Weekly Tech+Bio Highlights #63: Quantum vs. AI Small Molecule Design
FDA’s pathway for custom gene editing, UK’s non-animal testing roadmap, OpenAI leads a biosecurity seed, 100x mRNA delivery & over $1B in fresh life sciences VC funds
Hi! This is BiopharmaTrend’s weekly newsletter, Where Tech Meets Bio, where we explore technologies, breakthroughs, and cutting-edge companies.
If this newsletter is in your inbox, it’s because you subscribed, or someone thought you might enjoy it. In either case, you can subscribe directly by clicking this button:
🤖 AI x Bio
(AI applications in drug discovery, biotech, and healthcare)
🔹 Experiment-driven ML for small-molecule design — Terray launched EMMI, an integrated microarray+AI platform built on 13B+ compound–target measurements and its COATI molecular foundation model, adding a new module that uses uncertainty-aware selection to boost potency-optimization efficiency ~3x across discovery programs.
🔹 Small-molecule generation: Quantum vs AI — in a working paper, Kendall Byler and Shahar Keinan show that the quantum-based QuADD platform produced more drug-like, higher-affinity, synthesizable molecules in ~30 minutes, outperforming an AI diffusion model that took ~40 hours and generated more diverse but less viable candidates.
🔹 BenchSci and Mila launched a multi-year collaboration to develop advanced predictive and generative models that automate hypothesis generation and assay prediction to accelerate AI-driven drug discovery.
🔹 Generative AI for anti-fibrotic peptides— Animate Biosciences launched a generative-AI platform that converts multi-omic insights from regenerative species into tailored peptide therapeutics, building a pipeline for inflammatory and fibrotic diseases across multiple organs.
🔹 AI-designed muscle-targeting AAV & Increasing ‘genetic agency’ — Dyno Therapeutics unveiled an AI-engineered AAV vector with improved muscle delivery and reduced liver exposure, high skeletal and cardiac transduction in primates at ~25x lower dose than typical muscle gene therapies.
🔹 Multi-omic dataset effort — Tempus and IFLI launched a multi-year study to build a large follicular lymphoma dataset using genomics, proteomics, and methylation profiling, to accelerate biomarker discovery and precision research.
🔹 AI-guided oncology pipeline progress — Lantern Pharma reported Q clinical and regulatory advances across its AI-driven cancer programs: early efficacy signals in multiple solid tumors, regulatory guidance for a planned pediatric CNS trial, and broader commercialization of its AI drug-discovery platform, with $12.4M in cash extending runway into ~Q3 2026.
🔹 Owkin released its biology-focused AI co-pilot on AWS Marketplace as a SaaS offering, enabling faster procurement and deployment of agentic research workflows powered by multimodal data and biological reasoning.
🔹 AI toxicology — Cellarity published an AI + multi-omics framework that predicts drug-induced liver injury using a large human hepatocyte transcriptomics dataset, achieving high accuracy and outperforming >20 standard safety models, with the tools released open-source (model and validation data).
🔹 Multimodal CRC biomarkers — Nucleai and the University of Glasgow partnered to use AI and spatial multi-omics data from a large polyp cohort to identify predictive biomarkers for colorectal cancer and improve early-risk stratification and surveillance.
🔹 Esophageal cancer biomarkers — BostonGene and Kyoto University partner to analyze multiomic tumor data from an ESCC trial, aiming to identify immune-linked biomarkers that guide targeted therapy development and patient stratification.
🔹 TCR atlas for autoimmunity — A stealth Third Rock Ventures company partnered with Parse Biosciences to generate a ~5M-receptor TCR atlas via the GigaLab platform, creating a resource to map immune drivers and accelerate autoimmune drug discovery.
🔹 AI agent for women’s health — Millie launched an AI assistant built on a new unified data platform that personalizes maternity support, automates routine questions and scheduling, and streamlines clinical workflows to boost efficiency across its growing clinic network.
🔹 Modular protein nanomaterials — UW researchers designed reusable protein building blocks that assemble into cages, lattices, and 3D crystals by engineering binding interfaces first, creating a more programmable toolkit for protein-based nanostructures.
🔹 Foundation-model pathology forecasting — MIT researchers used Bioptimus’ H-optimus-1 model, trained on >1M pathology slides, to predict six-month tumour progression from a single image with ~0.8 AUC.
This newsletter reaches over 10K industry professionals from leading organizations across the globe. To reserve your sponsor slot in one of the upcoming issues, contact us at info@biopharmatrend.com
💰 Money Flows
(Funding rounds, IPOs, and M&A for startups and smaller companies)
🔹 AI + physics drug discovery — TandemAI raised a $22M Series A extension to scale its AI-and-physics-based discovery platform and wet-lab infrastructure, bringing total funding to ~$80M and supporting a pipeline that has produced 10+ clinical-bound candidates.
🔹 Reprogramming diseased cells — Scripta Therapeutics raised $12M seed funding to develop AI- and biology-driven drugs that modulate transcription factor activity, aiming to reshape disease pathways, starting with neurodegeneration.
