AIMC Topic: Machine Learning

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Meta-Learning Enables Complex Cluster-Specific Few-Shot Binding Affinity Prediction for Protein-Protein Interactions.

Journal of chemical information and modeling
Predicting protein-protein interaction (PPI) binding affinities in unseen protein complex clusters is essential for elucidating complex protein interactions and for the targeted screening of peptide- or protein-based drugs. We introduce MCGLPPI++, a ...

PathInHydro, a Set of Machine Learning Models to Identify Unbinding Pathways of Gas Molecules in [NiFe] Hydrogenases.

Journal of chemical information and modeling
Machine learning (ML) is a powerful tool for the automated data analysis of molecular dynamics (MD) simulations. Recent studies showed that ML models can be used to identify protein-ligand unbinding pathways and understand the underlying mechanism. T...

Polymer Biodegradation in Aquatic Environments: A Machine Learning Model Informed by Meta-Analysis of Structure-Biodegradation Relationships.

Environmental science & technology
Polymers are widely produced and contribute significantly to environmental pollution due to their low recycling rates and persistence in natural environments. Biodegradable polymers, while promising for reducing environmental impact, account for less...

Interpretable Machine Learning Models Delivering a New Perspective for the Reaction Mechanism between Organic Pollutants and Oxidative Radicals.

Environmental science & technology
Machine learning (ML) is expected to bring new insights into the impact of organic structures on the reaction mechanisms in reactive oxygen species oxidation. However, understanding the underlying chemical mechanisms still faces challenges due to the...

Seed Protein Content Estimation with Bench-Top Hyperspectral Imaging and Attentive Convolutional Neural Network Models.

Sensors (Basel, Switzerland)
Wheat is a globally cultivated cereal crop with substantial protein content present in its seeds. This research aimed to develop robust methods for predicting seed protein concentration in wheat seeds using bench-top hyperspectral imaging in the visi...

Innovative Approaches in Regulatory Affairs: Leveraging Artificial Intelligence and Machine Learning for Efficient Compliance and Decision-Making.

The AAPS journal
Artificial Intelligence (AI) and AI-driven technologies are transforming industries across the board, with the pharmaceutical sector emerging as a frontrunner beneficiary. This article explores the growing impact of AI and Machine Learning (ML) withi...

Prediction of late-onset depression in the elderly Korean population using machine learning algorithms.

Scientific reports
Late-onset depression (LOD) refers to depression that newly appears in elderly individuals without prior depression episodes. Predicting future depression is crucial for mitigating the risk of major depression in prospective patients. This study aims...

Leveraging machine learning algorithms to forecast delayed cerebral ischemia following subarachnoid hemorrhage: a systematic review and meta-analysis of 5,115 participants.

Neurosurgical review
It is feasible to predict delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH) using Artificial intelligence (AI) algorithms, which may offer significant improvements in early diagnosis and patient management. This systemat...

Rapid detection of drug abuse via tear analysis using surface enhanced Raman spectroscopy and machine learning.

Scientific reports
With the growing global challenge of drug abuse, there is an urgent need for rapid, accurate, and cost-effective drug detection methods. This study introduces an innovative approach to drug abuse screening by quickly detecting ephedrine (EPH) in tear...