Reflections
Reflections
Making data and data-science more human:
Design’s collaboration with data scientists
calibrate the model features and endpoints user find most helpful
Aligning on I/O for handover purposes:
Aligning on what is input needed for the model to run and what are the possible outputs, smoothens the handover for front and back-end development
Co-creation builds adoption
Regular co-creation with power users and others along the design-prototype-build journey builds long-term adoption
View into the process
Reflections
Making data and data-science more human:
Design’s collaboration with data scientists
calibrate the model features and endpoints user find most helpful
Aligning on I/O for handover purposes:
Aligning on what is input needed for the model to run and what are the possible outputs, smoothens the handover for front and back-end development
Co-creation builds adoption
Regular co-creation with power users and others along the design-prototype-build journey builds long-term adoption
Context & Impact
Context & Impact
Top PharmaCo was undergoing a global digital transformation of their R&D capabilities. Adapting their large molecule design capabilities to latest technologies is essential to meet patient needs and remain competitive.
60% of their R&D costs are driven by clinical failures. Out of 2.6 Bn USD development costs (accounted for failed assets), 1.5 Bn attributed to failed trials.
Insight synthesis of key personas across the mRNA-LNP design workflow including key responsibilities, pains and delights.
Qualitative expert personas
As is understanding
Building application and adoption
Documenting the end to end workflow for chemists and biologist LNP researchers to identify the areas for ‘in-silico’ unlocks.
Present-state workflow from desktop to lab
As is understanding
Quantification of pain-points and gain-creators
Documenting metrics around insights gathered to help prioritise ‘in-silico’ features.
As is understanding
Considering the tech architecture together with the user’s workflow to identify key data flows and enabling user interfaces needed to make models usable.
UX/UI Architecture design matching tech architecture
Algorithmic supported workflow
Considering in-vivo and in-vitro workflows that can be complemented via in-silico workflows. Anchoring the future-state journey for both MVP and V1 launch of the product.
Simplified ‘To-be’ workflow considering MVP and V1 releases
Algorithmic supported workflow
Digital whiteboarding the user-stories and syndicating with leadership + translating them to UX/UI explorations
Documenting key features as user-stories and high-fidelity wireframes
Algorithmic supported workflow
Digital whiteboarding the user-stories and syndicating with leadership + translating them to UX/UI explorations
Documenting key features as user-stories and high-fidelity wireframes
Algorithmic supported workflow
Documenting validated user-stories and product features to helping plan sprints considering technical feasibility, business impact and user requirements.
Maintaining product backlog for iterative deployment
Building application and adoption
Conveying senior stakeholders bi-weekly for design and adoption sessions where built product is demo’ed and workflow implications are discussed.
Conducting regular change management & adoption sessions
Building application and adoption
Mapping future-state journeys across mRNA-LNP and other downstream teams to help cement hybrid workflows enabled by advanced analytical workflows.
Envisioning cross-team collaboration enabled by in-silico workflows
Building application and adoption
Insight synthesis of key personas across the mRNA-LNP design workflow including key responsibilities, pains and delights.
Qualitative expert personas
As is understanding
Building application and adoption
Documenting the end to end workflow for chemists and biologist LNP researchers to identify the areas for ‘in-silico’ unlocks.
Present-state workflow from desktop to lab
As is understanding
Quantification of pain-points and gain-creators
Documenting metrics around insights gathered to help prioritise ‘in-silico’ features.
As is understanding
Considering the tech architecture together with the user’s workflow to identify key data flows and enabling user interfaces needed to make models usable.
UX/UI Architecture design matching tech architecture
Algorithmic supported workflow
Considering in-vivo and in-vitro workflows that can be complemented via in-silico workflows. Anchoring the future-state journey for both MVP and V1 launch of the product.
Simplified ‘To-be’ workflow considering MVP and V1 releases
Algorithmic supported workflow
Digital whiteboarding the user-stories and syndicating with leadership + translating them to UX/UI explorations
Documenting key features as user-stories and high-fidelity wireframes
Algorithmic supported workflow
Digital whiteboarding the user-stories and syndicating with leadership + translating them to UX/UI explorations
Documenting key features as user-stories and high-fidelity wireframes
Algorithmic supported workflow
Documenting validated user-stories and product features to helping plan sprints considering technical feasibility, business impact and user requirements.
