AI Drug Discovery

In the fast-evolving field of drug discovery, artificial intelligence (AI) is transforming how we identify, develop and validate new therapies. While AI holds immense potential to revolutionize the drug discovery and development process, the availability of high-quality data and the experimental validation of AI-driven predictions are critical for success. At Cyprotex, we support these two essential phases of the process. First is the development of robust ADME-Tox models, where AI-driven platforms depend on large volumes of high-quality, well-characterized training data to build and refine predictive algorithms. Our high-throughput, cost-efficient platforms and scientifically rigorous methodologies provide the foundational data needed to accelerate development timelines and turn virtual predictions into real-world therapeutic success. Second is the validation of AI predictions through rapid, high-quality experimental testing, where we help bridge the gap between computational insight and experimental verification. 

Cyprotex offers AI drug discovery companies a full spectrum of services that complement and enhance AI-generated predictions. Whether you are working with small molecules, biologics or other modalities, we will help validate the AI-driven drug discovery pipeline, accelerate the journey to clinical trials, and ultimately bring innovative therapies to market faster.

AI has transformed the way drug discovery companies approach the identification of new compounds, predicting everything from biological activity to toxicity profiles. To build accurate and reliable predictive models, access to large volumes of high-quality experimental data is essential. This foundational data is critical for training and refining AI algorithms, and Cyprotex supports this effort through scalable, standardized ADME-Tox data generation. But despite its potential, AI needs real-world validation to truly de-risk the process and ensure success in bringing new drugs to market.

AI-driven companies understand the importance of data-driven decisions, but also know that prediction without validation is just a hypothesis. This is where the experience and scientific excellence of Cyprotex can add critical value. Our ability to verify AI predictions with experimental evidence makes us an indispensable part of the process.

As artificial intelligence and machine learning (ML) transform the landscape of drug discovery, access to high-quality, precise and well-characterized datasets becomes essential for developing robust predictive computational models. Cyprotex, a leader in ADME-Tox services, plays a crucial role in supporting AI-driven drug discovery by generating high-quality in vitro data to train, validate, and optimize predictive models.

The reliability of ML models depends on high-quality training data. Experimental variability in both design and execution of assays can introduce noise, leading to inaccurate or poorly defined predictions. Cyprotex ensures data reliability through:

• Clearly defined and consistent assay designs.
• Built-in quality controls to define data acceptability.
• Clear focus on data integrity, including audit trail.
• Quantifiable processes for identifying gross experimental errors, ensuring that erroneous data points do not corrupt ML models.
• Deep understanding of systematic assay errors and their impact on computational models.
• Larger sample sizes, which help define robust population-level values for training datasets, reducing bias in ML models.

ML models depend on diverse and well-balanced training datasets that cover a broad range of chemical properties. Cyprotex enhances model training by:

• Providing guidance on global vs. localized models, helping AI developers determine whether target-specific or broad-spectrum models are appropriate.
• Selecting optimal compounds to maximize model predictive performance and return on investment.

ML models require a rich set of molecular descriptors to accurately predict drug behavior. Cyprotex can provide high-throughput experimental descriptors that complement in silico methods, for example, our facility has the capability to provide very rapid (typically < 3 days) high-quality biomimetic chromatography data, thereby offering a distinct competitive advantage over in silico 2D descriptors, including Chromatographic Hydrophobicity Index (CHI), Experimental Polar Surface Area (EPSA), Immobilized Artificial Membrane Binding (K-IAM), Human Serum Albumin Binding (K-HSA ) and others.

These in vitro descriptors serve as high-quality input features for ML models, improving predictions related to drug metabolism, pharmacokinetics, and toxicity. The ability to generate high-volume biomimetic chromatography data allows you to train your models on large datasets with biologically relevant parameters.

One of the greatest challenges in AI-driven drug discovery is obtaining large, high-quality datasets for model training. Cyprotex can address this by offering cost-saving strategies leading to optimized resource allocation without compromising data quality.

Cyprotex offers consultancy on critical aspects of your discovery project such as assay selection, assay formats, analytical methodologies, equipment requirements, and sample management. This process also includes seamless integration with Laboratory Information Management Systems (LIMS) to ensure efficient data handling. By customizing the project scope from the outset, Cyprotex aligns its capabilities with the specific needs of AI-driven research teams, maximizing efficiency and precision.

To facilitate a smooth transition into collaboration, Cyprotex provides indicative pricing and timelines early in the engagement process. This allows AI drug discovery companies to evaluate feasibility and budgetary considerations upfront. If further refinements are needed, Cyprotex’s technical teams engage in in-depth discussions to refine the project proposal, ensuring the final agreement is well suited to your specific research needs and objectives.

