In this review, we highlight recent applications of machine learning to virtual screening, focusing on the use of supervised techniques to train statistical learning algorithms to prioritize databases of molecules as active against a particular protein target. Both ligand-based similarity searching and structure-based docking have benefited from machine learning algorithms, including naïve Baye...

T he discovery of new bioactive compounds for specific biomolecular targets represents a significant hurdle in the early stages of drug discovery. Advances in automation and bioanalytical methods have provided high-throughput screening (HTS) techniques that can perform individual biochemical assays on as many as a million compounds or more. Even with HTS, the discovery of new lead compounds lar...

The main complicating factor in structure-based drug design is receptor rearrangement upon ligand binding (induced fit). It is the induced fit that complicates cross-docking of ligands from different ligand-receptor complexes. Previous studies have shown the necessity to include protein flexibility in ligand docking and virtual screening. Very few docking methods have been developed to predict ...

In drug discovery, one key step is to predict chemical activities of drug candidates such as toxicity, mutagenicity and solubility and thereby reduce the number of chemical compounds screened by actual experimental means. Such computational prediction is called “virtual screening”. Drug candidates are small molecules which are represented as attributed graphs where atoms and bonds correspond to...

BACKGROUND There are three main problems associated with the virtual screening of bioassay data. The first is access to freely-available curated data, the second is the number of false positives that occur in the physical primary screening process, and finally the data is highly-imbalanced with a low ratio of Active compounds to Inactive compounds. This paper first discusses these three problem...