Maintaining product backlog for iterative deployment
Building application and adoption
Conveying senior stakeholders bi-weekly for design and adoption sessions where built product is demo’ed and workflow implications are discussed.
Conducting regular change management & adoption sessions
Building application and adoption
Mapping future-state journeys across mRNA-LNP and other downstream teams to help cement hybrid workflows enabled by advanced analytical workflows.
Envisioning cross-team collaboration enabled by in-silico workflows
Building application and adoption
Insight synthesis of key personas across the mRNA-LNP design workflow including key responsibilities, pains and delights.
Qualitative expert personas
As is understanding
Building application and adoption
Documenting the end to end workflow for chemists and biologist LNP researchers to identify the areas for ‘in-silico’ unlocks.
Present-state workflow from desktop to lab
As is understanding
Quantification of pain-points and gain-creators
Documenting metrics around insights gathered to help prioritise ‘in-silico’ features.
As is understanding
Considering the tech architecture together with the user’s workflow to identify key data flows and enabling user interfaces needed to make models usable.
UX/UI Architecture design matching tech architecture
Algorithmic supported workflow
Considering in-vivo and in-vitro workflows that can be complemented via in-silico workflows. Anchoring the future-state journey for both MVP and V1 launch of the product.
Simplified ‘To-be’ workflow considering MVP and V1 releases
Algorithmic supported workflow
Digital whiteboarding the user-stories and syndicating with leadership + translating them to UX/UI explorations
Documenting key features as user-stories and high-fidelity wireframes
Algorithmic supported workflow
Digital whiteboarding the user-stories and syndicating with leadership + translating them to UX/UI explorations
Documenting key features as user-stories and high-fidelity wireframes
Algorithmic supported workflow
Documenting validated user-stories and product features to helping plan sprints considering technical feasibility, business impact and user requirements.
Maintaining product backlog for iterative deployment
Building application and adoption
Conveying senior stakeholders bi-weekly for design and adoption sessions where built product is demo’ed and workflow implications are discussed.
Conducting regular change management & adoption sessions
Building application and adoption
Mapping future-state journeys across mRNA-LNP and other downstream teams to help cement hybrid workflows enabled by advanced analytical workflows.
Envisioning cross-team collaboration enabled by in-silico workflows
Building application and adoption
Insight synthesis of key personas across the mRNA-LNP design workflow including key responsibilities, pains and delights.
Qualitative expert personas
As is understanding
Building application and adoption
Documenting the end to end workflow for chemists and biologist LNP researchers to identify the areas for ‘in-silico’ unlocks.
Present-state workflow from desktop to lab
As is understanding
Quantification of pain-points and gain-creators
Documenting metrics around insights gathered to help prioritise ‘in-silico’ features.
As is understanding
Considering the tech architecture together with the user’s workflow to identify key data flows and enabling user interfaces needed to make models usable.
UX/UI Architecture design matching tech architecture
Algorithmic supported workflow
Considering in-vivo and in-vitro workflows that can be complemented via in-silico workflows. Anchoring the future-state journey for both MVP and V1 launch of the product.
Simplified ‘To-be’ workflow considering MVP and V1 releases
Algorithmic supported workflow
Digital whiteboarding the user-stories and syndicating with leadership + translating them to UX/UI explorations
Documenting key features as user-stories and high-fidelity wireframes
Algorithmic supported workflow
Digital whiteboarding the user-stories and syndicating with leadership + translating them to UX/UI explorations
Documenting key features as user-stories and high-fidelity wireframes
Algorithmic supported workflow
Documenting validated user-stories and product features to helping plan sprints considering technical feasibility, business impact and user requirements.
Maintaining product backlog for iterative deployment
Building application and adoption
Conveying senior stakeholders bi-weekly for design and adoption sessions where built product is demo’ed and workflow implications are discussed.
Conducting regular change management & adoption sessions
Building application and adoption
Mapping future-state journeys across mRNA-LNP and other downstream teams to help cement hybrid workflows enabled by advanced analytical workflows.
Envisioning cross-team collaboration enabled by in-silico workflows
Building application and adoption
Insight synthesis of key personas across the mRNA-LNP design workflow including key responsibilities, pains and delights.
