  High-throughput screening, in which multiwell plates are used to test large
numbers of compounds against specific targets, is widely used across many areas
of the biological sciences and most prominently in drug discovery. We propose a
statistically principled approach to these screening experiments, using the
machinery of supersaturated designs and the Lasso. To accommodate limitations
on the number of biological entities that can be applied to a single microplate
well, we present a new class of row-constrained supersaturated designs. We
develop a computational procedure to construct these designs, provide some
initial lower bounds on the average squared off-diagonal values of their
main-effects information matrix, and study the impact of the constraint on
design quality. We also show via simulation that the proposed constrained row
screening method is statistically superior to existing methods and demonstrate
the use of the new methodology on a real drug-discovery system.
