Pathos AI Secures $237 Million in Series E Financing to Scale Agentic AI Drug Development Platform and Advance Oncology Pipeline

Funding will support Foundry expansion and clinical development, with JSKN016 registrational studies a priority.

See more from StockTitan in Google Search and AI answers. Adds StockTitan as a preferred source · opens Google

Add on Google Not now

Funding follows rapid portfolio expansion and will power the initiation of a registrational study for AI-identified asset JSKN016

NEW YORK--(BUSINESS WIRE)-- Pathos AI, Inc., a clinical-stage AI and technology company advancing its own therapeutic pipeline of drugs, today announced the closing of a $237 million Series E financing round at a post-money valuation of approximately $3 billion. The financing was led by Bristol Myers Squibb (NYSE: BMY), alongside participation from existing investors.

The capital raise reflects significant operational momentum driven by Pathos’s proprietary AI platform, Foundry. Over the past several months, the company has leveraged Foundry to aggressively expand its clinical-stage oncology portfolio through a series of rapid strategic transactions, bringing its pipeline to four active clinical assets.

Proceeds from the financing will be used to further scale the Foundry platform's capabilities and accelerate the clinical development of Pathos's AI-steered portfolio. A key priority for the funding will be advancing JSKN016, a first-in-class TROP2/HER3 bispecific antibody-drug conjugate (ADC) toward registrational studies. Additionally, capital will drive the clinical advancement of pocenbrodib in mCRPC and RRMM; AZD4241, a novel ER PROTAC partnered with AstraZeneca, and DO-2, a next-generation MET kinase inhibitor.

"We are building a platform to systematically improve the probability of success in drug development,” said Iker Huerga, CEO of Pathos. "Foundry has fundamentally accelerated our ability to identify highly promising clinical assets and integrate them into our pipeline. This capital allows us to rapidly scale our AI engine while actively steering our current programs toward key clinical milestones, ensuring we deliver the right therapies to the right patient populations.”

Pathos’s clinical velocity is powered by Foundry, which operates via thousands of specialized AI agents that aggregate the totality of information for a drug program in real time. By continuously synthesizing clinical data, genomics, biological specimen data, and the ever-changing competitive landscape, Foundry identifies the optimal next decision for a program. This continuous intelligence allows Pathos to pinpoint precise biological sub-populations, steer clinical trial pivots, and eliminate high-risk development paths early in a drug’s lifecycle.

About Pathos AI

Pathos AI is an AI and technology company developing drugs. The company redesigns drug development from first principles: identifying clinical assets, making portfolio decisions with continuously updated probability-of-success calculations, and guiding clinical trials with AI-native monitoring. Pathos’s pipeline includes clinical-stage programs in metastatic castration-resistant prostate cancer, multiple myeloma, MET-altered non-small-cell lung cancer, ER+/HER2‐ breast cancer, and TROP2/HER3-expressing solid tumors. The company is based in New York City. For more information, visit pathos.com.

View source version on businesswire.com: https://www.businesswire.com/news/home/20261023421846/en/

Media Contact
press@pathos.com

Source: Pathos AI, Inc.

Key Terms

antibody-drug conjugate medical

An antibody-drug conjugate is a targeted medicine that combines an antibody, which can identify specific cells, with a powerful drug designed to destroy those cells. This approach allows for precise treatment, minimizing damage to healthy tissue. For investors, developments in this area can signal advances in cancer therapies and potential growth opportunities in the biotech sector.

adc medical

An antibody-drug conjugate (ADC) is a targeted cancer medicine that pairs an antibody that recognizes specific markers on tumor cells with a potent cell-killing drug, connected so the toxic payload is delivered directly to the cancer. For investors, ADCs matter because successful ADCs can improve patient outcomes and reduce side effects compared with traditional chemotherapy, shaping clinical trial success, regulatory approval chances, commercial demand, and a company’s valuation much like a guided missile versus a general bomb.

mcrpc medical

mCRPC stands for metastatic castration‑resistant prostate cancer, a form of prostate cancer that has spread beyond the prostate and keeps progressing despite treatments that lower male hormones. It matters to investors because this stage is harder to treat, drives demand for new therapies, and often involves large, expensive clinical trials and regulatory decisions that can strongly influence a drug maker’s future revenue and stock value—think of it as a stubborn problem that creates both medical need and commercial opportunity.

rrmm medical

Relapsed/refractory multiple myeloma (RRMM) describes a stage of the blood cancer multiple myeloma where the disease has returned after prior treatment (relapsed) or no longer responds to available therapies (refractory). It matters to investors because trials and approvals in RRMM target patients with unmet needs and often command distinct regulatory paths, market demand, and pricing potential—similar to developing a solution for customers whose existing products no longer work.

protac technical

A PROTAC (proteolysis targeting chimera) is a small engineered molecule that tags a specific protein inside cells and brings it to the cell’s disposal machinery so the protein is destroyed rather than just blocked. Think of it as a targeted cleanup crew that removes a problematic part instead of temporarily turning it off. Investors care because PROTACs can tackle disease targets that traditional drugs cannot, creating potential for breakthrough therapies, larger markets, and binary clinical readouts that can sharply affect company value.