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Eunicella cavolini (Koch, 1887) generally recognized as yellow gorgonian, is a species of colonial soft coral in the family Gorgoniidae. Although it is common in the Mediterranean basin, the evaluation of biological potential of E. cavolini from the Adriatic Sea has not been performed yet. Taking this into account, as a part of our Center of excellence for Marine Bioprospecting of Adriatic Sea BioProCro, we have studied the chemical composition and biological activities of this gorgonian. [1] In this contribution, we will discuss the results of screenings of the antiproliferative, antibacterial, antioxidant, and anti-inflammatory activities of organic extracts and corresponding semipurified fractions. Additionally, we will show data concerning antiproliferative activity against four cancer cell lines and one non-transformed cell line, as well as its anti-inflammatory potential concerning the successfully isolated pregnane type steroid by employing dereplication methods. In final, we will present the synthesis of granulatamide B [2] isolated from the coral species of the Eunicella genus and its 12 derivatives by employing Fürstner’s Fe-catalyzed C–C coupling reaction. [3] The obtained small compound library of these N-acyl tryptamines differing in the number of C-atoms, saturation degree and conjugation of double bonds, were evaluated towards the formation and development of zebrafish Danio rerio embryos. [4]

Spirulina sp. is a widely cultivated cyanobacteria which contains a high amount of bioactive compounds with health-promoting effects and remarkable antioxidant activity due to the abundant presence of polyphenols and carotenoids. The effect of different drying temperatures (room temperature, 40, 50, 60, 80, and 100°C) on the phenolic profile and biological activity of Spirulina sp. was investigated. Samples were dried at various temperatures, extracted and then fractioned. Antioxidant activity of methanolic (F3) and dichloromethane (F4) fractions was evaluated using ABTS, FRAP, ORAC and DPPH assays. To determine the toxicity potential of prepared fractions, zebrafish embryotoxicity test was performed [2]. Phenolic compounds in Spirulina sp. samples were identified using HPLC, while LC/ MS- Q-TOF screening was used to predict the most probable bioactivities, chemical classes and drug- likeness properties of examined compounds. According to antioxidative assays, F3 fraction had higher ABTS and FRAP activity, while F4 fraction had higher DPPH and ORAC activity. The F4 fraction dried at 50°C showed the highest antioxidant activity using DPPH (687.06 +/- 11.41 mg AAE/g fraction), while ABTS and FRAP assays showed high activity for F3 fractions dried at 25, 30 and 100°C. There was no negative impact on the survival of zebrafish embryos and larvae observed at concentrations lower than 0.02 mg/mL. The most abundant polyphenols were epicatechin, vanillic acid, caffeic acid, diosmin, thymol, apigenin, while the presence of 3 alkanolamines and 25 lipid-like molecules was revealed by QTOF analysis. High antioxidant activity was observed at lower temperatures can be related to the presence of carotenoids and chlorophyll. Antioxidant potential of sample dried at 100°C can be attributed to pigment derivatives. The optimal temperature for preservation of polyphenols was 50 °C. A strong correlation between structural class of detected compounds and highly predicted activities (cardiovascular, antiinflammatory, anticancer) has been observed. The results indicate that drying at a lower temperature retains phenols and other components and maintains high antioxidant activity. From the obtained results, it can be concluded that lower drying temperatures better influence the content of phenolic compounds and biological activity of Spirulina sp., when compared to higher temperatures.

Natural products (NP) are enourmous, still largely unexplored resource of molecules with potentially therapeutic properties. More than 90 % of Earth species live in the marine environments, providing vast structural diversity and bioactivity potential of NPs associated with marine organisms. By investigating these organisms new compounds with applications in agriculture, medicine and various biotechnological processes may be discovered. While plants and terrestrial microorganisms have been more explored for new chemical entities so far, the increasing number of new drug candidates and drugs from marine organisms is showing that this resource will be of major interest in the future. Computational approaches offer a scalable, early approach for evaluating of bioactivity potential of large chemical space of natural products. We provide here a large scale, qualitative breakdown of properties of small molecules (less than 800 Da) stemming from different organisms and compare them against current drugs and nutraceuticals. We used COCONUT [1] (natural products) and DrugBank [2] (drugs and nutraceuticals) as sources of compounds, PASS software [3] to provide bioactivity estimates and Classyfire WEB service [4] to structuraly classify compounds. Table 1. depicts major structure-bioactivity profiles of compounds from different origins, with their rough druglikenes assessment. Using more rigorous computational (AI, machine learning based) approaches we are working to provide deeper structural-biaoctivity assessment of the compounds originating in marine organisms.

Machine learning (ML) approaches are extensively used to analyse chemical (synthetical, analytical, physicochemical) and biological (in vitro and in vivo) data and build models for discovery and design of novel small molecular weight molecules with optimized target activities. The basic ML requirement is to have a large and reliable enough dataset which is used for training and testing a predictive model. In the presentation, application of ML approaches on publicly available data sets for biologically active compounds such as drugs and agrochemicals will be demonstrated.