Qualitative expert personas
As is understanding
Algorithmic supported workflow
Building application and adoption
Documenting the end to end workflow for chemists and biologist LNP researchers to identify the areas for ‘in-silico’ unlocks.
Present-state workflow from desktop to lab
As is understanding
Algorithmic supported workflow
Building application and adoption
Documenting metrics around insights gathered to help prioritise ‘in-silico’ features.
Quantification of pain-points and gain-creators
As is understanding
Algorithmic supported workflow
Building application and adoption
Considering the tech architecture together with the user’s workflow to identify key data flows and enabling user interfaces needed to make models usable.
UX/UI Architecture design matching tech architecture
As is understanding
Algorithmic supported workflow
Building application and adoption
Considering in-vivo and in-vitro workflows that can be complemented via in-silico workflows. Anchoring the future-state journey for both MVP and V1 launch of the product.
Simplified ‘To-be’ workflow considering MVP and V1 releases
As is understanding
Algorithmic supported workflow
Building application and adoption
Digital whiteboarding the user-stories and syndicating with leadership + translating them to UX/UI explorations
Documenting key features as user-stories and high-fidelity wireframes
As is understanding
Algorithmic supported workflow
Building application and adoption
Documenting validated user-stories and product features to helping plan sprints considering technical feasibility, business impact and user requirements.
Maintaining product backlog for iterative deployment
As is understanding
Algorithmic supported workflow
Building application and adoption
Conveying senior stakeholders bi-weekly for design and adoption sessions where built product is demo’ed and workflow implications are discussed.
Conducting regular change management & adoption sessions
As is understanding
Algorithmic supported workflow
Building application and adoption
Mapping future-state journeys across mRNA-LNP and other downstream teams to help cement hybrid workflows enabled by advanced analytical workflows.
Envisioning cross-team collaboration enabled by in-silico workflows
As is understanding
Algorithmic supported workflow
Building application and adoption
Insight synthesis of key personas across the mRNA-LNP design workflow including key responsibilities, pains and delights.
Qualitative expert personas
As is understanding
Algorithmic supported workflow
Building application and adoption
Documenting the end to end workflow for chemists and biologist LNP researchers to identify the areas for ‘in-silico’ unlocks.
Present-state workflow from desktop to lab
As is understanding
Algorithmic supported workflow
Building application and adoption
Documenting metrics around insights gathered to help prioritise ‘in-silico’ features.
Quantification of pain-points and gain-creators
As is understanding
Algorithmic supported workflow
Building application and adoption
Considering the tech architecture together with the user’s workflow to identify key data flows and enabling user interfaces needed to make models usable.
UX/UI Architecture design matching tech architecture
As is understanding
Algorithmic supported workflow
Building application and adoption
Considering in-vivo and in-vitro workflows that can be complemented via in-silico workflows. Anchoring the future-state journey for both MVP and V1 launch of the product.
Simplified ‘To-be’ workflow considering MVP and V1 releases
As is understanding
Algorithmic supported workflow
Building application and adoption
Digital whiteboarding the user-stories and syndicating with leadership + translating them to UX/UI explorations
Documenting key features as user-stories and high-fidelity wireframes
As is understanding
Algorithmic supported workflow
Building application and adoption
Documenting validated user-stories and product features to helping plan sprints considering technical feasibility, business impact and user requirements.
Maintaining product backlog for iterative deployment
As is understanding
Algorithmic supported workflow
Building application and adoption
Conveying senior stakeholders bi-weekly for design and adoption sessions where built product is demo’ed and workflow implications are discussed.
Conducting regular change management & adoption sessions
As is understanding
Algorithmic supported workflow
Building application and adoption
Mapping future-state journeys across mRNA-LNP and other downstream teams to help cement hybrid workflows enabled by advanced analytical workflows.
Envisioning cross-team collaboration enabled by in-silico workflows
As is understanding
Algorithmic supported workflow
Building application and adoption
Insight synthesis of key personas across the mRNA-LNP design workflow including key responsibilities, pains and delights.
Qualitative expert personas
As is understanding
Algorithmic supported workflow
Building application and adoption
Documenting the end to end workflow for chemists and biologist LNP researchers to identify the areas for ‘in-silico’ unlocks.
