nach0: Multimodal Natural and Chemical Languages Foundation Model
This addresses the problem of integrating chemical and linguistic knowledge for researchers in chemistry and biology, though it is incremental as it builds on existing LLM and multimodal approaches.
The paper introduces nach0, a multimodal foundation model that tackles various chemical and biological tasks, such as biomedical question answering and molecular generation, and it outperforms state-of-the-art baselines on single-domain and cross-domain tasks.
Large Language Models (LLMs) have substantially driven scientific progress in various domains, and many papers have demonstrated their ability to tackle complex problems with creative solutions. Our paper introduces a new foundation model, nach0, capable of solving various chemical and biological tasks: biomedical question answering, named entity recognition, molecular generation, molecular synthesis, attributes prediction, and others. nach0 is a multi-domain and multi-task encoder-decoder LLM pre-trained on unlabeled text from scientific literature, patents, and molecule strings to incorporate a range of chemical and linguistic knowledge. We employed instruction tuning, where specific task-related instructions are utilized to fine-tune nach0 for the final set of tasks. To train nach0 effectively, we leverage the NeMo framework, enabling efficient parallel optimization of both base and large model versions. Extensive experiments demonstrate that our model outperforms state-of-the-art baselines on single-domain and cross-domain tasks. Furthermore, it can generate high-quality outputs in molecular and textual formats, showcasing its effectiveness in multi-domain setups.