ACS Medicinal Chemistry Letters最新文献

Provided herein are novel pyrrolopyrazine compounds as ERK5 inhibitors, pharmaceutical compositions, use of such compounds in treating cancer, and processes for preparing such compounds.

Artificial intelligence (AI) offers transformative potential in psychedelic research by addressing limitations in personalized treatment, predicting therapeutic outcomes, and understanding complex biological and environmental factors. AI-driven models provide new insights into long-term efficacy, set and setting optimization, and alternative treatment methods, advancing psychedelic therapy into personalized medicine.

The A₃ Adenosine Receptor (A₃AR) is an important therapeutic target due to its role in inflammation and immune response regulation. Herein, we synthesized and evaluated 5'-deoxy-adenosine derivatives with oxygen at the 4'-position, comparing them to previously studied 4'-thionucleosides. Compound 1h exhibited the highest binding affinity (K _i = 5.9 ± 1.1 nM), consistent with the trend observed in the 4'-thionucleosides. Notably, the 5'-deoxy-adenosine derivatives demonstrated enhanced agonistic activity. Docking studies with compound 1h revealed a shift in binding mode when oxygen replaced sulfur at the 4'-position. The compounds retained strong interactions with critical residues, such as Thr94, even without a hydrogen bond donor at the 5'-position. These results explain the increased agonistic effect observed when the ring heteroatom was changed from sulfur to oxygen.

Provided herein are novel compounds as CDK2 inhibitors, pharmaceutical compositions, use of such compounds in treating cancer, and processes for preparing such compounds.

Provided herein are novel 3-pyrrolidineindole derivatives as serotonergic psychedelic agents, pharmaceutical compositions, use of such compounds in treating psychosis and mental illnesses such as depression and post-traumatic stress disorder, and processes for preparing such compounds.

Provided herein are novel imidazotriazine derivatives as IL-17 modulators, pharmaceutical compositions, use of such compounds in treating inflammatory and autoimmune disorders, and processes for preparing such compounds.

Provided herein are novel TYK2 inhibitors, pharmaceutical compositions, use of such compounds in treating autoimmune and inflammatory diseases, and processes for preparing such compounds.

Inhibiting phosphofructokinase-1 (PFK1) is a promising approach for treating lactic acidosis and mitochondrial dysfunction by activating oxidative phosphorylation. Tryptolinamide (TLAM) has been shown as a PFK1 inhibitor, but its complex stereochemistry, with 16 possible isomers complicates further development. We conducted an asymmetric synthesis, determined the absolute configurations, and evaluated the PFK1 inhibitory activity of the TLAM isomers. Our structure-activity relationship (SAR) study of TLAM isomers revealed that both carboline and norbornene configurations influence PFK1 inhibitory activity. Among isomers 1a-1d, compound 1c was the most potent PFK1 inhibitor. Our elucidation of the SAR information on PFK1 inhibitors provides valuable insights for effective optimization.

Provided herein are novel bicyclic heterocyclic compounds as CD73 inhibitors, pharmaceutical compositions, use of such compounds in treating cancer, and processes for preparing such compounds.

One of the prominent challenges in breast cancer (BC) treatment is human epidermal growth factor receptor (EGFR) overexpression, which facilitates tumor proliferation and presents a viable target for anticancer therapies. This study integrates multiomics data to pinpoint promising therapeutic compounds and employs a machine learning (ML)-based similarity search to identify effective alternatives. We used BC cell line data from the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases and single-cell RNA sequencing (scRNA-seq) information that established afatinib as an efficacious candidate demonstrating superior IC₅₀ values. Next, ML models, including support vector machine (SVM), artificial neural networks (ANN), and random forest (RF), were trained on ChEMBL data to classify compounds with similar activity to the reference drug as active or inactive. The promising candidates underwent computational structural biology assessments for their molecular interactions and conformational dynamics. Our findings indicate that compounds ChEMBL233324, ChEMBL233325, ChEMBL234580, and ChEMBL372692 exhibit potent repressive action against EGFR, underscoring their potential as active antibreast cancer agents.