Assessing the performance of quantum-mechanical descriptors in physicochemical and biological property prediction

Alejandra Hinostroza; А. О. Кокорин; Alexandre Tkatchenko; Leonardo Medrano Sandonas

doi:10.1039/d5dd00411j

January 1, 2025Digital DiscoveryOpen Access

Assessing the performance of quantum-mechanical descriptors in physicochemical and biological property prediction

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Key Points

The aim is to evaluate how well quantum-mechanical descriptors predict physicochemical and biological properties.
Utilized machine learning techniques for analysis
Identified structure-property and property-property relationships
Focused on quantum-mechanical molecular descriptors
Quantum descriptors significantly impact prediction accuracy
Challenges in descriptor identification affect performance
Insights suggest improvements for future drug discovery models

Implication

Machine learning assesses molecular property predictions using quantum descriptors, highlighting challenges in drug discovery.

Abstract

Machine learning (ML) approaches have drastically advanced the exploration of structure-property and property-property relationships in computer-aided drug discovery. A central challenge in this field is the identification of molecular descriptors...

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Cite This Study

Hinostroza et al. (2025) studied this question.

synapsesocial.com/papers/69706d13b6488063ad5c1e55 https://doi.org/10.1039/d5dd00411j

View Full Paper

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Assessing the performance of quantum-mechanical descriptors in physicochemical and biological property prediction

Key Points

Implication

Abstract

Discussion

Cite This Study

Assessing the performance of quantum-mechanical descriptors in physicochemical and biological property prediction

Key Points

Implication

Abstract

Discussion

Cite This Study

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