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A Quantitative Structure-Activity Relationship (QSAR) approach for classification was used for the prediction of compounds as active/inactive relatively to overall biological activity, antitumor and antibiotic activities using a data set of 1746 compounds from PubChem with empirical CDK descriptors and semi-empirical quantum-chemical descriptors. A data set of 183 active pharmaceutical ingredients was additionally used for the external validation of the best models. The best classification models for antibiotic and antitumor activities were used to screen a data set of marine and microbial natural products from the AntiMarin database—25 and four lead compounds for antibiotic and antitumor drug design were proposed, respectively. The present work enables the presentation of a new set of possible lead like bioactive compounds and corroborates the results of our previous investigations. By other side it is shown the usefulness of quantum-chemical descriptors in the discrimination of biologically active and inactive compounds. None of the compounds suggested by our approach have assigned non-antibiotic and non-antitumor activities in the AntiMarin database and almost all were lately reported as being active in the literature. [ABSTRACT FROM AUTHOR]

The comprehensive information of small molecules and their biological activities in the PubChem database allows chemoinformatic researchers to access and make use of large-scale biological activity data to improve the precision of drug profiling. A Quantitative Structure-Activity Relationship approach, for classification, was used for the prediction of active/inactive compounds relatively to overall biological activity, antitumor and antibiotic activities using a data set of 1804 compounds from PubChem. Using the best classification models for antibiotic and antitumor activities a data set of marine and microbial natural products from the AntiMarin database were screened--57 and 16 new lead compounds for antibiotic and antitumor drug design were proposed, respectively. All compounds proposed by our approach are classified as non-antibiotic and non-antitumor compounds in the AntiMarin database. Recently several of the lead-like compounds proposed by us were reported as being active in the literature. [ABSTRACT FROM AUTHOR]

The risk of methicillin-resistant Staphylococcus aureus (MRSA) infection is increasing in both the developed and developing countries. New approaches to overcome this problem are in need. A ligand-based strategy to discover new inhibiting agents against MRSA infection was built through exploration of machine learning techniques. This strategy is based in two quantitative structure–activity relationship (QSAR) studies, one using molecular descriptors (approach A) and the other using descriptors (approach B). In the approach A, regression models were developed using a total of 6645 molecules that were extracted from the ChEMBL, PubChem and ZINC databases, and recent literature. The performance of the regression models was successfully evaluated by internal and external validation, the best model achieved R2 of 0.68 and RMSE of 0.59 for the test set. In general natural product (NP) drug discovery is a time-consuming process and several strategies for dereplication have been developed to overcome this inherent limitation. In the approach B, we developed a new NP drug discovery methodology that consists in frontloading samples with 1D NMR descriptors to predict compounds with antibacterial activity prior to bioactivity screening for NPs discovery. The NMR QSAR classification models were built using 1D NMR data (1H and 13C) as descriptors, from crude extracts, fractions and pure compounds obtained from actinobacteria isolated from marine sediments collected off the Madeira Archipelago. The overall predictability accuracies of the best model exceeded 77% for both training and test sets. [ABSTRACT FROM AUTHOR]

To discover new inhibitors against the human colon carcinoma HCT116 cell line, two quantitative structure–activity relationship (QSAR) studies using molecular and nuclear magnetic resonance (NMR) descriptors were developed through exploration of machine learning techniques and using the value of half maximal inhibitory concentration (IC50). In the first approach, A, regression models were developed using a total of 7339 molecules that were extracted from the ChEMBL and ZINC databases and recent literature. The performance of the regression models was successfully evaluated by internal and external validations, the best model achieved R2 of 0.75 and 0.73 and root mean square error (RMSE) of 0.66 and 0.69 for the training and test sets, respectively. With the inherent time-consuming efforts of working with natural products (NPs), we conceived a new NP drug hit discovery strategy that consists in frontloading samples with 1D NMR descriptors to predict compounds with anticancer activity prior to bioactivity screening for NPs discovery, approach B. The NMR QSAR classification models were built using 1D NMR data (1H and 13C) as descriptors, from 50 crude extracts, 55 fractions and five pure compounds obtained from actinobacteria isolated from marine sediments collected off the Madeira Archipelago. The overall predictability accuracies of the best model exceeded 63% for both training and test sets. [ABSTRACT FROM AUTHOR]

