Kristin Schmidt, PhD, Principal Research Scientist & Research Manager, IBM Research - shared that deep learning has emerged as a powerful tool for predicting molecular properties and generating molecule candidates, significantly advancing scientific exploration in various fields such as drug discovery and materials science. This progress can be attributed to the successful application of foundation models, which leverage large-scale pre-training methodologies to learn contextualized representations of input tokens through self-supervised learning on extensive unlabeled corpora. The pre-trained foundation models are subsequently fine-tuned for specific downstream tasks. In this presentation, we will introduce the suite of foundation models for chemistry and materials being developed by IBM Research. These models encompass a range of representation types, from SMILES annotations to 3D atomic positions of compounds. We will illustrate how these foundation models can be applied in diverse downstream use cases, showcasing their potential to accelerate scientific discovery.
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