If Isomorphic shrinks drug discovery cycles to weeks, the advantage may shift from who finds the molecule to who educates providers first. How does that reshape go-to-market strategy in pharma?
$600 M and Nobel-grade science behind AlphaFold makes “solving all diseases” feel less like hype. Which therapeutic areas do you think will adopt AI-driven pipelines next?
Moving from digital predictions to real oncology patients this fast raises a question: can existing trial frameworks capture the pace of learning, or do we need adaptive approvals?
The line between biotech and deep-tech just blurred. Will venture capital start valuing drug platforms like cloud-software multiples, or does biology still demand a different playbook?
If AlphaFold 3 can push molecules to trial in four years, the old “patent cliff” model may flip on its head. How does big pharma retool marketing when the pipeline never sleeps?
An AI engine that spits out on-demand cancer drugs is equal parts thrilling and daunting. Where do you see the biggest friction: clinical validation, reimbursement, or public trust?
We’ve talked about digital twins for years; now they’re dosing real patients. Curious how payers and regulators will price therapies that iterate as fast as the software behind them.
Isomorphic’s $600 M bet turns protein folding into a SaaS-like pipeline. When timelines collapse, does regulatory science keep up—or does the bottleneck just move downstream?
Wild to think “cure cancer with code” is moving from slogan to Phase I trials. If AI can compress drug discovery to software-level cycles, what new guardrails will the FDA and big pharma need?
If Isomorphic shrinks drug discovery cycles to weeks, the advantage may shift from who finds the molecule to who educates providers first. How does that reshape go-to-market strategy in pharma?
$600 M and Nobel-grade science behind AlphaFold makes “solving all diseases” feel less like hype. Which therapeutic areas do you think will adopt AI-driven pipelines next?
Moving from digital predictions to real oncology patients this fast raises a question: can existing trial frameworks capture the pace of learning, or do we need adaptive approvals?
Personalized, AI-generated cures sound amazing—but who owns the IP when the model designs the molecule?
The line between biotech and deep-tech just blurred. Will venture capital start valuing drug platforms like cloud-software multiples, or does biology still demand a different playbook?
If AlphaFold 3 can push molecules to trial in four years, the old “patent cliff” model may flip on its head. How does big pharma retool marketing when the pipeline never sleeps?
An AI engine that spits out on-demand cancer drugs is equal parts thrilling and daunting. Where do you see the biggest friction: clinical validation, reimbursement, or public trust?
We’ve talked about digital twins for years; now they’re dosing real patients. Curious how payers and regulators will price therapies that iterate as fast as the software behind them.
Isomorphic’s $600 M bet turns protein folding into a SaaS-like pipeline. When timelines collapse, does regulatory science keep up—or does the bottleneck just move downstream?
Wild to think “cure cancer with code” is moving from slogan to Phase I trials. If AI can compress drug discovery to software-level cycles, what new guardrails will the FDA and big pharma need?