Molecular Foundation Model

Molecular foundation models are large, pre-trained machine learning models designed to accelerate molecular design and discovery across diverse scientific domains. Current research emphasizes developing robust and versatile models, often based on transformer architectures, that can effectively handle various molecular representations (e.g., SMILES strings, graphs, and images) and integrate diverse data modalities (e.g., structures, properties, and textual descriptions). These models aim to improve the accuracy and efficiency of tasks such as property prediction, molecule generation, and cross-modal retrieval, ultimately impacting drug discovery, materials science, and other fields reliant on molecular understanding. A key focus is on overcoming data scarcity through techniques like transfer learning and self-supervised pretraining on large, publicly available datasets.