How AI in multimodal drug discovery and development helps scientists deliver life-saving therapies faster

This blog explains how pharmaceutical organizations can use an integrated digital thread to reduce drug development timelines, fully leveraging AI capabilities while maintaining regulatory compliance throughout the drug discovery and development lifecycle and how Siemens’ commitment to providing pharmaceutical industry-specific solutions can accelerate the journey from concept to cure.

The pharmaceutical industry stands at a pivotal moment, driven by the urgent need for new therapies and the transformative promise of digital innovation. As life expectancy rises, populations grow and more people require chronic and personalized treatments, the world looks to science for solutions. However, developing a new drug can often take anywhere between 10-15 years and cost upwards of $2.6 billion.¹ Therefore, even incremental improvements regarding speed, efficiency and reliability can translate into years saved.

Join us for our webinar on June 17 to learn more about accelerating drug discovery and development within a collaborative, data-driven environment. Register today.

What is the drug discovery and development digital thread? It’s an integrated data backbone that connects the design, make, test, analyze (DMTA) cycle with manufacturing and regulatory processes, ensuring scientific context and data integrity flow seamlessly across the entire drug lifecycle.

Transforming the pharma lifecycle involves taking a holistic, integrated approach using technology and expertise to meet the industry’s unique demands. This isn’t about adapting generic tools, but rather deploying solutions that resonate with the very core of pharmaceutical innovation.

Scientific complexity is a challenge, data fragmentation makes it worse

Each modality, such as antibodies, proteins and small molecules, lives in its own system, file structure or format. Critical context gets trapped in electronic lab notebooks (ELNs), instruments and analytics tools and lineage breaks at every handoff.

Fragmentation doesn’t just create inefficiency, it misses scientific opportunities your organization can recognize and, at the same time, make AI less reliable and relevant since information or data may be lacking.

What’s needed is a unified, AI-enabled discovery ecosystem that makes findable, accessible, interoperable and reusable (FAIR) data the default and turns multimodal data into connected, trustworthy knowledge – FAIR data standards promote maximum use of data by both humans and AI agents. When digital and physical workflows flow without interruption, scientific context stays intact, enabling teams to iterate faster, uncover insight earlier and advance the right candidates with confidence.

Did you know?

• 81% of pharma organizations are using AI in R&D²
• $60+ billion AI investment in drug development³
• 30% more viable drug candidates identified using AI⁴

If fragmentation keeps deciding what scientists can see, it will keep deciding what they can and can’t deliver. 

Connecting every stage with the digital thread 

Pharmaceutical scientists are driven by a common goal: to improve people’s lives and treat more diseases.  It’s this desire that motivates them to develop and discover the next life-changing therapy.

Yet, the journey of a drug, from initial discovery to market access, is a complex tapestry of research, development, testing and manufacturing, resulting in silos that can slow down progress and with loss of scientific knowledge at every handoff.

Siemens offers comprehensive coverage across the entire lifecycle, setting up the foundation of success in the earliest stages of discovery and design. This includes the critical tracking of data to ensure scientific continuity while maintaining compliance and quality, as well as the most advanced capabilities in simulation and modeling.

On top of that, structuring scientific work as AI-ready knowledge from the start, enabling AI-powered, simulation-driven, multimodal discovery provides full traceability from design to decision.

The pharmaceutical industry is increasingly adopting multi-physics simulation, marking a paradigm shift in how multimodal drug discovery and development are approached. This allows for virtual experimentation and optimization, drastically reducing the need for costly and time-consuming physical trials.

Furthermore, in one of the most highly regulated industrial sectors in the world, traceability and compliance with health authority requirements generate massive volumes of data that cannot be adequately managed without advanced digitalization tools and a digital thread that connects everything.

Indeed, data compromises integrity, which the Food and Drug Administration (FDA) cited as issues found in nearly 60% of its inspections.

Siemens and Dotmatics, a leader in Life Sciences R&D software, provide solutions that help pharmaceutical companies:

• Ensure the right data is being used in the right context
• Maintain data integrity and regulatory adherence throughout the entire drug lifecycle
• Provide structured data by design for optimal use and reliable AI analysis

Cerevel Therapeutics is a great example of a pharmaceutical company that was in need of solutions to support their complex research workflows, organize research data and help meet their ambitious goals. Here are three ways Cerevel Therapeutics is benefiting from Dotmatics solutions:

1. Have cross-functional research from design and synthesis, to registration, testing and data analyses. This solution united their data and equipped all scientific team members with the specific capabilities they needed.

2. Secure and safeguarded IP is critical in pharma multimodal drug discovery and development. With Dotmatics, Cerevel Therapeutics could now achieve regular distributed data backups, secure data transfer with encryption and valid web-app certification, trusted cloud security, full compliance with applicable rules and regulations, reliable data transfer and flow, secure version control and more.

3. Scalability via a cloud-based solution that would let them seamlessly scale as their projects increased and their internal and external teams grew.

“We evaluated a few different vendors, but when we looked at Dotmatics we thought, ‘Wow. Everything we need is already in the Dotmatics Platform!’…it was easy to see that the Dotmatics team would be flexible and easy to work with. We wanted to pick someone who would be a partner for the long haul and we found that with Dotmatics.” – Hanh Nho Nguyen, Director of Medicinal Chemistry, Cerevel Therapeutics

The focus on integration and interoperability means that all these solutions work together harmoniously, creating a truly connected ecosystem where data flows freely and intelligently.

AI reliability depends on integrated, standardized data foundations

Dotmatics is now part of Siemens, which showcases the commitment to using AI to revolutionize drug discovery and development. Considering the sheer volume of data in biological science and its inherent complexity, Siemens and Dotmatics are helping to transform AI and the processes related to science with the goal of accelerating the discovery and development of new drugs.

