Targeting mitochondrial complexes for cancer therapy.

Synthesis of silver nanoparticles using Nymphaea nouchali-derived 9-Octadecenal: Antimicrobial, antidiabetic properties and in Silico ADMET insight

Opioid analgesic drugs are widely used in modern medicine for pain management, anesthesia, and palliative care, making the study of their physicochemical properties essential for drug design and optimization. This study aims to investigate the potential of domination degree-based topological indices (DTIs) in quantitative structure–property relationship (QSPR) modeling and to apply a decision-making approach for ranking opioid analgesic drugs. QSPR models were developed using DTIs of chemical graphs, with logarithmic and multilinear regression analyses employed to establish correlations between DTIs and key physicochemical properties. In addition, the VIKOR multi-criteria decision-making method was integrated with QSPR analysis to systematically rank twenty opioid analgesic drugs. The results revealed strong correlations between DTIs and physicochemical properties, confirming their predictive ability, while the VIKOR-based ranking was found to be highly consistent across properties. These findings highlight the reliability of DTIs in predicting drug characteristics and demonstrate the practical utility of combining QSPR modeling with decision-making tools for drug characterization, discovery, and prioritization.

Integrative bioinformatics and machine learning combined with experimental validation in a doxorubicin-induced model identify BACH2, NXPH4, CD1E, and LIF as sodium overload-related molecular signatures in dilated cardiomyopathy.

Structure-based design of pyrazole derivatives targeting the human Cyclophilin D binding site.

Decoding Nipah virus polymerase: Cryo-EM reveals key targets for antiviral drug discovery

Continuous evolving humanoid for advanced cellular models.

Drug repurposing strategy to identify the putative leads against the Epidermal growth factor receptor (EGFR) from the USFDA-approved drug pool: Investigating the utility as an anticancer agent.

Echinoderm collagen as a neuromodulatory antioxidant: biochemical characterization and molecular interaction with NMDA receptor.

A veterinary illness called monkeypox is primarily spread by coming into intimate contact with the skin lesions, body fluids, and active vesicles of infected people. Prior to the conclusion of the COVID-19 pandemic, monkeypox is causing fresh concerns worldwide. There are at least five human pathogenic OPXVs (hpOPXVs), the two most hazardous of which are the smallpox and monkeypox viruses. Effective treatments for monkeypox infections do not exist. Monkeypox levels have significantly increased over the last ten years, which is associated with a decrease in smallpox immunity in the herd. Although the smallpox vaccine was 85% successful in preventing monkeypox, it was no longer regularly available since smallpox had been eradicated worldwide. Finding new indications for current medications is referred to as drug repurposing. The shift from conventional drug discovery to drug repurposing is being driven by the fact that the supply of new drugs is dwindling due to rising R&D costs, long development lead times for new drugs, low success rate of new drugs, regulatory hurdles, lost patent revenue, and competition from generics. The recent outbreak is connected to fresh, unidentified, copied and altered. The only drug currently approved by the U.S. Food and Drug Administration (FDA) to treat the smallpox virus is tecovirimat. However, research and pharmacopoeial interest in monkeypox is modest. This study explored the potential reuse of some drugs already approved by the FDA or other regulatory agencies for use in various applications using molecular dynamics and virtual screening.

Can artificial intelligence transform antiviral drug discovery?

RLBindDeep: A ResNet-LSTM based novel framework for protein-ligand binding affinity prediction.

Opportunities and challenges of PROTAC in the treatment of protein-driven diseases

Predicting pharmacokinetic (PK) profiles from molecular structures represents a significant advancement in pharmaceutical research with substantial implications for expediting drug discovery processes. We evaluated five approaches to systematically compare five distinct methodological frameworks for predicting rat plasma concentration-time profiles directly from molecular structures using a consistent dataset and evaluation framework: (1) NCA-ML, predicted non-compartmental analysis parameters with one compartmental PK modeling; (2) PBPK-ML, utilizing ML-predicted invitro characteristics in physiologically based PK models; (3) CMT-ML, neural networks predicting compartmental PK model parameters with two or three compartmental PK modeling; (4) CMT-PINN, employing physics-informed neural networks trained on concentration-time profiles predicting compartmental PK model parameters with two or three compartmental PK modeling; and (5) PURE-ML, using decision trees to predict concentration values at specific time points. The CMT-PINN approach achieved highest predictive performance closely followed by PURE-ML (R2-log: 0.854 vs. 0.789, Spearman: 0.933 vs. 0.896), with 65.9% versus 61.0% of predictions within a twofold error of the observed concentrations. The other three approaches showed substantially lower performance metrices and higher prediction error margins. Models trained directly on concentration-time profiles outperformed those trained using derived PK parameters, particularly with limited training datasets. Our findings confirm the viability of predicting PK behavior from molecular structures prior to synthesis. The implementation of these computational approaches enables informed compound selection early in discovery, concentrating resources on promising candidates, and potentially reducing animal studies while accelerating development timelines.

Brazilin and its oxidized form "brazilein", the natural fluorescently traceable agents, toxify Plasmodium falciparum to pyknotic death via disrupting its iron homeostasis.

Engineering the microenvironment of Cu-MOF nanozyme via modulating ligand hydrophobicity for array-based profiling of phenolic acids in natural products.

Integrative evaluation of Vernonia ambigua for antimalarial drug discovery: insights from ethnopharmacology, in vivo, and in silico studies

Medicinal plants have been serving as the foundation of traditional medicine, offering a vast reservoir of bioactive compounds with diverse therapeutic potential. In recent decades, the symbiotic association between plants and their endophytic fungi has emerged as a paradigm for novel natural product biosynthesis and sustainable pharmaceutical production. This review critically examines the parallel and complementary production of bioactive metabolites by medicinal plants and their endophytes, addressing problems in scalability of bioactive compounds. Advances in molecular techniques and genomic tools have expanded our ability to predict and characterize biosynthetic gene clusters (BGCs), linking genetic potential to chemical diversity. Integrative omics approaches, coupled with synthetic biology tools such as CRISPR-Cas9 mediated activation, heterologous expression, and bioprocess optimization, offer promising strategies to overcome scaleup barriers. • Critically compares metabolic mimicry between medicinal plants and fungal endophytes. • Addresses biosynthetic silencing and strain instability as barriers to scalability. • Evaluates BGC prediction and multi-omics for characterizing chemical diversity. • Highlights CRISPR-Cas9 and synthetic biology for activating cryptic pathways. • Proposes a framework for transitioning endophytes to industrial bio-factories.

HCAR1 antagonist screening based on boundary-selected negative sampling strategy and multi-level graph neural network.

Natural products as modulators of ferroptosis: Therapeutic implications and molecular mechanisms in disease treatment.