Meta Learning for Low-Resource Molecular Optimization

The goal of molecular optimization
(MO) is to discover molecules
that acquire improved pharmaceutical properties over a known starting
molecule. Despite many recent successes of new approaches for MO,
these methods were typically developed for particular properties with
rich annotated training examples. Thus, these approaches are difficult
to implement in real scenes where only a small amount of pharmaceutical
data is usually available due to the expense and significant effort
required for the data collection. Here, we propose a new approach,
Meta-MO, for molecular optimization with a handful of training samples
based on the well-recognized first-order meta-learning algorithms.
By using a set of meta tasks with rich training samples, Meta-MO trains
a meta model through the meta-learning optimization and adapts the
learned model to new low-resource MO tasks. Meta-MO was shown to consistently
outperform several pretraining and multitask training procedures,
providing an average improvement in the success rate of 4.3% on a
large-scale bioactivity data set with diverse target variations. We
also observed that Meta-MO resulted in the best performing models
across fine-tuning sets with only dozens of samples. To the best of
our knowledge, this is the first study to apply meta learning to MO
tasks. More importantly, such a strategy could be further extended
to many low-resource scenarios in real-world drug design.