AIMC Topic: Enzyme Inhibitors

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Machine-Learning-Driven Discovery of -Phenylbenzenesulfonamides as a Novel Chemotype for Lactate Dehydrogenase A Inhibition with Anti-Pancreatic Cancer Activity.

Journal of medicinal chemistry
Lactate dehydrogenase A (LDHA) is a promising target for cancer therapy due to its crucial role in aerobic glycolysis. Despite extensive efforts, the structural diversity of LDHA inhibitors remains limited. Here, we utilized machine learning techniqu...

Machine learning-based QSAR and structure-based virtual screening guided discovery of novel mIDH1 inhibitors from natural products.

Journal of computer-aided molecular design
Mutations in isocitrate dehydrogenase 1 (IDH1) have been widely observed in various tumors, such as gliomas and acute myeloid leukemia, and therefore has become one of the current research focal points. Therefore, it is crucial to find inhibitors tha...

Atomic Ga Site Enables Photonanozymes with Specific Inhibition Modes for Primary Drug Screening.

Analytical chemistry
Enzyme inhibition plays a crucial role in drug discovery by governing interactions between molecules and distinct enzymatic sites, facilitating the identification of early drug candidates. However, most nanozymes have been limited to single active si...

Machine learning framework coupled with CADD for predicting sphingosine kinase 1 inhibitors.

Computers in biology and medicine
Sphingosine kinase 1 (SphK1) plays a pivotal role in cancer progression, metastasis, and chemotherapy resistance, making it a key target for therapeutic interventions in cancer, cardiovascular diseases, and inflammation. Machine learning models, incl...

Bioactive structures for inhibitors of polymerase enzyme by artificial intelligence.

Future medicinal chemistry
AIMS: Present new bioactive compounds, created by De novo Drug Design and artificial intelligence (AI), as possible inhibitors of polymerase.

Prediction of newly synthesized heparin mimic's effects as heparanase inhibitor in cancer treatments via variational quantum neural networks.

Computational biology and chemistry
Cancer remains a leading global cause of death, primarily driven by the uncontrolled proliferation of abnormal cells. Malignant tumors, such as carcinomas, originate from unchecked epithelial cell growth and produce growth factors like FGF and VEGF, ...

Accelerating drug discovery targeting dihydroorotate dehydrogenase using machine learning and generative AI approaches.

Computational biology and chemistry
Dihydroorotate dehydrogenase (DHODH) is a key enzyme in pyrimidine biosynthesis, making it an attractive drug target for cancer, autoimmune diseases, and infections. Traditional DHODH inhibitor discovery is slow and costly. Our study integrated machi...

Mechanistic Study of Protein Interaction with Natto Inhibitory Peptides Targeting Xanthine Oxidase: Insights from Machine Learning and Molecular Dynamics Simulations.

Journal of chemical information and modeling
Bioactive peptides from food sources offer a safe and biocompatible approach to enzyme inhibition, with potential applications in managing metabolic disorders such as hyperuricemia and gout, conditions linked to excessive xanthine oxidase activity. U...

Targeting Bacterial RNA Polymerase: Harnessing Simulations and Machine Learning to Design Inhibitors for Drug-Resistant Pathogens.

Biochemistry
The increase in antimicrobial resistance presents a major challenge in treating bacterial infections, underscoring the need for innovative drug discovery approaches and novel inhibitors. Bacterial RNA polymerase (RNAP) has emerged as a crucial target...