AI Medical Compendium Topic

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

Ligands

Showing 401 to 410 of 600 articles

Clear Filters

Applicability Domain of Active Learning in Chemical Probe Identification: Convergence in Learning from Non-Specific Compounds and Decision Rule Clarification.

Molecules (Basel, Switzerland)
Efficient identification of chemical probes for the manipulation and understanding of biological systems demands specificity for target proteins. Computational means to optimize candidate compound selection for experimental selectivity evaluation are...

Artificial Intelligence Approach to Find Lead Compounds for Treating Tumors.

The journal of physical chemistry letters
It has been demonstrated that MMP13 enzyme is related to most cancer cell tumors. The world's largest traditional Chinese medicine database was applied to screen for structure-based drug design and ligand-based drug design. To predict drug activity, ...

Artificial Intelligence: A Novel Approach for Drug Discovery.

Trends in pharmacological sciences
Molecular dynamics (MD) simulations can mechanistically explain receptor function. However, the enormous data sets that they may imply can be a hurdle. Plante and colleagues (Molecules, 2019) recently described a machine learning approach to the anal...

A Ligand-Based Virtual Screening Method Using Direct Quantification of Generalization Ability.

Molecules (Basel, Switzerland)
Machine learning plays an important role in ligand-based virtual screening. However, conventional machine learning approaches tend to be inefficient when dealing with such problems where the data are imbalanced and features describing the chemical ch...

DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences.

PLoS computational biology
Identification of drug-target interactions (DTIs) plays a key role in drug discovery. The high cost and labor-intensive nature of in vitro and in vivo experiments have highlighted the importance of in silico-based DTI prediction approaches. In severa...

A Machine Learning Approach for the Discovery of Ligand-Specific Functional Mechanisms of GPCRs.

Molecules (Basel, Switzerland)
G protein-coupled receptors (GPCRs) play a key role in many cellular signaling mechanisms, and must select among multiple coupling possibilities in a ligand-specific manner in order to carry out a myriad of functions in diverse cellular contexts. Muc...

Design of Natural-Product-Inspired Multitarget Ligands by Machine Learning.

ChemMedChem
A virtual screening protocol based on machine learning models was used to identify mimetics of the natural product (-)-galantamine. This fully automated approach identified eight compounds with bioactivities on at least one of the macromolecular targ...

Applications of machine learning in GPCR bioactive ligand discovery.

Current opinion in structural biology
GPCRs constitute the largest druggable family having targets for 475 Food and Drug Administration (FDA) approved drugs. As GPCRs are of great interest to pharmaceutical industry, enormous efforts are being expended to find relevant and potent GPCR li...

Automated discovery of GPCR bioactive ligands.

Current opinion in structural biology
While G-protein-coupled receptors (GPCRs) constitute the largest class of membrane proteins, structures and endogenous ligands of a large portion of GPCRs remain unknown. Because of the involvement of GPCRs in various signaling pathways and physiolog...

Improved Method of Structure-Based Virtual Screening via Interaction-Energy-Based Learning.

Journal of chemical information and modeling
Virtual screening is a promising method for obtaining novel hit compounds in drug discovery. It aims to enrich potentially active compounds from a large chemical library for further biological experiments. However, the accuracy of current virtual scr...