AI Medical Compendium Journal:
Molecules (Basel, Switzerland)

Showing 61 to 70 of 243 articles

DeepmRNALoc: A Novel Predictor of Eukaryotic mRNA Subcellular Localization Based on Deep Learning.

Molecules (Basel, Switzerland)
The subcellular localization of messenger RNA (mRNA) precisely controls where protein products are synthesized and where they function. However, obtaining an mRNA's subcellular localization through wet-lab experiments is time-consuming and expensive,...

Comparative Studies on Resampling Techniques in Machine Learning and Deep Learning Models for Drug-Target Interaction Prediction.

Molecules (Basel, Switzerland)
The prediction of drug-target interactions (DTIs) is a vital step in drug discovery. The success of machine learning and deep learning methods in accurately predicting DTIs plays a huge role in drug discovery. However, when dealing with learning algo...

A Knowledge-Graph-Based Multimodal Deep Learning Framework for Identifying Drug-Drug Interactions.

Molecules (Basel, Switzerland)
The identification of drug-drug interactions (DDIs) plays a crucial role in various areas of drug development. In this study, a deep learning framework (KGCN_NFM) is presented to recognize DDIs using coupling knowledge graph convolutional networks (K...

Molecular Toxicity Virtual Screening Applying a Quantized Computational SNN-Based Framework.

Molecules (Basel, Switzerland)
Spiking neural networks are biologically inspired machine learning algorithms attracting researchers' attention for their applicability to alternative energy-efficient hardware other than traditional computers. In the current work, spiking neural net...

Acute Stress-Induced Changes in the Lipid Composition of Cow's Milk in Healthy and Pathological Animals.

Molecules (Basel, Switzerland)
Producers of milk and dairy products have been faced with the challenge of responding to European society's demand for guaranteed animal welfare production. In recent years, measures have been taken to improve animal welfare conditions on farms and e...

DEML: Drug Synergy and Interaction Prediction Using Ensemble-Based Multi-Task Learning.

Molecules (Basel, Switzerland)
Synergistic drug combinations have demonstrated effective therapeutic effects in cancer treatment. Deep learning methods accelerate identification of novel drug combinations by reducing the search space. However, potential adverse drug-drug interacti...

Explaining Accurate Predictions of Multitarget Compounds with Machine Learning Models Derived for Individual Targets.

Molecules (Basel, Switzerland)
In drug discovery, compounds with well-defined activity against multiple targets (multitarget compounds, MT-CPDs) provide the basis for polypharmacology and are thus of high interest. Typically, MT-CPDs for polypharmacology have been discovered seren...

Structural Analysis and Classification of Low-Molecular-Weight Hyaluronic Acid by Near-Infrared Spectroscopy: A Comparison between Traditional Machine Learning and Deep Learning.

Molecules (Basel, Switzerland)
Confusing low-molecular-weight hyaluronic acid (LMWHA) from acid degradation and enzymatic hydrolysis (named LMWHA-A and LMWHA-E, respectively) will lead to health hazards and commercial risks. The purpose of this work is to analyze the structural di...

Prediction and Construction of Energetic Materials Based on Machine Learning Methods.

Molecules (Basel, Switzerland)
Energetic materials (EMs) are the core materials of weapons and equipment. Achieving precise molecular design and efficient green synthesis of EMs has long been one of the primary concerns of researchers around the world. Traditionally, advanced mate...

3D Conformational Generative Models for Biological Structures Using Graph Information-Embedded Relative Coordinates.

Molecules (Basel, Switzerland)
Developing molecular generative models for directly generating 3D conformation has recently become a hot research area. Here, an autoencoder based generative model was proposed for molecular conformation generation. A unique feature of our method is ...