Machine learning methods for small data challenges in molecular science
Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
Geometric deep learning on molecular representations
Geometric deep learning (GDL) is based on neural network architectures that incorporate
and process symmetry information. GDL bears promise for molecular modelling applications …
and process symmetry information. GDL bears promise for molecular modelling applications …
[HTML][HTML] Highly accurate protein structure prediction with AlphaFold
Proteins are essential to life, and understanding their structure can facilitate a mechanistic
understanding of their function. Through an enormous experimental effort 1, 2, 3, 4, the …
understanding of their function. Through an enormous experimental effort 1, 2, 3, 4, the …
Applying and improving AlphaFold at CASP14
We describe the operation and improvement of AlphaFold, the system that was entered by
the team AlphaFold2 to the “human” category in the 14th Critical Assessment of Protein …
the team AlphaFold2 to the “human” category in the 14th Critical Assessment of Protein …
Before and after AlphaFold2: An overview of protein structure prediction
Three-dimensional protein structure is directly correlated with its function and its
determination is critical to understanding biological processes and addressing human …
determination is critical to understanding biological processes and addressing human …
MSA transformer
Unsupervised protein language models trained across millions of diverse sequences learn
structure and function of proteins. Protein language models studied to date have been …
structure and function of proteins. Protein language models studied to date have been …
High‐accuracy protein structure prediction in CASP14
The application of state‐of‐the‐art deep‐learning approaches to the protein modeling
problem has expanded the “high‐accuracy” category in CASP14 to encompass all targets …
problem has expanded the “high‐accuracy” category in CASP14 to encompass all targets …
De novo protein design by deep network hallucination
There has been considerable recent progress in protein structure prediction using deep
neural networks to predict inter-residue distances from amino acid sequences,–. Here we …
neural networks to predict inter-residue distances from amino acid sequences,–. Here we …
Improved protein structure prediction using potentials from deep learning
Protein structure prediction can be used to determine the three-dimensional shape of a
protein from its amino acid sequence. This problem is of fundamental importance as the …
protein from its amino acid sequence. This problem is of fundamental importance as the …
Map** the landscape of artificial intelligence applications against COVID-19
COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by
the World Health Organization, which has reported over 18 million confirmed cases as of …
the World Health Organization, which has reported over 18 million confirmed cases as of …