Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks
Finding synthesis routes for molecules of interest is essential in the discovery of new drugs and materials. To find such routes, computer-assisted synthesis planning (CASP) methods are employed, which rely on a single-step model of chemical reactivity. In this study, we introduce a template-based single-step retrosynthesis model based on Modern Hopfield Networks, which learn an encoding of both molecules and reaction templates in order to predict the relevance of templates for a given molecule. The template representation allows generalization across different reactions and significantly improves the performance of template relevance prediction, especially for templates with few or zero training examples. With inference speed up to orders of magnitude faster than baseline methods, we improve or match the state-of-the-art performance for top-k exact match accuracy for k ≥ 3 in the retrosynthesis benchmark USPTO-50k. Code to reproduce the results is available at github.com/ml-jku/mhn-react.
Code (1)
Tasks
RetrosynthesisSingle-step retrosynthesisSimilar Papers 제목 키워드 기반
Modern Hopfield Networks for Few- and Zero-Shot Reaction Template Prediction
Finding synthesis routes for molecules of interest is an essential step in the discovery of new drugs and materials. To find such routes, computer-assisted synthesis planning (CASP) methods are employed which rely on a m…
RetrosynthesisSingle-step retrosynthesisZero-Shot LearningA Transformer Model for Predicting Chemical Reaction Products from Generic Templates
The accurate prediction of chemical reaction outcomes is a major challenge in computational chemistry. Current models rely heavily on either highly specific reaction templates or template-free methods, both of which pres…
Computational chemistryEfficient ExplorationvalidWhat Makes Pre-trained Language Models Better Zero-shot Learners?
Current methods for prompt learning in zeroshot scenarios widely rely on a development set with sufficient human-annotated data to select the best-performing prompt template a posteriori. This is not ideal because in a r…
Language ModellingPrompt Learningtext-classificationText Classification+1RetroComposer: Composing Templates for Template-Based Retrosynthesis Prediction
The main target of retrosynthesis is to recursively decompose desired molecules into available building blocks. Existing template-based retrosynthesis methods follow a template selection stereotype and suffer from limite…
PredictionRetrosynthesisSingle-step retrosynthesisAssessing the Extrapolation Capability of Template-Free Retrosynthesis Models
Despite the acknowledged capability of template-free models in exploring unseen reaction spaces compared to template-based models for retrosynthesis prediction, their ability to venture beyond established boundaries rema…
Retrosynthesis