Name

Hyperlab

Legal name

(주)히츠

Website

https://hyperlab.ai

Company type

Private

Founded year

2016

Operating status

Operating

Headcount range

11–50 employees

Ownership category

akta.pro rank

Product category

AI Drug Discovery Platform

NAICS

Research and Development in Biotechnology (except Nanobiotechnology) (541714)

SIC

Services-Commercial Physical & Biological Research (8731), Services-Prepackaged Software (7372)

akta.pro primary industry

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

akta.pro secondary industry

AI/ML-Enabled Biologic Discovery Platforms (computational design, in silico screening) (HLAAAIAE)

Keywords

• AI drug discovery
• Generative molecular design
• Virtual compound screening
• Protein structure prediction
• ADMET property prediction

Headquarters

HQ city

Kuala Lumpur

HQ country

Malaysia

HQ region

Asia

Offices1 record

Markets served

GTM type

B2B

Offering type

Software

Cost components

Technology or R&D

Revenue model

1. Core Plan Subscription: Full-featured subscription providing AI docking, ADME/T prediction, and novel molecular design. Includes 6 tokens. Priced at $3,000/month per labspace.
2. Academic Plan: Core AI functionality with limited computational usage at reduced cost for academic and research institutions.
3. Custom Service Plan: 1:1 customized plan offering expert technical support and modular platform usage tailored to research purposes.
4. Hyper Screening X Add-on: Additional subscription for accessing 11 trillion+ compound library virtual screening and synthesis ordering.
5. Hyper Screening Points: Usage-based points for virtual screening across multiple targets. $1,000 per 1,000 points, available in 5-point increments.
6. Labspace Member Add-ons: Additional team members for collaboration with volume-based discounts based on member count and usage duration.

Pricing tiers

Go-to-market motion2 records

Distribution channels3 records

Marketing channels4 records

Core offering

Hyperlab is a cloud-based AI drug discovery SaaS platform that enables pharmaceutical and academic researchers to perform virtual screening over a vast compound library, generate novel synthesizable molecules via generative AI, predict drug-protein binding and ADME/T properties, and predict protein 3D structures, all within an integrated labspace workflow. The platform is sold via monthly subscription tiers (Core, Academic, Custom Service) with usage-based add-ons such as Hyper Screening Points, and is used by biotech/pharma researchers globally.

Product overview

Hyperlab is an AI-powered drug discovery platform structured as a core plan with modular capabilities plus specialized labspaces. The primary Small Molecule Discovery suite includes Hyper Screening, Hyper Screening X, Hyper Binding, Hyper Binding Co-Folding, Hyper Design, and Hyper ADME/T, unified through a Seamless Flow architecture. Premium features like Hyper Screening X leverage the proprietary RxnFlow generative AI (ICLR 2025) with an 11+ trillion compound library. A separate Biomedical Co-Scientist labspace, powered by the KAIST co-developed K-Fold protein structure prediction model, enables conversational research workflows for antibody and protein binder design. Subscription tiers include Academic Plan (for institutions), Core Plan ($3,000/month with 6 tokens), and Custom Service Plan (enterprise). Add-on purchases cover Hyper Screening Points and additional labspace members.

Differentiator

Problem solved

Functional benefit

Products and services

• Hyper Screening Cloud-based virtual screening module that searches through large molecular libraries to identify active substances with pharmacological potential, screening up to 1 million molecules per day. Used by pharmaceutical and biotech researchers for hit identification.
• Hyper Screening X Premium virtual screening add-on module powered by the proprietary RxnFlow generative AI, providing access to an 11+ trillion virtual compound library with custom AI training for target-specific molecules, purchasable building blocks, and an 80%+ synthesizability guarantee. Used by researchers seeking higher discovery probability and direct synthesis procurement.
• Hyper Binding Drug-protein binding prediction module that uses AI to predict binding affinity, helping researchers prioritize experiments and understand binding mechanisms. Targeted at medicinal chemists and computational biologists.
• Hyper Binding Co-Folding Advanced binding prediction that simultaneously predicts both bound and unbound protein structures to capture conformational changes during the binding process, used for more accurate binding mechanism analysis.
• Hyper Design AI-powered molecular structure design module that generates novel molecules with desired properties, employing multiple generative models including GFlowNet variants and diffusion models for structure-based drug design. Used by researchers to design novel molecular structures and understand factors that change derivative activity.
• Hyper ADME/T ADME/T prediction module that forecasts absorption, distribution, metabolism, excretion, and toxicity properties of drug candidates to optimize lead selection for pharmaceutical researchers.
• Biomedical Co-Scientist Specialized labspace for biomedical research combining chat-based literature research, molecular design, and structure verification in a single conversational workflow, powered by the K-Fold protein structure prediction model and supporting antibody and protein binder design.

