Synthace
Accelerate biological discovery through automated experiment design

Target Audience
- Drug discovery teams
- Pharmaceutical R&D departments
- Industrial biology researchers
- Biotech assay developers
Hashtags
Overview
Synthace combines specialized software with hands-on expert support to help drug discovery teams run smarter experiments. The platform helps scientists automate complex multi-variable assays that are normally time-consuming and difficult to standardize. Teams get faster, more reliable results while maintaining control over their experimental processes.
Key Features
Multifactorial Methods
Design complex experiments with multiple variables simultaneously
Lab Integration
Works with existing lab dispensers and equipment from day one
Expert Partnership
On-site specialists help implement and scale solutions
Standardized Insights
Structured approach for reliable, repeatable experiment results
Rapid Proof
Deliver measurable results within first experiments
Use Cases
Develop high-throughput biological assays
Optimize multi-variable experiments
Train teams in advanced methods
Scale discovery processes across labs
Accelerate drug development timelines
Pros & Cons
Pros
- Reduces experiment time by months according to case studies
- Maintains scientist control while improving throughput
- 10x+ experimental throughput reported by users
- Works with existing lab infrastructure
Cons
- Requires organizational adoption for full benefits
Frequently Asked Questions
What makes Synthace different from other lab automation tools?
Synthace combines software with dedicated expert support to implement multifactorial methods while working with existing lab equipment.
Can small teams use Synthace effectively?
The platform appears optimized for organizational adoption, with specialists helping scale solutions across drug discovery teams.
Does Synthace require new lab equipment?
No, it integrates with existing dispensers and infrastructure according to case studies.
Reviews for Synthace
Alternatives of Synthace
Accelerate medical research with AI-generated hypotheses and experimental designs
Design improved protein variants with machine learning assistance
Enhance drug development success with AI insights