The identification of interactions between drugs/compounds and their targets is crucial for
the development of new drugs. In vitro screening experiments (ie bioassays) are frequently …

The reproducibility of experiments has been a long standing impediment for further scientific
progress. Computational methods have been instrumental in drug discovery efforts owing to …

High-throughput screening technologies have provided a large amount of drug sensitivity
data for a panel of cancer cell lines and hundreds of compounds. Computational …

Motivation: Recent large-scale omics initiatives have catalogued the somatic alterations of
cancer cell line panels along with their pharmacological response to hundreds of …

The success of molecular modeling and computational chemistry efforts are, by definition,
dependent on quality software applications. Open source software development provides …

Metabolomics aims to measure and characterise the complex composition of metabolites in
a biological system. Metabolomics studies involve sophisticated analytical techniques such …

A major research area in machine learning is the development of algorithms to compute the
reliability of individual predictions. Such reliability estimates are essential to increase the …

Advances in machine learning have given rise to a plurality of data-driven methods for
predicting chemical properties from molecular structure. For many decades, the …

Background In recent years, predictive models based on machine learning techniques have
proven to be feasible and effective in drug discovery. However, to develop such a model …

Background More and more evidences from network biology indicate that most cellular
components exert their functions through interactions with other cellular components, such …