Docking, Molecular Dynamics, QSAR, and Machine Learning in Alzheimer's Small-Molecule Drug Discovery: Current Utility, Key Limitations, and Reproducibility Challenges
- 1Department of Chemistry
- 2Asian International University
Abstract
Alzheimer’s disease (AD) remains one of the most challenging therapeutic areas for small-molecule drug discovery because of its multifactorial pathology, complex target biology, and demanding central nervous system drug-development requirements. In this context, computational methods have become increasingly important for compound prioritization, hypothesis generation, and structure–activity interpretation. Among these, molecular docking, molecular dynamics (MD) simulations, quantitative structure–activity relationship (QSAR) modeling, and machine learning (ML) are widely used in AD-oriented medicinal chemistry, particularly in studies involving targets such as β-secretase 1 (BACE1) and tau. This review critically examines the practical roles of these four approaches in AD small-molecule discovery. Rather than presenting them as universally predictive solutions, the review evaluates their principal strengths, major limitations, and current challenges related to validation, interpretability, reproducibility, and experimental translation. Molecular docking remains useful for structure-based hypothesis generation and binding-pose analysis, especially for structurally characterized targets, but is frequently overinterpreted when numerical scores are treated as direct evidence of biological activity. MD simulations provide a complementary framework for examining protein–ligand stability, conformational flexibility, and interaction persistence, although simulation outcomes remain highly dependent on methodological choices and should not be regarded as definitive validation of binding or efficacy. QSAR continues to support ligand-based activity modeling and analogue prioritization, but its utility depends strongly on dataset quality, applicability domain, and validation strategy. ML offers greater flexibility for activity and property prediction, including multi-parameter prioritization and ADMET-related modeling, yet remains sensitive to dataset bias, overfitting, limited interpretability, and poor reproducibility. Across all four approaches, the review emphasizes that methodological rigor is at least as important as methodological sophistication. Particular attention is given to common problems including inadequate validation, overinterpretation of computational outputs, limited reproducibility, and weak integration with experimental medicinal chemistry. Overall, molecular docking, MD simulations, QSAR, and ML are best viewed as complementary support tools whose value depends on careful application, transparent reporting, and close integration with experimental evidence. A more critical, reproducible, and biologically informed use of these methods is likely to provide a stronger foundation for future AD small-molecule discovery. Keywords
Related
- Integrating artificial intelligence and molecular simulation for peptide discovery: limitations, validation and decision-centered workflows
- A Scoping Review of Diffusion-Based De Novo Molecular Generation: Architectures, Evaluation, and Open Challenges for Computational Drug Discovery
- Repurposing HIV Protease Inhibitors as Antifungal Chemosensitizers Against Multidrug Resistant Candida auris: Promise, Limitations, and Translational Challenges
- Comparative assessment of Au- and Ag-based SERS substrates for cancer and disease diagnosis: A review on analytical performance, reproducibility, and practical challenges
- A Risk-Tiered Validation Framework for Artificial Intelligence in Drug Discovery: From Reproducibility to Clinical Translation
BibTeX
@misc{saha2026docking,
title = {{Docking, Molecular Dynamics, QSAR, and Machine Learning in Alzheimer's Small-Molecule Drug Discovery: Current Utility, Key Limitations, and Reproducibility Challenges}},
author = {Poulami Saha and Anuja Chouhan},
howpublished = {ChemRxiv},
year = {2026},
month = sep,
doi = {10.26434/chemrxiv.15009354/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009354/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Docking, Molecular Dynamics, QSAR, and Machine Learning in Alzheimer's Small-Molecule Drug Discovery: Current Utility, Key Limitations, and Reproducibility Challenges
AU - Poulami Saha
AU - Anuja Chouhan
PY - 2026
DA - 2026/09/23
PB - ChemRxiv
DO - 10.26434/chemrxiv.15009354/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009354/v1
AB - Alzheimer’s disease (AD) remains one of the most challenging therapeutic areas for small-molecule drug discovery because of its multifactorial pathology, complex target biology, and demanding central nervous system drug-development requirements. In this context, computational methods have become increasingly important for compound prioritization, hypothesis generation, and structure–activity interpretation. Among these, molecular docking, molecular dynamics (MD) simulations, quantitative structure–activity relationship (QSAR) modeling, and machine learning (ML) are widely used in AD-oriented medicinal chemistry, particularly in studies involving targets such as β-secretase 1 (BACE1) and tau. This review critically examines the practical roles of these four approaches in AD small-molecule discovery. Rather than presenting them as universally predictive solutions, the review evaluates their principal strengths, major limitations, and current challenges related to validation, interpretability, reproducibility, and experimental translation. Molecular docking remains useful for structure-based hypothesis generation and binding-pose analysis, especially for structurally characterized targets, but is frequently overinterpreted when numerical scores are treated as direct evidence of biological activity. MD simulations provide a complementary framework for examining protein–ligand stability, conformational flexibility, and interaction persistence, although simulation outcomes remain highly dependent on methodological choices and should not be regarded as definitive validation of binding or efficacy. QSAR continues to support ligand-based activity modeling and analogue prioritization, but its utility depends strongly on dataset quality, applicability domain, and validation strategy. ML offers greater flexibility for activity and property prediction, including multi-parameter prioritization and ADMET-related modeling, yet remains sensitive to dataset bias, overfitting, limited interpretability, and poor reproducibility. Across all four approaches, the review emphasizes that methodological rigor is at least as important as methodological sophistication. Particular attention is given to common problems including inadequate validation, overinterpretation of computational outputs, limited reproducibility, and weak integration with experimental medicinal chemistry. Overall, molecular docking, MD simulations, QSAR, and ML are best viewed as complementary support tools whose value depends on careful application, transparent reporting, and close integration with experimental evidence. A more critical, reproducible, and biologically informed use of these methods is likely to provide a stronger foundation for future AD small-molecule discovery. Keywords
ER -