Gero

What Gero does

Gero is a Singapore-based AI biotech company developing physics-informed machine learning models for drug discovery, with a primary focus on aging and age-related diseases. Its core platform comprises Large Health Models trained on approximately 10 million curated longitudinal human medical records drawn from a broader pool of 100 million+ patient records, used to identify therapeutic targets linked to aging phenotypes. The company extends this into molecular design via ProtoBind-Diff, a proprietary foundation model enabling structure-free generation of protein binders and small molecules. Underpinning the platform is the Fedichev-Gruber theory of aging physics, which frames biological aging as a phase transition and provides a quantitative substrate for the company's predictive models. Scientific output has been validated through publications in Nature Communications and Science.

Gero monetizes through a target-generation business model, licensing AI-derived targets and design outputs to large pharmaceutical partners under milestone-based agreements. Disclosed enterprise collaborations include a partnership with Pfizer initiated in January 2023 and a Chugai (Roche group) collaboration announced in July 2025 valued at up to $250M+ in upfront and milestone payments. The company has raised approximately $34M in equity funding across multiple rounds between 2020 and 2026 and maintains a team of roughly 20 staff spanning physics, biology, and machine learning, with a US presence in San Francisco in addition to its Singapore headquarters. As of the most recent disclosed activity, Gero remains preclinical, with commercial value concentrated in upfront payments, research funding, and milestone recognition from its pharma collaborations.

Name

Gero

Legal name

GERO PTE. LTD.

Website

https://gero.ai

Company type

Private

Founded year

2015

Operating status

Operating

Headcount range

11–50 employees

Short description

Gero is a Singapore-based AI biotech that builds physics-informed foundation models to identify drug targets and design molecules for aging and age-related diseases, licensing outputs to large pharmaceutical partners under milestone-based agreements.

Ownership category

akta.pro rank

Product category

AI Drug Discovery

NAICS

Research and Development in Biotechnology (except Nanobiotechnology) (541714), Scientific Research and Development Services (5417)

SIC

Services-Commercial Physical & Biological Research (8731)

akta.pro primary industry

AI/ML Platforms for Drug Discovery & Experiment Optimization (HLAGAJAO)

akta.pro secondary industries

AI/ML-Enabled Biologic Discovery Platforms (computational design, in silico screening) (HLAAAIAE), Target Identification & Validation Platforms (omics-driven, chemoproteomics, genetic validation) (HLAAAIAI), Scientific Data Management & Data Platforms (SDMS/Data Lakehouse for R&D) (HLAGAJAC)

Keywords

• AI drug discovery
• Longevity therapeutics
• Target identification
• Aging biology research
• Pharmaceutical partnerships

Headquarters

HQ city

Singapore

HQ country

Singapore

HQ region

Asia

Offices2 records

Markets served

GTM type

B2B

Offering type

Software

Cost components

Personnel, Technology or R&D, Operations, Marketing or Sales, Infrastructure

Revenue model

1. Pharmaceutical Partnerships and Licensing: Gero operates as a target-generation platform, identifying molecular targets and licensing them to pharmaceutical partners. Revenue comes from upfront payments, milestone payments (up to $250M per partnership), and royalties on downstream development. This is the primary revenue model.
2. Collaborative Research Agreements: Research collaborations with pharma companies where Gero provides target discovery services. Partners include Pfizer (2023 fibrotic disease collaboration) and Chugai/Roche ($250M milestone deal for age-related diseases).

Go-to-market motion2 records

Distribution channels1 record

Marketing channels6 records

Core offering

Gero operates a physics-first AI drug discovery platform that combines Large Health Models trained on ~10 million curated longitudinal medical records with physics-based aging frameworks (the Fedichev-Gruber theory) to identify causal molecular targets for aging and age-related diseases. The company functions as a target-generation platform, licensing these targets to pharmaceutical partners (Chugai/Roche, Pfizer) who bear downstream clinical development risk, while also advancing select anti-aging interventions in-house. Its product portfolio includes the ProtoBind-Diff foundation model for structure-free drug design and a consumer-facing mobile app for biological age measurement.

Product overview

Gero is a physics-first AI drug discovery company offering a unified platform centered on its core AI models trained on large-scale longitudinal health data. The main offerings include the Gero AI Drug Discovery Platform, the ProtoBind-Diff foundation model for structure-free drug design, and Large Health Models that identify drug targets for aging and age-related diseases. For consumers, Gero provides a mobile app that calculates Resilience and Biological Age metrics from wearable health data. The company operates a target-generation model, proposing molecular targets to pharma partners who bear downstream clinical development risk.

Differentiator

Problem solved

Functional benefit

Products and services

• Gero AI Drug Discovery Platform A physics-first AI drug discovery platform that models how aging and disease emerge over time in humans, turning large-scale longitudinal health data into causal drug targets, novel molecules, and more translatable therapeutic programs. Offered to pharmaceutical partners for target identification and licensing in age-related indications.
• ProtoBind-Diff The first AI foundation model for structure-free drug design using only protein sequence, enabling targeted small molecule discovery without requiring experimental protein structure data. A novel computational drug discovery approach launched in 2025.
• Large Health Models AI models trained on approximately 10 million curated longitudinal medical records combined with physics-based aging frameworks to map aging trajectories across populations and identify causal drug targets that may slow aging and address age-related diseases.
• Gero Mobile App Consumer-facing mobile application that collects health data (step count, heart rate, heart rate variability) from Apple Health to calculate Resilience (recovery rate) and Biological Age for individual users.

