AI Medical Compendium Journal:
Journal of chemical information and modeling

Showing 11 to 20 of 934 articles

Improved Machine Learning Predictions of EC50s Using Uncertainty Estimation from Dose-Response Data.

Journal of chemical information and modeling
In early-stage drug design, machine learning models often rely on compressed representations of data, where raw experimental results are distilled into a single metric per molecule through curve fitting. This process discards valuable information abo...

Deep-Learning-Based Integration of Sequence and Structure Information for Efficiently Predicting miRNA-Drug Associations.

Journal of chemical information and modeling
Extensive research has shown that microRNAs (miRNAs) play a crucial role in cancer progression, treatment, and drug resistance. They have been recognized as promising potential therapeutic targets for overcoming drug resistance in cancer treatment. H...

ABP-Xplorer: A Machine Learning Approach for Prediction of Antibacterial Peptides Targeting -tRNA-Methyltransferase (TrmD).

Journal of chemical information and modeling
(MAB) infections pose a significant treatment challenge due to their intrinsic resistance to antibiotics, requiring prolonged multidrug regimens with limited success and frequent relapses. tRNA (m1G37) methyltransferase (TrmD), an enzyme essential f...

Heterogeneous Graph Contrastive Learning with Graph Diffusion for Drug Repositioning.

Journal of chemical information and modeling
Drug repositioning, which identifies novel therapeutic applications for existing drugs, offers a cost-effective alternative to traditional drug development. However, effectively capturing the complex relationships between drugs and diseases remains c...

Utilizing Dual-Channel Graph and Hypergraph Convolution Network to Discover Microbes Underlying Disease Traits.

Journal of chemical information and modeling
Discovering microbes underlying disease traits opens up opportunities for the diagnosis and effective treatment of diseases. However, traditional methods are often based on biological experiments, which are not only time-consuming but also costly, dr...

Advancements in Ligand-Based Virtual Screening through the Synergistic Integration of Graph Neural Networks and Expert-Crafted Descriptors.

Journal of chemical information and modeling
The fusion of traditional chemical descriptors with graph neural networks (GNNs) offers a compelling strategy for enhancing ligand-based virtual screening methodologies. A comprehensive evaluation revealed that the benefits derived from this integrat...

TIDGN: A Transfer Learning Framework for Predicting Interactions of Intrinsically Disordered Proteins with High Conformational Dynamics.

Journal of chemical information and modeling
Interactions between intrinsically disordered proteins (IDPs) are crucial for biological processes, such as intracellular liquid-liquid phase separation (LLPS). Experiments (e.g., NMR) and simulations used to study IDP interactions encounter a variet...

Triview Molecular Representation Learning Combined with Multitask Optimization for Enhanced Molecular Property Prediction.

Journal of chemical information and modeling
In molecular property prediction tasks, most methods rely on single-view representations, such as simplified molecular input line entry system (SMILES) strings. Some scholars have attempted to combine two graphical views for joint representation purp...

Prediction of Drug-Induced Nephrotoxicity Using Chemical Information and Transcriptomics Data.

Journal of chemical information and modeling
Prediction of drug-induced nephrotoxicity is an important task in the drug discovery and development pipeline. Chemical information-based machine learning models are used in general for nephrotoxicity prediction as a part of computational modeling. C...

Toward Accurate PAH IR Spectra Prediction: Handling Charge Effects with Classical and Deep Learning Models.

Journal of chemical information and modeling
Polycyclic aromatic hydrocarbons (PAHs) play a crucial role in astrochemistry, environmental studies, and combustion chemistry, yet interpreting their infrared (IR) spectra remains challenging due to the similarity of spectral features of many molecu...