Molecular Diversity最新文献

{"title":"Application of high-precision solubility prediction models in the assisted design of drug-like compounds.","authors":"Yutong Gao","doi":"10.1007/s11030-025-11239-x","DOIUrl":"https://doi.org/10.1007/s11030-025-11239-x","url":null,"abstract":"<p><p>Machine learning (ML) techniques are rapidly being applied to drug-assisted design. In order to provide more efficient methods to aid the solubility prediction aspect of drug design, two machine learning models are developed and trained with two distinct feature sets derived from the Zenodo dataset. The machine models are constructed with the multilayer perceptron as the core, combining Bayesian optimization and Monte Carlo methods to improve prediction accuracy. The training process leverages RMSprop to expedite convergence, utilizes Dropout to avert overfitting, and incorporates a Self-Attention mechanism to focus on important features. Based on the three types of compounds, the correlation coefficients all remain above 0.99 compared to the actual solubility. The average absolute errors of the solubility prediction results of the two models are less than 0.200 mol/L and 0.050 mol/L. Both trained models are capable of predicting the solubility of thousands of compounds in just 94.7 ms and 57.7 ms. Using these two models, it is possible to assist with faster and more rational design of drug-like compounds.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144155483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drug repurposing to identify potential FDA-approved drugs targeting three main angiogenesis receptors through a deep learning framework.","authors":"Mohammadreza Torabi, Soroush Sardari, Alejandro Rodríguez-Martínez, Nooshin Arabi, Horacio Pérez-Sánchez, Fahimeh Ghasemi","doi":"10.1007/s11030-025-11214-6","DOIUrl":"https://doi.org/10.1007/s11030-025-11214-6","url":null,"abstract":"<p><p>Tumor cell survival depends on the presence of oxygen and nutrients provided by existing blood vessels, particularly when cancer is in its early stage. Along with tumor growth in the vicinity of blood vessels, malignant cells require more nutrients; hence, capillary sprouting occurs from parental vessels, a process known as angiogenesis. Although multiple cellular pathways have been identified, controlling them with one single biomolecule as a multi-target inhibitor could be an attractive strategy for reducing medication side effects. Three critical pathways in angiogenesis have been identified, which are activated by the vascular endothelial growth factor receptor (VEGFR), fibroblast growth factor receptor (FGFR), and epidermal growth factor receptor (EGFR). This study aimed to develop a methodology to discover multi-target inhibitors among over 2000 FDA-approved drugs. Hence, a novel ensemble approach was employed, comprising classification and regression models. First, three different deep autoencoder classifications were generated for each target individually. The top 100 trained models were selected for the high-throughput virtual screening step. After that, all identified molecules with a probability of more than 0.9 in more than 70% of the models were removed to ensure accurate consideration in the regression step. Since the ultimate aim of virtual screening is to discover molecules with the highest success rate in the pharmaceutical industry, various aspects of the molecules in different assays were considered by integrating ten different regression models. In conclusion, this paper contributes to pharmaceutical sciences by introducing eleven diverse scaffolds and eight approved drugs that can potentially be used as inhibitors of angiogenesis receptors, including VEGFR, FGFR, and EGFR. Considering three target receptors simultaneously is another central concept and contribution used. This concept could increase the chance of success, while reducing the possibility of resistance to these agents.