  The drug discovery process currently employed in the pharmaceutical industry
typically requires about 10 years and $2-3 billion to deliver one new drug.
This is both too expensive and too slow, especially in emergencies like the
COVID-19 pandemic. In silicomethodologies need to be improved to better select
lead compounds that can proceed to later stages of the drug discovery protocol
accelerating the entire process. No single methodological approach can achieve
the necessary accuracy with required efficiency. Here we describe multiple
algorithmic innovations to overcome this fundamental limitation, development
and deployment of computational infrastructure at scale integrates multiple
artificial intelligence and simulation-based approaches. Three measures of
performance are:(i) throughput, the number of ligands per unit time; (ii)
scientific performance, the number of effective ligands sampled per unit time
and (iii) peak performance, in flop/s. The capabilities outlined here have been
used in production for several months as the workhorse of the computational
infrastructure to support the capabilities of the US-DOE National Virtual
Biotechnology Laboratory in combination with resources from the EU Centre of
Excellence in Computational Biomedicine.
