2026 Impact Report – A Letter from Amy Easton on The Future of Target Discovery and Drug Development
June 22, 2026 Amy Easton
The convergence of artificial intelligence and biomedical research is bringing new hope to the ALS community, offering the potential to identify novel therapeutic targets and accelerate drug development in a disease where time is everything. AI and ML now enable the rapid virtual design and testing of thousands of new molecules and the simulation of their interactions with therapeutic targets.
Leading pharmaceutical companies are investing heavily in these tools. Roche has built the largest announced GPU footprint of any pharmaceutical company, embedding accelerated computing into the core of how it discovers and develops new therapies. Eli Lilly partnered with NVIDIA to build a powerful AI supercomputer for medicine discovery and launched its TuneLab platform, backed by over $1 billion in proprietary research data, to give biotech companies access to its AI-enabled drug discovery models.
These topics and more were covered at this year’s Annual Meeting. We had the honor of sponsoring a roundtable moderated by Puneet Batra of Ember AI with Alexander McCampbell, Global Head of Research for Rare Disease at Roche, Kristina Kitko of Lilly Ventures, and Ajamete Kaykas, CXO of insitro. While all three companies are betting on AI, with insitro as one of the early Bay Area pioneers for new target discovery, all panelists conveyed a healthy dose of caution with regard to an immediate transformation of the industry. They shared optimism on the current impact of AI on drug discovery operations, but felt it would take 5 more years to see the impact of AI on improving efficiencies of clinical trials in the future.
Novel target discovery will always require benchwork to validate the target, though in silico models may become more prominent. One of the key takeaways from the researchers was clear—curation of high-quality clinical datasets representing diverse patient populations is needed to unlock the power of AI-based modeling of disease progression and patient stratification.
Amy Easton, PhD
VP, Scientific Programs
Target ALS
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