When yield prediction does not yield prediction: an overview of the current challenges
Machine Learning (ML) techniques face significant challenges when predicting advanced
chemical properties, such as yield, feasibility of chemical synthesis, and optimal reaction …
chemical properties, such as yield, feasibility of chemical synthesis, and optimal reaction …
Machine learning strategies for reaction development: toward the low-data limit
Machine learning models are increasingly being utilized to predict outcomes of organic
chemical reactions. A large amount of reaction data is used to train these models, which is in …
chemical reactions. A large amount of reaction data is used to train these models, which is in …
A machine-learning tool to predict substrate-adaptive conditions for Pd-catalyzed C–N couplings
Machine-learning methods have great potential to accelerate the identification of reaction
conditions for chemical transformations. A tool that gives substrate-adaptive conditions for …
conditions for chemical transformations. A tool that gives substrate-adaptive conditions for …
High-level data fusion enables the chemoinformatically guided discovery of chiral disulfonimide catalysts for atropselective iodination of 2-amino-6-arylpyridines
BT Rose, JC Timmerman, SA Bawel… - Journal of the …, 2022 - ACS Publications
The atropselective iodination of 2-amino-6-arylpyridines catalyzed by chiral disulfonimides
(DSIs) is described. Key to the development of this transformation was the use of a …
(DSIs) is described. Key to the development of this transformation was the use of a …
Bayesian optimization as a sustainable strategy for early-stage process development? A case study of Cu-catalyzed C–N coupling of sterically hindered pyrazines
E Braconi, E Godineau - ACS Sustainable Chemistry & …, 2023 - ACS Publications
Bayesian optimization is a powerful machine learning technique that is particularly well-
suited for optimizing chemical reactions in the early stages of process development. It can …
suited for optimizing chemical reactions in the early stages of process development. It can …
On the use of real-world datasets for reaction yield prediction
The lack of publicly available, large, and unbiased datasets is a key bottleneck for the
application of machine learning (ML) methods in synthetic chemistry. Data from electronic …
application of machine learning (ML) methods in synthetic chemistry. Data from electronic …
A Slug Flow Platform with Multiple Process Analytics Facilitates Flexible Reaction Optimization
F Wagner, P Sagmeister, CE Jusner… - Advanced …, 2024 - Wiley Online Library
Flow processing offers many opportunities to optimize reactions in a rapid and automated
manner, yet often requires relatively large quantities of input materials. To combat this, the …
manner, yet often requires relatively large quantities of input materials. To combat this, the …
ORDerly: Data Sets and Benchmarks for Chemical Reaction Data
Machine learning has the potential to provide tremendous value to life sciences by providing
models that aid in the discovery of new molecules and reduce the time for new products to …
models that aid in the discovery of new molecules and reduce the time for new products to …
Probing machine learning models based on high throughput experimentation data for the discovery of asymmetric hydrogenation catalysts
Enantioselective hydrogenation of olefins by Rh-based chiral catalysts has been extensively
studied for more than 50 years. Naively, one would expect that everything about this …
studied for more than 50 years. Naively, one would expect that everything about this …
Estimation of multicomponent reactions' yields from networks of mechanistic steps
This work describes estimation of yields of complex, multicomponent reactions (MCRs)
based on the modeled networks of mechanistic steps spanning both the main reaction …
based on the modeled networks of mechanistic steps spanning both the main reaction …