Present-state workflow from desktop to lab
As is understanding
Algorithmic supported workflow
Building application and adoption
Documenting metrics around insights gathered to help prioritise ‘in-silico’ features.
Quantification of pain-points and gain-creators
As is understanding
Algorithmic supported workflow
Building application and adoption
Considering the tech architecture together with the user’s workflow to identify key data flows and enabling user interfaces needed to make models usable.
UX/UI Architecture design matching tech architecture
As is understanding
Algorithmic supported workflow
Building application and adoption
Considering in-vivo and in-vitro workflows that can be complemented via in-silico workflows. Anchoring the future-state journey for both MVP and V1 launch of the product.
Simplified ‘To-be’ workflow considering MVP and V1 releases
As is understanding
Algorithmic supported workflow
Building application and adoption
Digital whiteboarding the user-stories and syndicating with leadership + translating them to UX/UI explorations
Documenting key features as user-stories and high-fidelity wireframes
As is understanding
Algorithmic supported workflow
Building application and adoption
Documenting validated user-stories and product features to helping plan sprints considering technical feasibility, business impact and user requirements.
Maintaining product backlog for iterative deployment
As is understanding
Algorithmic supported workflow
Building application and adoption
Conveying senior stakeholders bi-weekly for design and adoption sessions where built product is demo’ed and workflow implications are discussed.
Conducting regular change management & adoption sessions
As is understanding
Algorithmic supported workflow
Building application and adoption
Mapping future-state journeys across mRNA-LNP and other downstream teams to help cement hybrid workflows enabled by advanced analytical workflows.
Envisioning cross-team collaboration enabled by in-silico workflows
As is understanding
Algorithmic supported workflow
Building application and adoption
Insight synthesis of key personas across the mRNA-LNP design workflow including key responsibilities, pains and delights.
Qualitative expert personas
As is understanding
Algorithmic supported workflow
Building application and adoption
Documenting the end to end workflow for chemists and biologist LNP researchers to identify the areas for ‘in-silico’ unlocks.
Present-state workflow from desktop to lab
As is understanding
Algorithmic supported workflow
Building application and adoption
Documenting metrics around insights gathered to help prioritise ‘in-silico’ features.
Quantification of pain-points and gain-creators
As is understanding
Algorithmic supported workflow
Building application and adoption
Considering the tech architecture together with the user’s workflow to identify key data flows and enabling user interfaces needed to make models usable.
UX/UI Architecture design matching tech architecture
As is understanding
Algorithmic supported workflow
Building application and adoption
Considering in-vivo and in-vitro workflows that can be complemented via in-silico workflows. Anchoring the future-state journey for both MVP and V1 launch of the product.
Simplified ‘To-be’ workflow considering MVP and V1 releases
As is understanding
Algorithmic supported workflow
Building application and adoption
Digital whiteboarding the user-stories and syndicating with leadership + translating them to UX/UI explorations
Documenting key features as user-stories and high-fidelity wireframes
As is understanding
Algorithmic supported workflow
Building application and adoption
Documenting validated user-stories and product features to helping plan sprints considering technical feasibility, business impact and user requirements.
Maintaining product backlog for iterative deployment
As is understanding
Algorithmic supported workflow
Building application and adoption
Conveying senior stakeholders bi-weekly for design and adoption sessions where built product is demo’ed and workflow implications are discussed.
Conducting regular change management & adoption sessions
As is understanding
Algorithmic supported workflow
Building application and adoption
Mapping future-state journeys across mRNA-LNP and other downstream teams to help cement hybrid workflows enabled by advanced analytical workflows.
Envisioning cross-team collaboration enabled by in-silico workflows
As is understanding
Algorithmic supported workflow
Building application and adoption
Insight synthesis of key personas across the mRNA-LNP design workflow including key responsibilities, pains and delights.
Qualitative expert personas
As is understanding
Algorithmic supported workflow
Building application and adoption
Documenting the end to end workflow for chemists and biologist LNP researchers to identify the areas for ‘in-silico’ unlocks.
Present-state workflow from desktop to lab
As is understanding
Algorithmic supported workflow
Building application and adoption
Documenting metrics around insights gathered to help prioritise ‘in-silico’ features.
Quantification of pain-points and gain-creators
As is understanding
Algorithmic supported workflow
Building application and adoption
Considering the tech architecture together with the user’s workflow to identify key data flows and enabling user interfaces needed to make models usable.
