AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Ligands

Showing 101 to 110 of 599 articles

Clear Filters

Guided Docking as a Data Generation Approach Facilitates Structure-Based Machine Learning on Kinases.

Journal of chemical information and modeling
Drug discovery pipelines nowadays rely on machine learning models to explore and evaluate large chemical spaces. While including 3D structural information is considered beneficial, structural models are hindered by the availability of protein-ligand ...

MISATO: machine learning dataset of protein-ligand complexes for structure-based drug discovery.

Nature computational science
Large language models have greatly enhanced our ability to understand biology and chemistry, yet robust methods for structure-based drug discovery, quantum chemistry and structural biology are still sparse. Precise biomolecule-ligand interaction data...

Accurate structure prediction of biomolecular interactions with AlphaFold 3.

Nature
The introduction of AlphaFold 2 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design. Here we describe our AlphaFold 3 model with a substantially...

Large-scale chemoproteomics expedites ligand discovery and predicts ligand behavior in cells.

Science (New York, N.Y.)
Chemical modulation of proteins enables a mechanistic understanding of biology and represents the foundation of most therapeutics. However, despite decades of research, 80% of the human proteome lacks functional ligands. Chemical proteomics has advan...

Predicting FFAR4 agonists using structure-based machine learning approach based on molecular fingerprints.

Scientific reports
Free Fatty Acid Receptor 4 (FFAR4), a G-protein-coupled receptor, is responsible for triggering intracellular signaling pathways that regulate various physiological processes. FFAR4 agonists are associated with enhancing insulin release and mitigatin...

HBCVTr: an end-to-end transformer with a deep neural network hybrid model for anti-HBV and HCV activity predictor from SMILES.

Scientific reports
Hepatitis B and C viruses (HBV and HCV) are significant causes of chronic liver diseases, with approximately 350 million infections globally. To accelerate the finding of effective treatment options, we introduce HBCVTr, a novel ligand-based drug des...

Prospective de novo drug design with deep interactome learning.

Nature communications
De novo drug design aims to generate molecules from scratch that possess specific chemical and pharmacological properties. We present a computational approach utilizing interactome-based deep learning for ligand- and structure-based generation of dru...

Molecular Docking Improved with Human Spatial Perception Using Virtual Reality.

IEEE transactions on visualization and computer graphics
Adaptive steered molecular dynamics (ASMD) is a computational biophysics method in which an external force is applied to a selected set of atoms or a specific reaction coordinate to induce a particular molecular motion. Virtual reality (VR) based met...

PT-Finder: A multi-modal neural network approach to target identification.

Computers in biology and medicine
Efficient target identification for bioactive compounds, including novel synthetic analogs, is crucial for accelerating the drug discovery pipeline. However, the process of target identification presents significant challenges and is often expensive,...