AIMC Topic: Molecular Structure
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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...
A semiempirical and machine learning approach for fragment-based structural analysis of non-hydroxamate HDAC3 inhibitors.
Biophysical chemistry
Feb 15, 2025
Interest in HDAC3 inhibitors (HDAC3i) for pharmacological applications outside of cancer is growing. However, concerns regarding the possible mutagenicity of the commonly used hydroxamates (zinc-binding groups, ZBGs) are also increasing. Considering ...
A novel neural network-based nearest neighbor approach for drug function prediction from chemical structures.
European journal of pharmacology
Feb 11, 2025
Drug function prediction is a crucial task in drug discovery, design, and development, which involves the prediction of the biological functions of a drug molecule based on its chemical structure. Misleading drug function is a common reason for adver...
Machine Learning-Based Bioactivity Classification of Natural Products Using LC-MS/MS Metabolomics.
Journal of natural products
Feb 7, 2025
The rediscovery of known drug classes represents a major challenge in natural products drug discovery. Compound rediscovery inhibits the ability of researchers to explore novel natural products and wastes significant amounts of time and resources. Th...
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...
Progress of machine learning in the application of small molecule druggability prediction.
European journal of medicinal chemistry
Jan 10, 2025
Machine learning (ML) has become an important tool for predicting the pharmaceutical properties of small molecules. Recent advancements in ML algorithms enable the rapid and accurate evaluation of solubility, activity, toxicity, pharmacokinetics, and...
Coverage bias in small molecule machine learning.
Nature communications
Jan 9, 2025
Small molecule machine learning aims to predict chemical, biochemical, or biological properties from molecular structures, with applications such as toxicity prediction, ligand binding, and pharmacokinetics. A recent trend is developing end-to-end mo...
Combined usage of ligand- and structure-based virtual screening in the artificial intelligence era.
European journal of medicinal chemistry
Dec 10, 2024
Drug design has always been pursuing techniques with time- and cost-benefits. Virtual screening, generally classified as ligand-based (LBVS) and structure-based (SBVS) approaches, could identify active compounds in the large chemical library to reduc...
MPCD: A Multitask Graph Transformer for Molecular Property Prediction by Integrating Common and Domain Knowledge.
Journal of medicinal chemistry
Dec 2, 2024
Molecular property prediction with deep learning often employs self-supervised learning techniques to learn common knowledge through masked atom prediction. However, the common knowledge gained by masked atom prediction dramatically differs from the ...
Molecular tweaking by generative cheminformatics and ligand-protein structures for rational drug discovery.
Bioorganic chemistry
Oct 28, 2024
The purpose of this review is two-fold: (1) to summarize artificial intelligence and machine learning approaches and document the role of ligand-protein structures in directing drug discovery; (2) to present examples of drugs from the recent literatu...
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