Molecular Structure-Property Co-Trained Foundation Model for In Silico Chemistry

Recently, deep learning approaches have been extensively studied for various problems in chemistry, such as virtual screening, de novo molecule design, etc. Despite the impressive successes, end-to-end training for specific tasks usually requires separately designed networks, so it's often difficult to acquire a unified principle to synergistically combine existing architectures and training datasets for novel tasks. To address this, inspired by recent advances of pre-trained multi-modal foundation models such as Vision-Language Pretrained models (VLP), here we present a novel multimodal foundation model that can be used {\em in silico} for various downstream tasks in chemistry. Specifically, our framework, dubbed as the structure-property multi-modal (SPMM) foundation model, is based on the dual-stream transformer with X-shape attention, so that it can align the molecule structure and the chemical properties in a common embedding space. Accordingly, SPMM can simultaneously perform chemical property prediction from given structure-describing strings and allows the generation of molecular structures for given chemical properties, which was previously not possible with a single architecture. Furthermore, we show that the outstanding unimodal representation of a molecule emerges from multimodal learning, which has the potential to be fine-tuned for many other downstream tasks.