Search Results (290)

Antiviral peptides (AVPs) are promising therapeutic candidates, yet the rapid growth of sequence data and the field’s emphasis on predictors have left a gap: the lack of an integrated view linking peptide chemistry with biological context. Here, we map the AVP landscape through interactive data mining using Half-Space Proximal Networks (HSPNs) and Metadata Networks (MNs) in the StarPep toolbox. HSPNs minimize edges and avoid fixed thresholds, reducing computational cost while enabling high-resolution analysis. A threshold-free HSPN resolved eight chemically and biologically distinct communities, while MNs contextualized AVPs by source, function, and target, revealing structural–functional relationships. To capture diversity compactly, we applied centrality-guided scaffold extraction with redundancy removal (90–50% identity), producing four representative subsets suitable for modeling and similarity searches. Alignment-free motif discovery yielded 33 validated motifs, including 10 overlapping with reported AVP signatures and 23 apparently novel. Motifs displayed category-specific enrichment across antimicrobial classes, and sequences carrying multiple motifs (≥4–5) consistently showed higher predicted antiviral probabilities. Beyond computational insights, scaffolds provide representative “entry points” into AVP chemical space, while motifs serve as modular building blocks for rational design. Together, these resources provide an integrated framework that may inform AVP discovery and support scaffold- and motif-guided therapeutic design. Full article

Integrating artificial intelligence (AI) with the Quantitative Structure-Activity Relationship (QSAR) has transformed modern drug discovery by empowering faster, more accurate, and scalable identification of therapeutic compounds. This review outlines the evolution from classical QSAR methods, such as multiple linear regression and partial least squares, to advanced machine learning and deep learning approaches, including graph neural networks and SMILES-based transformers. Molecular docking and molecular dynamics simulations are presented as cooperative tools that boost the mechanistic consideration and structural insight into the ligand-target interactions. Discussions on using PROTACs and targeted protein degradation, ADMET prediction, and public databases and cloud-based platforms to democratize access to computational modeling are well presented with priority. Challenges related to authentication, interpretability, regulatory standards, and ethical concerns are examined, along with emerging patterns in AI-driven drug development. This review is a guideline for using computational models and databases in explainable, data-rich and profound drug discovery pipelines. Full article

A series of twenty-five salicylic acid derivatives was synthesized and structurally characterized by ¹H and ¹³C-APT NMR and IR spectroscopic techniques, and HRMS analysis. The derivatives were subjected to biological evaluation against species of the genus Candida (C. albicans ATCC 90028, C. albicans CBS 5602, C. tropicalis CBS 94, and C. krusei CBS 573). In assays were used the broth microdilution method to determine the minimum inhibitory concentration (MIC) and verify the probable mechanism of action for antifungal activity. In the antifungal evaluation, compounds N-isobutyl-2-hidroxybenzamide (14), N-cyclohexyl-2-hydroxybenzamide (15), N-benzyl-2-hydroxybenzamide (16), N-4-methylbenzyl-2-hydroxybenzamide (17), N-4-methoxybenzyl-2-hydroxybenzamide (18), N-2,4-dimethoxybenzyl-2-hydroxybenzamide (19), N-4-fluorbenzyl-2-hiydroxybenzamide (22), and N-4-chlorobenzyl-2-hydroxybenzamide (23) were bioactive against at least one fungal strain. The compound with the best antifungal profile was N-cyclohexyl-2-hydroxybenzamide (15), which presented a MIC of 570.05 μM against most of the strains tested. The tests using ergosterol and sorbitol demonstrated that the compound does not act by altering cell wall functions or the plasmatic membrane in Candida species. The in silico analysis of 15 for antifungal activity in various biological targets suggested a probable multitarget mechanism. Therefore, the synthesis of salicylic acid derivatives resulted in compounds with a good antifungal profile. Full article

Breast cancer faces significant therapeutic challenges, particularly for triple-negative breast cancer (TNBC), due to limited targeted therapies and drug resistance. Drug repositioning leverages existing safety and pharmacokinetic data to expedite the identification of new indications with cost-effective benefits compared to de novo drug discovery. In this critical narrative review, we examine recent advances in computational repositioning strategies for breast cancer, focusing on network-based methods, computer-aided drug design, artificial intelligence and machine learning, transcriptomic signature matching, and multi-omics integration. We highlight key case studies that have progressed to preclinical validation or clinical evaluation. We assess comparative performance metrics, experimental validation outcomes, and real-world success rates. We also present critical methodological challenges, including data heterogeneity, bias in real-world data, and the need for study reproducibility. Our review emphasizes the importance of window-of-opportunity trials and the need for standardized data sharing and reproducible pipelines. These insights highlight the groundbreaking potential of in silico repositioning in addressing unmet needs in breast cancer therapy. Full article

