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Abstract Background Haploinsufficient (HI) genes are those for which a reduction in copy number in a diploid from two to one results in significantly reduced fitness. Haploinsufficiency is increasingly implicated in human disease, and so predicting this phenotype could provide insights into the genetic mechanisms behind many human diseases, including some cancers. Results In the present work we show that orthologues of Saccharomyces cerevisiae HI genes are preferentially retained across the kingdom Fungi, and that the HI genes of S. cerevisiae can be used to predict haploinsufficiency in humans. Our HI gene predictions confirm known associations between haploinsufficiency and genetic disease, and predict several further disorders in which the phenotype may be relevant. Haploinsufficiency is also clearly relevant to the gene-dosage imbalances inherent in eukaryotic sex-determination systems. In S. cerevisiae, HI genes are over-represented on chromosome III, the chromosome that determines yeast's mating type. This may be a device to select against the loss of one copy of chromosome III from a diploid. We found that orthologues of S. cerevisiae HI genes are also over-represented on the mating-type chromosomes of other yeasts and filamentous fungi. In animals with heterogametic sex determination, accumulation of HI genes on the sex chromosomes would compromise fitness in both sexes, given X chromosome inactivation in females. We found that orthologues of S. cerevisiae HI genes are significantly under-represented on the X chromosomes of mammals and of Caenorhabditis elegans. There is no X inactivation in Drosophila melanogaster (increased expression of X in the male is used instead) and, in this species, we found no depletion of orthologues to yeast HI genes on the sex chromosomes. Conclusion A special relationship between HI genes and the sex/mating-type chromosome extends from S. cerevisiae to Homo sapiens, with the microbe being a useful model for species throughout the evolutionary range. Furthermore, haploinsufficiency in yeast can predict the phenotype in higher organisms.

There is an urgent need to make drug discovery cheaper and faster. This will enable the development of treatments for diseases currently neglected for economic reasons, such as tropical and orphan diseases, and generally increase the supply of new drugs. Here, we report the Robot Scientist 'Eve' designed to make drug discovery more economical. A Robot Scientist is a laboratory automation system that uses artificial intelligence (AI) techniques to discover scientific knowledge through cycles of experimentation. Eve integrates and automates library-screening, hit-confirmation, and lead generation through cycles of quantitative structure activity relationship learning and testing. Using econometric modelling we demonstrate that the use of AI to select compounds economically outperforms standard drug screening. For further efficiency Eve uses a standardized form of assay to compute Boolean functions of compound properties. These assays can be quickly and cheaply engineered using synthetic biology, enabling more targets to be assayed for a given budget. Eve has repositioned several drugs against specific targets in parasites that cause tropical diseases. One validated discovery is that the anti-cancer compound TNP-470 is a potent inhibitor of dihydrofolate reductase from the malaria-causing parasite Plasmodium vivax.