Current drug discovery technologies最新文献

Introduction: Benzodiazepine (BZD) and thienodiazepine (TND) congeners are highly effective in managing central nervous system (CNS) disorders such as anxiety, insomnia, depression, and epilepsy. However, this class of compounds should be revitalized through the development of new molecules with minimal side effects and reduced potential for dependence. The BZDs and TNDs can occupy the BZD receptor situated at the allosteric site of gamma-aminobutyric acid type A (GABAA) receptor and facilitate the GABAA-mediated chloride ion channel opening action.

Methods: The present study aims to structure-based docking of 29 BZDs and TNDs with binding affinity to enhance the allosteric action on GABAA and to predict the biochemical mechanisms at the target. This aspect has been scarcely explored; therefore, our objective is to predict the key amino acids within the target that favor interactions with BZDs and TNDs using quantitative structure-activity-amino acid relationship (QSAAR) analysis.

Results: The developed docking and QSAAR models can explain how interacting amino acids affect biological activity in terms of GABAA receptor binding affinity of BZDs and TNDs.

Discussion: The QSAAR establishes a quantitative relationship between biological activity and critical amino acids interacting with various groups of chemical compounds.

Conclusion: The above QSAAR model identifies GLN1239, SER1240, THR1242, and VAL1247 as significant contributors to activity with an R-value of 0.77. Therefore, these interacting amino acids are responsible for the compounds' agonistic activity.

Introduction: Drug discovery faces persistent challenges, including the need to handle heterogeneous datasets, extended timelines, and difficulties in accurately predicting drug-target interactions. These issues hinder the timely development of therapeutic interventions, especially during public health crises such as COVID-19. This study integrates ensemble machine learning with explainable artificial intelligence (XAI) to enhance predictive accuracy and transparency.

Methods: The dataset of 104 COVID-19-targeting compounds was used to train three regression models: Random Forest, Support Vector Regression, and Multi-Layer Perceptron. Ensemble strategies-Voting and Stacking Regressors-were implemented. SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) were employed to identify feature importance at global and local levels.

Results: The Drug Discovery Stack Regressor achieved the best performance, with a mean squared error (MSE) of 0.18 and R² of 0.88. SHAP and LIME analyses identified EffectiveRotorCount3D and YStericQuadrupole3D as the most influential descriptors. These features correspond to molecular flexibility and steric effects relevant to drug activity.

Discussion: Combining ensemble modeling with explainability improves both prediction robustness and interpretability. The integration of SHAP and LIME enables chemically meaningful insights into compound behavior, supporting informed molecular design and increasing model transparency. This dual-layer approach enhances confidence in AI-driven decision-making in the drug discovery process.

Conclusion: This study highlights that explainable ensemble models can improve the reliability, interpretability, and applicability of AI in drug discovery. The framework is scalable for broader datasets and offers actionable insights for rational therapeutic development and regulatory alignment.

AI comprises well-established technology for learning and identifying novel features, such as machine learning. The present article first discusses an overview of drug research, design, and development. We have also entrusted collaborations with pharmaceutical companies and artificial intelligence companies in drug development. Artificial Intelligence is primarily driven by neural net-works, namely deep neural networks (DNN) and recurrent neural networks (RNN). There are many different AI algorithms; this article describes the most widely used algorithms in the field. In recent years, the process of drug development has been encouraged by artificial Intelligence (AI). The article also provides examples of how AI and ML are being used to treat incurable diseases like cancer. A promising novel chemical found during drug discovery must proceed through the difficult and drawn-out drug development procedure; artificial intelligence techniques are being used more and more in drug discovery to deal with problems that have proven difficult to resolve.

Medicinal plants are a rich source of therapeutic agents. Tuberculosis (TB) is a highly infectious disease causing significant morbidity and mortality, primarily due to its causative agent, Mycobacterium tuberculosis. The incidence of TB is rising globally, exacerbated by the emergence of drug-resistant strains. Resistance has developed against first-line and second-line drugs, compli-cating TB control programmes and diminishing their effectiveness. The development of Multi-Drug-Resistant (MDR) and extensively-Drug-Resistant (XDR) strains of Mycobacterium tubercu-losis highlights the urgent need for novel anti-TB drugs with unique mechanisms of action. Medic-inal plants present promising alternative sources for TB treatment, especially for MDR and XDR strains. These plants produce various secondary metabolites, such as alkaloids, coumarins, flavo-noids, polyphenols, terpenoids, and quinones, which exhibit antimicrobial properties. These com-pounds, while not directly involved in the plant's growth and development, serve as defence mech-anisms and hold potential for TB control. According to the literature, phytochemical constituents with anti-tubercular activity have been identified in various plants. These phytochemicals show promise in treating MDR and XDR TB. This review provides an overview of the current synthetic drugs used for TB treatment and highlights the work done on anti-tubercular plants and their phyto-chemicals.

