Toward the third generation artificial intelligence
There have been two competing paradigms in artificial intelligence (AI) development ever
since its birth in 1956, ie, symbolism and connectionism (or sub-symbolism). While …
since its birth in 1956, ie, symbolism and connectionism (or sub-symbolism). While …
[PDF][PDF] Deep learning in protein structural modeling and design
Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and
powerful computational resources, impacting many fields, including protein structural …
powerful computational resources, impacting many fields, including protein structural …
Learning from protein structure with geometric vector perceptrons
Learning on 3D structures of large biomolecules is emerging as a distinct area in machine
learning, but there has yet to emerge a unifying network architecture that simultaneously …
learning, but there has yet to emerge a unifying network architecture that simultaneously …
Protein representation learning by geometric structure pretraining
Learning effective protein representations is critical in a variety of tasks in biology such as
predicting protein function or structure. Existing approaches usually pretrain protein …
predicting protein function or structure. Existing approaches usually pretrain protein …
Multi-scale representation learning on proteins
VR Somnath, C Bunne… - Advances in Neural …, 2021 - proceedings.neurips.cc
Proteins are fundamental biological entities mediating key roles in cellular function and
disease. This paper introduces a multi-scale graph construction of a protein–HoloProt …
disease. This paper introduces a multi-scale graph construction of a protein–HoloProt …
Improved protein structure refinement guided by deep learning based accuracy estimation
We develop a deep learning framework (DeepAccNet) that estimates per-residue accuracy
and residue-residue distance signed error in protein models and uses these predictions to …
and residue-residue distance signed error in protein models and uses these predictions to …
Structure-based protein design with deep learning
S Ovchinnikov, PS Huang - Current opinion in chemical biology, 2021 - Elsevier
Since the first revelation of proteins functioning as macromolecular machines through their
three dimensional structures, researchers have been intrigued by the marvelous ways the …
three dimensional structures, researchers have been intrigued by the marvelous ways the …
Shape-based generative modeling for de novo drug design
In this work, we propose a machine learning approach to generate novel molecules starting
from a seed compound, its three-dimensional (3D) shape, and its pharmacophoric features …
from a seed compound, its three-dimensional (3D) shape, and its pharmacophoric features …
Atom3d: Tasks on molecules in three dimensions
Computational methods that operate on three-dimensional molecular structure have the
potential to solve important questions in biology and chemistry. In particular, deep neural …
potential to solve important questions in biology and chemistry. In particular, deep neural …