AIMC Topic: Machine Learning

Clear Filters Showing 29021 to 29030 of 34417 articles

Including Physics-Informed Atomization Constraints in Neural Networks for Reactive Chemistry.

Journal of chemical information and modeling
Machine learning interatomic potentials (MLIPs) have emerged as powerful tools for investigating atomistic systems with high accuracy and a relatively low computational cost. However, a common and unaddressed challenge with many current neural networ...

IFPTML Multi-Output Model for Anti-Retroviral Compounds Including the Drug Structure and Target Protein Sequence Information.

Journal of chemical information and modeling
Retroviruses such as HIV cause significant diseases in humans and other organisms, making the discovery of antiretroviral (ARV) drugs a critical priority. While databases like ChEMBL contain valuable information, their complexity poses challenges. Th...

A Database for Large-Scale Docking and Experimental Results.

Journal of chemical information and modeling
The rapid expansion of readily accessible compounds over the past six years has transformed molecular docking, improving hit rates and affinities. While many millions of molecules may score well in a docking campaign, the results are rarely fully sha...

KNDM: A Knowledge Graph Transformer and Node Category Sensitive Contrastive Learning Model for Drug and Microbe Association Prediction.

Journal of chemical information and modeling
It has been proven that the microbiome in human bodies can promote or inhibit the treatment effects of the drugs by affecting their toxicities and activities. Therefore, identifying drug-related microbes helps in understanding how drugs exert their f...

Unraveling Disease-Associated PIWI-Interacting RNAs with a Contrastive Learning Methods.

Journal of chemical information and modeling
PIWI-interacting RNAs (piRNAs) are a class of small, non-coding RNAs predominantly expressed in the germ cells of animals and play a crucial role in maintaining genomic integrity, mediating transposon suppression, and ensuring gene stability. Beyond ...

Augmented and Programmatically Optimized LLM Prompts Reduce Chemical Hallucinations.

Journal of chemical information and modeling
Utilizing large Language models (LLMs) for handling scientific information comes with risk of the outputs not matching expectations, commonly called hallucinations. To fully utilize LLMs in research requires improving their accuracy, avoiding halluci...

Data-Driven Insights into Porphyrin Geometry: Interpretable AI for Non-Planarity and Aromaticity Analyses.

Journal of chemical information and modeling
Porphyrins are involved in numerous and very different chemical and biological processes, due to the sensitivity of their application-relevant properties to subtle structural changes. Applying modern machine learning methodology is very appealing for...

High throughput recurrent pregnancy loss screening: urine metabolic fingerprints LDI-MS and machine learning.

The Analyst
Infertility is a significant challenge faced by many families worldwide, with recurrent pregnancy loss (RPL) being a prevalent cause of infertility among women. This condition causes immense emotional and physical distress for affected individuals an...

Machine learning-assisted surface-enhanced raman spectroscopy for the rapid determination of the glutathione redox ratio.

The Analyst
Rapid and accurate detection of glutathione in its reduced (GSH) and oxidized (GSSG) forms is essential for monitoring oxidative stress in biological systems. Oxidative stress is a key indicator of various diseases, and glutathione plays a vital role...