AIMC Topic: Machine Learning

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Machine Learning of Molecular Dynamics Simulations Provides Insights into the Modulation of Viral Capsid Assembly.

Journal of chemical information and modeling
An effective approach in the development of novel antivirals is to target the assembly of viral capsids by using capsid assembly modulators (CAMs). CAMs targeting hepatitis B virus (HBV) have two major modes of function: they can either accelerate nu...

Machine Learning Prediction of Optical Properties of Coumarin Derivatives Using Gaussian-Weighted Graph Convolution and Subgraph Modular Input.

Journal of chemical information and modeling
Coumarin derivatives have been widely developed and utilized as chromophores and fluorophores in various research fields. In this study, we constructed an experimental database of the optical properties─specifically, absorption and emission wavelengt...

Multi-Knowledge Graph and Multi-View Entity Feature Learning for Predicting Drug-Related Side Effects.

Journal of chemical information and modeling
Computational prediction of potential drug side effects plays a crucial role in reducing health risks for clinical patients and accelerating drug development. Recent methods have constructed heterogeneous graphs that represent drugs and their side ef...

Atomic Energy Accuracy of Neural Network Potentials: Harnessing Pretraining and Transfer Learning.

Journal of chemical information and modeling
Machine learning-based interatomic potentials (MLIPs) have transformed the prediction of potential energy surfaces (PESs), achieving accuracy comparable to calculations. However, atomic energy predictions, often assumed to lack physical meaning, rem...

Predictive factors of hypoglycemia in type 2 diabetes: a prospective study using machine learning.

Scientific reports
Hypoglycemia is a serious complication in individuals with type 2 diabetes mellitus. Identifying who is most at risk remains challenging due to the non-linear relationships between hypoglycemia and its associated risk factors. The objective of this s...

Performance of machine learning models for predicting high-severity symptoms in multiple sclerosis.

Scientific reports
Current care in multiple sclerosis (MS) primarily relies on infrequently obtained data such as magnetic resonance imaging, clinical laboratory tests or clinical history, resulting in subtle changes that may occur between visits being missed. Mobile t...

A predictive model for hospital death in cancer patients with acute pulmonary embolism using XGBoost machine learning and SHAP interpretation.

Scientific reports
The prediction of in-hospital mortality in cancer patients with acute pulmonary embolism (APE) remains a significant clinical challenge. This study aimed to develop and validate a machine learning model using XGBoost to predict in-hospital mortality ...

Machine learning-based analysis on factors influencing blood heavy metal concentrations in the Korean CHildren's ENvironmental health Study (Ko-CHENS).

The Science of the total environment
Heavy metal concentration in pregnant women affects neurocognitive and behavioral development of their infants and children. The majority of existing research focusing on pregnant women's heavy metal concentration has considered individual environmen...

Machine learning reveals novel compound for the improved production of chitooligosaccharides in Escherichia coli.

New biotechnology
In order to improve predictability of outcome and reduce costly rounds of trial-and-error, machine learning models have been of increasing importance in the field of synthetic biology. Besides applications in predicting genome annotation, process par...

First nomogram for predicting interstitial lung disease and pulmonary arterial hypertension in SLE: a machine learning approach.

Respiratory research
BACKGROUND: Interstitial lung disease (ILD) and pulmonary arterial hypertension (PAH) are severe, life-threatening complications of systemic lupus erythematosus (SLE). Early identification of high-risk patients remains challenging due to the lack of ...