MolPLA: a molecular pretraining framework for learning cores, R-groups and their linker joints.
Bioinformatics
; 40(Supplement_1): i369-i380, 2024 Jun 28.
Article
em En
| MEDLINE
| ID: mdl-38940143
ABSTRACT
MOTIVATION Molecular core structures and R-groups are essential concepts in drug development. Integration of these concepts with conventional graph pre-training approaches can promote deeper understanding in molecules. We propose MolPLA, a novel pre-training framework that employs masked graph contrastive learning in understanding the underlying decomposable parts in molecules that implicate their core structure and peripheral R-groups. Furthermore, we formulate an additional framework that grants MolPLA the ability to help chemists find replaceable R-groups in lead optimization scenarios. RESULTS:
Experimental results on molecular property prediction show that MolPLA exhibits predictability comparable to current state-of-the-art models. Qualitative analysis implicate that MolPLA is capable of distinguishing core and R-group sub-structures, identifying decomposable regions in molecules and contributing to lead optimization scenarios by rationally suggesting R-group replacements given various query core templates. AVAILABILITY AND IMPLEMENTATION The code implementation for MolPLA and its pre-trained model checkpoint is available at https//github.com/dmis-lab/MolPLA.
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01-internacional
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MEDLINE
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Software
Idioma:
En
Revista:
Bioinformatics
Ano de publicação:
2024
Tipo de documento:
Article