3 secrets for drug discovery using AI

Here’s the scenario – You’re a passionate scientist in a leading pharmaceutical company hoping to uncover a transformative drug candidate. Naturally, you use artificial intelligence (AI) to help you target the most promising leads. After weeks of dedicated work, you start to realize that something seems a little off with your results. Maybe you recognize that the algorithm-proposed drug candidate has a history of poor tolerance in human clinical trials. Or perhaps the drug candidate fails to reproduce even the most basic PK/PD modeling results in vitro. Just like you, many drug discovery researchers have found themselves misled by the results proposed by AI.

Even with state-of-the-art algorithms, outcomes of AI for drug discovery heavily depend on the data and context backing them up. Many researchers, just like you, are seeking ways to navigate the intricacies and challenges of this rapidly evolving field. The path to successful AI-driven drug discovery may appear complex, but with the right guidance, AI can significantly enhance both the efficiency and effectiveness of your drug discovery journey.

Here are 3 of our best secrets to help ensure your success when using AI for drug discovery:

1. Start with quality data

The foundation of any successful AI model lies in the quality of its training data. Inconsistent or noisy biomedical data can introduce biases, potentially making the AI model veer off course. Imagine trying to master a language using an inaccurate dictionary; the outcome would be a garbled mess.

Similarly, training an AI model on low-quality biomedical data can lead to misguided conclusions. Data quality, integrity and relevance are paramount. Using expert-curated databases ensures the model begins with accurate and comprehensive knowledge.

That’s where our QIAGEN Biomedical Knowledge Base (BKB) database comes in. Curated by experts and continuously updated, QIAGEN BKB ensures you equip your AI models with the best possible start. It offers a strong foundation for building knowledge graphs and data models. Just as a building’s strength depends on its foundation, your AI model’s efficacy depends on starting with quality data.

2. Root AI inferences in real biological contexts

The power of AI lies in its ability to process vast amounts of information quickly. But it’s worth remembering that an AI model, regardless of its sophistication, doesn’t inherently understand the complexities of human biology. It sees numbers, patterns and correlations but not causations.

An AI model might draw associations that, at a glance, seem significant. However, without the biological context, these associations can be misleading. To avoid chasing after false positives, it’s crucial to ensure the AI’s conclusions are rooted within the biological realities.

Here’s the good news: QIAGEN BKB and QIAGEN Ingenuity Pathway Analysis (IPA) have built-in causality. With IPA you can quickly check the conclusions your AI generates. IPA’s intuitive GUI interface provides visual pathways, disease networks, upstream regulators/downstream effects and isoform-level differential expression analysis, all with the ability to bring in primary datasets for custom-tailored analyses.

3. Validate findings with peer-reviewed research

Science, at its core, thrives on collaboration, verification and iteration. A discovery today can be the stepping stone for a revolutionary breakthrough tomorrow. AI can be a potent tool in accelerating these discoveries, but its suggestions need validation.

While using AI for drug discovery can uncover potential candidates, it’s essential to validate these findings using published, peer-reviewed studies. Not only does this process lend credibility to your findings, but it also provides invaluable insights. For instance, understanding which cell lines have been used in previous studies can guide your preclinical testing, ensuring you’re on the right track.

For this crucial step, QIAGEN OmicSoft’s curated omics data collection is your ally, especially for enterprises in need of high-quality multi-omics datasets. You can tap into a comprehensive landscape of sources, offering validation from published studies beyond just a single public repository. Such validation lends credibility to your discoveries and provides invaluable insights. QIAGEN OmicSoft’s curated omics data collection facilitates this crucial step, bridging the gap between AI predictions and experimental data to construct disease models and digital twins of cells/organs/organisms.

Validating your cell line selection is also a critical factor for successful preclinical research. Using ATCC Cell Line Land, you can access authenticated cell line ‘omics data to make informed decisions before purchasing cell lines, helping to streamline your workflows, save time and resources, and enhance the predictability and reproducibility of your studies.