Causality-driven feature selection and domain adaptation for enhancing chemical foundation models in downstream tasks

Causality-driven feature selection and domain adaptation for enhancing chemical foundation models in downstream tasks

Recent advancements in large foundation models have revealed impressive capabilities in mastering complex chemical language representations. These models undergo a task-agnostic learning phase, characterized by pre-training on extensive unlabeled corpora followed by fine-tuning on specific downstrea...

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Bibliographic Details
Main Authors: Eduardo Soares, Victor Yukio Shirasuna, Emilio Vital Brazil, Karen Fiorella Aquino Gutierrez, Renato Cerqueira, Dmitry Zubarev, Kristin Schmidt, Daniel P Sanders
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:Machine Learning: Science and Technology
Subjects:
foundation models
causality
molecular properties prediction
QM9
Online Access:https://doi.org/10.1088/2632-2153/adabb1
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