SmilesT5: Domain-specific pretraining for molecular language models

• URL: http://arxiv.org/abs/2507.22514v1
• Date: Wed, 30 Jul 2025 09:36:18 GMT
• Title: SmilesT5: Domain-specific pretraining for molecular language models
• Authors: Philip Spence, Brooks Paige, Anne Osbourn,
• Abstract summary: Molecular property prediction is an increasingly critical task within drug discovery and development.<n>Recent advances in natural language processing have highlighted the capabilities of neural networks to learn complex human language.<n>We present novel domain-specific text-to-text pretraining tasks that yield improved performance in six classification-based molecular property prediction benchmarks.
• Score: 4.769637827387851
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Molecular property prediction is an increasingly critical task within drug discovery and development. Typically, neural networks can learn molecular properties using graph-based, language-based or feature-based methods. Recent advances in natural language processing have highlighted the capabilities of neural networks to learn complex human language using masked language modelling. These approaches to training large transformer-based deep learning models have also been used to learn the language of molecules, as represented by simplified molecular-input line-entry system (SMILES) strings. Here, we present novel domain-specific text-to-text pretraining tasks that yield improved performance in six classification-based molecular property prediction benchmarks, relative to both traditional likelihood-based training and previously proposed fine-tuning tasks. Through ablation studies, we show that data and computational efficiency can be improved by using these domain-specific pretraining tasks. Finally, the pretrained embeddings from the model can be used as fixed inputs into a downstream machine learning classifier and yield comparable performance to finetuning but with much lower computational overhead.

Related papers

• Pre-trained Molecular Language Models with Random Functional Group Masking [54.900360309677794]
  We propose a SMILES-based underlineem Molecular underlineem Language underlineem Model, which randomly masking SMILES subsequences corresponding to specific molecular atoms. This technique aims to compel the model to better infer molecular structures and properties, thus enhancing its predictive capabilities.
  arXiv  Detail & Related papers  (2024-11-03T01:56:15Z)
• In-Context Learning for Few-Shot Molecular Property Prediction [56.67309268480843]
  In this paper, we adapt the concepts underpinning in-context learning to develop a new algorithm for few-shot molecular property prediction. Our approach learns to predict molecular properties from a context of (molecule, property measurement) pairs and rapidly adapts to new properties without fine-tuning.
  arXiv  Detail & Related papers  (2023-10-13T05:12:48Z)
• Investigating Masking-based Data Generation in Language Models [0.0]
  A feature of BERT and models with similar architecture is the objective of masked language modeling. Data augmentation is a data-driven technique widely used in machine learning. Recent studies have utilized masked language model to generate artificially augmented data for NLP downstream tasks.
  arXiv  Detail & Related papers  (2023-06-16T16:48:27Z)
• Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property Prediction [53.06671763877109]
  We develop molecular embeddings that encode complex molecular characteristics to improve the performance of few-shot molecular property prediction. Our approach leverages large amounts of synthetic data, namely the results of molecular docking calculations. On multiple molecular property prediction benchmarks, training from the embedding space substantially improves Multi-Task, MAML, and Prototypical Network few-shot learning performance.
  arXiv  Detail & Related papers  (2023-02-04T01:32:40Z)
• Mitigating Data Scarcity for Large Language Models [7.259279261659759]
  In recent years, pretrained neural language models (PNLMs) have taken the field of natural language processing by storm. Data scarcity are commonly found in specialized domains, such as medical, or in low-resource languages that are underexplored by AI research. In this dissertation, we focus on mitigating data scarcity using data augmentation and neural ensemble learning techniques.
  arXiv  Detail & Related papers  (2023-02-03T15:17:53Z)
• Reprogramming Pretrained Language Models for Protein Sequence Representation Learning [68.75392232599654]
  We propose Representation Learning via Dictionary Learning (R2DL), an end-to-end representation learning framework. R2DL reprograms a pretrained English language model to learn the embeddings of protein sequences. Our model can attain better accuracy and significantly improve the data efficiency by up to $105$ times over the baselines set by pretrained and standard supervised methods.
  arXiv  Detail & Related papers  (2023-01-05T15:55:18Z)
• Dependency-based Mixture Language Models [53.152011258252315]
  We introduce the Dependency-based Mixture Language Models. In detail, we first train neural language models with a novel dependency modeling objective. We then formulate the next-token probability by mixing the previous dependency modeling probability distributions with self-attention.
  arXiv  Detail & Related papers  (2022-03-19T06:28:30Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• Reprogramming Language Models for Molecular Representation Learning [65.00999660425731]
  We propose Representation Reprogramming via Dictionary Learning (R2DL) for adversarially reprogramming pretrained language models for molecular learning tasks. The adversarial program learns a linear transformation between a dense source model input space (language data) and a sparse target model input space (e.g., chemical and biological molecule data) using a k-SVD solver. R2DL achieves the baseline established by state of the art toxicity prediction models trained on domain-specific data and outperforms the baseline in a limited training-data setting.
  arXiv  Detail & Related papers  (2020-12-07T05:50:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.