AI Medical Compendium Topic

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

Small Molecule Libraries

Showing 151 to 159 of 159 articles

Clear Filters

Deep inverse reinforcement learning for structural evolution of small molecules.

Briefings in bioinformatics
The size and quality of chemical libraries to the drug discovery pipeline are crucial for developing new drugs or repurposing existing drugs. Existing techniques such as combinatorial organic synthesis and high-throughput screening usually make the p...

ChemFLuo: a web-server for structure analysis and identification of fluorescent compounds.

Briefings in bioinformatics
BACKGROUND: Fluorescent detection methods are indispensable tools for chemical biology. However, the frequent appearance of potential fluorescent compound has greatly interfered with the recognition of compounds with genuine activity. Such fluorescen...

Application and assessment of deep learning for the generation of potential NMDA receptor antagonists.

Physical chemistry chemical physics : PCCP
Uncompetitive antagonists of the N-methyl d-aspartate receptor (NMDAR) have demonstrated therapeutic benefit in the treatment of neurological diseases such as Parkinson's and Alzheimer's, but some also cause dissociative effects that have led to the ...

A Deep Learning Approach to Antibiotic Discovery.

Cell
Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed pr...

Machine learning-based chemical binding similarity using evolutionary relationships of target genes.

Nucleic acids research
Chemical similarity searching is a basic research tool that can be used to find small molecules which are similar in shape to known active molecules. Despite its popularity, the retrieval of local molecular features that are critical to functional ac...

Elucidating Compound Mechanism of Action and Predicting Cytotoxicity Using Machine Learning Approaches, Taking Prediction Confidence into Account.

Current protocols in chemical biology
The modes of action (MoAs) of drugs frequently are unknown, because many are small molecules initially identified from phenotypic screens, giving rise to the need to elucidate their MoAs. In addition, the high attrition rate for candidate drugs in pr...

High-throughput screening and Bayesian machine learning for copper-dependent inhibitors of Staphylococcus aureus.

Metallomics : integrated biometal science
One potential source of new antibacterials is through probing existing chemical libraries for copper-dependent inhibitors (CDIs), i.e., molecules with antibiotic activity only in the presence of copper. Recently, our group demonstrated that previousl...

Use of a Machine Learning-Based High Content Analysis Approach to Identify Photoreceptor Neurite Promoting Molecules.

Advances in experimental medicine and biology
High content analysis (HCA) has become a leading methodology in phenotypic drug discovery efforts. Typical HCA workflows include imaging cells using an automated microscope and analyzing the data using algorithms designed to quantify one or more spec...