  Advances in biomedicine are largely fueled by exploring uncharted territories
of human biology. Machine learning can both enable and accelerate discovery,
but faces a fundamental hurdle when applied to unseen data with distributions
that differ from previously observed ones -- a common dilemma in scientific
inquiry. We have developed a new deep learning framework, called
{\textit{Portal Learning}}, to explore dark chemical and biological space.
Three key, novel components of our approach include: (i) end-to-end, step-wise
transfer learning, in recognition of biology's sequence-structure-function
paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection.
Portal Learning provides a practical solution to the out-of-distribution (OOD)
problem in statistical machine learning. Here, we have implemented Portal
Learning to predict chemical-protein interactions on a genome-wide scale.
Systematic studies demonstrate that Portal Learning can effectively assign
ligands to unexplored gene families (unknown functions), versus existing
state-of-the-art methods, thereby allowing us to target previously
"undruggable" proteins and design novel polypharmacological agents for
disrupting interactions between SARS-CoV-2 and human proteins. Portal Learning
is general-purpose and can be further applied to other areas of scientific
inquiry.
