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The development of matrices for Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI MS) has traditionally relied on experimental efforts. Here, we propose a Goal-Directed artificial intelligence generative model, fueled by computational chemistry calculated data, to construct a chemical space optimized for Electron Transfer (ET) processes in MALDI analysis. We utilized a group of 30 reported ET matrices, subjected to structural enumeration and molecular properties prediction using semiempirical and ab initio calculations, to establish a comprehensive database comprising diverse structural and property data. Subsequently, employing a protocol of structural enumeration with 68 canonical SMILES of Bemis-Murcko (BM) fragments, we expanded the structural complexity of the initial library. This process generated 82753 compounds organized into 10 scaffold levels, with a p50 index from the Cyclic System Retrieval (CSR) curve of scaffolds of 50%. From the resulting enumerated library, a diverse subset of structures was selected by using the Jarvis-Patrick clustering method. These structures, along with their associated properties measured from quantum mechanics and experimental data, were used to train a Machine Learning (ML) model to predict ionization energy ( E i ) values. Subsequently, a Scoring Neural Network (SNN), coupled with our Goal-Directed generative model using a Recurrent Neural Network (RNN) with Deep Learning (DL) architectures, was trained. The generative model was guided using a prior network within a Reinforcement/Transfer Learning environment. The final AI-generative model learned that structures with high unsaturation, H/C ratios under 1, and molecular weights between 100 and 300 u are favorable for ET MALDI matrices, as well as those with few aromatic rings and zero aliphatic rings. Other molecular features were also favored. The resulting AI-generated library exhibits E i values over 8.0 eV, akin to those of reported "good" ET MALDI matrices, indicating successful design with high synthesis accessibility scores. In conclusion, our generative model provided valuable insights into the molecular features ideal for ET MALDI compounds while generating a wide range of structurally diverse molecules within a similar molecular property space. The next critical step in this process is to synthesize a selection of these generated compounds for the experimental validation and further characterization.

The intricate composition of microalgal pigments plays a crucial role in various biological processes, from photosynthesis to biomarker identification. Traditional pigment analysis methods involve complex extraction techniques, posing challenges in maintaining analyte integrity. In this study, we employ Electron Transfer Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry (ET-MALDI-MS) to compare the pigmentary profiles of Chlorella vulgaris intact cells, chloroplasts, and solvent extracts. We aim to obtain comprehensive extracts rich in polar and non-polar compounds using ultrasound-assisted and supercritical fluid extraction methods. Additionally, intact chloroplasts are isolated using a lysis buffer and sucrose density gradient centrifugation. Our ET-MALDI-MS analysis reveals distinct compositional differences, highlighting the impact of extraction protocols on microalgal pigment identification. We observe prominent signals corresponding to radical cations of key pigments, including chlorophylls and carotenoids, which are crucial for C. vulgaris identification. Furthermore, ET-MALDI-MS facilitates the identification of specific lipids within chloroplast membranes and other organelles. This study underscores the rapid and precise nature of ET-MALDI-MS in microalgal biomarker analysis, providing valuable insights into phytoplankton dynamics, trophic levels, and environmental processes. C. vulgaris emerges as a promising model for studying pigment composition and membrane lipid diversity, enhancing our understanding of microalgal ecosystems.

Cacao pod husks (CHs), the most abundant by-product of cacao beans production, can potentially become a source of functional ingredients for the food, cosmetic, and pharmaceutical industries. Three pigment samples (yellow, red, and purple) from lyophilized and ground cacao pod husk epicarp (CHE), were isolated by ultrasound-assisted solvent extraction, with yields between 11 and 14 wt%. The pigments exhibited UV-Vis flavonoid-related absorption bands at 283 nm and 323 nm and, only for the purple extract, reflectance bands in the 400-700 nm range. As per the Folin-Ciocalteu method, the CHE extracts contain high yields of antioxidant phenolic compounds amounting to 161.6, 153.9, and 167.9 mg GAE per g extract for the yellow, red, and purple samples, respectively. Phloretin, quercetin, myricetin, jaceosidin, and procyanidin B1 were among the main flavonoids identified by MALDI-TOF MS. A biopolymeric bacterial-cellulose matrix can effectively retain up to 541.8 mg of CHE extract per g of cellulose in dry weight. Also, MTT assays revealed that CHE extracts are non-toxic and increase viability in cultured VERO cells., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

The method describes pigment analysis from microalgae/phytoplankton extracts using electron-transfer Matrix-Assisted Laser Desorption Ionization Mass Spectrometry (ET MALDI MS). Current microalgae/phytoplankton pigment analysis requires resource- and time-intensive chromatographic methods due to the broad polarity range of the target analytes. On the other hand, traditional MALDI MS chlorophyll analysis, using proton-transfer matrices such as 2,5-dihydroxybenzoic acid (DHB) or α-cyano-4-hydroxycinnamic acid (CHCA), results in central metal loss and phytol-ester cleavage. ET MALDI MS is an alternative for the rapid screening and detection of pigments in microalgae extracts.•MALDI matrices with ionization energies above 8.0 eV guarantee electron-transfer processes from photosynthetic and photoprotective pigments whose ionization energies lay below 7.5 eV.•ET MALDI MS pigment analysis agrees with data gathered from conventional chromatographic techniques (HPLC) and optical microscopy for pigment extracts from C. vulgaris cultures and freshwater phytoplankton samples.•The ET MALDI MS method allows fast and reliable detection of pigments in microalgae cultures and freshwater phytoplankton samples ., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors. Published by Elsevier B.V.)