Biology is believed to be the last forefront of natural sciences. Recent advances in biotechnologies have led to the exponential growth of biological data, which paves the way for biological sciences to transform from qualitative, phenomenological and descriptive to quantitative, analytical and predictive. Mathematics is becoming a driven force behind this historic transformation as it did for quantum physics a century ago. I will discuss how to combine differential geometry, algebraic topology, graph theory and partial differential equation with machine learning to give rise to the most accurate predictions of tens of thousands of experimental data in solvation free energy, partition coefficient, protein-drug binding affinity, and protein mutation impact.