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• W4385310178 endingPage "11948" @default.
• W4385310178 startingPage "11948" @default.
• W4385310178 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." @default.
• W4385310178 created "2023-07-28" @default.
• W4385310178 creator A5017440090 @default.
• W4385310178 creator A5038396319 @default.
• W4385310178 date "2023-07-26" @default.
• W4385310178 modified "2023-09-30" @default.
• W4385310178 title "Molecular Descriptors Property Prediction Using Transformer-Based Approach" @default.
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• W4385310178 doi "https://doi.org/10.3390/ijms241511948" @default.
• W4385310178 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37569322" @default.
• W4385310178 hasPublicationYear "2023" @default.
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