20 Must-Read QSAR Papers for a Solid Foundation in Drug Design and Discovery
Explore essential QSAR papers that lay the groundwork for understanding drug design, from foundational models to advanced predictive techniques.
15 min read
November 6th, 2024
Introduction
Quantitative Structure-Activity Relationship (QSAR) studies are crucial in drug design, allowing researchers to predict the biological activity of a molecule based on its chemical structure.
By utilizing QSAR models, researchers can save time and resources by prioritizing compounds with higher chances of success, leading to faster and more efficient drug discovery.
In the previous post, we introduced the basics of QSAR in cheminformatics and provided an in-depth guide for beginners.
In this blog post, we’ll explore 20 essential QSAR papers that have significantly influenced the field of drug design.
These selected papers will help you understand key methodologies, innovations, and applications in QSAR, enabling you to deepen your knowledge and apply QSAR techniques more effectively.
Alongside, we also recommend our in-depth curriculum focused on the practical aspects of QSAR modeling, guiding you through real-world applications and best practices.
Category I. Foundational QSAR Methodology and Pioneering Concepts
1. Hansch, C., & Fujita, T. (1964). "ρ-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure."
Hansch and Fujita are widely regarded as pioneers of the QSAR method. This paper is one of their earliest works establishing QSAR as a scientific approach. They introduced methods to relate biological activity to chemical properties through quantitative methods, emphasizing the role of hydrophobic, electronic, and steric factors in biological interactions.
2. Hansch, C., Maloney, P. P., Fujita, T., & Muir, R. M. (1962). "Correlation of Biological Activity of Phenoxyacetic Acids with Hammett Substituent Constants and Partition Coefficients."
Often cited as a foundational QSAR study, this work formalized the relationship between structure and activity by using mathematical correlations, making it a classic reference for understanding the interplay between molecular properties and biological effects.
3. Free, S. M., & Wilson, J. W. (1964). "A Mathematical Contribution to Structure-Activity Studies."
Known as the Free-Wilson approach, this paper introduced an additive model for understanding how different substituents affect biological activity. It’s especially useful in QSAR when exploring the independent effects of functional groups on molecular activity.
Category II. Advances in QSAR Methodologies
4. Cramer, R. D., Patterson, D. E., & Bunce, J. D. (1988). "Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins."
CoMFA introduced 3D-QSAR, which considers the three-dimensional shape and electronic properties of molecules in activity prediction. This paper represented a major step forward in QSAR by moving from simple linear models to spatial and electronic fields, allowing for better prediction accuracy and more insightful interpretations.
5. Kubinyi, H. (1993). "QSAR: Hansch Analysis and Related Approaches."
Although it is more of a review and textbook chapter than an original research article, Kubinyi’s work provides comprehensive insight into the various methodologies in QSAR, expanding on classical Hansch analysis and introducing newer approaches. It’s a foundational piece for anyone wanting an overview of QSAR techniques and their evolution.
6. Rogers, D., & Hopfinger, A. J. (1994). "Application of Genetic Function Approximation to Quantitative Structure-Activity Relationships and Quantitative Structure-Property Relationships."]
This paper introduced a novel approach to feature selection and optimization in QSAR modeling using Genetic Function Approximation (GFA). This technique leverages genetic algorithms—a type of evolutionary algorithm that simulates natural selection—to identify the best combination of descriptors for predicting a molecule's biological activity or property.
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Category III. Descriptor-Based QSAR and Molecular Similarity
7. Todeschini, R., & Consonni, V. (2000). "Handbook of Molecular Descriptors."
While technically a book, this work by Todeschini and Consonni is invaluable in QSAR as it provides an extensive list of molecular descriptors, key to creating robust models. It’s particularly important for understanding the quantitative variables that QSAR models rely on for prediction.
8. Carhart, R. E., Smith, D. H., & Venkataraghavan, R. (1985). "Atom pairs as molecular features in structure-activity studies: definition and applications."
This work by Carhart et al. represents a crucial step in the evolution of QSAR. The authors propose "atom pairs" as a way to represent molecular structures based on the interactions between pairs of atoms. This approach captures essential spatial and chemical information about the molecules. The atom pair approach remains a widely used descriptor in modern QSAR modeling and cheminformatics.
9. Kier, L. B., & Hall, L. H. (1986). "Molecular Connectivity in Chemistry and Drug Research."
This work introduces the concept of molecular connectivity indices as a means to quantify the structural features of molecules. It emphasizes the relationship between molecular connectivity—the arrangement and bonding of atoms within a molecule—and biological activity. By developing various connectivity indices, the authors demonstrate how these descriptors can effectively correlate chemical structure with biological properties, thereby enhancing predictive capabilities in QSAR models. This foundational work laid the groundwork for utilizing connectivity indices in cheminformatics and drug design, establishing a systematic approach for understanding how molecular topology influences pharmacological activity and guiding the rational design of new therapeutic agents.
10. Faulon, J.-L., Visco, D. P., & Pophale, R. S. (2003). The Signature Molecular Descriptor. 1. Using Extended Valence Sequences in QSAR and QSPR Studies."
This work presents the concept of signature molecular descriptors, focusing on the use of extended valence sequences (EVS) to characterize molecular structures in quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) studies. The authors introduce the signature descriptor as a novel approach to capture the essential features of molecular connectivity and atom types, which allows for the representation of complex molecular information in a systematic manner.
