Roman Kasianov

Tokyo-based Elix (founded 2016) has launched Elix Discovery, an AI drug discovery platform powered by federated learning models trained on proprietary compound data from 16 pharmaceutical companies. The platform is the first commercial deployment to result from Japan’s AMED-backed DAIIA initiative (2020–2025), which brought together 17 pharma companies and institutions including RIKEN, Kyoto University, and Nagoya University.

Xaira Therapeutics, the AI-native biotech launched in 2024 with over $1 billion in funding, has appointed biotech executive Jeff Jonker as President and Chief Operating Officer. Jonker will report to CEO Marc Tessier-Lavigne and oversee operational functions as Xaira advances its AI-driven platform for therapeutic development.

AbbVie announced a definitive agreement to acquire Capstan Therapeutics for up to $2.1 billion in cash, gaining access to its targeted lipid nanoparticle (tLNP) platform for in vivo cell engineering and its lead candidate, CPTX2309. The platform enables mRNA delivery directly to CD8+ T cells to induce transient anti-CD19 CAR expression in vivo, aiming to deplete B cells and reset immune function in B cell-mediated autoimmune diseases.

Chai Discovery has released Chai-2, a generative molecular design model for zero-shot antibody and binder discovery. The model is capable of designing fully de novo CDRs based solely on a given epitope and target structure, without reliance on existing antibody templates or training-time scaffolds. The company reports double-digit experimental hit rates (usually between 16–20%) validated across 52 previously unaddressed antigens using 20 candidates per target in a standard 24-well-plate format.

The Arc Institute has launched the Virtual Cell Challenge, a new open competition to benchmark computational models that predict how human cells respond to genetic perturbations. Announced today and published in Cell (Vol. 188, Issue 13), the challenge invites AI and computational biology researchers to submit predictive models for scoring on a live leaderboard, using single-cell RNA sequencing data from human embryonic stem cells.

See also: Arc Institute Releases its First Virtual Cell Model

SandboxAQ has launched what it claims is the largest public dataset of protein-ligand complexes with associated binding affinity data. The database, called SAIR (Structurally Augmented IC50 Repository), contains approximately 5.2 million synthetic 3D structures covering over 1 million protein-ligand systems, designed to support the development and benchmarking of AI models in drug discovery.

Context

Spun out of Alphabet in 2022, SandboxAQ operates at the intersection of AI and quantum simulation, with a dedicated biopharma arm known as AQBioSim. Backed by over $300 million in funding (~$5B valuation) and partnerships with AstraZeneca, Sanofi, and the Michael J. Fox Foundation, the company develops Large Quantitative Models (LQMs) to accelerate molecular design and biological simulation.

A new AI model from Google DeepMind, called AlphaGenome, has been released in preview for non-commercial research, offering a high-resolution, long-context DNA sequence model designed to predict the regulatory effects of genetic variants at scale.

XtalPi has signed a letter of intent with DoveTree LLC, a company founded by Harvard University professor Gregory Verdine, to develop small molecule and antibody drug candidates using XtalPi’s AI- and robotics-powered discovery platform. The collaboration will focus on multiple targets selected by DoveTree in oncology, autoimmune disorders, and neurological diseases.

Arc Institute has launched its first virtual cell model, State, designed to predict gene expression changes in response to chemical, genetic, or cytokine perturbations across diverse cell types. The model is publicly released for noncommercial use and trained on observational data from nearly 170 million cells and perturbational data from over 100 million cells across 70 human cell lines, making it the largest single-cell perturbation model to date.

NVIDIA has released a new AI-driven workflow, the Biomedical AI-Q Research Agent Developer Blueprint, designed to accelerate the early phases of drug discovery by automating literature review and hypothesis generation. Built on top of NVIDIA’s AI-Q and Retrieval-Augmented Generation (RAG) blueprints, the system combines a reasoning agent with virtual screening capabilities, targeting use cases such as target identification and small molecule pairing.