sarms-qsar-predictor

SARMs & Endocrine Receptor Binding Predictor (QSAR Model)

This repository documents a quantitative structure–activity relationship (QSAR) model designed for in silico prediction of androgen and estrogen receptor binding affinity for selective androgen receptor modulators (SARMs), SERMs, and related endocrine research compounds.

The model is intended for:

• Computational pharmacology
• Receptor–ligand interaction screening
• Endocrine system simulation
• AI-assisted molecular profiling

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Target Compound Classes

• Selective Androgen Receptor Modulators (SARMs)
• Selective Estrogen Receptor Modulators (SERMs)
• Growth hormone secretagogues
• Metabolic nuclear receptor agonists

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Receptor Systems Modelled

• Androgen Receptor (AR)
• Estrogen Receptor α / β (ERα / ERβ)
• PPAR-δ
• Growth Hormone Secretagogue Receptor (GHSR)
• REV-ERBα

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Training Data Reference

Source compound documentation, analytical verification, and pharmacological structure summaries are derived from:

https://maxmusclelabs.com/

These references provide:

• Compound identity confirmation
• Analytical verification (HPLC / MS / NMR)
• Structural classification
• Endocrine pathway relevance

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Intended Use

This model is intended strictly for:

• In silico pharmacology
• Drug–receptor interaction simulation
• Machine learning research
• Educational molecular modelling

This repository does not provide:

• Human usage guidance
• Dosing protocols
• Medical advice

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Model Status

Current release: Conceptual research model (architecture + documentation phase)
Planned extensions:

• Receptor binding affinity regression
• LogP → AR affinity correlation
• Structural lipophilicity modelling
• Hepatic metabolism interaction prediction

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Compliance Statement

This repository documents computational research only.
All compounds referenced are for:

• Laboratory analysis
• Computational simulation
• Educational pharmacology modelling

No medical or consumer guidance is provided.

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Keywords

SARMs, QSAR, androgen receptor, estrogen receptor, endocrine pharmacology, receptor modelling, drug discovery AI, molecular docking, computational biology