AI in Healthcare

Today's Stories

1. 1

   MindWalk Holdings joins Russell 3000E ahead of Q4 earnings

   MindWalk Holdings Corp. (NASDAQ: HYFT), an Austin-based AI drug-discovery company, was added to the Russell 3000E Index effective after the U.S. market close on June 26, 2026. The company will report fourth-quarter and full fiscal year 2026 results on July 22, 2026, at 5:00 p.m. Eastern Time, and has filed a European patent application for its proprietary HYFT Technology and ReefIQ biological context layer. The index inclusion broadens MindWalk's eligibility for index-tracking funds, giving it fresh institutional visibility at a time when AI-driven drug discovery names have been among the market's stronger performers. The company is positioning itself in two major pharmaceutical frontiers—GLP-1 metabolic health and infectious disease—by applying its LensAI platform to programs aimed at sustaining high-quality signaling alongside healthy-aging pathways.

   MindWalk's earnings call on July 22, 2026 will offer investors a near-term look at the company's revenue trajectory and program progress. The company's competitive differentiation rests on HYFT Technology, a proprietary, function-aware representation of biology built on roughly 660 million biological patterns that encode conserved relationships between sequence, structure, and function.

2. 2

   OpenAI researcher Miles Wang raises $200M at $2B for AI drug discovery startup

   Miles Wang, an OpenAI researcher, is leaving to launch a startup focused on AI models for drug discovery. He is in talks to raise about $200 million（約320億円） at a $2 billion（約3200億円） valuation, with Lightspeed in discussions to lead the funding round; several other OpenAI researchers are expected to join. The move reflects investor appetite for AI applied to life sciences. Comparable startups have raised substantial funding—Chai Discovery announced a $400 million（約640億円） raise at a $3.8 billion（約6100億円） valuation this week, and Google DeepMind's Isomorphic Labs raised a $2.1 billion（約3400億円） Series B in May. The new company may focus on finding new uses for existing FDA-approved drugs, which can reach revenue faster than developing drugs from scratch.

   Wang, who joined OpenAI in 2024 after dropping out from Harvard, has co-authored research on how AI models can automate and accelerate scientific discovery. Wang disputed the reported funding figures and company description but did not specify the correct details; talks are ongoing and the deal may not be final.

3. 3

   Amgen, Scientists Say AI Speeds Drug Discovery but Is Not a Shortcut

   Amgen and leading academic institutions held a roundtable conversation (Meeting the Moment) exploring how AI is shaping drug discovery. The discussion underscores that while AI can help teams analyze complex data, generate hypotheses, and identify patterns, it does not replace the scientific process. Developing a new medicine typically takes more than a decade. AI offers the potential to help researchers learn faster, but key challenges remain—including predicting how drugs behave in the human body and ensuring safety. The conversation highlights that real progress depends on high-quality data, deeper biological understanding, and expertise and judgment of scientists working alongside these tools.

   Amgen is investing in this direction through a recent South San Francisco lab expansion designed to support the Design, Make, Test, Analyze (DMTA) process, which brings together chemistry, biology, automation, and data science to generate insights faster and better connect decisions across the research process.

4. 4

   Insilico starts Phase III trial for AI-discovered lung-fibrosis drug

   Insilico Medicine initiated a Phase III clinical trial for Rentosertib, an oral small-molecule inhibitor targeting TNIK for idiopathic pulmonary fibrosis (IPF). The drug was discovered using Insilico's AI platforms—PandaOmics identified TNIK as a high-priority fibrosis target, and Chemistry42 designed the molecule. The Phase IIa trial showed manageable safety and tolerability, with the 60 mg once-daily arm demonstrating mean forced vital capacity improvement of +98.4 mL at 12 weeks. IPF is a progressive lung-scarring disease with median survival commonly reported at approximately two to four years after diagnosis, and current approved antifibrotic therapies can only slow progression but do not reverse the disease. Rentosertib represents a potentially first-in-class medicine whose target was identified by AI, whose chemical structure was designed by generative AI, and whose development is aimed at this severe age-related disease with high unmet medical need. This marks a major late-stage milestone for AI-driven drug discovery moving from research into late-stage clinical validation.

   The Phase III trial is expected to enroll 320 patients with IPF and is designed to systematically evaluate efficacy and safety of once-daily Rentosertib administered over 52 weeks. The trial will be led by Professor Zuojun Xu of Peking Union Medical College Hospital as Leading Principal Investigator, with Academician Nanshan Zhong and President Chang Chen as Co-Leading Principal Investigators.

5. 5

   Pharma Giants Back AI Drug Discovery with Billions as Clinical Proof Lags

   Isomorphic Labs raised $2.1 billion（約3400億円） in May led by Thrive Capital and secured major partnerships with Novartis, Eli Lilly, and Johnson & Johnson. The company's IsoDD platform predicts protein-ligand interactions and identifies cryptic binding pockets to expand the druggable landscape. Separately, Genesis Molecular AI and Incyte announced an expanded collaboration worth potentially over $1 billion（約1600億円）, while Chai Discovery licensed its Chai-3 antibody design model to Pfizer, and Inceptive partnered with Alnylam Pharmaceuticals in a deal worth up to $2 billion（約3200億円） with $30 million（約48億円） upfront. The investments reflect conviction in AI platforms and proprietary datasets as the foundation of drug discovery, even though few AI-designed drugs have reached the clinic. For pharma companies, embedding AI-driven workflows into R&D pipelines—using foundation models trained on internal genomics, transcriptomics, and proteomics data—may unlock previously intractable problems like neurological disease and accelerate candidate nomination timelines. However, commentators note the valuation cycle may have decoupled from clinical proof, meaning capital is chasing computational promise before real-world efficacy is proven.

   Foresite-backed Xaira Therapeutics, which launched in 2024 with more than $1 billion（約1600億円） in funding, is building virtual cell models to advance target discovery. Inductive Bio gained external validation in February by placing first in the OpenADMET-ExpansionRx blind challenge for predicting drug compound properties. The key differentiator for investors is whether AI can solve previously unsolvable problems and change the pace or probability of clinical success, not just model accuracy alone.