Novel, new, undiscovered. Whether you are one of the ever-expanding crop of AI drug discovery firms or a more traditional biopharma company, looking for new, effective drug candidates is what it’s all about. But every time a new molecule is envisioned or made, it begs the question: how novel are your molecules, really?
However, as anyone in drug discovery knows, a molecule can appear new but may already be covered by existing intellectual property (IP) claims. For decision-makers, ensuring true novelty requires more than just laboratory creativity. True molecule novelty demands a thorough assessment of the molecule’s broader context in terms of what has been made, patented, and discussed before.
As artificial intelligence (AI) reshapes the molecular discovery landscape, the question of novelty becomes more pressing than ever. Generative AI platforms are trained on vast datasets that may include patented molecules. While they excel at optimizing drug-like properties and predicting potential biological activity, they often lack an inherent awareness of IP constraints or the capacity for true creativity. As such, AI cannot single-handedly guarantee molecule novelty. In fact, a set of AI-generated molecules may initially appear to be structurally different. Still, it may be more likely to fall within the scope of an existing patent than those following a more human-centric design path, especially if broad claims regarding molecular scaffolds or specific functional groups cover the compound.
Freedom to operate (FTO) analysis, i.e., assessing whether a company can make, use, or sell a new molecule without infringing on existing patents, has long been a cornerstone of the R&D process for traditional drug discovery teams. However, the nature of traditional FTO analysis is almost always time-consuming and resource-intensive. Conducting a comprehensive, often manual patent search, relevant patent claim review, and new-to-patented compound chemical structure comparison, can take a long time. Inevitably, this means the FTO process has to be a distinct, often later step in the development process, which can lead to significant setbacks, both in time and resources.
So, what can we do to change this? The good news is that the big data-led AI explosion now means that integrating IP considerations into every step of drug discovery is finally possible. Accencio has growing capabilities to enable research teams to embed data and IP analysis and contextualize the research landscape from the early stages of molecule design. The ability to proactively identify potential IP risks and other infringements to tailor strategies to navigate around them empowers decision-makers to act early rather than waiting until a late-stage roadblock threatens the entire project. A holistic, continuous approach also frees researchers to design in the context of the existing idea and data space, which can support them to step back and truly see the bigger picture, leading to greater design creativity.
In this way, as drug discovery moves further into the AI era, the challenge of ensuring true molecule novelty becomes a question of changing how high-level decision-makers in both traditional and AI-driven discovery teams see IP. The road to success isn’t just about discovering something new—it’s about ensuring that what you’ve discovered is usable, defensible, and innovative within the bounds of an increasingly crowded and competitive field. The solution is simple: creatively designing in the context of everything that has gone before.
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