AIMC Topic: Drug Discovery

Clear Filters Showing 131 to 140 of 1550 articles

metaCDA: A Novel Framework for CircRNA-Driven Drug Discovery Utilizing Adaptive Aggregation and Meta-Knowledge Learning.

Journal of chemical information and modeling
In the emerging field of RNA drugs, circular RNA (circRNA) has attracted much attention as a novel multifunctional therapeutic target. Delving deeper into the intricate interactions between circRNA and disease is critical for driving drug discovery e...

A novel neural network-based nearest neighbor approach for drug function prediction from chemical structures.

European journal of pharmacology
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...

AGDIFF: Attention-Enhanced Diffusion for Molecular Geometry Prediction.

Journal of chemical information and modeling
Accurate prediction of molecular geometries is crucial for drug discovery and materials science. Existing fast conformer prediction algorithms often rely on approximate empirical energy functions, resulting in low accuracy. More accurate methods like...

Single-Cell Array Enhanced Cell Damage Recognition Using Artificial Intelligence for Anticancer Drug Discovery.

Analytical chemistry
This work developed a cell damage recognition method based on single-cell arrays using an artificial intelligence tool. The method uses micropatterns (single-cell micropatches and microwells) to isolate each cell in an ordered array to minimize cell ...

Modeling Boltzmann-weighted structural ensembles of proteins using artificial intelligence-based methods.

Current opinion in structural biology
This review highlights recent advances in AI-driven methods for generating Boltzmann-weighted structural ensembles, which are crucial for understanding biomolecular dynamics and drug discovery. With the rise of deep learning models such as AlphaFold2...

Machine Learning-Based Bioactivity Classification of Natural Products Using LC-MS/MS Metabolomics.

Journal of natural products
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
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...

Artificial Intelligence in Natural Product Drug Discovery: Current Applications and Future Perspectives.

Journal of medicinal chemistry
Drug discovery, a multifaceted process from compound identification to regulatory approval, historically plagued by inefficiencies and time lags due to limited data utilization, now faces urgent demands for accelerated lead compound identification. I...

CaBind_MCNN: Identifying Potential Calcium Channel Blocker Targets by Predicting Calcium-Binding Sites in Ion Channels and Ion Transporters Using Protein Language Models and Multiscale Feature Extraction.

Journal of chemical information and modeling
Calcium ions (Ca) are crucial for various physiological processes, including neurotransmission and cardiac function. Dysregulation of Ca homeostasis can lead to serious health conditions such as cardiac arrhythmias and hypertension. Ion channels and ...

CL-GNN: Contrastive Learning and Graph Neural Network for Protein-Ligand Binding Affinity Prediction.

Journal of chemical information and modeling
In the realm of drug discovery and design, the accurate prediction of protein-ligand binding affinity is of paramount importance as it underpins the functional interactions within biological systems. This study introduces a novel self-supervised lear...