AI for organic and polymer synthesis
Recent years have witnessed the transformative impact from the integration of artificial
intelligence with organic and polymer synthesis. This synergy offers innovative and …
intelligence with organic and polymer synthesis. This synergy offers innovative and …
[HTML][HTML] Machine learning-guided strategies for reaction conditions design and optimization
This review surveys the recent advances and challenges in predicting and optimizing
reaction conditions using machine learning techniques. The paper emphasizes the …
reaction conditions using machine learning techniques. The paper emphasizes the …
AutoTemplate: enhancing chemical reaction datasets for machine learning applications in organic chemistry
This paper presents AutoTemplate, an innovative data preprocessing protocol, addressing
the crucial need for high-quality chemical reaction datasets in the realm of machine learning …
the crucial need for high-quality chemical reaction datasets in the realm of machine learning …
An artificial intelligence course for chemical engineers
Artificial intelligence and machine learning are revolutionising fields of science and
engineering. In recent years, process engineering has widely benefited from this novel …
engineering. In recent years, process engineering has widely benefited from this novel …
A green, facile, and practical preparation of capsaicin derivatives with thiourea structure
L Chen, Z Gao, Y Zhang, X Dai, F Meng, Y Guo - Scientific Reports, 2024 - nature.com
Capsaicin derivatives with thiourea structure (CDTS) is highly noteworthy owing to its higher
analgesic potency in rodent models and higher agonism in vitro. However, the direct …
analgesic potency in rodent models and higher agonism in vitro. However, the direct …
Artificial Intelligence-Driven Development of Nickel-Catalyzed Enantioselective Cross-Coupling Reactions
The conventional approach to developing asymmetric synthetic methods relies heavily on
empirical optimization. However, the integration of artificial intelligence (AI) and high …
empirical optimization. However, the integration of artificial intelligence (AI) and high …
Self-Supervised Contrastive Molecular Representation Learning with a Chemical Synthesis Knowledge Graph
Self-supervised molecular representation learning has demonstrated great promise in
bridging machine learning and chemical science to accelerate the development of new …
bridging machine learning and chemical science to accelerate the development of new …
Transfer learning for a foundational chemistry model
E King-Smith - Chemical Science, 2024 - pubs.rsc.org
Data-driven chemistry has garnered much interest concurrent with improvements in
hardware and the development of new machine learning models. However, obtaining …
hardware and the development of new machine learning models. However, obtaining …
A focus on molecular representation learning for the prediction of chemical properties
Y Harnik, A Milo - Chemical Science, 2024 - pubs.rsc.org
Molecular representation learning (MRL) is a specialized field in which deep-learning
models condense essential molecular information into a vectorized form. Whereas recent …
models condense essential molecular information into a vectorized form. Whereas recent …
De novo drug design through gradient-based regularized search in information-theoretically controlled latent space
Over the last decade, automatic chemical design frameworks for discovering molecules with
drug-like properties have significantly progressed. Among them, the variational autoencoder …
drug-like properties have significantly progressed. Among them, the variational autoencoder …