UX/UI Architecture design matching tech architecture
As is understanding
Algorithmic supported workflow
Building application and adoption
Considering in-vivo and in-vitro workflows that can be complemented via in-silico workflows. Anchoring the future-state journey for both MVP and V1 launch of the product.
Simplified ‘To-be’ workflow considering MVP and V1 releases
As is understanding
Algorithmic supported workflow
Building application and adoption
Digital whiteboarding the user-stories and syndicating with leadership and translating them to UX/UI explorations
Documenting key features as user-stories and high-fidelity wireframes
As is understanding
Algorithmic supported workflow
Building application and adoption
Documenting validated user-stories and product features to helping plan sprints considering technical feasibility, business impact and user requirements.
Maintaining product backlog for iterative deployment
As is understanding
Algorithmic supported workflow
Building application and adoption
Conveying senior stakeholders bi-weekly for design and adoption sessions where built product is demo’ed and workflow implications are discussed.
Conducting regular change management & adoption sessions
As is understanding
Algorithmic supported workflow
Building application and adoption
Mapping future-state journeys across mRNA-LNP and other downstream teams to help cement hybrid workflows enabled by advanced analytical workflows.
Envisioning cross-team collaboration enabled by in-silico workflows
As is understanding
Algorithmic supported workflow
Building application and adoption
Insight synthesis of key personas across the mRNA-LNP design workflow including key responsibilities, pains and delights.
Qualitative expert personas
As is understanding
Algorithmic supported workflow
Building application and adoption
Documenting the end to end workflow for chemists and biologist LNP researchers to identify the areas for ‘in-silico’ unlocks.
Present-state workflow from desktop to lab
As is understanding
Algorithmic supported workflow
Building application and adoption
Documenting metrics around insights gathered to help prioritise ‘in-silico’ features.
Quantification of pain-points and gain-creators
As is understanding
Algorithmic supported workflow
Building application and adoption
Considering the tech architecture together with the user’s workflow to identify key data flows and enabling user interfaces needed to make models usable.
UX/UI Architecture design matching tech architecture
As is understanding
Algorithmic supported workflow
Building application and adoption
Considering in-vivo and in-vitro workflows that can be complemented via in-silico workflows. Anchoring the future-state journey for both MVP and V1 launch of the product.
Simplified ‘To-be’ workflow considering MVP and V1 releases
As is understanding
Algorithmic supported workflow
Building application and adoption
Digital whiteboarding the user-stories and syndicating with leadership and translating them to UX/UI explorations
Documenting key features as user-stories and high-fidelity wireframes
As is understanding
Algorithmic supported workflow
Building application and adoption
Documenting validated user-stories and product features to helping plan sprints considering technical feasibility, business impact and user requirements.
Maintaining product backlog for iterative deployment
As is understanding
Algorithmic supported workflow
Building application and adoption
Conveying senior stakeholders bi-weekly for design and adoption sessions where built product is demo’ed and workflow implications are discussed.
Conducting regular change management & adoption sessions
As is understanding
Algorithmic supported workflow
Building application and adoption
Mapping future-state journeys across mRNA-LNP and other downstream teams to help cement hybrid workflows enabled by advanced analytical workflows.
Envisioning cross-team collaboration enabled by in-silico workflows
As is understanding
Algorithmic supported workflow
Building application and adoption
Insight synthesis of key personas across the mRNA-LNP design workflow including key responsibilities, pains and delights.
Qualitative expert personas
As is understanding
Algorithmic supported workflow
Building application and adoption
Documenting the end to end workflow for chemists and biologist LNP researchers to identify the areas for ‘in-silico’ unlocks.
Present-state workflow from desktop to lab
As is understanding
Algorithmic supported workflow
Building application and adoption
Documenting metrics around insights gathered to help prioritise ‘in-silico’ features.
Quantification of pain-points and gain-creators
As is understanding
Algorithmic supported workflow
Building application and adoption
Considering the tech architecture together with the user’s workflow to identify key data flows and enabling user interfaces needed to make models usable.
UX/UI Architecture design matching tech architecture
As is understanding
Algorithmic supported workflow
Building application and adoption
Considering in-vivo and in-vitro workflows that can be complemented via in-silico workflows. Anchoring the future-state journey for both MVP and V1 launch of the product.
