A brief introduction to chemical reaction optimization

CJ Taylor, A Pomberger, KC Felton, R Grainger… - Chemical …, 2023 - ACS Publications
From the start of a synthetic chemist's training, experiments are conducted based on recipes
from textbooks and manuscripts that achieve clean reaction outcomes, allowing the scientist …

Machine learning for electrocatalyst and photocatalyst design and discovery

H Mai, TC Le, D Chen, DA Winkler… - Chemical …, 2022 - ACS Publications
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …

The rise of self-driving labs in chemical and materials sciences

M Abolhasani, E Kumacheva - Nature Synthesis, 2023 - nature.com
Accelerating the discovery of new molecules and materials, as well as developing green
and sustainable ways to synthesize them, will help to address global challenges in energy …

A field guide to flow chemistry for synthetic organic chemists

L Capaldo, Z Wen, T Noël - Chemical science, 2023 - pubs.rsc.org
Flow chemistry has unlocked a world of possibilities for the synthetic community, but the idea
that it is a mysterious “black box” needs to go. In this review, we show that several of the …

[HTML][HTML] Biocatalysis

EL Bell, W Finnigan, SP France, AP Green… - Nature Reviews …, 2021 - nature.com
Biocatalysis has become an important aspect of modern organic synthesis, both in
academia and across the chemical and pharmaceutical industries. Its success has been …

Combining machine learning and computational chemistry for predictive insights into chemical systems

JA Keith, V Vassilev-Galindo, B Cheng… - Chemical …, 2021 - ACS Publications
Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

A multi-objective active learning platform and web app for reaction optimization

JAG Torres, SH Lau, P Anchuri… - Journal of the …, 2022 - ACS Publications
We report the development of an open-source experimental design via Bayesian
optimization platform for multi-objective reaction optimization. Using high-throughput …

Drug discovery with explainable artificial intelligence

J Jiménez-Luna, F Grisoni, G Schneider - Nature Machine Intelligence, 2020 - nature.com
Deep learning bears promise for drug discovery, including advanced image analysis,
prediction of molecular structure and function, and automated generation of innovative …

The open reaction database

SM Kearnes, MR Maser, M Wleklinski… - Journal of the …, 2021 - ACS Publications
Chemical reaction data in journal articles, patents, and even electronic laboratory notebooks
are currently stored in various formats, often unstructured, which presents a significant …

Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR

A Tropsha, O Isayev, A Varnek, G Schneider… - Nature Reviews Drug …, 2024 - nature.com
Quantitative structure–activity relationship (QSAR) modelling, an approach that was
introduced 60 years ago, is widely used in computer-aided drug design. In recent years …