Introduction: Biofouling poses a significant economic threat to various marine industries, leading to financial losses that can reach billions of euros annually. This study highlights the urgent need for effective alternatives to traditional antifouling agents, particularly following the global ban on organotin compounds. Material and methods: Streptomyces aculeolatus PTM-346 was isolated from sediment samples on the shores of the Madeira Archipelago, Portugal. The crude extract was fractionated using silica flash chromatography and preparative HPLC, resulting in two isolated marinone compounds: madeirone ( 1 ), a novel marinone derivative discovered in this study, and neomarinone ( 2 ). The antifouling activities of these compounds were tested against five marine bacterial species and the larvae of the mussel Mytilus galloprovincialis . Additionally, in silico and in vivo environmental toxicity evaluations of madeirone ( 1 ) and neomarinone ( 2 ) were conducted. Results: Madeirone ( 1 ) demonstrated significant antibiofilm efficacy, inhibiting Phaeobacter inhibens by up to 66%, Marinobacter hydrocarbonoclasticus by up to 60%, and Cobetia marina by up to 40%. Neomarinone (2) also exhibited substantial antibiofilm activity, with inhibition rates of up to 41% against P. inhibens , 40% against Pseudo-oceanicola batsensis , 56% against M. hydrocarbonoclasticus , 46% against C. marina, and 40% against Micrococcus luteus . The growth inhibition activity at the same concentrations of these compounds remained below 20% for the respective bacteria, highlighting their effectiveness as potent antibiofilm agents without significantly affecting bacterial viability. Additionally, both compounds showed potent effects against the settlement of Mytilus galloprovincialis larvae, with EC 50 values of 1.76 µg/mL and 0.12 µg/mL for compounds ( 1 ) and ( 2 ), respectively, without impairing the viability of the targeted macrofouling species. In silico toxicity predictions and in vivo toxicity assays both support their potential for further development as antifouling agents. Conclusion: The newly discovered metabolite madeirone ( 1 ) and neomarinone ( 2 ) effectively inhibit both micro- and macrofouling. This distinct capability sets them apart from existing commercial antifouling agents and positions them as promising candidates for biofouling prevention. Consequently, these compounds represent a viable and environmentally friendly alternative for incorporation into paints, primers, varnishes, and sealants, offering significant advantages over traditional copper-based compounds., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision. The handling editor LP declared a past co-authorship with the author JA., (Copyright © 2024 Wissner, Almeida, Grilo, Oliveira, Brízida, Escobedo-Hinojosa, Pissaridou, Vasquez, Cunha, Sobral, Vasconcelos and Gaudêncio.)

The risk of methicillin-resistant Staphylococcus aureus (MRSA) infection is increasing in both the developed and developing countries. New approaches to overcome this problem are in need. A ligand-based strategy to discover new inhibiting agents against MRSA infection was built through exploration of machine learning techniques. This strategy is based in two quantitative structure⁻activity relationship (QSAR) studies, one using molecular descriptors (approach A) and the other using descriptors (approach B). In the approach A, regression models were developed using a total of 6645 molecules that were extracted from the ChEMBL, PubChem and ZINC databases, and recent literature. The performance of the regression models was successfully evaluated by internal and external validation, the best model achieved R² of 0.68 and RMSE of 0.59 for the test set. In general natural product (NP) drug discovery is a time-consuming process and several strategies for dereplication have been developed to overcome this inherent limitation. In the approach B, we developed a new NP drug discovery methodology that consists in frontloading samples with 1D NMR descriptors to predict compounds with antibacterial activity prior to bioactivity screening for NPs discovery. The NMR QSAR classification models were built using 1D NMR data (¹H and 13 C) as descriptors, from crude extracts, fractions and pure compounds obtained from actinobacteria isolated from marine sediments collected off the Madeira Archipelago. The overall predictability accuracies of the best model exceeded 77% for both training and test sets.

Fijiolide A, a potent inhibitor of TNF-alpha-induced NFkappaB activation, along with fijiolide B, were isolated from a marine-derived bacterium of the genus Nocardiopsis. The planar structures of fijiolides A (1) and B (2) were elucidated by interpretation of 2D NMR spectroscopic data, while the absolute configurations of these compounds were defined by interpretation of circular dichroism and 2D NMR data combined with application of the advanced Mosher's method. Fijiolides A and B are related to several recently isolated chloroaromatic compounds, which appear to be the Bergman cyclization products of enediyne precursors. Fijiolide A reduced TNF-alpha-induced NFkappaB activation by 70.3%, with an IC(50) value of 0.57 micro-M. Fijiolide B demonstrated less inhibition, only 46.5%, without dose dependence. The same pattern was also observed with quinone reductase (QR) activity: fijiolide A was found to induce quinone reductase-1 (QR1) with an induction ratio of 3.5 at a concentration of 20 microg/mL (28.4 microM). The concentration required to double the activity was 1.8 microM. Fijiolide B did not affect QR1 activity, indicating the importance of the nitrogen substitution pattern for biological activity. On the basis of these data, fijiolide A is viewed as a promising lead for more advanced anticancer testing.