Machine intelligence for chemical reaction space
Discovering new reactions, optimizing their performance, and extending the synthetically
accessible chemical space are critical drivers for major technological advances and more …
accessible chemical space are critical drivers for major technological advances and more …
[HTML][HTML] Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery
The field of predictive chemistry relates to the development of models able to describe how
molecules interact and react. It encompasses the long-standing task of computer-aided …
molecules interact and react. It encompasses the long-standing task of computer-aided …
The open reaction database
Chemical reaction data in journal articles, patents, and even electronic laboratory notebooks
are currently stored in various formats, often unstructured, which presents a significant …
are currently stored in various formats, often unstructured, which presents a significant …
Machine learning may sometimes simply capture literature popularity trends: a case study of heterocyclic Suzuki–Miyaura coupling
Applications of machine learning (ML) to synthetic chemistry rely on the assumption that
large numbers of literature-reported examples should enable construction of accurate and …
large numbers of literature-reported examples should enable construction of accurate and …
Unified deep learning model for multitask reaction predictions with explanation
There is significant interest and importance to develop robust machine learning models to
assist organic chemistry synthesis. Typically, task-specific machine learning models for …
assist organic chemistry synthesis. Typically, task-specific machine learning models for …
Data sharing in chemistry: lessons learned and a case for mandating structured reaction data
The past decade has seen a number of impressive developments in predictive chemistry
and reaction informatics driven by machine learning applications to computer-aided …
and reaction informatics driven by machine learning applications to computer-aided …
Automated chemical reaction extraction from scientific literature
Access to structured chemical reaction data is of key importance for chemists in performing
bench experiments and in modern applications like computer-aided drug design. Existing …
bench experiments and in modern applications like computer-aided drug design. Existing …
[HTML][HTML] Inferring experimental procedures from text-based representations of chemical reactions
The experimental execution of chemical reactions is a context-dependent and time-
consuming process, often solved using the experience collected over multiple decades of …
consuming process, often solved using the experience collected over multiple decades of …
[HTML][HTML] Predicting reaction conditions from limited data through active transfer learning
Transfer and active learning have the potential to accelerate the development of new
chemical reactions, using prior data and new experiments to inform models that adapt to the …
chemical reactions, using prior data and new experiments to inform models that adapt to the …
[HTML][HTML] The challenge of balancing model sensitivity and robustness in predicting yields: a benchmarking study of amide coupling reactions
Accurate prediction of reaction yield is the holy grail for computer-assisted synthesis
prediction, but current models have failed to generalize to large literature datasets. To …
prediction, but current models have failed to generalize to large literature datasets. To …