CRISPR-Cas systems have revolutionized genome engineering due to their exceptional precision, programmability, and cost-effectiveness. While rooted in microbial defense mechanisms, expanding their application particularly in therapeutics demands a chemically oriented framework that enables tunable, reversible, and safe gene editing.This review presents a multidisciplinary exploration of recent advances in the structural, synthetic, and computational dimensions of CRISPR-Cas technologies. Structural analyses focus on domain architectures of Cas enzymes, including the recognition (REC), nuclease (HNH and RuvC), and PAM-interacting domains, emphasizing the catalytic role of divalent metal ions. Comparative insights into Cas9, Cas12, and Cas13 reveal functional diversity across DNA- and RNA-targeting systems, supported by high-resolution structural data on guide RNA pairing and conformational dynamics.The review highlights advances in chemical modulation, such as anti-CRISPR proteins, small molecule inhibitors, and stimuli-responsive switches, with emphasis on structure–activity relationships. Additionally, bioorganic delivery system lipid nanoparticles, polymers, and cell-penetrating peptides are examined for their role in enhancing in vivo delivery through formulation chemistry.Finally, computational chemistry approaches molecular docking, molecular dynamics simulations, and virtual screening are discussed as key enablers in modulator discovery and optimization. Integration with AI-driven tools is proposed as a promising direction for rational CRISPR design. Overall, this chemistry-centric perspective highlights the importance of molecular-level control in developing the next generation of programmable and clinically safe CRISPR-based interventions.

Odorant receptors (ORs) in Drosophila melanogaster are critical components of the insect olfactory system, mediating the detection of environmental cues such as host plants, food sources, and mating signals. Targeting these receptors with natural ligands offers a promising approach for sustainable pest control, especially through the utilization of bioactive compounds derived from agricultural crop and food production residues (ACFPR). In this study, we employed AlphaFold-predicted model of Drosophila odorant receptor Q9W1P8 (AF-Q9W1P8-F1-model_v4), retrieved from the UniProt database for structure-based virtual screening. Molecular docking was conducted using GNINA, a deep learning-enhanced docking platform that provides refined binding affinity predictions. The most favourable predicted binding affinities for 164 components of ACFPR from different sources were in the range -11.5 to -8.5 kcal/mol. Among the tested compounds, α-tomatine achieved the most favorable GNINA-affinity score (–11.5 kcal/mol). Other compounds, such as peonidin 3-rutinoside (–11.08 kcal/mol) and cinnamtannin B1 (–11.04 kcal/mol), also demonstrated strong predicted binding affinities according to GNINA scoring, supporting the potential of plant-derived molecules to modulate insect olfactory receptors. The binding modes of these molecules suggest potential interactions with multiple regions of the receptor surface, including the predicted binding site. These interactions may modulate receptor function through mechanisms such as direct inhibition of the binding site or allosteric regulation at distal sites. These findings highlight the relevance of odorant receptors as molecular targets for eco-friendly pest control, and demonstrate the utility of AlphaFold models and GNINA scoring in guiding the rational selection of natural ligands.

Phthalocyanines (Pcs) are macrocyclic ligands of special importance due to their great stability, good photophysical characteristics, and ease of structural alteration, which may be utilized to regulate the properties of associated materials and devices. Because of their small size, high sensitivity, low cost, simplicity of synthesis, and low processing temperature, metal phthalocyanines (TM-Pcs) sensors are one of the finest materials available for detecting gases. MPc aromatic macrocycles have the capacity to stack and produce crystalline and poly-crystalline films, making them suitable for field-effect transistor construction. TM-Pc-based sensors have also demonstrated significant absorption in the ultraviolet-visible and near-infrared ranges, which is why these molecules are employed in cancer photodynamic treatment. We investigated quantum chemical parameters for single-molecule magnets using theoretical calculations using the Density Functional Theory (DFT), which includes the Hubbard component (PBE+U). An investigation is conducted into the transition metal phthalocyanine molecules TM-Pc (3d transition metal with TM = Ti, Cr, Mn, Co, and Cu). The energy of the frontier molecular orbitals, gap (HOMO-LUMO), electronegativity, chemical potential, global hardness, softness, and electrophilicity index are among the electronic characteristics and reactivity indices associated with TM-Pc molecules that are displayed. These characteristics are intended to help comprehend and predict the future course of innovative experimental research. As a result, the suggested materials exhibit promising properties for spintronic applications.

