Graph neural networks for materials science and chemistry
Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …
and materials science, being used to predict materials properties, accelerate simulations …
Drug discovery with explainable artificial intelligence
Deep learning bears promise for drug discovery, including advanced image analysis,
prediction of molecular structure and function, and automated generation of innovative …
prediction of molecular structure and function, and automated generation of innovative …
State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis
We investigated the effect of different training scenarios on predicting the (retro) synthesis of
chemical compounds using text-like representation of chemical reactions (SMILES) and …
chemical compounds using text-like representation of chemical reactions (SMILES) and …
Improving few-and zero-shot reaction template prediction using modern hopfield networks
Finding synthesis routes for molecules of interest is essential in the discovery of new drugs
and materials. To find such routes, computer-assisted synthesis planning (CASP) methods …
and materials. To find such routes, computer-assisted synthesis planning (CASP) methods …
Molecule edit graph attention network: modeling chemical reactions as sequences of graph edits
M Sacha, M Błaz, P Byrski… - Journal of Chemical …, 2021 - ACS Publications
The central challenge in automated synthesis planning is to be able to generate and predict
outcomes of a diverse set of chemical reactions. In particular, in many cases, the most likely …
outcomes of a diverse set of chemical reactions. In particular, in many cases, the most likely …
Recent advances in deep learning for retrosynthesis
Retrosynthesis is the cornerstone of organic chemistry, providing chemists in material and
drug manufacturing access to poorly available and brand‐new molecules. Conventional rule …
drug manufacturing access to poorly available and brand‐new molecules. Conventional rule …
Spatial–temporal complex graph convolution network for traffic flow prediction
Traffic flow prediction remains an ongoing hot topic in the field of Intelligent Transportation
System. The state-of-the-art traffic flow prediction models can effectively extract both spatial …
System. The state-of-the-art traffic flow prediction models can effectively extract both spatial …
Explainable machine learning for property predictions in compound optimization: miniperspective
R Rodríguez-Pérez, J Bajorath - Journal of medicinal chemistry, 2021 - ACS Publications
The prediction of compound properties from chemical structure is a main task for machine
learning (ML) in medicinal chemistry. ML is often applied to large data sets in applications …
learning (ML) in medicinal chemistry. ML is often applied to large data sets in applications …
Artificial intelligence in reaction prediction and chemical synthesis
V Venkatasubramanian, V Mann - Current Opinion in Chemical Engineering, 2022 - Elsevier
Recent years have seen a sudden spurt in the use of artificial intelligence (AI) methods for
computational reaction modeling and prediction. Given the diversity of the techniques, we …
computational reaction modeling and prediction. Given the diversity of the techniques, we …
Modern machine learning for tackling inverse problems in chemistry: molecular design to realization
The discovery of new molecules and materials helps expand the horizons of novel and
innovative real-life applications. In pursuit of finding molecules with desired properties …
innovative real-life applications. In pursuit of finding molecules with desired properties …