Molecular machine learning (ML) underpins critical workflows in drug discovery, material science, and catalyst optimization ...

Models rewrite code to avoid being shut down. That’s why ‘alignment’ is a matter of such urgency.

Endogenous intracellular allosteric modulators of GPCRs remain largely unexplored, with limited binding and phenotype data available. This gap arises from the lack of robust computational methods for ...

A breakthrough study reveals carbyne's unique vibrational states within carbon nanotubes, enhancing its potential for ...

This code example demonstrates how to run through the ModusToolbox™ machine learning (ModusToolbox™-ML) development flow with PSoC™ 6 MCU, where the end user has a pre-trained neural network (NN) ...

Machine learning models were trained with all variables as inputs to classify patients likely to have favorable outcomes. For the deep neural network model, 3 hidden layers with 15 artificial neural ...

Machine-learning algorithms can now estimate the "brain age" of infants with unprecedented precision by analyzing electrical ...

Researchers developed a machine learning model that can evaluate patients' PPD risk using readily accessible clinical and demographic factors. Findings demonstrate the model's promising predictive ...

Abstract: Over the past decades, numerous practical applications of machine learning techniques have shown the potential of AI-driven and data-driven approaches in a large number of computing fields.

A research team from Kumamoto University has developed a promising deep learning model that significantly enhances the accuracy of subgraph matching—a critical task in fields ranging from drug ...

Learning microscopic properties of a material from its macroscopic ... toward materials evaluation and design via spectroscopic measurements. Herein, we exploit machine-learned vibrational ...