Computational and machine learning approaches are playing a pivotal role in the identification, characterization and targeting of noncanonical DNA structures including G-quadruplexes, Z-DNA, hairpins, and triplexes. These configurations play critical roles in maintaining genomic stability, facilitating DNA repair, and regulating chromatin organization. Although the human genome predominantly adopts the B DNA conformation, evidence indicates that non B DNA forms exert significant influence on gene expression and disease development. This highlights the need for dedicated computational frameworks to systematically investigate these alternative structures. Machine learning models encompassing supervised and unsupervised algorithms such as K Nearest Neighbors, Support Vector Machines, and deep learning architectures including Convolutional Neural Networks have shown considerable potential in predicting sequence motifs predisposed to forming non B DNA conformations. These predictive tools contribute to identifying genomic regions associated with disease susceptibility. Complementary bioinformatics platforms and molecular docking tools, notably AutoDock, along with chemical libraries like ZINC, facilitate the virtual screening of small molecules targeting specific DNA structures. Stabilizers of G quadruplexes, exemplified by CX 5461, have demonstrated therapeutic promise in BRCA deficient cancers, highlighting the translational impact of computational methods on drug discovery. Anticipating DNA structural shifts opens new avenues in personalized medicine for complex diseases, with computational chemistry and machine learning deepening our understanding of DNA topology and guiding smarter ligand design. The integrated approach proposed in this review addresses the previous studies done in this field and highlights the current limitations in structural genomics and advances the development of precision therapeutics aligned with individual genomic profiles.

Triple-negative breast cancer (TNBC) accounts for approximately 10–15% of breast cancer cases and poses a significant clinical challenge due to its aggressive nature and poorer survival outcomes compared to other subtypes. This is primarily attributed to the lack of estrogen, progesterone, and HER2 receptors, which renders conventional hormone-based therapies ineffective. In this study, we employed in silico approaches to design and evaluate novel chalcone derivatives as potential inhibitors of Lyn tyrosine kinase, a critical enzyme implicated in TNBC progression. The designed compounds were screened for drug-likeness and toxicity, all meeting Lipinski’s rule of five and demonstrating favorable toxicity profiles. Molecular docking studies and post dock analysis identified five promising ligands; CHCN1, CHCN19, CHCN48, CHCN333, and CHCN94 that exhibited strong binding affinities to key active site residues of Lyn kinase, including Asp385, Phe386, Gly387, Lys275, and Glu290. Among these, CHCN1 showed the highest binding affinity at –8.4 kcal/mol, likely due to interactions with Asp385 and Lys275. These results suggest that the chalcone derivatives may effectively disrupt Lyn-mediated signaling pathways essential for cancer cell survival, potentially inhibiting proliferation, metastasis, and invasion. Overall, this study provides valuable insights into the therapeutic potential of chalcone derivatives for Triple Negative Breast Cancer, offering promising avenues for targeted intervention.

Diabetic Retinopathy(DR), eye disease that threaten cause of irreversible blindness. It always challenging to detect and diagnose early. There are several invasive procedures exists in Ophthalmology for diagnosis. All are required highly skilled medical practitioners with operational knowledge of diagnosing the sensitive organs like retina and its tiny vessels, due to dearth of retina specialist, eye’s organs sensitivity and complexity of retinal therapy the invasive procedure is time consuming, costly and have slow progress. The fundus images are the visual information of the rear part of the retina having progression of lesions around the retinal tissue’s surface, the electric signals not able to reach at visual cortex, then blurry vision or the vision loss experienced by the patients. The older methods of retinal fundus images for diagnosing lesions and symptoms of DR takes time, that causes delay in treatment and hence reducing the chance of success. Therefore, early diagnosis using fundus images can save the required efforts and time of doctors and patients. Artificial Intelligence (AI) techniques have the capability to learn the tissues structure of eye’s anatomy and to give the analysis about disease through the fundus images. Firstly, apply the image preprocessing techniques followed by splitting of dataset, creating multi head self-attention blocks, then classify the disease using the AI model. The proposed model should be trained over balanced dataset of DR images for prediction of accurate results followed by explain the decisions that diagnosed by model is correctly classified or not using Explainable AI algorithm.

