AIMC Topic: Proteins

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DeepTargetClass: a web-based platform for predicting protein target classes of small molecules.

Journal of computer-aided molecular design
The identification of protein target classes is a key step in drug discovery, as it enables prioritization of screening campaigns and supports target-based drug repurpose. In this study, we developed a deep-learning pipeline based on a multilayer per...

A scalable equivariant graph network framework for precise protein function prediction.

Genome biology
BACKGROUND: Protein function research helps in understanding the complex biological processes that occur within cells. However, the intricate nature of protein structures and functions, along with the rapid growth of protein sequence data, presents a...

Rapid and Accurate Protein Structure Database Search Using Inverse Folding Model and Contrastive Learning.

Journal of chemical information and modeling
Protein structure database search has become increasingly challenging due to the growing number of experimental and computational structures. We introduce mTM-align2, a novel two-step approach for rapid and accurate protein structure database search....

Proximity Effects Leveraged in Ligand-Directed Chemical Labeling of Natural Proteins under Live Conditions.

Accounts of chemical research
ConspectusCovalent chemical labeling of proteins is central to chemical biology, offering functional modifications beyond imaging probes. While genetically engineered systems using self-labeling tags (e.g., HaloTag, SNAP-Tag) or genetic code expansio...

An interpretable geometric graph neural network for enhancing the generalizability of drug-target interaction prediction.

BMC biology
BACKGROUND: Accurate prediction of drug-target interactions (DTIs) is essential for advancing drug discovery. Although numerous computational methods have been proposed, many exhibit limited generalization, particularly when dealing with unseen drugs...

Accurate prediction of protein-ATP binding sites based on a protein pretrained large language model and a fractional-order convolutional neural network.

Scientific reports
ATP, a high-energy phosphate compound also known as adenosine triphosphate, serves as a direct energy source for living organisms. Proteins, composed of amino acids, are fundamental macromolecules and essential building blocks of life. The interactio...

Artificial intelligence in protein-based detection and inhibition of AMR pathways.

Journal of computer-aided molecular design
Antimicrobial Resistance (AMR) is a global concern demanding high-throughput and precise AMR surveillance strategies. This review provides a comprehensive list of Artificial Intelligence (AI) driven frameworks widely employed in the early detection, ...

Benchmarking Sequence-Based Compound-Protein Interaction Prediction through Constructing a Debiased Data Set CDPN.

Journal of chemical information and modeling
Accurate prediction of compound-protein interactions (CPIs) is critical for drug discovery, but existing data sets often suffer from biases that hinder model generalization. Here, we first highlighted that over-represented molecular scaffolds and imb...

High-accuracy protein complex structure modeling based on sequence-derived structure complementarity.

Nature communications
In living organisms, proteins perform key functions required for life activities by interacting to form complexes. Determining the protein complex structure is crucial for understanding and mastering biological functions. Although AlphaFold2 makes a ...

A multi-geometric graph fusion network for protein-ligand affinity prediction.

Physical chemistry chemical physics : PCCP
Protein-ligand binding affinity prediction plays a crucial role in drug discovery. While recent works use two-dimensional graph neural networks to improve affinity prediction, we find that the three-dimensional geometric information of proteins and l...