Drug-decorated nanoparticles (DDNPs) have important medical applications. The current work combined Perturbation Theory with Machine Learning and Information Fusion (PTMLIF). Thus, PTMLIF models were proposed to predict the probability of nanoparticle–compound/drug complexes having antimalarial activity (against Plasmodium). The aim is to save experimental resources and time by using a virtual screening for DDNPs. The raw data was obtained by the fusion of experimental data for nanoparticles with compound chemical assays from the ChEMBL database. The inputs for the eight Machine Learning classifiers were transformed features of drugs/compounds and nanoparticles as perturbations of molecular descriptors in specific experimental conditions (experiment-centered features). The resulting dataset contains 107 input features and 249,992 examples. The best classification model was provided by Random Forest, with 27 selected features of drugs/compounds and nanoparticles in all experimental conditions considered. The high performance of the model was demonstrated by the mean Area Under the Receiver Operating Characteristics (AUC) in a test subset with a value of 0.9921 ± 0.000244 (10-fold cross-validation). The results demonstrated the power of information fusion of the experimental-centered features of drugs/compounds and nanoparticles for the prediction of nanoparticle–compound antimalarial activity. The scripts and dataset for this project are available in the open GitHub repository.

The development of new molecules for the treatment of leishmaniasis is, a neglected parasitic disease, is urgent as current anti-leishmanial therapeutics are hampered by drug toxicity and resistance. The pyrrolo[1,2-b]isoquinoline core was selected as starting point, and palladium-catalyzed Heck-initiated cascade reactions were developed for the synthesis of a series of C-10 substituted derivatives. Their in vitro leishmanicidal activity against visceral (L. donovani) and cutaneous (L. amazonensis) leishmaniasis was evaluated. The best activity was found, in general, for the 10-arylmethyl substituted pyrroloisoquinolines. In particular, 2ad (IC50 = 3.30 μM, SI > 77.01) and 2bb (IC50 = 3.93 μM, SI > 58.77) were approximately 10-fold more potent and selective than the drug of reference (miltefosine), against L. amazonensis on in vitro promastigote assays, while 2ae was the more active compound in the in vitro amastigote assays (IC50 = 33.59 μM, SI > 8.93). Notably, almost all compounds showed low cytotoxicity, CC50 > 100 μg/mL in J774 cells, highest tested dose. In addition, we have developed the first Perturbation Theory Machine Learning (PTML) algorithm able to predict simultaneously multiple biological activity parameters (IC50, Ki, etc.) vs. any Leishmania species and target protein, with high values of specificity (>98%) and sensitivity (>90%) in both training and validation series. Therefore, this model may be useful to reduce time and assay costs (material and human resources) in the drug discovery process.

