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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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Structure-Selectivity Relationship Prediction of Tau Imaging Tracers Using Machine Learning-Assisted QSAR Models and

Maryam Gholampour¹, Hassan Seradj¹, Amirhossein Sakhteman²

¹Department of Medicinal Chemistry, Faculty of Pharmacy, Shiraz University of Medical Sciences, Shiraz 71468-64685, Iran.

ACS Chemical Neuroscience

|April 10, 2023

Summary

Computational methods reveal key molecular features for selective tau imaging agents. Understanding these structural properties aids in designing better positron emission tomography tracers for tauopathies.

Keywords:

artificial intelligence molecular docking molecular dynamics simulation selective binding structural features tauopathies

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Area of Science:

Neuroscience
Computational Chemistry
Pharmacology

Background:

Tau fibrils are key pathological hallmarks in tauopathies.
Current imaging agents lack well-understood molecular binding mechanisms.
Highly selective agents are needed for accurate tau imaging.

Purpose of the Study:

Investigate the structural basis of selective tau aggregate binding.
Identify essential molecular moieties for selective binding.
Facilitate rational design of novel tau imaging agents.

Main Methods:

Machine learning-based quantitative structure-activity relationship (QSAR) classification.
Molecular docking and molecular dynamics (MD) simulations.
MM/PBSA binding free-energy calculations.

Main Results:

A Random Forest QSAR model achieved high accuracy (96.6% selective, 97.6% non-selective).
Ligand 63 demonstrated superior binding energy to tau and PHF6.
Key selective moieties include N-heterocycles, nitrogen atom positions, and tertiary amines.

Conclusions:

Computational approaches elucidate the structure-selectivity relationship for tau binding.
Identified molecular features are crucial for designing selective tau imaging agents.
This knowledge will guide the development of improved positron emission tomography tracers.