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How Integrated Expertise is Shaping the Future of Drug Discovery
View the full article in Drug Discovery News here.
We are pleased to share that Manjula Dissanayake, PhD, Principal of Technology Innovation at bioXcelerate AI, has been featured in an article published by Drug Discovery News, exploring the transformative role of integrated expertise in shaping the future of drug discovery.
The article, titled Breaking Down Silos: How Combined Expertise is Shaping the Future of Drug Discovery, outlines the urgent need for a more collaborative, cross-disciplinary approach in drug development – one that bridges biology, chemistry, data science and clinical insight from the outset.
Key Insights from the Article
The Cost of Siloed Research
Traditional drug discovery models often operate in functional silos, where limited data visibility and poor integration lead to missed opportunities, duplicated effort, and costly failures further down the pipeline.
The Value of Cross-Disciplinary Collaboration
Manju highlights the growing shift towards integrated teams that work together from early discovery through to translational planning. This alignment enables better-informed decisions and more agile project development.
The Role of AI in Context
While AI and machine learning are increasingly central to R&D, they are only as effective as the context in which they are applied. Collaborative environments allow AI to be grounded in relevant biological and clinical insight, improving model outputs and driving more meaningful results.
Building for the Future
To succeed, organisations must move beyond isolated functions and adopt a more connected model; one that fosters transparency, co-creation and rapid iteration. Integrated discovery is no longer optional; it is a requirement for scientific and commercial progress.
About Dr Manjula Dissanayake
Dr. Manju Dissanayake, Principal of Technology Innovation at BioXcelerate, started his career with an undergraduate degree in medical microbiology at the University of Edinburgh but quickly shifted to computer science due to his strong interest. He completed a PhD at Heriot-Watt University, specialising in machine learning, particularly in feature selection and classification within large biological datasets using Evolutionary Algorithms.
