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An end-to-end framework for the prediction of protein structure and fitness from single sequence.

Nature communications
Significant research progress has been made in the field of protein structure and fitness prediction. Particularly, single-sequence-based structure prediction methods like ESMFold and OmegaFold achieve a balance between inference speed and prediction...

Protein Classes Predicted by Molecular Surface Chemical Features: Machine Learning-Assisted Classification of Cytosol and Secreted Proteins.

The journal of physical chemistry. B
Chemical structures of protein surfaces govern intermolecular interaction, and protein functions include specific molecular recognition, transport, self-assembly, etc. Therefore, the relationship between the chemical structure and protein functions p...

Holistic in silico developability assessment of novel classes of small proteins using publicly available sequence-based predictors.

Journal of computer-aided molecular design
The development of novel therapeutic proteins is a lengthy and costly process, with an average attrition rate of 91% (Thomas et al. Clinical Development Success Rates and Contributing Factors 2011-2020, 2021). To increase the probability of success a...

FitScore: a fast machine learning-based score for 3D virtual screening enrichment.

Journal of computer-aided molecular design
Enhancing virtual screening enrichment has become an urgent problem in computational chemistry, driven by increasingly large databases of commercially available compounds, without a commensurate drop in in vitro screening costs. Docking these large d...

MGNDTI: A Drug-Target Interaction Prediction Framework Based on Multimodal Representation Learning and the Gating Mechanism.

Journal of chemical information and modeling
Drug-Target Interaction (DTI) prediction facilitates acceleration of drug discovery and promotes drug repositioning. Most existing deep learning-based DTI prediction methods can better extract discriminative features for drugs and proteins, but they ...

Hierarchical Graph Capsule Networks for Molecular Function Classification With Disentangled Representations.

IEEE/ACM transactions on computational biology and bioinformatics
In biochemistry, graph structures have been widely used for modeling compounds, proteins, functional interactions, etc. A common task that divides these graphs into different categories, known as graph classification, highly relies on the quality of ...

Predicting Drug-Target Interactions Via Dual-Stream Graph Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics
Drug target interaction prediction is a crucial stage in drug discovery. However, brute-force search over a compound database is financially infeasible. We have witnessed the increasing measured drug-target interactions records in recent years, and t...

Protein ligand binding site prediction using graph transformer neural network.

PloS one
Ligand binding site prediction is a crucial initial step in structure-based drug discovery. Although several methods have been proposed previously, including those using geometry based and machine learning techniques, their accuracy is considered to ...

Evolutionary Probability and Stacked Regressions Enable Data-Driven Protein Engineering with Minimized Experimental Effort.

Journal of chemical information and modeling
Protein engineering through directed evolution and (semi)rational approaches is routinely applied to optimize protein properties for a broad range of applications in industry and academia. The multitude of possible variants, combined with limited scr...

AI-Driven Deep Learning Techniques in Protein Structure Prediction.

International journal of molecular sciences
Protein structure prediction is important for understanding their function and behavior. This review study presents a comprehensive review of the computational models used in predicting protein structure. It covers the progression from established pr...