  Deep learning is now widely used in drug discovery, providing significant
acceleration and cost reduction. As the most fundamental building block,
molecular representation is essential for predicting molecular properties to
enable various downstream applications. Most existing methods attempt to
incorporate more information to learn better representations. However, not all
features are equally important for a specific task. Ignoring this would
potentially compromise the training efficiency and predictive accuracy. To
address this issue, we propose a novel approach, which treats language models
as an agent and molecular pretraining models as a knowledge base. The agent
accentuates task-relevant features in the molecular representation by
understanding the natural language description of the task, just as a tailor
customizes clothes for clients. Thus, we call this approach MolTailor.
Evaluations demonstrate MolTailor's superior performance over baselines,
validating the efficacy of enhancing relevance for molecular representation
learning. This illustrates the potential of language model guided optimization
to better exploit and unleash the capabilities of existing powerful molecular
representation methods. Our code is available at
https://github.com/SCIR-HI/MolTailor.
