Data as a Product for Structure-Based Drug Design (SBDD)

Unlocking strategic value: Data as a key asset in SBDD

by Dr. Neil Taylor

Structure-Based Drug Design (SBDD) has evolved significantly over the decades, transitioning from a largely experimental technique to a sophisticated computational approach. At the core of this transformation is data – not merely as a byproduct of research but as a critical product in its own right. By recognising data as a product for SBDD, researchers can unlock new opportunities in drug discovery and development.

In this article, we explore the transformative role of data in Structure-Based Drug Design (SBDD), examining its evolution from a research byproduct to a critical asset. We delve into the defining characteristics of valuable data, discuss the integration of diverse datasets essential for effective SBDD, and highlight the strategic advantages of treating data as a product. Additionally, we consider the future of SBDD data products, including their integration with AI and the emergence of federated data ecosystems.

Traditionally, gathering raw experimental data was an intermediate step in drug hunting. Today, well-curated bioinformatics and cheminformatics datasets have become valuable products themselves, because the technological capability to mine and combine data in different ways opens up new possibilities to generate value from raw data. These datasets integrate protein sequences, protein structures, chemical reactions, small molecule structures, binding affinities, pharmacokinetic properties, molecular interactions, and other metadata into cohesive, accessible resources that accelerate discovery. Knowledge-based compchem methods and molecular dynamics simulations play a crucial role in further refining these datasets, ensuring they provide accurate and actionable insights for drug development.

Defining data as a product in SBDD

The value of data as a product is determined by its validation, organisation, reliability, accessibility, and actionability. High-quality, high-value structural data products are characterised by:

• Rigorous validation to ensure accuracy and reliability.
• Standardised formats for seamless integration across platforms.
• Comprehensive metadata to enhance usability.
• Intuitive interfaces that democratise access across multidisciplinary teams.

These attributes are essential for making structure-based drug discovery more efficient and effective.

Generating value through data as a product

Treating data as a product in SBDD generates value through multiple channels:

• Internal efficiency gains through better decision-making in research portfolios.
• Reduced failure rates in clinical development through improved target validation.
• Accelerated structure determination via efficient data flow between experimental and computational teams.
• Improved reproducibility through comprehensive tracking of data transformations and processing parameters.
• Enhanced collaboration between structural specialists and biological domain experts.
• Scalable infrastructure that adapts to technological advances in structural determination methods.
• Democratised access to structural insights for non-specialists (e.g., medicinal chemists, cell biologists).

Forward-thinking organisations increasingly recognise the strategic importance of leveraging 3D protein structure data as high-value data products, acknowledging their role in gaining a competitive advantage within modern drug discovery.

The need for integrated SBDD data products

To be truly effective, SBDD data products must integrate data from multiple domains, including:

• Structural repositories with validated crystallographic, NMR and cryo-EM experimental data of target proteins and protein-ligand docking complexes, crucial for docking ligand information.
• Sequence repositories containing data from a wide range of organisms.
• Small molecule repositories, including in-house registration systems, inventory systems, virtual libraries, and supplier databases – particularly important for fragment-based discovery and fragment-based lead discovery.
• Binding affinity databases correlating molecular structures with quantitative interaction measurements, essential for protein-ligand docking.
• Conformational ensembles representing protein dynamics across biologically relevant states. Molecular dynamics (MD) simulations play a key role in gathering this information.
• Physicochemical property collections detailing solubility, permeability, and metabolic stability.
• Annotation layers linking structural features to biological functions and pathological states.

Transforming raw data into a strategic asset

The process of transforming raw protein structural data into data as a product can sometimes require substantial investment in data science, engineering, quality control, and user experience design. Global leaders in SBDD typically maintain dedicated data product teams comprising experts in structural biology, bioinformatics, cheminformatics, databases, enterprise software development, and computing.

For other organisations, enterprise software solutions provide a practical alternative. DesertSci’s Proasis platform is one such system, offering a fully functional enterprise solution that translates 3D protein structural data into a powerful strategic asset. Proasis combines years of scientific expertise with world-class knowledge to streamline drug discovery.

Accelerating drug discovery with high-value data

Investing in an advanced SBDD data system delivers substantial benefits, including faster discovery timelines. When researchers can confidently query high-quality, high-value structural databases, explore binding site characteristics, and visualise molecular interactions in real time, the journey from concept to candidate is significantly shortened.

By recognising data as a strategic asset, researchers can drive innovation, enhance drug discovery efficiency, and unlock new opportunities in Structure-Based Drug Design (SBDD).

The future of SBDD data products

The future of SBDD data products lies in their integration with AI systems. As machine learning algorithms become more advanced in predicting ligand binding modes and protein-ligand interactions, the quality and organisation of training data will be paramount. Organisations maintaining pristine structural data products will gain a competitive edge in developing next-generation AI tools for drug design.

Additionally, federated data ecosystems are emerging, enabling organisations to share structural information while safeguarding proprietary interests. These collaborative platforms accelerate discovery across the industry while preserving competitive differentiation.

Viewing data as a product rather than a byproduct represents a paradigm shift in SBDD. Organisations that invest in commercial software systems capable of ensuring data quality, accessibility, and seamless integration gain a decisive edge in drug discovery efficiency.

DesertSci’s Proasis platform is at the forefront of this transformation, providing a powerful enterprise solution that transforms 3D protein structural data into a strategic asset. By leveraging Proasis, researchers can confidently harness high-value datasets, accelerate ligand discovery, and optimise molecular design with greater precision. As computational methods – such as GROMACS molecular dynamics – continue to evolve, companies that adopt cutting-edge data solutions like DesertSci’s Proasis will lead the way in therapeutic innovation, ultimately driving faster, more effective drug development.

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Dr. Neil Taylor, founder of DesertSci, is a leading expert in SBDD data solutions – connect with him on LinkedIn to learn more.