🔹 Programmable miniprotein drug design — AI Proteins raised ~$42M Series A to advance its first-principles, de novo miniprotein platform and launch programs through a hub-and-spoke model, building on its earlier ~$18M seed round and a Bristol Myers Squibb collaboration valued up to ~$400M.
🔹 AI–brain data scaling — Beacon Biosignals raised $86M Series B to expand its AI platform built on large-scale home EEG and sleep data, growing its neurobiomarker dataset and supporting CNS drug development across major pharma partners (incl. Takeda, UCB, Syndeio).
🔹 Sofinnova Partners closed a €650M ($750M) early-stage life sciences fund, lifting its total capital raised over the past year to €1.5B and expanding support for biotech and medtech startups across Europe and the US.
🔹 Medicxi raised €500M for its latest fund to back asset-focused biotechs across stages, bringing its total capital raised over the past decade to >€2B and building on recent exits and acquisitions that generated >$1B in returns.
🔹 Oncology CRO transfer — JSR Life Sciences will sell Crown Bioscience to Adicon, moving its tumor-model and translational oncology services to a new owner while keeping its biospecimens unit in-house.
🔹 Fabric acquired UCM Digital Health, adding ~1M covered lives and 400 payer/employer clients, reinforcing its scale across a platform now serving 100M+ people nationwide.
🔹 AI-enabled biosecurity push — OpenAI led a $15M seed round for Red Queen Bio, a Helix Nano spinout developing AI-driven defenses against misuse of biological design tools, extending OpenAI’s recent biosecurity investing efforts.
🔹 Japan’s new global biotech CVC — Hiroki Takai announced the launch of TOHO Ventures, a ¥10B (~$64M) fund created by TOHO HOLDINGS and Global Brain to back drug-discovery and modality-focused startups mainly in Europe and the U.S., leveraging TOHO’s nationwide clinical and distribution networks to support commercialization.
🔹 UK accelerates shift to non-animal testing — The UK unveiled a national roadmap, announced by Lord Vallance, committing £75M to scale organ-on-chip, 3D bioprinting, and AI/in-silico models, alongside a new UK Centre for the Validation of Alternative Methods to speed regulatory acceptance and phase out multiple animal tests by 2026–2030.
⚙️ Other Tech
(Innovations across quantum computing, BCIs, gene editing, and more)
🔹 BCI-enabled camera control — Neuralink brain-computer implant allowed an ALS patient to control a motorized webcam and communicate more fluidly, marking one of 12 implanted users with a combined ~15,000 hours of device use.
🔹 100x more potent mRNA delivery — MIT researchers engineered a new degradable lipid nanoparticle that boosts mRNA vaccine potency ~100x in mice while lowering liver tox, enabling equal immune responses at ~1/100 the dose of current FDA-approved LNPs.
🔹 Induced-proximity oncology collaboration — General Proximity partnered with Daiichi Sankyo to use its OmniTAC platform to discover effector–target pairs for next-gen induced-proximity cancer therapeutics, expanding the platform’s reach to traditionally undruggable targets.
🏛️ Bioeconomy & Society
(News on centers, regulatory updates, and broader biotech ecosystem developments)
🔹 New FDA path for custom gene editing — FDA leaders outlined a “plausible mechanism” pathway enabling individualized gene-editing therapies to gain approval based on demonstrated target engagement and biologic plausibility, aiming to scale single-patient successes like baby KJ’s treatment for rare diseases.
Read also:
Three Big Ideas in Aging Research That Could Shift the Therapeutic Landscape
A lot of chemists I've met aren't really don't care about AI which is pretty strange to me. Their argument is that the hardest part of making molecules isn't the design process, it's in optimizing the synthetic route, reaction kinetics, or troubleshooting solubility issues that AI might not have caught. It's only going to get better, so we'll see how long their feelings are valid...
This issue reads like standing on a cliff edge and watching two forces reshape the landscape at the same time — AI accelerating beyond its own shadow, and biology cracking itself open into something programmable, modular, almost language-like.
What struck me most is not the flood of innovations but the pattern underneath: everyone is trying to compress the distance between data → mechanism → therapy.
Quantum shortcuts for molecular space, foundation models for pathology, TCR atlases at population scale — all of it is a race to collapse the search time between “we think this works” and “we know why it works.”
And then there’s the other thread you didn’t spell out but is woven through the entire issue:
regulators are finally admitting they can’t keep up unless they switch paradigms too.
The FDA’s “plausible mechanism” route for individualized gene editing might be the quietest but most revolutionary line in the whole newsletter.
That’s not incremental change — that’s the beginning of medicine leaving the era of population-level treatments and entering the era of personalized interventions built on-the-fly.
What I kept thinking while reading is:
the pace isn’t just increasing — the system is starting to self-accelerate.
AI tools design molecules; multimodal datasets train the AI; regulators adapt to AI-powered biology; and new funding waves chase the possibilities opened by the first cycle.
Your roundup captures that tipping point feeling perfectly:
we’re crossing from “tech in bio” to “bio becoming an information technology.”
Curious what you think will snap first:
the scientific bottlenecks or the regulatory ones?