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,phe-nomenological and descriptive to quantitative,analytical and predictive.Mathematics is becoming a driven force behind this historic transformation as it did to quantum physics a century ago.I will discuss how to combine differential geometry,algebra-ic topology,graph theory and partial differential equation with machine learning to arrive at the cutting edge predictions of a vast variety of experimental data,includ-ing solvation free energies,partition coefficients,protein-drug binding affinities,and protein mutation impacts.