This thesis describes the importance of being able to control the selectivity of potential drug candidates. It explains how computational models are employed to predict and rationalize compound... Show moreThis thesis describes the importance of being able to control the selectivity of potential drug candidates. It explains how computational models are employed to predict and rationalize compound-protein binding (affinity) and therewith, selectivity of compounds. Moreover, it shows that selectivity can purposely be tuned to target either a single protein or an entire panel of proteins. The challenges of selectivity modeling are addressed based on case studies in the sodium-dependent glucose co-transporters, G protein-coupled receptors, and kinases. Show less
Over the last decades several disciplines relevant to medicinal chemistry and preclinical drug discovery have made gigantic leaps; this includes chemistry, biology and measurement of bioactivity.... Show moreOver the last decades several disciplines relevant to medicinal chemistry and preclinical drug discovery have made gigantic leaps; this includes chemistry, biology and measurement of bioactivity. Better techniques have led to massive amounts of data. Moreover, sources of chemical and bioactivity data have become available in the public domain. Hence there is a need for new techniques combining and mining these data sources. This thesis focuses on computational methods combining data from these disciplines and demonstrates that the sum of these methods leads to better quality predictions than models using the individual data sources. One of the techniques central in this thesis is proteochemometric modeling, a machine learning approach linking chemical descriptors and protein descriptors to a biologically relevant output variable. This output variable describes the activity of molecules on biological macromolecules and hence proteochemometric models can make relevant predictions for both unseen molecules and unseen macromolecules (e.g. novel viral mutants). Secondly we present a novel technique that is able to combine information from multiple crystal structures in such a way that shared and unique pharmacophoric features can be isolated and visualized. Approaches presented here have been validated prospectively and have been shown to be widely applicable. Show less
One of the main problems of drug design is that it is quite hard to discover compounds that have all the required properties to become a drug (efficacy against the disease, good biological... Show moreOne of the main problems of drug design is that it is quite hard to discover compounds that have all the required properties to become a drug (efficacy against the disease, good biological availability, low toxicity). This thesis describes the use of data mining and interactive evolutionary algorithms to design novel classes of molecules. Using data mining, we split a 250,000 compound database into ring systems, substituents and linkers. We then counted the occurrence of the different fragments, as well as their co-occurrence. Our resulting lists of common and uncommon chemical substructures and substructure combinations can be used to increase the diversity of drug screening libraries and hence increase their chance to yield new drugs. We also developed a computer program, the Molecule Evoluator. This program uses an interactive evolutionary algorithm to propose novel molecules or molecule modifications. Using the Molecule Evoluator, our chemists were able to discover three novel classes of compounds, resulting in the synthesis of eight new compounds. Four of these proved to bind to biogenic amine targets such as the norepinephrine transport protein and the alpha-adrenergic receptors. So, our computer methods offer inspiration to chemists, helping them to get new ideas for drug molecules. Show less