AI has the ability, among other things, to help scientists:

• More efficiently select drug candidates
• Improve drug design
• Identify new disease targets
• Manage toxicity
• Enhance patient safety

To do this, AI needs the right data, because artificial intelligence is not inherently intelligent.

Its effectiveness hinges mainly on the quality of its inputs and operates based on the probability derived from the data it’s fed. If that data isn’t aligned, standardized, relevant or connected to its context, the AI’s output will be compromised.

Siemens provides the end-to-end standardized and integrated data foundation upon which AI can operate, enabling scientists to select drug candidates more efficiently, gain deeper insights into molecular interactions with diseases and more effectively address toxicity. AI isn’t replacing human expertise; it empowers them by acting as a powerful assistant.

With Dotmatics, Siemens provides the structure, the architecture and the foundational tools necessary to develop the best AI applications, ensuring that the pharmaceutical industry can harness this transformative technology with confidence and precision.

Siemens delivers pharma-specific solutions built for AI-ready discovery

Siemens’ integrated solutions are designed to ensure data quality across the entire lifecycle that leads to trustworthy AI outcomes and reliable results crucial from the discovery stages through to market approval. Siemens’ continuous investment in and development of capabilities specifically tailored for the life sciences industry means its solutions are constantly evolving to meet the demands of an ever-changing industry.

This includes software-as-a-service (SaaS) offerings that provide scalability, making advanced digitalization accessible to companies of all sizes, from agile startups, biologics, and CROs, to established large pharmaceutical corporations. This flexibility ensures that innovation is not limited by infrastructure or budget.

Siemens’ comprehensive software portfolio, which includes solutions like Dotmatics’ Luma, covers the entire drug discovery and development spectrum with a unified, comprehensive, and integrated scientific platform.

Deliver life-saving therapies faster

With Siemens’ drug discovery and development digital thread, pharmaceutical organizations can establish a scientific operating model that improves R&D efficiency and turns AI into a catalyst for trustworthy, enterprise-scale decision-making.

The key is replacing fragmented, tool‑dependent workflows with a connected scientific operating environment where AI and simulation are grounded in structured, reusable scientific knowledge. This also fosters continuous, real‑time data capture across modalities, leveraging multiscale in-silico modeling and enterprise knowledge graphs.

The result is:

• Precision at the bench
• Intelligence across the drug lifecycle
• Continuity that accelerates confident decisions and brings lifesaving therapies to clinical trial stages and commercialization faster
• Reduced experimental burden
• Derisked candidate selection earlier

Siemens is committed to transforming the pharmaceutical industry through its end-to-end digitalization strategy, AI-driven innovation and solutions developed specifically for the pharmaceutical industry, born from a deep consideration for customer needs and rigorous industry requirements.

Read more about our vision and explore our solutions now.

FAQs about multimodal data and drug discovery and development

1. Are there examples of pharma companies who have successfully unified their data with Siemens solutions?

Yes. Pharma organizations are using Siemens and Dotmatics to help scientists bring life changing drugs and therapies to patients in need faster and more efficiently.

• Croda Pharma’s challenge was its fragmented, localized data management across global R&D sites as well as manual processes and disconnected systems, which slowed experiment setup and analysis. By adopting an enterprise R&D platform to unify data, workflows and teams as well as standardizing experiment capture and formulation design across disciplines, they had a scalable infrastructure to support growing scientific diversity and AI readiness. Other benefits include:
  • Faster experiment setup and reduced duplicate work
  • Easier data access and sharing with searchable, auditable platform
  • Consistent workflows improving quality and reproducibility
  • Scalable, future-ready foundation for enterprise knowledge management and AI application and automation
• Sygnature Discovery was challenged with fragmented data and inconsistent processes across global sites, manual workflows that slowed analysis and reporting and limited visibility for cross-site collaboration and client updates. They needed standardization without losing flexibility and implemented Siemens solutions to unify workflows and data, centralize lab operations, request tracking and registration processes across disciplines. Strengthening drug discovery services and client communication led to:
  • Consistent, standardized workflows across teams and research sites
  • Significant time savings through automation and reduced manual effort
  • Enhanced collaboration and transparency across internal and client stakeholders
  • Centralized, searchable data environment improving accessibility and oversight

2. How does data unification improve AI reliability in drug discovery?

An integrated data backbone that connects research, design, testing, manufacturing and regulatory processes ensures that all scientific context and data integrity flow seamlessly across the entire drug lifecycle. With standardized, aligned and connected data, AI can operate on high-quality inputs, leading to more accurate predictions, better drug design and more efficient candidate selection.

3. What are the main challenges the pharmaceutical industry faces in drug discovery and how does Siemens address them?

The pharmaceutical industry faces significant challenges, including lengthy development timelines (10-15 years), high costs (upwards of $2.6 billion per drug) and scientific complexity exacerbated by data fragmentation. Siemens addresses these challenges through its commitment to end-to-end digitalization and AI-driven innovation. By providing extended drug discovery and development solutions, Siemens and Dotmatics helps pharmaceutical organizations:

• Reduce drug development timelines
• Improve AI reliability through integrated and standardized data foundations
• Ensure regulatory compliance and data integrity
• Enable AI-powered, simulation-driven discovery with full traceability
• Foster continuous, real-time data capture and multiscale in silico modeling

1. https://phrma.org/blog/research-and-development-continues-long-after-a-medicine-is-initially-approved ↩︎
2. https://www.allaboutai.com/resources/ai-statistics/drug-development/ ↩︎
3. https://www.allaboutai.com/resources/ai-statistics/drug-development/ ↩︎
4. https://gitnux.org/digital-transformation-in-the-pharmaceutical-industry-statistics/ ↩︎