AIMC Topic: Structure-Activity Relationship
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Structural Similarity, Activity, and Toxicity of Mycotoxins: Combining Insights from Unsupervised and Supervised Machine Learning Algorithms.
Journal of agricultural and food chemistry
Feb 27, 2025
A large number of mycotoxins and related fungal metabolites have not been assessed in terms of their toxicological impacts. Current methodologies often prioritize specific target families, neglecting the complexity and presence of co-occurring compou...
Machine Learning-Driven Discovery of Structurally Related Natural Products as Activators of the Cardiac Calcium Pump SERCA2a.
ChemMedChem
Feb 6, 2025
A key molecular dysfunction in heart failure is the reduced activity of the cardiac sarcoplasmic reticulum Ca-ATPase (SERCA2a) in cardiac muscle cells. Reactivating SERCA2a improves cardiac function in heart failure models, making it a validated targ...
Rational design and synthesis of pyrazole derivatives as potential SARS-CoV-2 M inhibitors: An integrated approach merging combinatorial chemistry, molecular docking, and deep learning.
Bioorganic & medicinal chemistry
Feb 3, 2025
The global impact of SARS-CoV-2 has highlighted the urgent need for novel antiviral therapies. This study integrates combinatorial chemistry, molecular docking, and deep learning to design, evaluate and synthesize new pyrazole derivatives as potentia...
High-Accuracy Identification and Structure-Activity Analysis of Antioxidant Peptides via Deep Learning and Quantum Chemistry.
Journal of chemical information and modeling
Jan 7, 2025
Antioxidant peptides (AOPs) hold great promise for mitigating oxidative-stress-related diseases, but their discovery is hindered by inefficient and time-consuming traditional methods. To address this, we developed an innovative framework combining ma...
DPFunc: accurately predicting protein function via deep learning with domain-guided structure information.
Nature communications
Jan 2, 2025
Computational methods for predicting protein function are of great significance in understanding biological mechanisms and treating complex diseases. However, existing computational approaches of protein function prediction lack interpretability, mak...
Artificial intelligence-driven rational design of ionizable lipids for mRNA delivery.
Nature communications
Dec 30, 2024
Lipid nanoparticles (LNPs) have proven effective in mRNA delivery, as evidenced by COVID-19 vaccines. Its key ingredient, ionizable lipids, is traditionally optimized by inefficient and costly experimental screening. This study leverages artificial i...
Cubosomes as Delivery System to Repositioning Nitrofurantoin in Breast Cancer Management.
Drug design, development and therapy
Dec 19, 2024
PURPOSE: Nitrofurantoin (NITRO), a long-standing antibiotic to treat urinary tract infections, is activated by Nitro reductases. This activation mechanism has led to its exploration for repositioning applications in controlling and treating breast ca...
Machine learning-based rational design for efficient discovery of allatostatin analogs as promising lead candidates for novel IGRs.
Pest management science
Nov 8, 2024
BACKGROUND: Insect neuropeptide allatostatins (ASTs) play a vital role in regulating insect growth, development, and reproduction, making them potential candidates for new insect growth regulators (IGRs). However, the practical use of natural ASTs in...
Wee1 inhibitor optimization through deep-learning-driven decision making.
European journal of medicinal chemistry
Sep 29, 2024
Deep learning has gained increasing attention in recent years, yielding promising results in hit screening and molecular optimization. Herein, we employed an efficient strategy based on multiple deep learning techniques to optimize Wee1 inhibitors, w...
Deep-Learning-Driven Discovery of SN3-1, a Potent NLRP3 Inhibitor with Therapeutic Potential for Inflammatory Diseases.
Journal of medicinal chemistry
Sep 20, 2024
The NLRP3 inflammasome plays a central role in the pathogenesis of various intractable human diseases, making it an urgent target for therapeutic intervention. Here, we report the development of SN3-1, a novel orally potent NLRP3 inhibitor, designed ...
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