Advanced API-based solution designed to accelerate small molecule discovery through 3D shape-constrained generation

We are grateful to our speakers and engaged audience for driving a dynamic discussion. As David Walker, Quantori’s VP of Life Sciences, said: “The Q&A sparked thoughtful dialogue on how AI is reshaping competitive advantage across techbio, biotech, and pharma. While core AI capabilities are becoming increasingly standardized, true differentiation will come from innovative business models and flawless execution.”

Machine learning (ML) is transforming microbiome research, offering new ways to analyze complex biological data and unlock insights into human health. However, challenges such as small dataset sizes, demographic biases, and model validation issues must be addressed to ensure reliable and fair applications in clinical settings. Overcoming these hurdles will require collaboration across disciplines and adherence to best practices to maximize the potential of ML in microbiomics.

Yesterday, we were excited to host our first Quantori Connect Roundtable! Bringing together industry leaders from Pfizer, Takeda, Vertex, and Applied Insights Consulting, we explored the value proposition of AI/ML in biopharma—discussing adoption challenges, real-world applications, and future opportunities.

Everest Group highlighted Quantori’s strong technology partner ecosystem, skilled talent pool, and investment in emerging technologies as key factors in its leadership position.

Quantori Deck.AI is an enterprise-level knowledge management tool designed to streamline the process of creating and managing presentations.

Medical imaging plays a crucial role within both clinical practice and scientific research, assisting healthcare professionals with patient diagnosis and subsequent treatment monitoring, using minimally invasive techniques.

Medical imaging has long been a cornerstone of modern healthcare, offering a window into the human body that guides diagnosis and treatment. Among its many applications, histopathology — the examination of biological tissues to observe the appearance of diseased cells and tissues — plays a pivotal role in diagnosing various conditions, including cancer. However, traditional histopathological techniques are labor-intensive and subject to variability in interpretation. This is where artificial intelligence (AI) steps in, promising to transform histopathology by enhancing accuracy, efficiency, and consistency.

Despite recent progress in the development of Machine Learning models, their application in clinical practice remains limited. Several barriers to entry exist, with the lack of transparency in a model's decision-making process being critical. This article reviews a recent study where explainability was a core component of Machine Learning methodology applied to medical image analysis.