AI Medical Compendium Journal:
Chemical science

Showing 1 to 10 of 55 articles

Accurate prediction of the kinetic sequence of physicochemical states using generative artificial intelligence.

Chemical science
Capturing the time evolution and predicting kinetic sequences of states of physicochemical systems present significant challenges due to the precision and computational effort required. In this study, we demonstrate that 'Generative Pre-trained Trans...

Chemometric sensing of stereoisomeric compound mixtures with a redox-responsive optical probe.

Chemical science
The analysis of mixtures of chiral compounds is a common task in academic and industrial laboratories typically achieved by laborious and time-consuming physical separation of the individual stereoisomers to allow interference-free quantification, fo...

PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences.

Chemical science
The last few years have seen the development of numerous deep learning-based protein-ligand docking methods. They offer huge promise in terms of speed and accuracy. However, despite claims of state-of-the-art performance in terms of crystallographic ...

Designing solvent systems using self-evolving solubility databases and graph neural networks.

Chemical science
Designing solvent systems is key to achieving the facile synthesis and separation of desired products from chemical processes, so many machine learning models have been developed to predict solubilities. However, breakthroughs are needed to address d...

Keeping an "eye" on the experiment: computer vision for real-time monitoring and control.

Chemical science
This work presents a generalizable computer vision (CV) and machine learning model that is used for automated real-time monitoring and control of a diverse array of workup processes. Our system simultaneously monitors multiple physical outputs (, liq...

Transfer learning for a foundational chemistry model.

Chemical science
Data-driven chemistry has garnered much interest concurrent with improvements in hardware and the development of new machine learning models. However, obtaining sufficiently large, accurate datasets of a desired chemical outcome for data-driven chemi...

Natural product drug discovery in the artificial intelligence era.

Chemical science
Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the ...

Organocatalytic enantioselective S1-type dehydrative nucleophilic substitution: access to bis(indolyl)methanes bearing quaternary carbon stereocenters.

Chemical science
A highly general and straightforward approach to access chiral bis(indolyl)methanes (BIMs) bearing quaternary stereocenters has been realized enantioconvergent dehydrative nucleophilic substitution. A broad range of 3,3'-, 3,2'- and 3,1'-BIMs were o...

Predicting glycosylation stereoselectivity using machine learning.

Chemical science
Predicting the stereochemical outcome of chemical reactions is challenging in mechanistically ambiguous transformations. The stereoselectivity of glycosylation reactions is influenced by at least eleven factors across four chemical participants and t...

Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors.

Chemical science
Accurate and rapid evaluation of whether substrates can undergo the desired the transformation is crucial and challenging for both human knowledge and computer predictions. Despite the potential of machine learning in predicting chemical reactivity s...