Vitiligo, also known as leukoderma, is a chronic autoimmune skin disorder characterized by the progressive loss of melanocytes, leading to depigmented patches on the skin. While it is not life-threatening, the visible nature of the condition can significantly impact a patient's psychological and emotional well-being. This review aimed to provide a comprehensive overview of vitiligo, in-cluding its clinical presentation, pathogenesis, diagnostic methods, and current therapeutic options. Although various synthetic treatments are available, ranging from corticosteroids to phototherapy, their long-term effectiveness is often limited, and adverse effects are more common. As a result, there is a growing interest in natural and plant-based therapies that may offer safer and more sustainable alternatives. This review has highlighted the use of herbal bioactives and traditional medicines for vitiligo management, drawing upon the data sourced from PubMed, Google Scholar, Springer, and ClinicalTrials.gov databases. Key search terms for this review included vitiligo, herbal therapies, traditional medicine, animal models, Shwitra, and Baras. The review has also explored findings from animal models and clinical trials, contributing to our understanding of disease mechanisms and ther-apeutic efficacy. By integrating traditional knowledge with modern research, there is emerging po-tential for plant-derived compounds to serve as complementary or alternative options in vitiligo treat-ment. In conclusion, advancing our understanding of vitiligo's underlying mechanisms and embrac-ing safer, evidence-based herbal therapies may pave the way toward more effective and holistic pa-tient care.

Introduction: Today's world is grappling with numerous infectious diseases and pandemics caused by bacteria, viruses, fungi, or parasites, which are affecting people at an alarming rate. Molecular topology, a field that significantly influences drug design and discovery, involves the algebraic description of chemical compounds, enabling their distinctive and straightforward characterization.

Materials and methods: Among various applications, the topological indices can be generated from ℳ-polynomial. ℳ-polynomial is a generating function that has been proposed to unify the computation of diverse topological indices. It contains degree-based topological data of molecular graphs and facilitates the derivation of multiple degree-based topological indices in an efficient manner. The ℳ-polynomial can be used to derive different degree-based topological indices by using different transformations. Computational efficiency offers a common method for calculating several topological indices. QSAR/QSPR Models are employed to examine molecular properties and biological activity in drug design.

Results: The Sombor index, a molecular descriptor, was studied in the context of several antibacterial medications, including Amoxicillin, Ampicillin, Tetracycline, Doxycycline, Cefalexin, and Ciprofloxacin. These drugs are commonly used to treat conditions such as bladder infections, rickettsial infections, pneumonia, bronchitis, and other respiratory tract infections.

Discussion: In this study, the edge partition technique is employed to derive the ℳ-polynomial for selected antibacterial drug molecules. The graphical representation of the respective molecular structures is calculated and discussed based on the derived ℳ-polynomial.

Conclusion: To construct the ℳ -polynomial and derive the Sombor index for antibiotic drugs, then correlate them with the physicochemical properties of these drugs to analyze the regression models for the best fit.

Introduction: This study investigates the molecular docking of 306 phytochemicals from Iris, Daphne, and Chrysosplenium species against three key proteins of the H5N1 influenza virus: neuraminidase, polymerase, and hemagglutinin. Phytochemicals are recognized for their antiviral potential, but interactions between compounds from these genera and H5N1 proteins remain underexplored. Given the ongoing threat of H5N1, identifying novel inhibitors is essential. The main intent is to evaluate the binding affinities of selected phytochemicals through molecular docking and assess the drug-likeness of top candidates using pharmacokinetic and physicochemical filters.

Methods: Molecular docking was performed for 306 phytochemicals against the three H5N1 proteins. Fourteen promising compounds were further screened for physicochemical properties, compliance with Lipinski's Rule of Five, Veber's Rule, and PAINS alerts.

