AI Medical Compendium Journal:
Molecules (Basel, Switzerland)

Showing 1 to 10 of 243 articles

Enhancing Unconditional Molecule Generation via Online Knowledge Distillation of Scaffolds.

Molecules (Basel, Switzerland)
Generating new drug-like molecules is an essential aspect of drug discovery, and deep learning models significantly accelerate this process. Language models have demonstrated great potential in generating novel and realistic SMILES representations of...

Seeking Correlation Among Porin Permeabilities and Minimum Inhibitory Concentrations Through Machine Learning: A Promising Route to the Essential Molecular Descriptors.

Molecules (Basel, Switzerland)
Developing effective antibiotics against Gram-negative bacteria remains challenging due to their protective outer membrane. With this study, we investigated the relationship between antibiotic permeation through the OmpF porin of and antimicrobial e...

Application of Machine Learning for FOS/TAC Soft Sensing in Bio-Electrochemical Anaerobic Digestion.

Molecules (Basel, Switzerland)
This study explores the application of various machine learning (ML) models for the real-time prediction of the FOS/TAC ratio in microbial electrolysis cell anaerobic digestion (MEC-AD) systems using data collected during a 160-day trial treating bre...

Beef Traceability Between China and Argentina Based on Various Machine Learning Models.

Molecules (Basel, Switzerland)
Beef, as a nutrient-rich food, is widely favored by consumers. The production region significantly influences the nutritional value and quality of beef. However, current methods for tracing the origin of beef are still under development, necessitatin...

Machine Learning Tool for New Selective Serotonin and Serotonin-Norepinephrine Reuptake Inhibitors.

Molecules (Basel, Switzerland)
Depression, a serious mood disorder, affects about 5% of the population. Currently, there are two groups of antidepressants that are the first-line treatment for depressive disorder: selective serotonin reuptake inhibitors and serotonin-norepinephrin...

Unlocking Antimicrobial Peptides: In Silico Proteolysis and Artificial Intelligence-Driven Discovery from Cnidarian Omics.

Molecules (Basel, Switzerland)
Overcoming the growing challenge of antimicrobial resistance (AMR), which affects millions of people worldwide, has driven attention for the exploration of marine-derived antimicrobial peptides (AMPs) for innovative solutions. Cnidarians, such as cor...

Machine Learning-Based Spectral Analyses for Cultivar Identification.

Molecules (Basel, Switzerland)
is a plant species with high cultural and biological relevance. Besides being used as an ornamental plant species, has relevant biological properties. Due to hybridization, thousands of cultivars are known, and their accurate identification is mand...

MCF-DTI: Multi-Scale Convolutional Local-Global Feature Fusion for Drug-Target Interaction Prediction.

Molecules (Basel, Switzerland)
Predicting drug-target interactions (DTIs) is a crucial step in the development of new drugs and drug repurposing. In this paper, we propose a novel drug-target prediction model called MCF-DTI. The model utilizes the SMILES representation of drugs an...

Deep Learning Approaches for the Prediction of Protein Functional Sites.

Molecules (Basel, Switzerland)
Knowing which residues of a protein are important for its function is of paramount importance for understanding the molecular basis of this function and devising ways of modifying it for medical or biotechnological applications. Due to the difficulty...

Study on SHP2 Conformational Transition and Structural Characterization of Its High-Potency Allosteric Inhibitors by Molecular Dynamics Simulations Combined with Machine Learning.

Molecules (Basel, Switzerland)
The src-homology 2 domain-containing phosphatase 2 (SHP2) is a human cytoplasmic protein tyrosine phosphatase that plays a crucial role in cellular signal transduction. Aberrant activation and mutations of SHP2 are associated with tumor growth and im...