Evaluation Pattern is able to comprehensively evaluate the risk of the queried molecule based on the combination of credible prediction models and useful substructure rules.Learn More
Screening Mode is able to screen a molecular dataset and detect potential frequent hitters, thus improving the credibility of experimental results and decreasing unnecessary cost.Learn More
Compound aggregation tends to start when the concentration is above the CAC and end as aggregators form with radius of approximately 30-600 nm. The resulting colloidal aggregators would non-specifically bind to the surface of proteins, thus inducing local protein unfolding, which usually results in destabilization or denaturation of enzymes.
Due to its unique catalysis mechanism, FLuc is widely used in a variety of HTS bioluminescence assays, especially in the assay which aims to study gene expression at the transcriptional level. However, the inhibition of Fluc by unexpected FLuc inhibitors would produce interference to HTS assays.
Fluorescence is the process by which a molecule, called fluorophore or fluorescent dye, absorbs a photon of light, exciting an electron to a higher energy state. Fluorophores have many applications, including as enzyme substrates, labels for biomolecules, cellular stains and environmental indicators. However, the appearance of fluorescent compound would produce interference to related HTS assays.
Chemical reactive compounds typically result in the chemical modification of reactive protein residues or, less frequently, the modification of nucleophilic assay reagents。
Promiscuous compounds refer to compounds that specifically bind to different macromolecular targets. These multiple interactions may include unintended targets, thus triggering adverse reactions and other safety issues.
Alpha-screen, FRET, TR-FRET, absorbance artifacts are included.
Based on the collection from related literatures and large databases, a high-quality dataset containing more than 810,000 compounds, which has been rigorously prepared by a multi-step scheme, is used for the model development and substructure compilation for frequent hitters.
Graph neural networks (GNNs) were used to process molecular graph structures, learn molecular features and develop the credible models for frequent hitters in ChemFH.
The predictions on representative false positive mechanisms are provided, including colloidal aggregate, firefly luciferase reporter enzyme inhibition, fluorescence, and chemical reactivity. Besides, to provide more reference about the authenticity, the prediction on promiscuity is also provided in ChemFH.
Based on the large and high-quality collected dataset, a total of 102 representative substructures with an averaged precision value higher than 0.85 were used for a comprehensive exploration of frequent hitters.