AIMC Topic: Kinetics

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A Three-Module Machine Learning Framework for Protein Sequence- and Temperature-Dependent / Prediction in β-Glucosidases.

ACS synthetic biology
The catalytic activity of enzymes is intricately determined by their amino acid sequences and assay conditions, particularly temperature. Navigating the complex interplay among sequence, temperature, and catalytic function is crucial for unlocking a ...

Interpretating SPR-Derived Reaction Kinetics via Self-Organizing Maps for Diagnostic Applications.

ACS sensors
Biosensors emerge as promising, cost-effective infectious disease diagnostics in resource-limited settings, requiring neither laboratory infrastructure nor specialized personnel. Surface plasmon resonance (SPR)-based biosensors remain preeminent for ...

Neural CRNs: A Natural Implementation of Learning in Chemical Reaction Networks.

ACS synthetic biology
Molecular circuits capable of autonomous learning could unlock novel applications in fields such as bioengineering and synthetic biology. To this end, existing chemical implementations of neural computing have primarily relied on emulating discrete-l...

Kinetic and Mechanistic Discrepancies of Single/Dual-Atom Nanozymes Drive a Triple-Channel Sensing Array for Machine Learning-Assisted Antioxidant Discrimination.

Analytical chemistry
Current colorimetric sensing arrays for antioxidant detection often struggle with discrimination due to cross-reactive signals from individual nanozymes. These signals are typically modulated by external factors such as pH or chromogenic substrates, ...

Explainable machine learning for comprehensive characterization of poly (6-(Ethoxybenzothiazole acrylamide)) resin for removal of Th(IV), As(V), and Hg(II) ions from aqueous solution.

Environmental geochemistry and health
Adsorption is a promising technique with significant potential for water purification. In this context, the present study examines the adsorption efficiency of poly(6-(ethoxybenzothiazole acrylamide) (PEBTA) in removing high-valent metal ions from aq...

The chronODE framework for modelling multi-omic time series with ordinary differential equations and machine learning.

Nature communications
Many genome-wide studies capture isolated moments in cell differentiation or organismal development. Conversely, longitudinal studies provide a more direct way to study these kinetic processes. Here, we present an approach for modeling gene-expressio...

Advancing Whole-Cell Biosensors: Kinetics-Dependent Metabolic SERS Analytics for Pollutant Differentiation and Quantification.

Analytical chemistry
Whole-cell biosensors (WCBs), which detect targeting analytes through cellular responses, have become powerful tools for environmental monitoring. However, existing WCBs often rely on the single-channel low-dimension signal outputs (e.g., fluorescenc...

Machine Learning Predicts Drug Release Profiles and Kinetic Parameters Based on Tablets' Formulations.

The AAPS journal
Direct compression (DC) remains a popular manufacturing technology for producing solid dosage forms. However, the formulation optimisation is a laborious process, costly and time-consuming. The aim of this study was to determine whether machine learn...

Biomacromolecular engineering of redox-active chitosan-polypyrrole-clay hybrid materials for machine learning-assisted desulfurization and supercapacitor application.

International journal of biological macromolecules
The bentonite chitosan polypyrrole (Bent-CS-PPy) composite was engineered as a multifunctional material with dual capabilities: the efficient adsorption of dibenzothiophene (DBT) from model fuel and application as an electrode in electrochemical ener...

Self-driving microscopy detects the onset of protein aggregation and enables intelligent Brillouin imaging.

Nature communications
The process of protein aggregation, central to neurodegenerative diseases like Huntington's, is challenging to study due to its unpredictable nature and relatively rapid kinetics. Understanding its biomechanics is crucial for unraveling its role in d...