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Proteins

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DeePNAP: A Deep Learning Method to Predict Protein-Nucleic Acid Binding Affinity from Their Sequences.

Journal of chemical information and modeling
Predicting the protein-nucleic acid (PNA) binding affinity solely from their sequences is of paramount importance for the experimental design and analysis of PNA interactions (PNAIs). A large number of currently developed models for binding affinity ...

Machine learning to predict continuous protein properties from binary cell sorting data and map unseen sequence space.

Proceedings of the National Academy of Sciences of the United States of America
Proteins are a diverse class of biomolecules responsible for wide-ranging cellular functions, from catalyzing reactions to recognizing pathogens. The ability to evolve proteins rapidly and inexpensively toward improved properties is a common objectiv...

Heterogeneous sampled subgraph neural networks with knowledge distillation to enhance double-blind compound-protein interaction prediction.

Structure (London, England : 1993)
Identifying binding compounds against a target protein is crucial for large-scale virtual screening in drug development. Recently, network-based methods have been developed for compound-protein interaction (CPI) prediction. However, they are difficul...

Identification of Protein-Protein Interaction Associated Functions Based on Gene Ontology.

The protein journal
Protein-protein interactions (PPIs) involve the physical or functional contact between two or more proteins. Generally, proteins that can interact with each other always have special relationships. Some previous studies have reported that gene ontolo...

A Comprehensive Review on Machine Learning Techniques for Protein Family Prediction.

The protein journal
Proteomics is a field dedicated to the analysis of proteins in cells, tissues, and organisms, aiming to gain insights into their structures, functions, and interactions. A crucial aspect within proteomics is protein family prediction, which involves ...

Building Representation Learning Models for Antibody Comprehension.

Cold Spring Harbor perspectives in biology
Antibodies are versatile proteins with both the capacity to bind a broad range of targets and a proven track record as some of the most successful therapeutics. However, the development of novel antibody therapeutics is a lengthy and costly process. ...

Target-specific novel molecules with their recipe: Incorporating synthesizability in the design process.

Journal of molecular graphics & modelling
Application of Artificial intelligence (AI) in drug discovery has led to several success stories in recent times. While traditional methods mostly relied upon screening large chemical libraries for early-stage drug-design, de novo design can help ide...

HormoNet: a deep learning approach for hormone-drug interaction prediction.

BMC bioinformatics
Several experimental evidences have shown that the human endogenous hormones can interact with drugs in many ways and affect drug efficacy. The hormone drug interactions (HDI) are essential for drug treatment and precision medicine; therefore, it is ...

Automated model building and protein identification in cryo-EM maps.

Nature
Interpreting electron cryo-microscopy (cryo-EM) maps with atomic models requires high levels of expertise and labour-intensive manual intervention in three-dimensional computer graphics programs. Here we present ModelAngelo, a machine-learning approa...

Deep learning in modeling protein complex structures: From contact prediction to end-to-end approaches.

Current opinion in structural biology
Protein-protein interactions play crucial roles in many biological processes. Traditionally, protein complex structures are normally built by protein-protein docking. With the rapid development of artificial intelligence and its great success in mono...