  Methods for designing organic materials with desired properties have high
potential impact across fields such as medicine, renewable energy,
petrochemical engineering, and agriculture. However, using generative modeling
to design substances with desired properties is difficult because candidate
compounds must satisfy multiple constraints, including synthetic accessibility
and other metrics that are intuitive to domain experts but challenging to
quantify. We propose C5T5, a novel self-supervised pretraining method that
enables transformers to make zero-shot select-and-replace edits, altering
organic substances towards desired property values. C5T5 operates on IUPAC
names -- a standardized molecular representation that intuitively encodes rich
structural information for organic chemists but that has been largely ignored
by the ML community. Our technique requires no edited molecule pairs to train
and only a rough estimate of molecular properties, and it has the potential to
model long-range dependencies and symmetric molecular structures more easily
than graph-based methods. C5T5 also provides a powerful interface to domain
experts: it grants users fine-grained control over the generative process by
selecting and replacing IUPAC name fragments, which enables experts to leverage
their intuitions about structure-activity relationships. We demonstrate C5T5's
effectiveness on four physical properties relevant for drug discovery, showing
that it learns successful and chemically intuitive strategies for altering
molecules towards desired property values.