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144141085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harnessing virtual screening and MD simulations: a multistage approach to identifying potent and nontoxic agonists for protein kinase A.","authors":"Muneeb Ali, Nadeem Ahmad, Madiha Sardar, Sajjad Haider, Mamona Mushtaq, Mohammad Nur-E-Alam, Mohammed F Hawwal, Pinghua Sun, Zaheer Ul-Haq","doi":"10.1007/s11030-025-11223-5","DOIUrl":"https://doi.org/10.1007/s11030-025-11223-5","url":null,"abstract":"<p><p>Obesity-induced insulin resistance impairs glucose tolerance and β-cell function, significantly contributing to the pathogenesis of type 2 diabetes (T2D). Protein kinase A (PKA), being one of the key effector molecules of the cyclic AMP (cAMP) pathway, increases insulin secretion via membrane activity, gene expression, and exocytosis of insulin granules. The previous studies were limited to either target cAMP analogs as PKA agonist or mostly flavonoids using In vivo and In vitro studies (Hameed in Int J Biol Macromol 119:149-156, 2018;Shahab in Biomed Pharmacother 177, 2024;Hameed in Eur J Pharmacol 820:245-255, 2018;Hameed in Eur J Pharmacol 858, 2019;Hafizur in Med Chem Res 27:1408-1418, 2018;). To speed up the process, this study aimed to identify potential PKA activators as therapeutic agents for restoring β-cell function in Type 2 Diabetes (T2D) using a multistage virtual screening approach. In the initial phase, a ligand-based pharmacophore model was constructed to screen an in-house small molecule database for potential PKA agonists. By targeting the essential pharmacophoric features necessary for interaction with the cyclic nucleotide-binding (CNB) domain of PKA, the goal was to identify compounds with strong binding affinities and therapeutic promise. To gain deeper insights into the molecular mechanisms of PKA activation and evaluate key interactions and dynamic stability, a subset of promising hits was subjected to all-atom molecular dynamics simulations. Simulations showed significant conformational changes in PKA complexes, with average backbone root mean square deviations (RMSD) of 0.37 ± 0.15 nm for Comp-03, 0.53 ± 0.18 nm for Comp-11, 0.31 ± 0.06 nm for Comp-17, 0.28 ± 0.03 nm for Comp-38, and 0.48 ± 0.13 nm for Comp-41. The N3A motif showed consistent fluctuations, suggesting increased flexibility. Binding free energy calculations showed binding free energies (ΔGbind) for cAMP, Comp-03, Comp-17, Comp-38, and Comp-41, with ΔGbind values of - 62.87 ± 10.04, - 68.57 ± 12.77, - 78.13 ± 16.36, - 62.67 ± 13.06, and - 80.87 ± 10.45 kcal/mol, respectively. To further probe the conformational stability of these complexes, multidimensional scaling and free energy profiling were carried out. This exhaustive research study, involving examination of stability dynamics, deviation patterns, interaction networks, conformational changes, and energy profiles, provides profound understanding about mechanisms that activate PKA. The findings highlight several promising lead compounds, notably Comp-03, Comp-17, Comp-38, and Comp-41, which exhibit superior potential to activate PKA compared to cAMP. These findings lay a strong foundation for the development of novel PKA activators as potential therapeutic agents for managing T2D.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144141089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"De novo synthesis of tryptanthrin and its derivatives from indole: engineering a sustainable photocatalytic strategy.","authors":"Ya Zhou, Junrong Zhang, Ya Xiao, Hongwu Liu, Guoqing Wang, Linli Yang, Wujun Jian, Dan Zeng, Xiang Zhou, Song Yang","doi":"10.1007/s11030-025-11222-6","DOIUrl":"https://doi.org/10.1007/s11030-025-11222-6","url":null,"abstract":"<p><p>Tryptanthrin is a natural alkaloidal molecule [IUPAC name 6,12-dihydro-6,12-dioxoindolo-(2,1-b)-quinazoline], possessing an indoloquinazoline moiety. Since first being discovered in 1979, it and its analogs generated great interest in the fields of medicinal chemistry, life science, and materials science. Protocols for synthesizing tryptanthrins with the features of facile, eco-friendly, and mild reaction conditions remain an underdeveloped area. This new protocol, leveraging Rose Bengal as a photo-catalyst and commercially available substituted indoles as starting materials, enables selective synthesis under mild reaction conditions, expanding the substrate scope, complementing existing methods, and investigating the possible reaction mechanism. This approach emphasizes eco-friendliness, cost-effectiveness, and scalability, making it suitable for diverse applications. This innovation holds promise for synthesizing tryptanthrin-based bioactive compounds and materials relevant to chemistry and beyond.