Machine learning-driven computer-aided synthesis planning (CASP) tools have become important tools for idea generation in the design of complex molecule synthesis but do not adequately address the stereochemical features of the target compounds. A novel approach to automated extraction of templates used in CASP that includes stereochemical information included in the US Patent and Trademark Office (USPTO) and an internal AstraZeneca database containing reactions from Reaxys, Pistachio, and AstraZeneca electronic lab notebooks is implemented in the freely available AiZynthFinder software. Three hundred sixty-seven templates covering reagent- and substrate-controlled as well as stereospecific reactions were extracted from the USPTO, while 20,724 templates were from the AstraZeneca database. The performance of these templates in multistep CASP is evaluated for 936 targets from the ChEMBL database and an in-house selection of 791 AZ designs. The potential and limitations are discussed for four case studies from ChEMBL and examples of FDA-approved drugs.
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http://dx.doi.org/10.1021/acs.jcim.4c00370 | DOI Listing |
Commun Med (Lond)
January 2025
Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, UK.
Background: Sotrovimab is a neutralising monoclonal antibody (nMAB) currently available to treat extremely clinically vulnerable COVID-19 patients in England. Trials have shown it to have mild to moderate side effects, however, evidence regarding its safety in real-world settings remains insufficient.
Methods: Descriptive and multivariable logistic regression analyses were conducted to evaluate uptake, and a self-controlled case series analysis performed to measure the risk of hospital admission (hospitalisation) associated with 49 pre-specified suspected adverse outcomes in the period 2-28 days post-Sotrovimab treatment among eligible patients treated between December 11, 2021 and May 24, 2022.
Orphanet J Rare Dis
January 2025
Cardiovascular, Renal and Metabolism (CVRM) Evidence, BioPharmaceuticals Medical, AstraZeneca, Gothenburg, Sweden.
Introduction: Significant advances in the treatment of transthyretin (ATTR) amyloidosis has led to an evolving understanding of the epidemiology of this condition. This systematic literature review (SLR) aims to synthesize current evidence on epidemiology and mortality outcomes in ATTR amyloidosis, addressing the need for a comprehensive understanding of its current global impact.
Methods: An SLR of the literature from January 2018 to April 2023 was conducted using the Medline and Embase databases.
Transl Cancer Res
December 2024
Tokyo Metropolitan Cancer and Infectious Disease Center, Komagome Hospital, Tokyo, Japan.
Background: Numerous studies have demonstrated that immune cell infiltration is a significant predictor in the prognosis of those with breast cancer. This study aimed to develop a prognostic model for undifferentiated breast cancer using immune-related markers.
Methods: Differentially expressed genes (DEGs) and prognostic factors were identified from The Cancer Genome Atlas (TCGA) database.
Sci Rep
January 2025
Department of Pediatrics and Pediatric Hematology/Oncology, University Children's Hospital, Carl Von Ossietzky Universität, Klinikum Oldenburg AöR, Rahel-Straus-Straße 10, 26133, Oldenburg, Germany.
Survivors of sellar/suprasellar tumors involving hypothalamic structures face a risk of impaired quality of life, including tumor- and/or treatment-related hypothalamic obesity (TTR-HO) defined as abnormal weight gain resulting in severe persistent obesity due to physical, tumor- and/or treatment related damage of the hypothalamus. We analyze German claims data to better understand treatment pathways for patients living TTR-HO during the two years following the index surgical treatment. A database algorithm identified patients with TTR-HO in a representative German payer claims database between 2010 and 2021 (n = 5.
View Article and Find Full Text PDFClin Exp Nephrol
January 2025
Kawasaki Medical School, Department of Nephrology and Hypertension, Kurashiki, Japan.
Background: Chronic kidney disease (CKD) represents a significant public health challenge, with rates consistently on the rise. Enhancing kidney function prediction could contribute to the early detection, prevention, and management of CKD in clinical practice. We aimed to investigate whether deep learning techniques, especially those suitable for processing missing values, can improve the accuracy of predicting future renal function compared to traditional statistical method, using the Japan Chronic Kidney Disease Database (J-CKD-DB), a nationwide multicenter CKD registry.
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