Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: EC:3.1.1.7 (acetylcholinesterase)
 28,390 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Twenty eight enzymatic activities and four macromolecular substances have been histochemically compared in rat and rabbit aortas, embedded in a common block. The study was carried out at different stages of development: 3 days, 3 months, 7-9 months and 17-19 months. In addition, lipase and cholinesterase were biochemically assayed in adult rat and rabbit aortas. The rat aortas (atheroresistant) had a better supply of aerobic oxidoreductases [linked to the pentose pathway (G6PD, 6PGD) as well as to the Krebs cycle (SD, ICD)], lipolytic enzymes (acid esterases, cholinesterase, lipase), lysosomal enzymes (acid PH/ase, Aryl-sulf/ase - Betaglu/ase), ADPase - ATPase - AlK Ph/ase Alpha GPD and acid lipids. Rabbit aortas (atherosensitive) were richer in metachromatic GAG, UDPGD (GAG Anabolism), glycogen, and related enzymes (phosphorylase, glycogen synthetase) as well as 5'-nucleotidase, Beta HBD, Lactate D and Aldolase. These differences support the hypothesis that arterial atherosensitivity is related to the activity and efficiency of smooth muscle cell energetic and catabolic processes, which govern the behaviour of lipids, proteins and carbohydrates as they penetrate the arterial wall. The factors that determine the proliferative and sclerogenic responses of arterial tissues to aggressions and, in particular, the response to lipids, remain, however, to be determined.
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PMID:A comparative study of the arterial tissue metabolism in atherosensitive and atheroresistant species. I. Comparison between rabbit and rat aortas. 734 89

Artificial intelligence and multiobjective optimization represent promising solutions to bridge chemical and biological landscapes by addressing the automated de novo design of compounds as a result of a humanlike creative process. In the present study, we conceived a novel pair-based multiobjective approach implemented in an adapted SMILES generative algorithm based on recurrent neural networks for the automated de novo design of new molecules whose overall features are optimized by finding the best trade-offs among relevant physicochemical properties (MW, logP, HBA, HBD) and additional similarity-based constraints biasing specific biological targets. In this respect, we carried out the de novo design of chemical libraries targeting neuraminidase, acetylcholinesterase, and the main protease of severe acute respiratory syndrome coronavirus 2. Several quality metrics were employed to assess drug-likeness, chemical feasibility, diversity content, and validity. Molecular docking was finally carried out to better evaluate the scoring and posing of the de novo generated molecules with respect to X-ray cognate ligands of the corresponding molecular counterparts. Our results indicate that artificial intelligence and multiobjective optimization allow us to capture the latent links joining chemical and biological aspects, thus providing easy-to-use options for customizable design strategies, which are especially effective for both lead generation and lead optimization. The algorithm is freely downloadable at https://github.com/alberdom88/moo-denovo and all of the data are available as Supporting Information.
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PMID:De Novo Drug Design of Targeted Chemical Libraries Based on Artificial Intelligence and Pair-Based Multiobjective Optimization. 3284 50