The parasite species of genus Plasmodium causes Malaria, which remains a major global health problem due to parasite resistance to available Antimalarial drugs and increasing treatment costs. Consequently, computational prediction of new Antimalarial compounds with novel targets in the proteome of Plasmodium sp. is a very important goal for the pharmaceutical industry. We can expect that the success of the pre-clinical assay depends on the conditions of assay per se, the chemical structure of the drug, the structure of the target protein to be targeted, as well as on factors governing the expression of this protein in the proteome such as genes (Deoxyribonucleic acid, DNA) sequence and/or chromosomes structure. However, there are no reports of computational models that consider all these factors simultaneously. Some of the difficulties for this kind of analysis are the dispersion of data in different datasets, the high heterogeneity of data, etc. In this work, we analyzed three databases ChEMBL (Chemical database of the European Molecular Biology Laboratory), UniProt (Universal Protein Resource), and NCBI-GDV (National Center for Biotechnology Information—Genome Data Viewer) to achieve this goal. The ChEMBL dataset contains outcomes for 17,758 unique assays of potential Antimalarial compounds including numeric descriptors (variables) for the structure of compounds as well as a huge amount of information about the conditions of assays. The NCBI-GDV and UniProt datasets include the sequence of genes, proteins, and their functions. In addition, we also created two partitions (c_assayj = c_aj and c_dataj = cd_j) of categorical variables from theChEMBL dataset. These partitions contain variables that encode information about experimental conditions of preclinical assays (c_aj) or about the nature and quality of data (c_dj). These categorical variables include information about 22 parameters of biological activity (c_a0), 28 target proteins (c_a1), and 9 organisms of assay (c_a2), etc. We also created another partition of (c_protj = c_pj) including categorical variables with biological information about the target proteins, genes, and chromosomes. These variables cover32 genes (c_p0), 10 chromosomes (c_p1), gene orientation (c_p2), and 31 protein functions (c_p3). We used a Perturbation-Theory Machine Learning Information Fusion (IFPTML) algorithm to map all this information (from three databases) into and train a predictive model. Shannon’s entropy measure Sh_k (numerical variables) was used to quantify the information about the structure of drugs, protein sequences, gene sequences, and chromosomes in the same information scale. Perturbation Theory Operators (PTOs) with the form of Moving Average (MA) operators have been used to quantify perturbations (deviations) in the structural variables with respect to their expected values for different subsets (partitions) of categorical variables. We obtained three IFPTML models using General Discriminant Analysis (GDA), Classification Tree with Univariate Splits (CTUS), and Classification Tree with Linear Combinations (CTLC). The IFPTML-CTLC presented the better performance with Sensitivity Sn(%) = 83.6/85.1, and Specificity Sp(%) = 89.8/89.7 for training/validation sets, respectively. This model could become a useful tool for the optimization of preclinical assays of new Antimalarial compounds vs. different proteins in the proteome of Plasmodium.

The importance of Piptocoma discolor is a predominantly fast-growing native species of secondary forest ideal for a sustainable forest due to its predominance in the Amazon of Ecuador and Latin America. Currently it is wood and its waste does not have an alternative to produce electricity. Scarce information on its biomass potential and energy value. The objective was to obtain fresh biomass and drying kinetics, we worked in the four cantons of the Pastaza province with 20 plots of 400 m2 and 600 m2 that had an average of 15 to 20 trees, whose chest height diameters ranged from 10 and 33 cm., Establishing the following values: a) Average number of trees per hectare (1274 NA2/ha); b) Biomass in 100 987 hectares with an average of 41 097 686.87 tons, which means that it contributes with 49.72 % of water and 50.28 % of solid matter; c) A kinetic model of R2 = 0.9855, the estimate of a = 1.1318, k = 0.0637765, n = 0.560617 and b = -0.193993, based on a standard error of 0.0301682 and residual autocorrection of 0.182239 on drying kinetics; and d) Total energy means more than 13.5 GW of thermal energy using 100 % biomass.

Los Modelos de Lenguaje de Gran Tamaño (LLM) están revolucionando la eficiencia y automatización en la generación de código de programación. Este trabajo tiene como objetivo proporcionar una visión general del estado del arte de estos modelos y su aplicación específica en la programación. La metodología permitió identificar 549 publicaciones iniciales, cribadas en fases sucesivas hasta seleccionar 27 artículos clave para el análisis cualitativo. Los resultados muestran que los LLM mejoran significativamente la calidad del código generado, optimizan la productividad de los programadores y apoyan el aprendizaje mediante herramientas como ChatGPT, Codex y GitHub Copilot. El análisis destaca que el 66,7% de los artículos revisados se publicaron en el 2024, reflejando el creciente interés en este campo. Estados Unidos lidera la investigación con 11 publicaciones, seguido de Países Bajos y Suiza. Los estudios seleccionados se filtraron para generar una síntesis crítica que evalúa la calidad y aplicabilidad de los LLM en la escritura y depuración de código. Esta revisión analiza las principales ventajas de los LLM, como la mejora en la productividad y la generación de código optimizado. Sin embargo, también aborda desafíos como la precisión y el riesgo de errores o código no seguro. Los LLM pueden mejorar significativamente la calidad del código generado. También pueden facilitar la curva de aprendizaje de los nuevos programadores. No obstante, se resalta la necesidad de mantener una supervisión humana rigurosa para garantizar la fiabilidad de las soluciones generadas por los modelos.