Phthalates are widely used plasticizers recognized as endocrine-disrupting chemicals (EDCs) with well-documented adverse effects on reproductive health. These compounds act either directly or through their metabolites and can influence various biochemical pathways. Key phthalates that have been associated with potential toxic outcomes include di(2-ethylhexyl) phthalate (DEHP), dibutyl phthalate (DBP), butyl benzyl phthalate (BBP), diisononyl phthalate (DiNP), and diisodecyl phthalate (DiDP). The presence of these compounds in everyday consumer products has been associated with various adverse effects on human reproductive health, including hormonal disruption, issues in gonadal function, and other hormone related problems. This systematic review provides an overview and critical synthesis of the most recent research regarding phthalate reproductive toxicity. The scope is to summarize and aggregate correlations between phthalate exposure and reproductive health outcomes and highlight factors, such as age, sex, and extent of exposure, that have the most significant impacts on clinical outcomes. The reported studies focus on the gender-specific outcomes of various phthalates, while the epidemiological data reveal the importance of exposure duration and age. The reported results highlight the need for strict regulations regarding phthalate usage and the importance of developing safer alternatives. Full article

Identifying the most reactive conformation of a molecule is a central challenge in computational chemistry, particularly when reactivity depends on subtle conformational effects. While most conformation search tools aim to find the lowest-energy structure, they often overlook the electronic descriptors that govern chemical reactivity. In this work, we present GAELLE, a cheminformatics tool that combines conformer generation with quantum reactivity descriptors to identify the most reactive structure of a molecule in solution. GAELLE integrates an evolutionary algorithm with fast semiempirical quantum chemical calculations (xTB), enabling the automated ranking of conformers based on HOMO–LUMO gap minimization (Pearson’s principle of maximum hardness) and electrophilicity index (Parr’s electrophilicity scale). Solvent effects are accounted for via implicit solvation models (GBSA/ALPB) to ensure realistic evaluation of reactivity in solution. The method is fully SMILES-driven, open-source, and scalable to medium-sized drug-like molecules. Applications to reactive intermediates, bioactive conformations, and pre-reactive complexes demonstrate the method’s relevance for mechanism elucidation, molecular design, and in silico screening. GAELLE is publicly available and offers a reactivity-focused alternative to traditional energy-minimization tools in conformational analysis. Full article

Background/Objectives: Cofilin, a key regulator of actin cytoskeleton dynamics, contributes to neuroinflammation, synaptic damage, and blood–brain barrier disruption in ischemic stroke. Despite its established role in stroke pathology, cofilin remains largely untargeted by existing therapeutics. This study aimed to identify potential cofilin-binding molecules by repurposing LIMK1 inhibitors through an integrated computational strategy. Methods: A cheminformatics pipeline combined QSAR modeling with four molecular fingerprint sets and multiple machine learning algorithms. The best-performing QSAR model (substructure–Random Forest) achieved R²_train = 0.8747 and R²_test = 0.8078, supporting the reliability of compound prioritization. Feature importance was assessed through SHAP analysis. Top candidates were subjected to molecular docking against cofilin, followed by 300 ns molecular dynamics simulations, MM-GBSA binding energy calculations, principal component analysis (PCA), and dynamic cross-correlation matrix (DCCM) analyses. Network pharmacology identified overlapping targets between selected compounds and stroke-related genes. Results: Three compounds, CHEMBL3613624, ZINC000653853876, and Gandotinib, were prioritized based on QSAR performance, binding affinity (−6.68, −6.25, and −5.61 Kcal/mol, respectively), and structural relevance. Docking studies confirmed key interactions with Asp98 and His133 on cofilin. Molecular dynamics simulations supported the stability of these interactions, with Gandotinib showing the highest conformational stability, and ZINC000653853876 exhibiting the most favorable energetic profile. Network pharmacology analysis revealed eight intersecting targets, including MAPK1, PRKCB, HDAC1, and serotonin receptors, associated with neuroinflammatory and vascular pathways in strokes. Conclusions: This study presents a rational, integrative repurposing framework for identifying cofilin-targeting compounds with potential therapeutic relevance in ischemic stroke. The selected candidates warrant further experimental validation. Full article

This paper investigates the effectiveness of Word2Vec-based molecular representation learning on SMILES (Simplified Molecular Input Line Entry System) strings for a downstream prediction task related to the market approvability of chemical compounds. Here, market approvability is treated as a proxy classification label derived from approval status, where only the molecular structure is analyzed. We train character-level embeddings using Continuous Bag of Words (CBOW) and Skip-Gram with Negative Sampling architectures and apply the resulting embeddings in a downstream classification task using a multi-layer perceptron (MLP). To evaluate the utility of these lightweight embedding techniques, we conduct experiments on a curated SMILES dataset labeled by approval status under both imbalanced and SMOTE-balanced training conditions. In addition to our Word2Vec-based models, we include a ChemBERTa-based baseline using the pretrained ChemBERTa-77M model. Our findings show that while ChemBERTa achieves a higher performance, the Word2Vec-based models offer a favorable trade-off between accuracy and computational efficiency. This efficiency is especially relevant in large-scale compound screening, where rapid exploration of the chemical space can support early-stage cheminformatics workflows. These results suggest that traditional embedding models can serve as viable alternatives for scalable and interpretable cheminformatics pipelines, particularly in resource-constrained environments. Full article

