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Molecular Descriptors Property Prediction Using Transformer-Based Approach.
- Source :
-
International Journal of Molecular Sciences . Aug2023, Vol. 24 Issue 15, p11948. 15p. - Publication Year :
- 2023
-
Abstract
- In this study, we introduce semi-supervised machine learning models designed to predict molecular properties. Our model employs a two-stage approach, involving pre-training and fine-tuning. Particularly, our model leverages a substantial amount of labeled and unlabeled data consisting of SMILES strings, a text representation system for molecules. During the pre-training stage, our model capitalizes on the Masked Language Model, which is widely used in natural language processing, for learning molecular chemical space representations. During the fine-tuning stage, our model is trained on a smaller labeled dataset to tackle specific downstream tasks, such as classification or regression. Preliminary results indicate that our model demonstrates comparable performance to state-of-the-art models on the chosen downstream tasks from MoleculeNet. Additionally, to reduce the computational overhead, we propose a new approach taking advantage of 3D compound structures for calculating the attention score used in the end-to-end transformer model to predict anti-malaria drug candidates. The results show that using the proposed attention score, our end-to-end model is able to have comparable performance with pre-trained models. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 16616596
- Volume :
- 24
- Issue :
- 15
- Database :
- Academic Search Index
- Journal :
- International Journal of Molecular Sciences
- Publication Type :
- Academic Journal
- Accession number :
- 169926428
- Full Text :
- https://doi.org/10.3390/ijms241511948