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144126361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel pyrazole carboxylate derivatives as lonazolac bioisosteres with selective COX-2 inhibition: design, synthesis and anti-inflammatory activity.","authors":"Wael A A Fadaly, Ahmed Elshewy, Ahmed H A Abusabaa, Dina M E Amin, Hoda Khalifa Abdelhady, Haredy Hassan Haredy, Asmaa M Mahmoud, Nashwa A Ibrahim, Mohamed T M Nemr","doi":"10.1007/s11030-025-11220-8","DOIUrl":"https://doi.org/10.1007/s11030-025-11220-8","url":null,"abstract":"<p><p>Two novel series of di-aryl/tri-aryl substituted pyrazole ester derivatives 15a-h and 19a-d were designed, synthesized as novel non-acidic lonazolac analogs and tested for its COX-2, 5-LOX, 15-LOX, iNOS, pro-inflammatory cytokines TNF-α and PGE2 inhibitory activities. All the tested compounds showed excellent COX-2 inhibitory activity (IC₅₀ = 0.059-3.89 μM), compared to that of celecoxib (IC₅₀ = 0.22 μM), where derivatives 15c, 15d, 15 h and 19d were found to be the most potent showing COX-2 selectivity index in range of (S.I. = 28.56-98.71) compared to celecoxib (S.I. = 13.65). Moreover, the most potent four derivatives 15c, 15d, 15 h and 19d showed outstanding 5-LOX and 15-LOX inhibitory activities (IC₅₀ = 0.24-0.81, 0.20-2.2 respectively, compared to zileuton IC₅₀ = 1.52 and 0.54, respectively). Further investigation of the anti-inflammatory mechanistic study of derivatives 15c, 15d, 15 h and 19d revealed that these four compounds exhibited comparable TNF-α and PGE2 (LPS-induced pro-inflammatory cytokines) inhibitory activities (IC₅₀ = 0.77-1.20 μM and 0.28-0.52 μM respectively) when compared to celecoxib (IC₅₀ = 0.87 μM and 0.38 μM respectively) as reference drug using lipopolysaccharide-activated RAW 264.7 macrophages. Based on the advanced inhibitory activity of compounds 15c, 15d, 15 h and 19d against LPS-induced pro-inflammatory mediators (TNF-α and PGE2), inducible nitric oxide synthase (iNOS) inhibition assay was carried out. Remarkably, compounds 15c, 15d, 15 h and 19d showed higher potency with lower IC₅₀ (0.41-0.61 µM) when compared to the reference drug celecoxib (0.48 µM). Prior to in vivo anti-inflammatory activity screening, cytotoxicity testing was performed to ascertain safe and non-toxic concentrations of each compound. Safe doses of compounds were determined using lipopolysaccharide-activated RAW 264.7 macrophages, moreover results showed that compounds 15c, 15d, 15 h and 19d were more safer (less cytotoxic) with higher IC₅₀ (178.95-301.40 µM) when compared to the reference drug celecoxib (148.90 µM). In vivo anti-inflammatory activity of the target compounds 15c, 15d, 15 h and 19d reinforced the results of in vitro screening as the derivatives 15c, 15d, 15 h and 19d showed (ED₅₀ = 8.22-31.22 mg/kg, respectively) and were more potent than celecoxib (ED₅₀ = 40.39 mg/kg). All screened derivatives 15c, 15d, 15 h and 19d were less ulcerogenic (ulcer indexes = 1.22-3.93) than lonazolac (ulcer index = 20.30) and comparable to celecoxib (ulcer index = 3.02). In silico docking and ADME studies were carried out in order to clarify the interactions of the most active derivatives 15c, 15d, 15 h and 19d with the target enzymes and their pharmacokinetic parameters.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144126431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discovery of novel potential 11β-HSD1 inhibitors through combining deep learning, molecular modeling, and bio-evaluation.","authors":"Xiaodie Chen, Liang Zou, Lu Zhang, Jiali Li, Rong Liu, Yueyue He, Mao Shu, Kuilong Huang","doi":"10.1007/s11030-025-11171-0","DOIUrl":"https://doi.org/10.1007/s11030-025-11171-0","url":null,"abstract":"<p><p>11β-Hydroxysteroid dehydrogenase type 1 (11β-HSD1) has been shown to play an important role in the treatment of impaired glucose tolerance, insulin resistance, dyslipidemia, and obesity and is a promising drug target. In this study, we built a gated recurrent unit (GRU)-based recurrent neural network using 1,854,484 (processed) drug-like molecules from ChEMBL and the US patent database and successfully built a molecular generative model of 11βHSD1 inhibitors by using the known 11β-HSD1 inhibitors that have undergone transfer learning, our constructed GRU model