  Artificial intelligence has demonstrated immense potential in scientific
research. Within molecular science, it is revolutionizing the traditional
computer-aided paradigm, ushering in a new era of deep learning. With recent
progress in multimodal learning and natural language processing, an emerging
trend has targeted at building multimodal frameworks to jointly model molecules
with textual domain knowledge. In this paper, we present the first systematic
survey on multimodal frameworks for molecules research. Specifically,we begin
with the development of molecular deep learning and point out the necessity to
involve textual modality. Next, we focus on recent advances in text-molecule
alignment methods, categorizing current models into two groups based on their
architectures and listing relevant pre-training tasks. Furthermore, we delves
into the utilization of large language models and prompting techniques for
molecular tasks and present significant applications in drug discovery.
Finally, we discuss the limitations in this field and highlight several
promising directions for future research.
