AIMC Topic: Databases, Chemical

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Active-learning strategies in computer-assisted drug discovery.

Drug discovery today
High-throughput compound screening is time and resource consuming, and considerable effort is invested into screening compound libraries, profiling, and selecting the most promising candidates for further testing. Active-learning methods assist the s...

Data Exploration for Target Predictions Using Proprietary and Publicly Available Data Sets.

Chemical research in toxicology
When applying machine learning (ML) approaches for the prediction of bioactivity, it is common to collect data from different assays or sources and combine them into single data sets. However, depending on the data domains and sources from which thes...

PolyCrit: An Online Collaborative Platform for Polymer Characterization.

Journal of chromatography. A
Polymer liquid chromatography at critical conditions (LCCC) is a chromatographic separation condition achieved by carefully balancing the interaction of a polymer with stationary and mobile phases to make the elution time of a polymer in chromatograp...

SuperPred 3.0: drug classification and target prediction-a machine learning approach.

Nucleic acids research
Since the last published update in 2014, the SuperPred webserver has been continuously developed to offer state-of-the-art models for drug classification according to ATC classes and target prediction. For the first time, a thoroughly filtered ATC da...

Machine learned calibrations to high-throughput molecular excited state calculations.

The Journal of chemical physics
Understanding the excited state properties of molecules provides insight into how they interact with light. These interactions can be exploited to design compounds for photochemical applications, including enhanced spectral conversion of light to inc...

Deep fusion learning facilitates anatomical therapeutic chemical recognition in drug repurposing and discovery.

Briefings in bioinformatics
The advent of large-scale biomedical data and computational algorithms provides new opportunities for drug repurposing and discovery. It is of great interest to find an appropriate data representation and modeling method to facilitate these studies. ...

An effective self-supervised framework for learning expressive molecular global representations to drug discovery.

Briefings in bioinformatics
How to produce expressive molecular representations is a fundamental challenge in artificial intelligence-driven drug discovery. Graph neural network (GNN) has emerged as a powerful technique for modeling molecular data. However, previous supervised ...

A modified binary particle swarm optimization with a machine learning algorithm and molecular docking for QSAR modelling of cholinesterase inhibitors.

SAR and QSAR in environmental research
The acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) inhibitors play a key role in treating Alzheimer's disease. This study proposes an approach that integrates a modified binary particle swarm optimization (PSO) with a machine learning ...

ChemGenerator: a web server for generating potential ligands for specific targets.

Briefings in bioinformatics
In drug discovery, one of the most important tasks is to find novel and biologically active molecules. Given that only a tip of iceberg of drugs was founded in nearly one-century's experimental exploration, it shows great significance to use in silic...

Various machine learning approaches coupled with molecule simulation in the screening of natural compounds with xanthine oxidase inhibitory activity.

Food & function
Gout is a common inflammatory arthritis associated with various comorbidities, such as cardiovascular disease and metabolic syndrome. Xanthine oxidase inhibitors (XOIs) have emerged as effective substances to control gout. Much attention has been giv...