A systematic survey in geometric deep learning for structure-based drug design
Structure-based drug design (SBDD) utilizes the three-dimensional geometry of proteins to
identify potential drug candidates. Traditional methods, grounded in physicochemical …
identify potential drug candidates. Traditional methods, grounded in physicochemical …
[HTML][HTML] Protein representations: Encoding biological information for machine learning in biocatalysis
D Harding-Larsen, J Funk, NG Madsen… - Biotechnology …, 2024 - Elsevier
Enzymes offer a more environmentally friendly and low-impact solution to conventional
chemistry, but they often require additional engineering for their application in industrial …
chemistry, but they often require additional engineering for their application in industrial …
From proteins to ligands: decoding deep learning methods for binding affinity prediction
Accurate in silico prediction of protein–ligand binding affinity is important in the early stages
of drug discovery. Deep learning-based methods exist but have yet to overtake more …
of drug discovery. Deep learning-based methods exist but have yet to overtake more …
Binding affinity predictions with hybrid quantum-classical convolutional neural networks
Central in drug design is the identification of biomolecules that uniquely and robustly bind to
a target protein, while minimizing their interactions with others. Accordingly, precise binding …
a target protein, while minimizing their interactions with others. Accordingly, precise binding …
Fusion-based deep learning architecture for detecting drug-target binding affinity using target and drug sequence and structure
Accurately predicting drug-target binding affinity plays a vital role in accelerating drug
discovery. Many computational approaches have been proposed due to costly and time …
discovery. Many computational approaches have been proposed due to costly and time …
Geometry-complete perceptron networks for 3d molecular graphs
A Morehead, J Cheng - Bioinformatics, 2024 - academic.oup.com
Motivation The field of geometric deep learning has recently had a profound impact on
several scientific domains such as protein structure prediction and design, leading to …
several scientific domains such as protein structure prediction and design, leading to …
Knowledge graph convolutional network with heuristic search for drug repositioning
Drug repositioning is a strategy of repurposing approved drugs for treating new indications,
which can accelerate the drug discovery process, reduce development costs, and lower the …
which can accelerate the drug discovery process, reduce development costs, and lower the …
Prediction of protein–ligand binding affinity via deep learning models
H Wang - Briefings in Bioinformatics, 2024 - academic.oup.com
Accurately predicting the binding affinity between proteins and ligands is crucial in drug
screening and optimization, but it is still a challenge in computer-aided drug design. The …
screening and optimization, but it is still a challenge in computer-aided drug design. The …
Multi-task bioassay pre-training for protein-ligand binding affinity prediction
Protein–ligand binding affinity (PLBA) prediction is the fundamental task in drug discovery.
Recently, various deep learning-based models predict binding affinity by incorporating the …
Recently, various deep learning-based models predict binding affinity by incorporating the …
Innovative Mamba and graph transformer framework for superior protein-ligand affinity prediction
K Han, C Shi, Z Wang, W Liu, Z Li, Z Wang, L Lei… - Microchemical …, 2024 - Elsevier
Accurate measurement of the affinity between drug targets is of great importance in the field
of drug discovery, as it provides significant information about drug action. However, the …
of drug discovery, as it provides significant information about drug action. However, the …