The use of machine leaming models in biochemical research has specific requirements in terms of reliability, robustness, and interpretability of such models. The complexity and constraints of many applications in these fields require tailored and/or advanced methods to successfully deal with them. In the context of risk assessment, we investigated existing metrics for the quantification of confidence estimates of predictions in the context of chemoinformatics. Gaussian processes have been successfully applied in chemoinformatics to predict properties of new chemical compounds. They provide an estimate of variance, or error bar, along with the prediction itself. This error bar may be used, e.g., to discard individual uncertain predictions. We investigated the limits of error bars and compared different methods to assess the quality of confidence estimates. Visual explanations were introduced by us to explain individual predictions of kernel-based leaming models. In our approach, the training examples that contribute most to a classifier decision are visualized, along with a quantification of their importance for the prediction. This allows users to understand predictions in terms of the objects in question, here molecules. A comprehensive study with test persons was conducted to quantify the effectiveness of our approach. The study used Ames mutagenicity as biochemical application, and revealed significant improvements of user's abihty to judge the reliability of a prediction. Besides improvements in prediction accuracy the explanatory components spotted insufficient coverage and important chemical characteristics of the training data. Screening large libraries of chemical compounds against a biological target, e.g., a receptor or enzyme, is crucial in the hit discovery phase of drug discovery. Virtual screening can be seen as a ranking problem that prefers as many actives as possible at the top of the ranking. Current methods use regression to predict each molecule's activity, and then sort to obtain a ranking. We developed a top-k ranking algorithm (StructRank) that solves this problem directly, without the intermediate regression step. Our approach empirically outperforms regression methods and a common ranking algorithm (RankSVM) in terms of actives found. StructRank is publicly available. It's corresponding journal publication is one of the most read ones in the Journal of Chemical Information and Modeling. When investigating new biological targets, often few measurement data are available for the new target, while at the same time there is more data for related targets. Similarly, in classical quantitative structure-property relationships, for the same property, separate linear models are established per group of compounds, assuming substituent effects to be additive inside each group, but not across groups. We investigated the use of multi-task learning to exploit relationships between data sets. Improvements over single models where empirically found in situations where only limited annotated data was available. The Institute of Pure and Applied Mathematics (IPAM) long program on "Navigating Chemical Compound Space for Materials and Bio Design" (2011) at the University of California, Los Angeles, USA brought together researchers from physics, chemistry, biology, and mathematics. Three new international collaborations were established, dealing with estimation of atomization energies of organic molecules, estimation of kinetic energies based on electron densities, and characterization of transition state surfaces. The program proved to be highly productive, yielded new applications of machine learning, and raised awareness of machine learning in several communities, including physical chemistry and materials science.