was able to accurately capture drug-like molecules evaluated using traditional machine model-related syntax, and transfer learning can also easily generate potential 11β-HSD1 inhibitors. By combining Lipinski's and absorption, distribution, metabolism, excretion, and toxicity (ADME/T) analyses to filter nonconforming molecules and stepwise screening through molecular docking and molecular dynamics simulation, we finally obtained 5 potential compounds. We found that compound 02 is identical to a previously published inhibitor of 11β-HSD1. We selected compounds 02 and 05 with the lowest binding free energy for in vitro activity validation and found that compound 02 possessed inhibitory activity but was not as potent as the control. In conclusion, our study provides new ideas and methods for the development of new drugs and the discovery of new 11β-HSD1 inhibitors.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144109463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning approaches for predicting the small molecule-miRNA associations: a comprehensive review.","authors":"Ashish Panghalia, Vikram Singh","doi":"10.1007/s11030-025-11211-9","DOIUrl":"https://doi.org/10.1007/s11030-025-11211-9","url":null,"abstract":"<p><p>MicroRNAs (miRNAs) are evolutionarily conserved small regulatory elements that are ubiquitous in cells and are found to be abnormally expressed during the onset and progression of several human diseases. miRNAs are increasingly recognized as potential diagnostic and therapeutic targets that could be inhibited by small molecules (SMs). The knowledge of SM-miRNA associations (SMAs) is sparse, mainly because of the dynamic and less predictable 3D structures of miRNAs that restrict the high-throughput screening of SMs. Toward augmenting the costly and laborious experiments determining the SM-miRNA interactions, machine learning (ML) has emerged as a cost-effective and efficient platform. In this article, various aspects associated with the ML-guided predictions of SMAs are thoroughly reviewed. Firstly, a detailed account of the SMA data resources useful for algorithms training is provided, followed by an elaboration of various feature extraction methods and similarity measures utilized on SMs and miRNAs. Subsequent to a summary of the ML algorithms basics and a brief description of the performance measures, an exhaustive census of all the 32 ML-based SMA prediction methods developed so far is outlined. Distinctive features of these methods have been described by classifying them into six broad categories, namely, classical ML, deep learning, matrix factorization, network propagation, graph learning, and ensemble learning methods. Trend analyses are performed to investigate the patterns in ML algorithms usage and performance achievement in SMA prediction. Outlining key principles behind the up-to-date methodologies and comparing their accomplishments, this review offers valuable insights into critical areas for future research in ML-based SMA prediction.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144109420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synthesis, anti-tumor evaluation, and mechanistic investigation of 3-indolylpyrazole phenoxyacetamide derivatives against chronic myeloid leukemia cells.","authors":"Mengting Liu, Guiyun Wu, Yue Zhou, Chengpeng Li, Danping Chen, Yan Li, Zhurui Li, Chenchen Li, Zhenchao Wang","doi":"10.1007/s11030-025-11219-1","DOIUrl":"10.1007/s11030-025-11219-1","url":null,"abstract":"<p><p>Both indole and phenol structures are important active components of drug structure. In this study, a series of novel 3-indole pyrazole derivatives incorporating phenolic moieties were rationally designed and synthesized through molecular hybridization strategy. A comprehensive evaluation of antitumor activity demonstrated that these target compounds exhibited remarkable cytotoxicity across four distinct cancer cell lines. Notably, compound O11 emerged as the most potent compound, showing exceptional activity against human chronic myeloid leukemia (K562) cells with an IC₅₀ value of 2.64 μM. The investigation into the mechanism of transcription and protein validation revealed that the anti-tumor effects were produced through multiple pathways: potential interference with the mitochondrial membrane, modulated the intracellular levels of reactive oxygen species, inhibited the activation of the NF-κB signaling pathway, blockade of the cell cycle at the G2/M phase, and ultimately, apoptosis of the K562 cells. These findings underscore the potential of O11, a promising compound that offers new possibilities for the further development of cancer therapeutics.