Automating reward function configuration for drug design
Marius Urbonas Temitope Ajileye Paul Gainer Douglas Pires
Designing reward functions that guide generative molecular design (GMD)
algorithms to desirable areas of chemical space is of critical importance in
AI-driven drug discovery. Traditionally, this has been a manual and error-prone
task; the selection of appropriate computational methods to approximate
biological assays is challenging and the aggregation of computed values into a
single score even more so, leading to potential reliance on trial-and-error
approaches. We propose a novel approach for automated reward configuration that
relies solely on experimental data, mitigating the challenges of manual reward
adjustment on drug discovery projects. Our method achieves this by constructing
a ranking over experimental data based on Pareto dominance over the
multi-objective space, then training a neural network to approximate the reward
function such that rankings determined by the predicted reward correlate with
those determined by the Pareto dominance relation. We validate our method using
two case studies. In the first study we simulate Design-Make-Test-Analyse
(DMTA) cycles by alternating reward function updates and generative runs guided
by that function. We show that the learned function adapts over time to yield
compounds that score highly with respect to evaluation functions taken from the
literature. In the second study we apply our algorithm to historical data from
four real drug discovery projects. We show that our algorithm yields reward
functions that outperform the predictive accuracy of human-defined functions,
achieving an improvement of up to 0.4 in Spearman's correlation against a
ground truth evaluation function that encodes the target drug profile for that
project. Our method provides an efficient data-driven way to configure reward
functions for GMD, and serves as a strong baseline for future research into
transformative approaches for the automation of drug discovery.