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Structure-Activity Relationships and Drug Design

Structure-Activity Relationships and Drug Design

Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...

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Related Experiment Video

Updated: Jan 15, 2026

Structure and Coordination Determination of Peptide-metal Complexes Using 1D and 2D 1H NMR

Structure and Coordination Determination of Peptide-metal Complexes Using 1D and 2D 1H NMR

Published on: December 16, 2013

Manifold-constrained nucleus-level denoising diffusion model for structure-based drug design.

Shengchao Liu¹, Liang Yan^2,3, Weitao Du⁴

¹Department of Electrical Engineering and Computer Sciences (EECS), University of California, Berkeley, CA 94720.

Proceedings of the National Academy of Sciences of the United States of America

|October 6, 2025

Summary

This summary is machine-generated.

NucleusDiff, a novel AI approach, prevents atomic collisions in drug design by enforcing spatial constraints. This method significantly reduces collisions and improves ligand binding affinity for better therapeutic development.

Keywords:

generative AI manifold learning statistical machine learning structure-based drug design

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Last Updated: Jan 15, 2026

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Atomic Scale Structural Studies of Macromolecular Assemblies by Solid-state Nuclear Magnetic Resonance Spectroscopy

Atomic Scale Structural Studies of Macromolecular Assemblies by Solid-state Nuclear Magnetic Resonance Spectroscopy

Published on: September 17, 2017

Area of Science:

Computational chemistry
Artificial intelligence in drug discovery

Background:

AI models excel at generating high-affinity ligands for drug design.
Existing models often neglect the physical constraint of minimum atomic distance, leading to collisions.

Purpose of the Study:

To introduce NucleusDiff, an AI model designed to mitigate atomic collisions in structure-based drug design.
To improve ligand binding affinity by enforcing physical priors.

Main Methods:

NucleusDiff enforces spatial distance constraints using auxiliary mesh points around atomic nuclei.
The model approximates van der Waals boundaries to prevent atomic collisions.
Evaluation involved the CrossDocked2020 dataset and a COVID-19 therapeutic target.

Main Results:

NucleusDiff reduced atomic collision rates by up to 100%.
The model enhanced ligand binding affinity by up to 22.16%.
Results surpassed state-of-the-art structure-based drug design models.

Conclusions:

NucleusDiff effectively reduces atomic collisions in AI-driven drug design.
The method improves binding affinity, offering a significant advancement.
Qualitative analysis confirmed the model's visual effectiveness in optimizing molecular structures.