AI Drug Discovery Surges: Galux, LG Chem, Cheiron, Peptris Announce Major Deals

AI-driven drug discovery companies are closing major financing rounds and forging new partnerships. Galux, a South Korea-based biotech, completed a $29 million (KRW 42 billion) Series B financing, while LG Chem signed a research and license option agreement with U.K.-based LabGenius Therapeutics to discover multispecific antibody candidates for cancer. Cheiron secured $8 million in seed funding, and Peptris raised INR 70 crore (USD 7.7 million) in a Series A round.

Galux, a South Korea-based biotech startup pioneering AI-driven protein therapeutics design, announced the successful completion of a $29 million (KRW 42 billion) Series B financing. With this round, the company's total capital raised reaches $47 million (KRW 68 billion). Existing investors InterVest, DAYLI Partners, and PATHWAY Investment participated, alongside new investors including Yuanta Investment, Korea Development Bank (KDB), SL Investment, NCORE Ventures, SneakPeek Investments, Korea Investment & Securities, and Mirae Asset Securities. Galux previously closed an $18 million (KRW 21 billion) Series A financing in 2022. Proceeds will be used to further enhance its AI platform, expand R&D infrastructure, and accelerate preclinical validation of proprietary pipelines.

Galux has developed GaluxDesign, an AI-driven protein design platform specializing in de novo antibody design. The company confirmed that its AI-designed antibody structure was highly consistent with the cryo-EM experimental structure, demonstrating the precision and generalizability of the platform. The platform is being extended to challenging target classes including GPCRs and ion channels. Galux has established co-development partnerships with major domestic pharmaceutical companies such as Celltrion and LG Chem, and recently announced a collaboration with Boehringer Ingelheim for AI-based precision protein design. At BIO USA 2026, Galux held meetings with about 30 companies, seeking to turn early technology validation projects into larger partnerships. Its collaboration with AstraZeneca was introduced through the KHIDI-AstraZeneca Project NOVA Global Connect program, focusing on testing whether Galux's AI platform can design proteins with predefined functional properties. Galux has also signed an investment and joint research agreement with antibody-drug conjugate developer AimedBio to develop a blood-brain barrier (BBB)-penetrating protein delivery platform. The companies plan to design protein candidates that can bind to specific receptors using antibody structures. In conventional antibody discovery, companies may screen from one million to one trillion candidates; Galux says it has obtained antibodies by designing only dozens of candidates.

LG Chem has signed a joint research and license option agreement with U.K.-based LabGenius Therapeutics to discover novel multispecific antibody candidates for cancer treatment. The multi-year collaboration will focus on developing tumour-selective antibodies addressing on-target, off-tumour toxicities in solid tumour treatment. LabGenius will lead the programme through early-stage preclinical research, including in vitro efficacy studies; LG Chem will then undertake additional preclinical development, including in vivo studies, and will have the option to acquire rights to the programme through an in-licensing agreement. LabGenius will receive an upfront payment and may be eligible for early milestone payments. Should LG Chem exercise its licensing option, LabGenius could receive potential triple-digit million-dollar clinical, regulatory and commercial milestone payments, in addition to royalties on future net sales. LG Chem will fund all research and development activities. LG Chem expects the collaboration to significantly shorten the timeline for discovering antibody drug candidates, cutting the more-than-five-year process roughly in half. LabGenius's EVA platform combines machine learning with high-throughput experimentation, using a fully autonomous laboratory system to design, robotically test, and analyze antibody candidates iteratively.

Cheiron, an AI-native life sciences platform, has secured $8 million in seed funding led by Menlo Ventures. Strategic backers include the co-founder of Moderna, a former Pfizer chief medical officer, the CEO of Chai Discovery, a former Starbucks CEO, and a former Apple AI chief. Cheiron is designed to replace fragmented spreadsheets, papers, and filings by representing an entire drug development lifecycle as a single, connected, and auditable operating system. At its core is the Life Sciences Knowledge Graph (LKG), which unifies global clinical, biomedical, regulatory, patent, and commercial evidence into a single model, enabling cross-workflow inferences, discrepancy and risk detection, and hypothesis stress-testing. The company reported rapid adoption within six months of launch across tens of thousands of biopharma professionals, including top-five South Korean pharmaceutical giant Boryung.

Bengaluru-based drug discovery startup Peptris has raised INR 70 crore (USD 7.7 million) in a Series A funding round co-led by IAN Alpha Fund and Speciale Invest, with participation from Tenacity Ventures, BYT Ventures and other investors. The company said the funds will be used to advance existing programs toward clinical readiness and expand the discovery pipeline over the next 24 months. Peptris develops artificial intelligence models that generate novel molecules and predict key drug development parameters, aiming to reduce failures in early-stage drug development. Its platform has led to the discovery of Novel Chemical Entities (NCEs) and identified drug repurposing and rescue opportunities. The company focuses on therapeutic areas such as rare diseases, inflammation, oncology and women's health, and operates a B2B model, partnering with pharmaceutical and biotechnology firms.

Creative Biolabs has upgraded its AI-driven functional protein solutions for metabolic disease drug discovery, using proprietary deep learning algorithms to computationally design multi-receptor agonists for conditions like obesity and type 2 diabetes. The platform screens millions of peptide sequences and aims to compress research cycles from hit identification to lead optimization to 2 to 14 weeks. It also addresses peptide stability by engineering ultra-long-acting profiles and uses high-fidelity pharmacological dataset training to predict ADMET properties early. The platform integrates molecular dynamics simulations for allosteric modulation to improve selectivity.