandwidth of 5.1 & micro;m). In contrast, the y-polarization mode maintains a flat anomalous dispersion profile. When pumped with a 200 fs width pulse at a pump wavelength of 4.0 & micro;m and a peak power of 2.0 kW, the 5.0 cm long PM-PCF generates a MIR SC spanning from 1.7 & micro;m to 5.1 & micro;m (a bandwidth of 3.4 & micro;m) and 1.08 & micro;m to 8.15 & micro;m (a bandwidth of 7.07 & micro;m) for the x- and y-polarizations, respectively. Owing to its high nonlinearity and larger birefringence, the proposed PM-PCF-based SC source is a promising candidate for applications in the MIR region.
Metal corrosion is a major degradation mechanism that reduces the load-carrying capacity and serviceability of steel structural members. Quantitative identification of corrosion-induced stiffness loss from limited structural response measurements remains a challenging inverse problem, particularly under sparse sensing and measurement noise. This study presents a physics-informed neural network (PINN)-based framework for identifying spatially varying flexural rigidity in steel I-beams. The proposed approach embeds the Euler–Bernoulli beam equation directly into the loss function, enabling simultaneous inference of the transverse displacement field and the stiffness degradation field from sparse displacement data. The framework is evaluated through numerical case studies involving uniform, localized, and multi-point corrosion scenarios. Gaussian noise levels of up to 5
