Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C1175175 (SARS)
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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

Coronavirus disease 2019 (COVID-19) accounts for over 180,000 deaths in the USA. Although COVID-19 affects all racial ethnicities, non-Hispanic Blacks have the highest mortality rates. Evidence continues to emerge, linking the disproportion of contagion and mortality from severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a result of adverse social determinants of health. Yet, genetic predisposition may also play a credible role in disease transmission. SARS-CoV-2 enters cells by interaction between SARS-CoV-2 spike protein and the receptor molecule angiotensin converting enzyme 2 (ACE2) expressed on the surface of the target cells, such that polymorphisms and the expression level of ACE2 influence infectivity and consequent pathogenesis of SARS-CoV-2. Genetic polymorphisms in other multiple genes, such as acetylcholinesterase (AChE) and interleukin-6, are also closely associated with underlying diseases, such as hypertension and type 2 diabetes mellitus, which substantially raise SARS-CoV-2 mortality. However, it is unknown how these genetic polymorphisms contribute to the disparate mortality rates, with or without underlying diseases. Of particular interest is the potential that genetic polymorphisms in these genes may be influencing the disparity of COVID-19 mortality rates in Black communities. Here, we review the evidence that biological predisposition for high-risk comorbid conditions may be relevant to our ability to fully understand and therefore address health disparities of COVID-19 deaths in Blacks.
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PMID:The Perfect Storm: COVID-19 Health Disparities in US Blacks. 3296 60