AI led drug discovery experience

 

The use of AI in drug discovery dates back several decades. Initially, AI was used for predicting bioactivity and identifying potential drug candidates. With recent advancements in machine learning and deep learning, AI is now used for predicting toxicity and pharmacokinetics, designing and optimizing drug molecules, and more.

In today's era, the drug discovery scientists in our client's team devote an inequitable chunk of their time compiling information from disparate origins and readying it for examination.

 As the design and strategy lead, I spearheaded an ambitious project for one of the world's largest pharmaceutical companies. The project aimed to introduce a new AI technology that effectively supports and boosts the confidence of discovery scientists in generating well-informed hypotheses for gene identification in the context of diseases.

 

Confidential information, including the company name, roles, and the detailed outcomes including research ouputs, system/service blueprints and UI/UX design under discussion, has been omitted and obscured in this case study in compliance with the non-disclosure agreement.

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The challenge

Extracting meaning from millions of disparate data points

Our objective for the project was to revamp the process of drug discovery for research scientists by leveraging our newly developed AI technology at the innovation center.

Our high level goal was to comprehend the impact of integrating this new AI technology on the drug discovery process across the organization, and subsequently create a user-friendly experience that would enable scientists to concentrate on their research instead of grappling with intricate technologies or managerial duties.

The approach and the objectives

Multidisciplinary understanding

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Insight study

Build a common understanding  of the client and technology needs. Generate insights to discover where the solution will bring value spanning from end user upto C-suite.

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Technology experiment

Conduct a series of technology experiments to confirm the drug discovery approach and evaluate the impact of any incremental technological changes on the strategic, operational, and tactical decision-making process of end-users.

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Design & build

Design and build for a solution that covers both physical and digital experiences.

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Business & Strategy

Develop an understanding of how the solution will fit within the ecosystem of service providers, data vendors, and authorities.

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Discovery

 Scientist Empowerment through Human-Centered Design: A Research Phase for Drug Discovery Processes

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Deeper Insights

Generating insights by merging the qualitative and the quantitave data

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Mapping the as-is

It is more complicated than it initially appears, and it was already quite complicated to begin with

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Reframing the problem

A pain point is a symptom; the root cause is the disease. To truly heal, you must address both

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Introducing our solution

System level re-design including workflows, technology and policies

User experience in 5 steps

Putting human collaboration at the heart of experience

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User Interface

A re-imagined digital lab experience

Roadmap

The future is computational drug discovery 

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