Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery
W Wilman, S Wróbel, W Bielska… - Briefings in …, 2022 - academic.oup.com
Antibodies are versatile molecular binders with an established and growing role as
therapeutics. Computational approaches to developing and designing these molecules are …
therapeutics. Computational approaches to developing and designing these molecules are …
Critical assessment of methods for predicting the 3D structure of proteins and protein complexes
SJ Wodak, S Vajda, MF Lensink… - Annual review of …, 2023 - annualreviews.org
Advances in a scientific discipline are often measured by small, incremental steps. In this
review, we report on two intertwined disciplines in the protein structure prediction field …
review, we report on two intertwined disciplines in the protein structure prediction field …
Hierarchical graph learning for protein–protein interaction
Abstract Protein-Protein Interactions (PPIs) are fundamental means of functions and
signalings in biological systems. The massive growth in demand and cost associated with …
signalings in biological systems. The massive growth in demand and cost associated with …
ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction
Predicting the functional sites of a protein from its structure, such as the binding sites of small
molecules, other proteins or antibodies, sheds light on its function in vivo. Currently, two …
molecules, other proteins or antibodies, sheds light on its function in vivo. Currently, two …
DeepRank-GNN: a graph neural network framework to learn patterns in protein–protein interfaces
Motivation Gaining structural insights into the protein–protein interactome is essential to
understand biological phenomena and extract knowledge for rational drug design or protein …
understand biological phenomena and extract knowledge for rational drug design or protein …
Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins
Metal ions are essential cofactors for many proteins and play a crucial role in many
applications such as enzyme design or design of protein-protein interactions because they …
applications such as enzyme design or design of protein-protein interactions because they …
Accelerating antibody discovery and design with artificial intelligence: Recent advances and prospects
G Bai, C Sun, Z Guo, Y Wang, X Zeng, Y Su… - Seminars in Cancer …, 2023 - Elsevier
Therapeutic antibodies are the largest class of biotherapeutics and have been successful in
treating human diseases. However, the design and discovery of antibody drugs remains …
treating human diseases. However, the design and discovery of antibody drugs remains …
Open-source machine learning in computational chemistry
A Hagg, KN Kirschner - Journal of Chemical Information and …, 2023 - ACS Publications
The field of computational chemistry has seen a significant increase in the integration of
machine learning concepts and algorithms. In this Perspective, we surveyed 179 open …
machine learning concepts and algorithms. In this Perspective, we surveyed 179 open …
Recent advances in predicting protein–protein interactions with the aid of artificial intelligence algorithms
Protein–protein interactions (PPIs) are essential in the regulation of biological functions and
cell events, therefore understanding PPIs have become a key issue to understanding the …
cell events, therefore understanding PPIs have become a key issue to understanding the …
Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens
The design of new biomolecules able to harness immune mechanisms for the treatment of
diseases is a prime challenge for computational and simulative approaches. For instance, in …
diseases is a prime challenge for computational and simulative approaches. For instance, in …