Dr. Alfonso T. García-Sosa, Assoc. Prof. @UniTartu https://xitter.cf/t.co/qp4ZmMFKWx @ATGarciaSosa@mastodon.social
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ALT Spatial representation of α (cyan) and β (pink) anomers of 1 in CAL-B binding site
ALT Since many of the currently available antileishmanial treatments exhibit toxicity, low effectiveness, and resistance, search and validation of new therapeutic targets allowing the development of innovative drugs have become a worldwide priority. This work presents a structure-based drug discovery strategy to validate the Lmj_04_BRCT domain as a novel therapeutic target in Leishmania spp. The structure of this domain was explored using homology modeling, virtual screening, and molecular dynamics studies. Candidate compounds were validated in vitro using promastigotes of Leishmania major, L. amazonensis, and L. infantum, as well as primary mouse macrophages infected with L. major. The novel inhibitor CPE2 emerged as the most active of a group of compounds against Leishmania, being able to significantly reduce the viability of promastigotes. CPE2 was also active against the intracellular forms of the parasites and significantly reduced parasite burden in murine macrophages without toxicit
ALT Substances that can modify the androgen receptor pathway in humans & animals are entering the environment & food chain with the proven ability to disrupt hormonal systems and leading to toxicity & adverse effects on reproduction, brain development, & prostate cancer, among others. State-of-the-art databases with experimental data of human, chimp, & rat effects by chemicals have been used to build machine-learning classifiers & regressors & to evaluate these on independent sets. Deep neural networks (DNNs) on user-defined physicochemically relevant features developed for this work outperforming graph convolutional, random forest, & large featurizations. The results show that these user-provided structure-, ligand-, & statistically based features & specific DNNs provided the best results as determined by AUC (0.87), MCC (0.47), & other metrics and by their interpretability achemical meaning of the features .Techniques of this type may improve AR and toxicity description and prediction