Spirulina sp. is a widely cultivated cyanobacteria which contains a high amount of bioactive compounds with health-promoting effects and remarkable antioxidant activity due to the abundant presence of polyphenols and carotenoids. The effect of different drying temperatures (room temperature, 40, 50, 60, 80, and 100°C) on the phenolic profile and biological activity of Spirulina sp. was investigated. Samples were dried at various temperatures, extracted and then fractioned. Antioxidant activity of methanolic (F3) and dichloromethane (F4) fractions was evaluated using ABTS, FRAP, ORAC and DPPH assays. To determine the toxicity potential of prepared fractions, zebrafish embryotoxicity test was performed. Phenolic compounds in Spirulina sp. samples were identified using HPLC, while LC/ MS-Q-TOF screening was used to predict the most probable bioactivities, chemical classes and drug-likeness properties of examined compounds. According to antioxidative assays, F3 fraction had higher ABTS and FRAP activity, while F4 fraction had higher DPPH and ORAC activity. The F4 fraction dried at 50°C showed the highest antioxidant activity using DPPH (687.06 +/- 11.41 mg AAE/g fraction), while ABTS and FRAP assays showed high activity for F3 fractions dried at 25, 30 and 100°C. There was no negative impact on the survival of zebrafish embryos and larvae observed at concentrations lower than 0.02 mg/mL. The most abundant polyphenols were epicatechin, vanillic acid, caffeic acid, diosmin, thymol, apigenin, while the presence of 3 alkanolamines and 25 lipid-like molecules was revealed by QTOF analysis. High antioxidant activity observed at lower temperatures can be related to the presence of carotenoids and chlorophyll. Antioxidant potential of sample dried at 100°C can be attributed to pigment derivatives. The optimal temperature for preservation of polyphenols was 50°C. A strong correlation between structural class of detected compounds and highly predicted activities (cardiovascular, anti-inflammatory, anticancer) has been observed. The results indicate that drying at a lower temperature retains phenols and other components and maintains high antioxidant activity. It can also be concluded that lower drying temperatures better influence the content of phenolic compounds and biological activity of Spirulina sp., when compared to higher temperatures.

Bioactive potential of brown alga Padina pavonica from the Adriatic Sea was described according to the tests conducted.

Natural products are a great source of novel molecules that can have beneficial effects on human health, not only as medicines, but also as nutrients and cosmetic ingredients. We have investigated compounds from the largest natural product database COCONUT, using data science and machine learning, and compared them with approved drugs from the ChEMBL database [1, 2]. Among the various machine learning techniques, reducing the dimensionality of the multidimensional space for the purpose of visualization enables better understanding of large data sets. In the presented case study, we used advanced exploration and visualization tools to analyze structure-activity relationships (SAR) and reveal preliminary activities for subsets of natural products. References: [1] M. Sorokina, P. Merseburger, K. Rajan, M. A. Yirik, C. Steinbeck, J. Cheminform. 13 (2021) 2. [2] A. Gaulton, L. J. Bellis, A. P. Bento, J. Chambers, M. Davies, A. Hersey, Y. Light, S. McGlinchey, D. Michalovich, B. Al-Lazikani, J. P. Overington, Nucleic Acids Res. 40 (2012) D1100–D1107.

Spirulina sp. is a widely cultivated microalgae that contains numerous bioactive compounds with health-promoting properties. Phenolic profile and biological activity were investigated in regard to different drying temperatures (25, 40, 50, 60, 80, 100°C). Antioxidant activity (ABTS, FRAP, ORAC, DPPH) of methanolic (F3) and dichloromethane (F4) fractions was evaluated. To assess the toxicity of the fractions, zebrafishembryos were used. Phenolic compounds were identified using HPLC, while prediction of most probable bioactivities, chemical classes, and drug-likeness properties were made using LC/MS-Q-TOF. The F4 (50°C) showed the highest altioxidant activity using DPPH, while ABTS and FRAP showed high activity for F3 (25, 30 and 100°C). No negative impact on the survival of zebrafish was observed at concentrations lower than 0.02 mg/mL. Most abundant polyphenols were epicatechin, vanillic and caffeic acid, while the presence of alkanolamines and lipid-like molecules was revealed by LC/MS-Q-TOF. High antioxidant activity was observed at lower temperatures, due to the presence of carotenoids and chlorophylls, while for the sample dried at 100°C this can be attributed to pigment derivatives. Optimal temperature for preservation of polyphenols was 50°C. A strong correlation between structural class of detected compounds and highly predicted activities (cardiovascular, anti- inflammatory, anticancer) has been observed.

There is enormous public interest in the EU, but also the interest of the pharmaceutical industry in the possibility of finding significant factors that would make it possible to distinguish between approved drugs and drugs that have been withdrawn after several years of use. The EU responded to this interest by funding a large AI4EU project, which launched calls for proposals to develop various artificial intelligence (AI)-based computing solutions.[1] Among them was the task to develop a predictive AI solution based on the deep neural network method to distinguish drugs from withdrawn drugs based on their structure.[2] In this presentation, the process of defining the problem and creating a dataset will be elaborated, as well as related issues such as the different status of the same drug in different countries or regions, or the problem of cleaning datasets, as many drugs are not pure compounds but complexes. We have calculated comprehensive sets of molecular structure descriptors for the final sets of approved and withdrawn drugs. The analysis of the importance/usefulness of each structure-based molecular descriptor (including those related to ADMETox properties of drugs) in discriminating between approved and withdrawn drugs will be presented. As expected, the classification accuracy of the multivariate models based on descriptors is not high. Nevertheless, it is comparable to (or sometimes better than) more complex models and significantly higher than the accuracy achieved by random guessing (random model). REFERENCES [1] The AI4EU Call for Solutions Seeking Companies to Solve AI Challenges, https://internationaldataspaces.org/the-ai4eu-call-for-solutions-seeking-companies-to-solve-aichallenges/ (assessed on April 15, 2022) [2] The AI-on-demand platform, https://www.ai4europe.eu/ and https://www.ai4media.eu/projectoverview/ (assessed on April 15, 2022)