  Is there a unified model for generating molecules considering different
conditions, such as binding pockets and chemical properties? Although
target-aware generative models have made significant advances in drug design,
they do not consider chemistry conditions and cannot guarantee the desired
chemical properties. Unfortunately, merging the target-aware and chemical-aware
models into a unified model to meet customized requirements may lead to the
problem of negative transfer. Inspired by the success of multi-task learning in
the NLP area, we use prefix embeddings to provide a novel generative model that
considers both the targeted pocket's circumstances and a variety of chemical
properties. All conditional information is represented as learnable features,
which the generative model subsequently employs as a contextual prompt.
Experiments show that our model exhibits good controllability in both single
and multi-conditional molecular generation. The controllability enables us to
outperform previous structure-based drug design methods. More interestingly, we
open up the attention mechanism and reveal coupling relationships between
conditions, providing guidance for multi-conditional molecule generation.