Quantifiable outcome

• 1 million molecules screened within 24 hours
• +2 more outcomes

Named customers4 records

Segments3 records

Ideal customer profiles3 records

Technology focussed Yes

API detail

Has API

No

API docs

API detail

Core technology

AI maturity

App detail

AI capability8 records

Feature10 records

Partnerships

Two partnerships are on record, tiered core and supporting.

• KAIST (Korea Advanced Institute of Science and Technology)coreStrategic or Co-development PartnerHyperLab co-developed the K-Fold biomedical foundation model with KAIST. K-Fold is a next-generation AI model for protein structure prediction that uses protein's own characteristics rather than evolutionary information (MSA). It can predict protein-ligand and nucleic acid complex structures for binding site analysis and binder/antibody design. This partnership represents a core technological foundation for HyperLab's capabilities.
• Synple ChemsupportingTechnology or IntegrationHyperLab integrates with Synple Chem's synthesis service to provide 80%+ synthesis feasibility guarantee for molecules generated by Hyper Screening X. Users can request synthesis quotes and purchase building blocks directly through the platform, enabling seamless transition from virtual screening to actual synthesis.

Scale indicators4 records

Recent moves4 records

Expansion highlights5 records

Direct peers

• Atomwise: Pioneer in AI virtual screening of small molecules against targets, with AtomNet platform and multiple pharma partnerships. Directly comparable to HyperLab's Hyper Screening / Hyper Screening X virtual screening capability.
• Insilico Medicine: AI-driven drug discovery platform combining generative chemistry, target identification, and clinical development. Closest direct peer to HyperLab's end-to-end small-molecule AI workflow including generative design and virtual screening.
• Schrödinger: Physics-based and AI-augmented computational chemistry platform (LiveDesign) serving pharma R&D with molecular design, FEP+, and binding prediction. Comparable in serving the same pharma computational-chemistry buyer as HyperLab, with overlap in binding/co-folding and ADMET prediction.
• BenevolentAI: AI platform for drug discovery spanning target identification, molecular design, and clinical prioritization. Comparable in delivering SaaS-style AI discovery tools to enterprise pharma R&D teams.
• XtalPi: AI + robotics drug discovery company combining crystal-structure prediction, generative molecular design, and automated synthesis — directly mirrors HyperLab's AI-design-to-synthesis loop with Synple Chem.

Broad incumbents

• Recursion Pharmaceuticals: AI-driven biotech combining high-throughput wet-lab experimentation with ML models for target ID and discovery. Larger, publicly listed incumbent operating across the full discovery pipeline rather than a pure-software platform.
• Absci: AI generative biology company pairing zero-shot AI protein design with high-throughput wet-lab validation. Larger publicly listed player in adjacent generative-design space — relevant peer for evaluating enterprise AI-discovery business models.
• Isomorphic Labs: Alphabet-owned drug discovery company building on AlphaFold for structure-based drug design and novel therapeutic development. A much larger, well-capitalized incumbent in AI protein-structure-based discovery — overlaps directly with HyperLab's K-Fold value proposition.

Emerging players

• Iambic Therapeutics: AI-driven drug discovery combining physics-based models with neural networks for clinical candidate design. Early-stage but well-funded emerging player with comparable generative chemistry and ADMET prediction capability to HyperLab.
• Cradle Bio: Generative AI platform for protein and biological molecule design with wet-lab validation. Emerging competitor focused on biologics design — adjacent to HyperLab's small-molecule focus but overlapping on generative AI methodology and pharma enterprise sales motion.

Market position

Strengths5 records

Weaknesses5 records

Competitive moat5 records

Key risks6 records

Key highlights7 records

Customer concentration

Revenue estimate

Valuation estimate

Number of profiles

Profiles1 record

Funding overview

Funding rounds1 record

Investors

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 Hyperlab