Quantifiable outcome

• Potential to add 30-40 additional healthy years through noise-reducing drugs (Level 2 interventions)
• +2 more outcomes

Named customers1 record

Segments2 records

Ideal customer profiles2 records

Technology focussed Yes

API detail

Has API

No

API docs

API detail

Core technology

AI maturity

App detail

Integration2 records

AI capability4 records

Feature4 records

Partnerships

Two partnerships are on record, tiered flagship and core.

• Chugai Pharmaceutical (Roche Group)flagshipStrategic or Co-development Partner · 7 July 2025Joint research and license agreement to discover antibody therapies for age-related diseases. Gero provides target identification using its Large Health Models; Chugai leads antibody development. Deal includes upfront payment and up to $250M in milestone payments plus royalties. Announced July 2025.
• PfizercoreStrategic or Co-development Partner · 6 January 2023Research collaboration to discover targets for fibrotic disease. Gero applied its AI platform to identify molecular targets; Pfizer conducted downstream validation and development.

Scale indicators4 records

Recent moves6 records

Expansion highlights5 records

Broad incumbents

• Isomorphic Labs: Isomorphic Labs is Alphabet's AI drug discovery spin-out applying DeepMind's protein structure prediction (AlphaFold) to therapeutic discovery. It competes for the same pharma partnerships as Gero and represents a much larger, better-capitalized competitor in AI-driven target identification and molecular design.
• Schrödinger: Schrödinger provides computational chemistry and physics-based simulation software (and internal drug discovery) used by pharma companies for molecular design. Its physics-based drug discovery platform competes with the physics-informed aspects of Gero's approach and serves overlapping pharma customers.

Direct peers

• Exscientia: Exscientia pioneered AI-driven small molecule design and has been a key reference customer profile for Gero's Head of IP, who previously led IP and licensing at Exscientia. Exscientia's end-to-end AI drug design platform (now part of Recursion) directly overlaps with Gero's ProtoBind-Diff and target-generation capabilities.
• Calico Life Sciences: Calico is an Alphabet-backed research organization focused on the biology of aging and longevity. Calico's mission and aging-biology focus align closely with Gero's thesis, though Calico operates at significantly greater scale and with internal development rather than a partnership-first model.
• Recursion Pharmaceuticals: Recursion is a clinical-stage AI-biotech combining high-throughput biology, automation, and foundation models for drug discovery. Following its acquisition of Exscientia, Recursion is among the largest pure-play AI drug discovery platforms. It serves a similar pharma partnership customer base and competes for capital and partnerships with Gero.
• BenevolentAI: BenevolentAI is an AI-drug discovery company using knowledge graphs and machine learning to identify novel targets and drug candidates, with pharma partnerships as a key revenue stream. Its target identification and partnership-driven model closely parallels Gero's go-to-market.
• BioAge Labs: BioAge Labs is a clinical-stage biotech focused on the biology of aging and developing therapeutics that target aging mechanisms to extend human healthspan. Direct overlap with Gero's aging biology thesis and pharma partnership model, with BioAge having advanced to clinical trials.
• Insilico Medicine: Insilico Medicine is an AI-driven biotech focused on aging and age-related diseases, combining generative AI, target discovery, and internal drug development. It is the most direct competitor to Gero, with overlapping customer base (pharma partnerships) and a similar AI drug discovery thesis applied to aging biology. Insilico has a more vertically integrated model (internal pipeline plus PharmaGen platform), making it a useful benchmark.

Others

• Owkin: Owkin is an AI-biotech applying machine learning to real-world patient data for drug discovery and development, primarily in oncology. It is comparable as an AI-drug discovery platform leveraging large-scale patient data, though its therapeutic focus is more oncology-centric than aging.

Emerging players

• Loyal for Dogs: Loyal is a clinical-stage biotech developing longevity therapeutics for dogs, with regulatory milestones from the FDA. It addresses aging biology using a pharma-style development model and is an emerging peer in the broader longevity therapeutic space, though its focus on veterinary applications is a different go-to-market.

Market position

Strengths5 records

Weaknesses5 records

Competitive moat4 records

Key risks6 records

Key highlights7 records

Customer concentration

• Xx.com/hacking_aging
• LinkedInlinkedin.com/company/gero
• Facebookfacebook.com/geroscience

Revenue estimate

Valuation estimate

Number of profiles

Profiles10 records

Funding overview

Funding rounds6 records

Investors3 records

Funding detail is available on the Subscription and Enterprise plan.Contact sales →

M&A

Investments

M&A and investment is available on the Subscription and Enterprise plan.Contact sales →

Frequently asked questions about Gero