The continuous increase in microbial resistance to therapeutic agents has become one of the greatest challenges to global health. In this context, the present study investigated the bioactivity of 25 chroman-4-one and homoisoflavonoid derivatives—17 of which are novel—against pathogenic microorganisms, including Staphylococcus epidermidis, Pseudomonas aeruginosa, Salmonella enteritidis, Candida albicans, C. tropicalis, Nakaseomyces glabratus (formerly C. glabrata), Aspergillus flavus, and Penicillium citrinum. Antimicrobial assay was performed using the microdilution technique in 96-well microplates to determine the minimum inhibitory concentration (MIC). Thirteen compounds exhibited antimicrobial activity, with compounds 1, 2, and 21 demonstrating greater potency than the positive control, especially against Candida species. Molecular modeling suggested distinct mechanisms of action in Candida albicans: 1 potentially inhibits cysteine synthase, while 2 and 21 possibly target HOG1 kinase and FBA1, key proteins in fungal virulence and survival. Our findings indicated that the addition of alkyl or aryl carbon chains at the hydroxyl group at position 7 reduces antimicrobial activity, whereas the presence of methoxy substituents at the meta position of ring B in homoisoflavonoids enhances bioactivity. These findings highlight key structural features of these compound classes, which may aid in the development of new bioactive agents against pathogenic microorganisms. Full article

Background: Nitrogen fixation (diazotrophy) is a desirable trait present in some cyanobacteria with potential applications in sustainable agriculture and chemical feedstock production. This study discovers a predictive relationship modeled between secondary metabolites and diazotrophic cyanobacteria by leveraging chemical structure similarity to identify diazotrophic strains. Methods: An algorithm was developed using chemical fingerprint similarity of metabolites curated from CyanoMetDB and evaluated with leave-one-out cross-validation on 133 manually labeled metabolites. Results: The model demonstrated strong predictive performance, achieving 88% accuracy and a ROC-AUC of 0.96. We then applied this approach to prioritize likely diazotrophic strains among 1980 unlabeled metabolites and their associated organisms, providing a rank order of most likely undetected diazotrophic strains. Toxicity analysis showed that diazotrophic-associated metabolites show similar toxicity to non-diazotrophic metabolites in rats, with less toxicity in Daphnia magna, suggesting that these metabolites are not playing a defensive role. However, these metabolites did have relatively high nitrogen presence, and many were cyclic peptides, potentially serving as signaling molecules. Conclusions: This study underscores the potential of secondary metabolites in identifying diazotrophs, even when they may not be actively demonstrating diazotrophic physiology. Discovering more diazotrophic cyanobacteria has strong implications for advancing agricultural biotechnology towards the goal of self-fertilizing crops. Full article

Background/Objectives: Smart Formulation is an artificial intelligence-based platform designed to predict the Beyond Use Dates (BUDs) of compounded oral solid dosage forms. The study aims to develop a decision-support tool for pharmacists by integrating molecular, formulation, and environmental parameters to assist in optimizing the stability of extemporaneous preparations. Methods: A tree ensemble regression model was trained using a curated dataset of 55 experimental BUD values collected from the Stabilis database. Each formulation was encoded with molecular descriptors, excipient composition, packaging type, and storage conditions. The model was implemented using the KNIME platform, allowing the integration of cheminformatics and machine learning workflows. After training, the model was used to predict BUDs for 3166 APIs under various formulation and storage scenarios. Results: The analysis revealed a significant impact of excipient type, number, and environmental conditions on API stability. APIs with lower LogP values generally exhibited greater stability, particularly when formulated with a single excipient. Excipients such as cellulose, silica, sucrose, and mannitol were associated with improved stability, whereas HPMC and lactose contributed to faster degradation. The use of two excipients instead of one frequently resulted in reduced BUDs, possibly due to moisture redistribution or phase separation effects. Conclusions: Smart Formulation represents a valuable contribution to computational pharmaceutics, bridging theoretical formulation design with practical compounding needs. The platform offers a scalable, cost-effective alternative to traditional stability testing and is already available for use by healthcare professionals. Its implementation in hospital and community pharmacies may help mitigate drug shortages, support formulation standardization, and improve patient care. Future developments will focus on real-time stability monitoring and adaptive learning for enhanced precision. Full article