Achieve Breakthrough Drug Discovery With Al-Powered Virtual Screening
The Untapped Frontier of Drug Development: Innovation in Hit Discovery
Drug development is a long and complex journey that begins with basic research to uncover the causes of specific diseases and define drug targets, and extends through preclinical and clinical stages. Among these, the stage of initial candidate compound discovery (Hit Discovery) remains the most persistent bottleneck. Unlike other stages that progress in a structured manner, this phase still relies heavily on random and inefficient approaches. As a result, it has become a key driver of low success rates, enormous costs, and development delays that hinder the entire pipeline. However, AI-driven rational strategies and innovative technologies can deliver a transformative impact, especially at this bottleneck. LM-VS™ turns this challenge into an opportunity for innovation.
Why Does Hit Discovery Fail?
Unsystematic Approach
Reliance on random HTS and inefficient, non-systematic methodsHigh Costs and Delays
Repeated setup of new conditions and experiments, leading to prolonged timelinesUncertain Outcomes
Low reproducibility and poor reliability from unpredictable trial-and-error processesHow is LM-VS™ Different?
Rational Approach
AI-driven foundational design through precise 3D structural analysis of targets integrated with AI-powered screeningBreakthrough in Cost & Time
Swift exploration of Bn. compounds, built on a rational design foundation through AIHigher Success Rate
Enhanced accuracy through structure-informed simulation and multi-step validation Innovative Hit Discovery through a Hybrid Approach
Combining AI-Based Design and Precision Screening
With the Designed Scaffold, LM-VS™ applies a hybrid approach that unites AI-driven design with intelligent screening, enabling faster and more accurate Hit discovery anchored in a solid scientific foundation.
What is a Designed Scaffold?
- A structural anchor (pre-organized conformational anchor) designed to maintain stable orientation and positioning within the target protein's binding pocket
- Serves as the launch point for AI-driven molecular exploration
Scaffold Design Process
- 3D structural analysis of the binding pocket to identify key interactions
- Rigorous validation via MD simulations
- a. Assess structural flexibility and stability
- b. Evaluate feasibility of maintaining distances and angles upon functional group binding
- Confirm suitability as a scaffold for rational design
Application of AI-Based Screening
- Scientific Foundation: Establishes clear anchor points for AI-driven exploration
- Efficient Exploration: Identifies optimal functional groups based on the scaffold foundation
- Outcome: Greater precision and shorter timelines, leading to maximized efficiency in Hit discovery
How LM-VS™ Identifies Optimal Candidates from Billions of Compounds
Using rational scaffold points within the target protein’s binding site, LM-VS™ quantitatively evaluates compound binding potential, enabling the selection of the most promising HIT candidates
Binding Pocket and Key Residue Definition
Designed Scaffold Construction
AI-Driven Structural Learning and Exploration
Quantitative Evaluation & Hit Selection
See how it transforms Hit Discovery into a simpler, faster, and more innovative process.
Designed Scaffold LM-VS™: Proven in Real Applications
Designed Scaffold LM-VS™ is adaptable to a wide range of targets and has been successfully applied to the discovery of compounds with diverse mechanisms of action. Explore the representative cases below to see how scaffold-based structural design drives both precision and broad applicability in drug discovery.
From Target to Hit: Four Streamlined Steps
Designed Scaffold LM-VS™ provides a simple, user-friendly workflow that begins as soon as you enter a protein target. From pocket definition to scaffold design, compound exploration, and results review, the entire hit discovery process is streamlined into four clear steps.
Step 1. Select Target Protein
- Search by protein or gene name
- Even when no experimental PDB structure is available, structures can be predicted with AlphaFold and supported for exploration
Step 2. Pocket Definition & Scaffold Design
- AI defines the binding pocket for the target with optimized precision
- A tailored scaffold is proposed for the identified site — with options for user input or customization if desired
Step 3. LLM-Based Compound Exploration
- The Language Model explores compounds on the foundation of the scaffold
- From tens of millions of candidates, extracts high-scoring hit compounds
Step 4. Review Hit Discovery Outcomes
- Receive a comprehensive report in PDF (figures and tables)
- Download hit lists and structural files in EXCEL / CSV / PDB formats
From Hits to Full Insights: Reports, Structures, and Raw Data
Along with a prioritized list of candidate hits selected from a library of up to 10 billion compounds, we provide all the essential data you need for analysis — including raw data, 3D structures (PDB), and comprehensive reports. Explore below to see actual analysis results and sample deliverables in detail.
Curious about what the results look like?
Download a Sample LM-VS™ Screening Report
The sample includes lead compound candidates, structural visuals, and scoring insights—just like what clients receive.
Submit your request today and start screening within a week.
Hybrid Hit Discovery Reinvented: Innovation in AI-Driven Design + Screening
LM-VS™ Workflow: LM-VS™ (Language Model-based Virtual Screening Service) begins by analyzing the selected protein target, precisely defining its binding pocket and key residues to establish a Designed Scaffold — a structural anchor optimized for the pocket. Using this scaffold as the reference point, LM-VS™ explores up to 10 billion compounds to identify optimal functional group combinations, while structure-based pattern learning predicts binding potential. Candidates are then quantitatively evaluated through CNN-based structure-activity metrics, and the most promising hits are iteratively refined across multiple learning rounds to enhance accuracy.
[Advanced Option] DeepMatcher®: The DeepMatcher® stage provides in-depth analysis, integrating evaluations of binding stability, monitoring of conformational changes, and binding energy calculations. This enables the selection of physiologically relevant hit candidates, delivered within two weeks.
Simple Pricing, All-Inclusive Plan
One plan, end-to-end—scaffold design through AI screening for fast, accurate discovery.
Compound Library
10 Billion
Price
$20,000
AI Training
50 Rounds
Timeline
1 Week
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