11. Faulon, J.-L., Churchwell, C. J., & Visco, D. P. (2003). The Signature Molecular Descriptor. 2. Enumerating Molecules from Their Extended Valence Sequences."
This work by the same authors introduces a new algorithm for enumerating molecular structures based on predefined extended valence sequences (signatures). At that time, this algorithm could efficiently generate molecular structures with approximately 50 non-hydrogen atoms within a matter of seconds, making it a powerful tool for computational chemistry.
Category IV. Model Validation and Reliability Standards
12. Golbraikh, A., & Tropsha, A. (2002). "Beware of q²!."
This paper discusses limitations of using cross-validation metrics like ( q^2 ) for QSAR model validation, advocating for external validation as the standard for QSAR models. It’s a key resource for understanding pitfalls in model validation and the importance of assessing external predictivity.
13. Tropsha, A., Gramatica, P., & Gombar, V. K. (2003). "The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models."
This work emphasizes the critical role of validation in quantitative structure-activity relationship (QSAR) modeling. The authors argue that robust validation practices are necessary to ensure the reliability and applicability of QSAR models in predicting biological activity. The authors assert that validation is not merely a supplementary aspect of QSAR modeling but a fundamental requirement. They highlight that without proper validation, the predictive power of a model cannot be confidently established.
14. OECD (2007). "Guidance Document on the Validation of (Q)SAR Models"
While not a research paper, this OECD guidance document outlines the validation principles required for QSAR models used in regulatory settings, such as environmental toxicology. It presents principles that are essential for any QSAR model to be considered reliable, especially in fields where regulatory acceptance is crucial.
Please note: The new OECD guidelines 2024 are now available and recommended for reading.
15. Consonni, V., Ballabio, D., & Todeschini, R. (2010). "Evaluation of model predictive ability by external validation techniques."]
This paper goes in-depth into statistical techniques for external validation, including bootstrapping and permutation tests, which are now standard practices in QSAR to ensure model robustness. It’s an essential guide for rigorous validation.
Category V. Modern Techniques: Machine Learning, Big Data, and Deep Learning in QSAR
16. Tropsha, A., & Golbraikh, A. (2007). "Predictive QSAR Modeling Workflow and Applicability Domains."
This review covers machine learning applications in QSAR, including model workflows, the importance of applicability domain, and virtual screening strategies. It’s a helpful resource for anyone interested in integrating machine learning and QSAR for drug discovery.
17. Ma, J., Sheridan, R. P., Liaw, A., Dahl, G. E., & Svetnik, V. (2015). "Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships."
This is one of the early works that explores the resurgence of deep neural networks (DNNs) in QSAR modeling, demonstrating their superior predictive capabilities compared to traditional methods like random forest (RF) across diverse datasets from Merck’s drug discovery efforts. The study highlights the efficiency of DNNs, showing that a single set of recommended parameters can outperform RF without the need for extensive optimization for individual datasets. Reading this paper is beneficial for understanding the latest advancements in machine learning techniques applied to QSAR and their practical implications in drug discovery.
18. Muratov, E. N., et al. (2020). "QSAR Without Borders."
This work is significant as it highlights the evolution of QSAR modeling and its expanding applicability beyond traditional chemical research into diverse fields such as nanotechnology and clinical informatics. Reading this paper will deepen your understanding of how QSAR methodologies can enhance predictive capabilities across a broad range of scientific disciplines, making it essential for anyone involved in modern data analysis and computational modeling.
Category VI. Specialized Topics: Toxicity, Consensus Modeling, and Interpretability
19. Debnath, A. K., Lopez de Compadre, R. L., Debnath, G., Shusterman, A. J., & Hansch, C. (1991). "Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity."
This study presents a comprehensive QSAR analysis of over 200 aromatic and heteroaromatic nitro compounds tested for mutagenicity, identifying key determinants such as hydrophobicity and molecular orbital energies. By demonstrating that fused ring structures correlate with increased mutagenic potency, this work provides valuable insights for predictive toxicology, aiding in the design of safer bioactive compounds. Reading this paper will enhance your understanding of how structural variations influence biological activity, making it a key resource for researchers in medicinal chemistry and toxicology.
20. Guha, R. (2008). On the interpretation and interpretability of quantitative structure-activity relationship models."
The last in our recommendation is this paper by Rajarshi Guha which discusses the critical aspect of interpretability in QSAR modeling, emphasizing how QSAR models can reveal valuable structure-activity relationships (SARs) that inform molecular design. Guha covers key factors influencing interpretability, from the selection of meaningful descriptors to statistical model validation. Through diverse examples, the paper explores how QSAR models can aid in understanding molecular activity, pinpointing outliers, and guiding modifications to improve efficacy. It's a valuable resource for anyone interested in the practical application of QSAR models to enhance drug discovery through interpretable and actionable insights.
Conclusion
QSAR modeling has transformed in recent years, fueled by innovations in ML and AI that make predictions more precise, interpretable, and adaptable across various applications.
Yet, to truly grasp the evolving landscape, it’s crucial to build a strong foundation. The selected papers provide essential insights into QSAR’s core principles and methodologies, from classical techniques to interpretability and validation practices.
We encourage you to explore these works, as they offer a comprehensive grounding that will enhance your understanding of QSAR. Engaging with both these foundational studies and the latest advancements will help you build a solid foundation to apply QSAR techniques effectively and keep pace with the exciting developments in this field.
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