Simplified ‘To-be’ workflow considering MVP and V1 releases
As is understanding
Algorithmic supported workflow
Building application and adoption
Digital whiteboarding the user-stories and syndicating with leadership and translating them to UX/UI explorations
Documenting key features as user-stories and high-fidelity wireframes
As is understanding
Algorithmic supported workflow
Building application and adoption
Documenting validated user-stories and product features to helping plan sprints considering technical feasibility, business impact and user requirements.
Maintaining product backlog for iterative deployment
As is understanding
Algorithmic supported workflow
Building application and adoption
Conveying senior stakeholders bi-weekly for design and adoption sessions where built product is demo’ed and workflow implications are discussed.
Conducting regular change management & adoption sessions
As is understanding
Algorithmic supported workflow
Building application and adoption
Mapping future-state journeys across mRNA-LNP and other downstream teams to help cement hybrid workflows enabled by advanced analytical workflows.
Envisioning cross-team collaboration enabled by in-silico workflows
As is understanding
Algorithmic supported workflow
Building application and adoption
Insight synthesis of key personas across the mRNA-LNP design workflow including key responsibilities, pains and delights.
Qualitative expert personas
As is understanding
Algorithmic supported workflow
Building application and adoption
Documenting the end to end workflow for chemists and biologist LNP researchers to identify the areas for ‘in-silico’ unlocks.
Present-state workflow from desktop to lab
As is understanding
Algorithmic supported workflow
Building application and adoption
Documenting metrics around insights gathered to help prioritise ‘in-silico’ features.
Quantification of pain-points and gain-creators
As is understanding
Algorithmic supported workflow
Building application and adoption
Considering the tech architecture together with the user’s workflow to identify key data flows and enabling user interfaces needed to make models usable.
UX/UI Architecture design matching tech architecture
As is understanding
Algorithmic supported workflow
Building application and adoption
Considering in-vivo and in-vitro workflows that can be complemented via in-silico workflows. Anchoring the future-state journey for both MVP and V1 launch of the product.
Simplified ‘To-be’ workflow considering MVP and V1 releases
As is understanding
Algorithmic supported workflow
Building application and adoption
Digital whiteboarding the user-stories and syndicating with leadership and translating them to UX/UI explorations
Documenting key features as user-stories and high-fidelity wireframes
As is understanding
Algorithmic supported workflow
Building application and adoption
Documenting validated user-stories and product features to helping plan sprints considering technical feasibility, business impact and user requirements.
Maintaining product backlog for iterative deployment
As is understanding
Algorithmic supported workflow
Building application and adoption
Conveying senior stakeholders bi-weekly for design and adoption sessions where built product is demo’ed and workflow implications are discussed.
Conducting regular change management & adoption sessions
As is understanding
Algorithmic supported workflow
Building application and adoption
Mapping future-state journeys across mRNA-LNP and other downstream teams to help cement hybrid workflows enabled by advanced analytical workflows.
Envisioning cross-team collaboration enabled by in-silico workflows
As is understanding
Algorithmic supported workflow
Building application and adoption
Impact
Candidate drug in phase 1 trial expected to start 12-18 months after program start
2x
Endpoint improvement over scientific baseline
2-3x
Acceleration in the
design of molecules>4x
In silico leads and vectors currently being tested in lab
>1K
Research and data scientists using products daily
50+
Of engagement with SMEs on
UX and design topics520+ minutes
We integrated algorithms into a single digital product experience for analysis, tracking, and reporting of large molecules in-silico analysis.
Making Data Science Models Usable
Making Data Science Models Usable
We integrated algorithms into a single digital product experience for analysis, tracking, and reporting of large molecules in-silico analysis.
We integrated algorithms into a single digital product experience for analysis, tracking, and reporting of large molecules in-silico analysis.
We integrated algorithms into a single digital product experience for analysis, tracking, and reporting of large molecules in-silico analysis.
Life-Sciences
R&D Large Molecules
Life-Sciences
R&D Large Molecules
Life-Sciences
R&D Large Molecules
Life-Sciences
R&D Large Molecules
Life-Sciences
R&D Large Molecules
Life-Sciences
R&D Large Molecules