LM-VS™ Designed Scaffold Technology

LM-VS™ Designed Scaffold Technology

AI learns from 10B compounds
to autonomously identify
the most promising hit candidates

AI learns from 10B compounds to autonomously identify
the most promising hit candidates

Over 70% cost reduction in hit discovery
achieved through AI automation start now.

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Compound Library

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Compound Library

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Compound Library

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AI Learning Loops

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AI Learning Loops

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AI Learning Loops

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Per Target Protein

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Per Target Protein

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Per Target Protein

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Top Candidates

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Top Candidates

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Top Candidates

LM-VS™ delivers proven results
even for the toughest targets.

Membrane Protein

AI stabilizes dynamic membrane pockets to predict precise binding interactions.

Membrane Protein

AI stabilizes dynamic membrane pockets to predict precise binding interactions.

Membrane Protein

AI stabilizes dynamic membrane pockets to predict precise binding interactions.

Membrane Protein

AI stabilizes dynamic membrane pockets to predict precise binding interactions.

Molecular Glue

AI fine-tunes inter-protein affinity and forms stable complexes.

Molecular Glue

AI fine-tunes inter-protein affinity and forms stable complexes.

Molecular Glue

AI fine-tunes inter-protein affinity and forms stable complexes.

Molecular Glue

AI fine-tunes inter-protein affinity and forms stable complexes.

TPD (Targeted Protein Degradation)

AI identifies E3–target interactions and discovers optimal degrader candidates.

TPD (Targeted Protein Degradation)

AI identifies E3–target interactions and discovers optimal degrader candidates.

TPD (Targeted Protein Degradation)

AI identifies E3–target interactions and discovers optimal degrader candidates.

TPD (Targeted Protein Degradation)

AI identifies E3–target interactions and discovers optimal degrader candidates.

With a single input,
AI designs, screens, and generatesoptimized candidates for your target.

Step 1. INPUT

Scaffold Derivatives

Enter your target pocket or binding-site structure.
Our team reviews the data and designs an optimized scaffold that fits the binding environment.
After client review, the finalized scaffold becomes a precise and reliable starting point for all AI screening and learning.

Step 2. LM-VS™

Listening to Your Vision

We begin by discussing your goals, audience, and expectations to ensure our approach aligns perfectly with your vision.

Step 3. OUTPUT

Listening to Your Vision

We begin by discussing your goals, audience, and expectations to ensure our approach aligns perfectly with your vision.

February 2023

November 2024

Step 1. INPUT

Scaffold Derivatives

Enter your target pocket or binding-site structure.
Our team reviews the data and designs an optimized scaffold that fits the binding environment.
After client review, the finalized scaffold becomes a precise and reliable starting point for all AI screening and learning.

Step 2. LM-VS™

Listening to Your Vision

We begin by discussing your goals, audience, and expectations to ensure our approach aligns perfectly with your vision.

Step 3. OUTPUT

Listening to Your Vision

We begin by discussing your goals, audience, and expectations to ensure our approach aligns perfectly with your vision.

February 2023

November 2024

January 2019

January 2019

January 2019

AI redefines the pace of discovery.
Curious how it works?

Explore LM-VS™ — from input to insight.

LM-VS™ simulates
molecular dynamics to predict drug-target
binding patterns and delivers
hit candidates in just two weeks.

Speed. Reliability. Scale.

Our 5MW AI Bio Supercom Center runs 5,000+ GPUs in parallel,
screening 10 billion compounds and generating results instantly.

5MW AI BIO Supercom Center

5층 독립 구조, 5,000+ CPU/GPU 클러스터

다수의 고성능 코어로 최적화된 병렬 연산 환경 제공

4kW + 20Mbps 대역폭 구성

독립 클러스터 기반의 실시간 연산 안정성 확보

데이터 손실 없이 즉시 결과 생성

AI efficiency redefines discovery.
Complete hit discovery for just $20K.

LM-VS™ isn’t about being cheaper — it’s about being smarter.

From early stages requiring rapid validation to strategic co-development, you can choose the most efficient collaboration method based on the speed and goals of your research.
Check out the optimal program for your research stage.

Popular

$20,000

/Per Target Protein

LM-VS™ isn’t about being cheaper —
it’s about being smarter.

Included

INPUT (Scaffold Derivatives)
Custom scaffold and pocket design optimized for your target

LM-VS™ (Automated Virtual Screening)
AI-driven screening of 10B compounds with up to 50 iterative learning loops

OUTPUT (Instant Report Generation)
Automated ranking of top candidates and report creation

Timeline

Within 3 weeks

Deliverables

Top 200 Candidates Report + Structure Dataset

FAQ

FAQ

How is AI screening different from traditional experiments?

What are the key benefits of using LM-VS™?

Can AI-generated results be directly applied to experiments?

How long does the analysis take?

What deliverables are included?

Is LM-VS™ applicable to all protein targets?

How is data security managed?

How do I request a quote or start a project?

How is AI screening different from traditional experiments?

What are the key benefits of using LM-VS™?

Can AI-generated results be directly applied to experiments?

How long does the analysis take?

What deliverables are included?

Is LM-VS™ applicable to all protein targets?

How is data security managed?

How do I request a quote or start a project?

How is AI screening different from traditional experiments?

What are the key benefits of using LM-VS™?

Can AI-generated results be directly applied to experiments?

How long does the analysis take?

What deliverables are included?

Is LM-VS™ applicable to all protein targets?

How is data security managed?

How do I request a quote or start a project?

No waiting. Just results — LM-VS™

Achieve results beyond what’s possible.

Contact Us

Contact Us

© Syntekabio Co., Ltd. All rights reserved.

Sales/Partnerships

bd@syntekabio.com

Family Sites

Family Sites

Sales/Partnerships

bd@syntekabio.com

© Syntekabio Co., Ltd. All rights reserved.

Family Sites

Sales/Partnerships

bd@syntekabio.com

© Syntekabio Co., Ltd. All rights reserved.