Abstract: Advances in genomic, transcriptomic, and proteomic technologies now enable the routine measurement of thousands of biomolecules within any biological sample. With the advent of single-cell techniques that multiplex these measurements over millions of cells, the scale of biological data generation is outpacing our human ability to comprehend this data. Approaches based on machine intelligence offer the possibility of augmenting our human capacity for understanding in order to enable biological discovery. In this seminar, I will explore applications of machine learning to enable biological discovery from genomic, proteomic, transcriptomic, and metabolomic datasets, and discuss implications for the diagnosis and treatment of human disease. Areas of focus will include the discovery of novel antibiotics from nature using genomic and metabolomic datasets; charting protein-protein interactions within mammalian tissues and across the tree of life using proteomic datasets; mapping neural circuits in high-throughput using single-cell and spatial transcriptomics datasets; and the identification of novel designer drugs of abuse from metabolomic datasets. Looking forward, I will explore how machine learning might bridge the most significant remaining gap in our ability to measure biological systems: namely, our ability to measure all of the small molecules within any biological sample. I will discuss the use of chemical artificial intelligence to identify unknown molecules within mass spectrometry data, and the implications of this technology for our understanding of cancer metabolism and germline cancer risk.