$\texttt{MiniMol}$: A Parameter-Efficient Foundation Model for Molecular Learning

arXiv:2404.14986v1 Announce Type: new
Abstract: In biological tasks, data is rarely plentiful as it is generated from hard-to-gather measurements. Therefore, pre-training foundation models on large quantities of available data and then transfer to low-data downstream tasks is a promising direction. However, how to design effective foundation models for molecular learning remains an open question, with existing approaches typically focusing on models with large parameter capacities. In this work, we propose $\texttt{MiniMol}$, a foundational model for molecular learning with 10 million …

abstract arxiv cs.ai cs.lg data design foundation foundation model gather generated however low pre-training tasks training transfer type