AIMC Topic: Quantitative Structure-Activity Relationship
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Extended Activity Cliffs-Driven Approaches on Data Splitting for the Study of Bioactivity Machine Learning Predictions.
Molecular informatics
Nov 18, 2024
The presence of Activity Cliffs (ACs) has been known to represent a challenge for QSAR modeling. With its high data dependency, Machine Learning QSAR models will be directly influenced by the activity landscape. We propose several extended similarity...
Explainable machine learning models for predicting the acute toxicity of pesticides to sheepshead minnow (Cyprinodon variegatus).
The Science of the total environment
Nov 16, 2024
A quantitative structure-activity relationship (QSAR) study was conducted on 313 pesticides to predict their acute toxicity to Sheepshead minnow (Cyprinodon variegatus) by using DRAGON descriptors. Essentials accounting for a reliable model were all ...
IMPACT OF REAL-LIFE ENVIRONMENTAL EXPOSURES ON REPRODUCTION: A contemporary review of machine learning to predict adverse pregnancy outcomes from pharmaceuticals, including DDIs.
Reproduction (Cambridge, England)
Nov 15, 2024
IN BRIEF: Clinical drug trials often do not include pregnant people due to health risks; therefore, many medications have an unknown effect on the developing fetus. Machine learning QSAR models have been used successfully to predict the fetal risk of...
Synergizing Machine Learning, Conceptual Density Functional Theory, and Biochemistry: No-Code Explainable Predictive Models for Mutagenicity in Aromatic Amines.
Journal of chemical information and modeling
Nov 11, 2024
This study synergizes machine learning (ML) with conceptual density functional theory (CDFT) to develop OECD-compliant predictive models for the mutagenic activity of aromatic amines (AAs) with a fully No-Code methodology using a comprehensive data s...
Molecular mechanism underlying effect of D93 and D289 protonation states on inhibitor-BACE1 binding: exploration from multiple independent Gaussian accelerated molecular dynamics and deep learning.
SAR and QSAR in environmental research
Nov 8, 2024
BACE1 has been regarded as an essential drug design target for treating Alzheimer's disease (AD). Multiple independent Gaussian accelerated molecular dynamics simulations (GaMD), deep learning (DL), and molecular mechanics general Born surface area (...
Small molecule inhibitors of IL-1R1/IL-1β interaction identified via transfer machine learning QSAR modelling.
International journal of biological macromolecules
Nov 7, 2024
The human interleukin-1 receptor I (IL-1R1) is a cytokine receptor recognized by interleukin 1β (IL-1β), among other cytokines. Over activation of IL-1R1 has been implicated in various inflammatory conditions. This research aims to identify small-mol...
Using the super-learner to predict the chemical acute toxicity on rats.
Journal of hazardous materials
Oct 28, 2024
With the rapid increase in the number of commercial chemicals, testing methods regarding on median lethal dose (LD) relying animal experiments face challenges such as high costs and ethical concerns. Classical quantitative structure-activity relation...
QSPR modeling to predict surface tension of psychoanaleptic drugs using the hybrid DA-SVR algorithm.
Journal of molecular graphics & modelling
Oct 23, 2024
A robust Quantitative Structure-Property Relationship (QSPR) model was presented to predict the surface tension property of psychoanaleptic (psychostimulant and antidepressant) drugs. A dataset of 112 molecules was utilized, and three feature selecti...
Machine Learning-Based Prediction of the Inhibitory Activity of Chemical Substances Against Rat and Human Cytochrome P450s.
Chemical research in toxicology
Oct 20, 2024
The prediction of cytochrome P450 inhibition by a computational (quantitative) structure-activity relationship approach using chemical structure information and machine learning would be useful for toxicity research as a simple and rapid tool. Howev...
Chemoinformatics for corrosion science: Data-driven modeling of corrosion inhibition by organic molecules.
Molecular informatics
Oct 15, 2024
This paper reviews the application of machine learning to the inhibition of corrosion by organic molecules. The methodologies considered include quantitative structure-property relationships (QSPR) and related data-driven approaches. The characterist...
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