Recognizing the importance of time-sensitive data in AI-driven research, Cyprotex meticulously optimizes workflows for compound management, logistics, and assay delivery. The alignment of compound supply and data delivery schedules ensures that research teams receive high-quality, reliable data within the required timelines. Additionally, Cyprotex collaborates with clients to define compound information requirements and data formats, ensures seamless integration with ML models for predictive analytics.

Cyprotex provides continuous support throughout the project’s lifecycle with dedicated points of contact. Regular meetings with study managers, multidisciplinary scientific leaders and business development teams ensure transparency, facilitate real-time updates, and allow for the discussion of new requirements as projects evolve. This hands-on approach ensures that clients receive proactive support, allowing them to adapt and optimize their AI-driven research without delays or data inconsistencies.

AI-driven drug discovery relies on high-quality experimental data at every stage from building robust predictive models to validating compound designs. At Cyprotex, we specialize in generating large-scale, reliable ADME-Tox datasets that power ML model development and refinement. Our high-throughput platforms and standardized assays provide the foundation needed to train predictive algorithms with confidence. Once models are developed, we support the next critical phase: validating AI-generated predictions through rigorous, real-world experimental testing to ensure safety, efficacy, and development viability.

Here is why leading AI drug discovery companies choose us as their trusted partner:

The predictive power of computational models in drug discovery is fundamentally dependent on the quality and consistency of input data. Cyprotex has more than 25 years of experience in generating high-quality, standardized ADME-Tox datasets using fully validated assays. Our expertise extends from early-stage discovery to preclinical development, enabling AI-driven companies to build and refine models based on robust, reproducible experimental data. Through a diverse portfolio of in vitro assays, we support companies in optimizing ADME properties while assessing toxicity risks with scientifically rigorous human relevant methodologies.

Developing machine learning models for drug discovery requires access to large, diverse, and well-annotated datasets to train algorithms effectively. Cyprotex has invested extensively in automation and high-throughput screening technologies to generate high-volume, high-integrity data at an industrial scale. With two state-of-the-art facilities in the UK and the USA, we ensure seamless scalability and consistency in data generation, making it possible to support large AI-driven projects with reliable, standardized outputs.

Our advanced liquid handling robotics and automated screening platforms provide a level of precision and reproducibility that is critical for computational model training. This commitment to automation minimizes variability, enhances throughput, and ensures that datasets remain consistent and comparable across experiments, ultimately improving the reliability of predictive models.

At Cyprotex, we prioritize quality, cost-effectiveness, and speed. Our high-throughput assay platforms are designed to generate large volumes of data without compromising accuracy or reproducibility. We focus on ensuring that our data is precise, consistent, and scientifically robust, providing a reliable foundation for AI-driven predictive modeling.

We also offer cost-effective assay formats and globally competitive pricing models, allowing AI companies to access high-quality experimental data at scale. Our ability to deliver large datasets within aggressive timelines ensures that ML models can be trained and validated rapidly and efficiently, enabling faster iterations and improved predictions.

We understand that AI-driven drug discovery requires a seamless flow of structured data. That is why we provide end-to-end data management solutions, ensuring that every dataset we generate is easily accessible, well-organized, and compatible with ML modeling platforms. Our secure LIMS system ensures full data traceability, while our API-driven approach enables real-time data transfer into preferred analytics tools.

By offering flexible and standardized data formats, we remove barriers to AI adoption, allowing researchers to focus on model development and validation rather than time-consuming data processing. With our streamlined data infrastructure, AI teams can quickly extract insights, optimize predictions, and accelerate drug discovery.

Reliable compound management is crucial for AI-driven drug discovery. We provide centralized compound ordering and storage solutions, allowing seamless access to high-quality screening compounds. Our processes ensure that compounds are assay-ready, minimizing handling errors and maintaining data consistency across experiments.

With a storage capacity exceeding one million compounds, we can efficiently support large-scale screening projects, ensuring that AI-driven companies have uninterrupted access to high-quality experimental data. By optimizing our logistical workflows, we make it easier for ML models to be trained on accurate and reproducible datasets.

As part of the parent company, Evotec, Cyprotex provides access to a broader ecosystem of drug discovery services, allowing AI-driven companies to integrate their predictive models with additional experimental validation. This includes hit screening, in vitro and in vivo therapeutic area models, and synthetic and medicinal chemistry capabilities. The ability to correlate AI-generated predictions with real-world experimental results enhances model refinement and improves confidence in computational drug discovery approaches. Additionally, Evotec also has internal AI experts who understand the specific needs and challenges of AI developers, enabling more effective collaboration across computational and experimental domains.