Antimicrobial resistance represents a growing threat to global public health. The indiscriminate use of antibiotics has accelerated the emergence of resistant strains, reducing the therapeutic effectiveness of currently available drugs, fluoroquinolones being no exception. In this context, the design of new antimicrobials remains a significant challenge. This study evaluated, using in silico tools, the binding affinity of four novel fluoroquinolone derivatives against the DNA gyrase of six bacterial species, using moxifloxacin as the reference compound. Target protein sequences were retrieved from the Protein Data Bank and GenBank and subsequently modeled using SwissModel, I-TASSER, Phyre, and AlphaFold. The generated structures were assessed with MolProbity, and those with the best scores were selected for molecular docking. Proteins were prepared using Chimera 1.18 and AutoDockTools 1.5.7. The active site was identified with Discovery Studio 2024. Ligands were built in ZINC, prepared using Open Babel v3.1.1.60, and docked with AutoDock Vina v1.2.3.57. Docking validation was performed with DockRMSD. Residues SER83 (within the QRDR region), ARG121, and PTR122 (outside this region) were involved in ligand-enzyme interactions. Molecule C showed the highest binding affinity across all bacterial species, outperforming the control, while molecules A, B, and D displayed similar values to the reference compound. These findings suggest that molecule C exhibits a favorable profile as a potential antimicrobial agent against resistant strains.

Developing predictive models for drug efficacy is challenged by the complexity and heterogeneity of bioassay data. Here, we present LIFE.PTML, a methodology integrating drug Lifecycle (L), Information Fusion (IF), Encoding (E), Perturbation Theory (PT), and Machine Learning (ML), to predict compound activity across diverse experimental conditions. Using a dataset of 3748 molecule-assay combinations targeting calmodulin (CaM) and related proteins, LIFE.PTML combines chemical and protein descriptors, quantifies experimental variability via perturbation operators, and trains non-linear classifiers, including XGBoost and Gradient Boosting. XGBoost achieved the best performance, with 88.9% test accuracy and ROC AUC of 0.959, while feature importance analysis highlighted contributions from both drug- and protein-level descriptors. The results demonstrate that LIFE.PTML provides a robust, flexible, and interpretable framework for predictive chemoinformatics, facilitating the integration of multi-source data for drug discovery applications.

CYP1B1 (Cytochrome P450 1B1) is a key enzyme involved in the metabolic activation of carcinogens and is gaining importance as a therapeutic target, especially in hormone-dependent cancers like breast cancer. In the present work, we have developed QSAR (Quantitative Structure–Activity Relationship) models for 63 previously reported compounds with known inhibitory activity (pIC₅₀) against CYP1B1. Rather than relying on conventional 2D/3D descriptors, we focused on extracting quantum chemical and thermodynamic descriptors using xTB, a semi-empirical quantum tool. Parameters like HOMO-LUMO energy gap, dipole moment, zero-point energy, entropy, and enthalpy were computed from optimized geometries. Using recursive feature elimination (RFE), the top 8 descriptors were shortlisted and used for both regression and classification modelling. Several machine learning techniques were applied, including Multiple Linear Regression (MLR), Support Vector Regression (SVR), Random Forest, and K-Nearest Neighbours for regression, and Support Vector Classification (SVC), ensemble voting, stacking, and XGBoost for classification. Among the models tested, classification models gave better performance compared to regression. The stacking classifier achieved an accuracy of 92.3% with an AUC of 0.86, while the XGBoost model showed comparable accuracy and a slightly higher AUC of 0.94. These findings show that quantum and thermodynamic descriptors, even without conventional structural fingerprints, can provide meaningful insights for activity prediction. This study provides a foundation for further work, where we plan to use docked conformers to capture interaction-based features for improved biological relevance.

The mixture of enantiomers in most cases leads to unfavorable outcomes in a biological organism. Historically, one of the most representative examples is thalidomide—an antiemetic drug synthesized and administered to pregnant women in its racemic form during the 1950s—where the R-enantiomer exhibited the desired effect, while the S-enantiomer showed teratogenic effects.

In this context, another group of drugs for which it is important to regulate the administration of only one enantiomer are non-steroidal anti-inflammatory drugs (NSAIDs). Some, like R-naproxen, are marketed in enantiomerically pure form, while others such as ibuprofen and ketoprofen, are sold as racemates.

Given that patients requiring this type of drug often take them for extended periods, research has focused on the chiral and efficient synthesis of only the active stereoisomer. Therefore, the present work describes the synthesis of a new chiral catalyst that features a palladium atom as the central metal and the dipeptide L-lysine-glycine as the ligand. The catalyst was characterized by electron microscopy, infrared spectroscopy, nuclear magnetic resonance, and mass spectrometry.

Its ability to induce chirality was tested in an aldol reaction between various aromatic aldehydes and cyclohexanone, achieving good yields and enantiomeric excesses of up to 75%, as determined by high-performance liquid chromatography. Additionally, due to the presence of a palladium metal center, it was evaluated in a Heck cross-coupling reaction, where it was observed to promote C–C bond formation. This suggests that the catalyst could potentially be used in one-pot processes involving aldol–Heck reactions.