Results: All compounds exhibited no PAINS alerts, with several conforming to Lipinski's Rule of Five and Veber's Rule. Edgeworoside A emerged as the top-performing compound, showing strong binding affinity across all three targets and favorable interaction profiles. Triumbellin and daphnogi-rin A exhibited significant binding affinity for hemagglutinin and neuraminidase, as well as for polymerase, respectively. Compounds such as 3-isobutenylquercetin, irisoid E, junipegenin A, daphne-toxin, and excoecariatoxin exhibited high binding potential without violating drug-likeness criteria.

Conclusion: Several phytochemicals, particularly edgeworoside A, demonstrate promising multi-target potential against H5N1 influenza proteins. These findings highlight the therapeutic relevance of compounds from underexplored plant genera and support their further development through in vitro, in vivo, and preclinical studies.

Ethnopharmacology is the study of traditional medicinal knowledge and its application in modern drug discovery. It combines ethnobotanical insights with scientific research to identify bio-active compounds with therapeutic potential. Sustainable agriculture refers to farming practices that maintain ecological balance, support biodiversity, and ensure the long-term availability of resources. Integrating these fields can enhance drug discovery while preserving medicinal plants and promoting environmental sustainability. This review examines the collaboration between ethnopharmacology and sustainable agriculture in advancing drug discovery, conservation, and global food security. This review examines the role of ethnopharmacology in drug discovery, analyzing traditional medicinal practices, bioactivity-guided fractionation, and metabolomic profiling. It also investigates sustainable agriculture techniques, including organic farming, controlled cultivation, and conservation strategies for medicinal plants. Data were collected from peer-reviewed literature using sources such as Google Scholar, PubMed, Scopus, and journal databases like ScienceDirect. Ethnopharmacology has con-tributed to the discovery of key drugs with anticancer, anti-inflammatory, and antimicrobial proper-ties. Sustainable agriculture ensures a steady supply of medicinal plants while optimizing their bio-active compound production through improved cultivation techniques. The combination of these ap-proaches strengthens drug discovery efforts and supports ecological conservation. Integrating eth-nopharmacology with sustainable agriculture is a promising strategy for developing new drugs while protecting natural resources. Future research should focus on innovative cultivation techniques, com-munity-led conservation efforts, and advanced analytical methods to enhance the discovery of new drugs. The adoption of agroecological practices, technological advancements, and policy support will be crucial in ensuring sustainable and equitable benefits for healthcare and agriculture. Bridging tra-ditional knowledge with scientific research will foster new therapeutic discoveries while promoting environmental sustainability.

Introduction: Streptomyces species have complex genomes, including various biosynthetic gene clusters, frequently responsible for producing antibacterial and bioactive secondary metabolites under certain environmental conditions. To assess the impact of Magnesium and Iron on Streptomyces sp. VITGV100 secondary metabolite production and bioactivity, including molecular docking studies to predict their therapeutic potential.

Methods: Streptomyces sp. VITGV100 was grown in a nutrient broth supplemented with Magnesium and Iron elicitors. The secondary metabolites were analyzed for antioxidant activity via 2,2-diphenyl-1-picrylhydrazyl radical scavenging activity, antimicrobial activity against Escherichia coli and Bacillus subtilis, and molecular docking studies of selected compounds.

Results: Magnesium and Iron supplementation elevated the production of metabolites with antioxidant activity (90% scavenging, IC50 value 0.025 mg/ml) at 6 mg/ml of Magnesium, and antimicrobial properties show the highest inhibition zone of 23 mm against Escherichia coli. Statistical analysis showed significant differences (p < 0.05) through two-way ANOVA. Docking study revealed substantial binding energy, supported by favorable Chemical Absorption, Distribution, Metabolism, Excretion, and Toxicity profiles.

Discussion: Magnesium and iron elicitation in Streptomyces sp. VITGV100 significantly enhances its antioxidant and antibacterial capabilities. Strong bioactivity and in-silico study confirmed. Although results lack in vivo efficacy and mechanistic insights, they are consistent with previous studies on trace element-induced metabolite synthesis. Clinical evaluations and mechanistic investigations of the discovered bioactive compounds should be prioritized.

Conclusion: Magnesium and Iron significantly improve the synthesis of bioactive compounds in Streptomyces sp. VITGV100, showing strong antioxidant and antimicrobial activities of these metabolites, combined with promising docking and ADMET profiles, shows promising therapeutic potential.