AI Medical Compendium Journal:
Biochemistry

Showing 1 to 10 of 10 articles

Predicting Protein Function in the AI and Big Data Era.

Biochemistry
It is an exciting time for researchers working to link proteins to their functions. Most techniques for extracting functional information from genomic sequences were developed several years ago, with major progress driven by the availability of big d...

Fine-Tuned Deep Transfer Learning Models for Large Screenings of Safer Drugs Targeting Class A GPCRs.

Biochemistry
G protein-coupled receptors (GPCRs) remain a focal point of research due to their critical roles in cell signaling and their prominence as drug targets. However, directly linking drug efficacy to the receptor-mediated activation of specific intracell...

Targeting Bacterial RNA Polymerase: Harnessing Simulations and Machine Learning to Design Inhibitors for Drug-Resistant Pathogens.

Biochemistry
The increase in antimicrobial resistance presents a major challenge in treating bacterial infections, underscoring the need for innovative drug discovery approaches and novel inhibitors. Bacterial RNA polymerase (RNAP) has emerged as a crucial target...

Adaptive Workflows of Machine Learning Illuminate the Sequential Operation Mechanism of the TAK1's Allosteric Network.

Biochemistry
Allostery is a fundamental mechanism driving biomolecular processes that holds significant therapeutic concern. Our study rigorously investigates how two distinct machine-learning algorithms uniquely classify two already close-to-active DFG-in states...

AmorProt: Amino Acid Molecular Fingerprints Repurposing-Based Protein Fingerprint.

Biochemistry
As protein therapeutics play an important role in almost all medical fields, numerous studies have been conducted on proteins using artificial intelligence. Artificial intelligence has enabled data-driven predictions without the need for expensive ex...

Prioritizing Pain-Associated Targets with Machine Learning.

Biochemistry
While hundreds of genes have been associated with pain, much of the molecular mechanisms of pain remain unknown. As a result, current analgesics are limited to few clinically validated targets. Here, we trained a machine learning (ML) ensemble model ...

The Rational Discovery of a Tau Aggregation Inhibitor.

Biochemistry
Intrinsically disordered proteins play vital roles in biology, and their dysfunction contributes to many major disease states. These proteins remain challenging targets for rational ligand discovery or drug design because they are highly dynamic and ...

Sequence-Based Prediction of Cysteine Reactivity Using Machine Learning.

Biochemistry
As one of the most intrinsically reactive amino acids, cysteine carries a variety of important biochemical functions, including catalysis and redox regulation. Discovery and characterization of cysteines with heightened reactivity will help annotate ...