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144101289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"First report on analysis of chemical space, scaffold diversity, critical structural features of HDAC11 inhibitors.","authors":"Rinki Prasad Bhagat, Jyotisha, Indrasis Dasgupta, Sk Abdul Amin, Pranay Jakkula, Arijit Bhattacharya, Insaf Ahmed Qureshi, Shovanlal Gayen","doi":"10.1007/s11030-025-11217-3","DOIUrl":"https://doi.org/10.1007/s11030-025-11217-3","url":null,"abstract":"<p><p>In the histone deacetylase (HDAC) family, HDAC11 is the smallest and a single member under the class IV subtype. It is important as a drug target mainly in cancer, inflammatory and autoimmune diseases. The design and development of selective HDAC11 inhibitors is quite a challenge for the chemist community due to the unavailability of the crystal structure of HDAC11. Ligand-based drug design (LBDD) strategies are the hope to speed up the development of its inhibitors. Here, an in-depth analysis of 712 HDAC11 inhibitors is performed through compound space networks and various cheminformatics approaches. The analyses demonstrated significant clustering of similar compounds based on their chemical structures, offering valuable insights into the chemical space occupied by HDAC11 inhibitors. Furthermore, the current work aimed to develop robust classification-based QSAR models that deliver the essential structural fingerprints. This study highlighted that the compounds bearing scaffolds such as isoindoline, benzimidazole, carboxamide/hydroxamate moieties, etc., are important for HDAC11 inhibitors. Molecular docking and MD simulations further provide an in-depth analysis of the binding interaction of the identified fingerprints in the catalytic site of HDAC11. In brief, our study delivers some important structural attributes that will aid medicinal chemists in designing and developing future potent HDAC11 inhibitors.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"\"Acne vulgaris: key insights, treatment, and future prospects\".","authors":"Jyotsana Bhatt, Vijay Jyoti Kumar, Chahat","doi":"10.1007/s11030-025-11209-3","DOIUrl":"https://doi.org/10.1007/s11030-025-11209-3","url":null,"abstract":"<p><p>Acne vulgaris is an immune-mediated inflammatory skin condition. It is the 8th most treated skin condition, affecting over 80% of teenagers globally. The pathophysiology of the disease is intricate and involves the interaction of multiple factors, including hyperactivation of sebaceous glands, abnormal hyperkeratinization of pilosebaceous follicles, dysbiosis of microflora, and, subsequently, immune-mediated inflammation. Due to the multifaceted nature of the disorder, a combination of different anti-acne agents is preferred. However, synthetic topical anti-acne agents such as retinol derivatives and benzoyl peroxide are often associated with common side effects such as redness, dryness, peeling, irritation, and eczema. Also, increasing cases of multidrug resistance against commonly used antibiotics further reduce the therapeutic effectiveness of the treatment and upsurge the overall expense. Many studies validate using plant extracts and secondary metabolites for treating acne. Alongside synergistic formulations, the co-delivery of synthetic and herbal agents is emerging as the latest target for developing novel and efficient acne treatments. This article summarizes the comprehensive pathophysiology of the disease, classification, and grading system evolution, along with detailed information on different FDA-approved synthetic topical and systemic anti-acne agents (retinoids, antibiotics, and diverse medications). In addition, we highlight the new investigation on several phytoconstituents and secondary metabolites for treating acne vulgaris. Finally, we have attached the details of all the ongoing clinical trials on acne therapy.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144075276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}