Drug discovery is no longer relying on the one gene-one disease paradigm nor on target-based screening alone to discover new drugs. Phenotypic-based screening is regaining momentum to discover new compounds since those assays provide an environment closer to the physiological state of the disease and allow to better anticipate off-target effects and other factors that can limit the efficacy of the drugs. However, uncovering the mechanism of action of the compounds active in those assays relies on in vitro techniques that are expensive and time- consuming. In silico approaches are therefore beneficial to prioritise mechanism of action hypotheses to be tested in such systems.

In this thesis, the use of machine learning algorithms for in silico ligand-target prediction for target deconvolution in phenotypic screening datasets was investigated. A computational workflow is presented in Chapter 2, that allows to improve the coverage of mechanism of action hypotheses obtained by combining two conceptually different target prediction algorithms.

These models rely on the principle that two structurally similar compounds are likely to have the same target. In Chapter 3 of this thesis, it was shown that structural similarity and the similarity in phenotypic activity are correlated, and the fraction of phenotypically similar compounds that can be expected for an increase in structural similarity was subsequently quantified. Morgan fingerprints were also found to be less sensitive to the dataset employed in these analyses than two other commonly used molecular descriptors.

In Chapter 4, the mechanism of action hypotheses obtained through target prediction was compared to those obtained by extracting experimental bioactivity data of compounds active in phenotypic assays. It was then showed that the mechanism of action hypotheses generated from these two types of approach agreed where a large number of compounds were active in the phenotypic assay. When there were fewer compounds active in the phenotypic assay, target prediction complemented the use of experimental bioactivity data and allowed to uncover alternative mechanisms of action for compounds active in these assays.

Finally, the in silico target prediction workflow described in Chapter 2 was applied in Chapter 5 to deconvolute the activity of compounds in a kidney cyst growth reduction assay, aimed at discovering novel therapeutic opportunities for polycystic kidney disease. A metric was developed to rank predicted targets according to the activity of the compounds driving their prediction. Gene expression data and occurrences in the literature were combined with the target predictions to further narrow down the most probable mechanisms of action of cyst growth reducing compounds in the screen. Two target predictions were proposed as a potential mechanism for the reduction of kidney cyst growth, one of which agreed with docking studies.