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Structure-based drug design with geometric deep learning
C Isert, K Atz, G Schneider - Current Opinion in Structural Biology, 2023 - Elsevier
Abstract Structure-based drug design uses three-dimensional geometric information of
macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric …
macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric …
Multimodal learning with graphs
Y Ektefaie, G Dasoulas, A Noori, M Farhat… - Nature Machine …, 2023 - nature.com
Artificial intelligence for graphs has achieved remarkable success in modelling complex
systems, ranging from dynamic networks in biology to interacting particle systems in physics …
systems, ranging from dynamic networks in biology to interacting particle systems in physics …
Protst: Multi-modality learning of protein sequences and biomedical texts
Current protein language models (PLMs) learn protein representations mainly based on
their sequences, thereby well capturing co-evolutionary information, but they are unable to …
their sequences, thereby well capturing co-evolutionary information, but they are unable to …
Equibind: Geometric deep learning for drug binding structure prediction
H Stärk, O Ganea, L Pattanaik… - International …, 2022 - proceedings.mlr.press
Predicting how a drug-like molecule binds to a specific protein target is a core problem in
drug discovery. An extremely fast computational binding method would enable key …
drug discovery. An extremely fast computational binding method would enable key …
Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
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 …
ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction
J Tubiana, D Schneidman-Duhovny, HJ Wolfson - Nature Methods, 2022 - nature.com
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 …
Independent se (3)-equivariant models for end-to-end rigid protein docking
Protein complex formation is a central problem in biology, being involved in most of the cell's
processes, and essential for applications, eg drug design or protein engineering. We tackle …
processes, and essential for applications, eg drug design or protein engineering. We tackle …
Machine learning and deep learning in synthetic biology: Key architectures, applications, and challenges
MK Goshisht - ACS omega, 2024 - ACS Publications
Machine learning (ML), particularly deep learning (DL), has made rapid and substantial
progress in synthetic biology in recent years. Biotechnological applications of biosystems …
progress in synthetic biology in recent years. Biotechnological applications of biosystems …
A new age in protein design empowered by deep learning
H Khakzad, I Igashov, A Schneuing, C Goverde… - Cell Systems, 2023 - cell.com
The rapid progress in the field of deep learning has had a significant impact on protein
design. Deep learning methods have recently produced a breakthrough in protein structure …
design. Deep learning methods have recently produced a breakthrough in protein structure …