Short menu

AI-platform for Accelerate discovery by simulation biochemical in vitro experiments

The aim of the AI platform is to replace verification and intermediate in vitro experiments by predicting experimental data
A proteomics focused exploration
ai platform for biochemical experiments in‑vitro experiment prediction software predictive analytics for lab research
Pharma and biotech spend tens of billions each year on in‑vitro work, where variability and low first‑try success increse cost and timelines. BinomLabs applies state‑of‑the‑art ML to deliver experiment prognoses that align with lab data up to R = 0.93, demonstrated on real in‑vitro studies.

With this guidance, teams typically reduce ~10 experiments to 3–4, lifting the proportion of successful first attempts and freeing capital for the highest‑value questions. We enhance expert judgment—turning every scientist into a faster, more precise decision‑maker.

Reduce the number of in vitro experiments from 10 to 4 by obtaining a forecast of experimental data

Financial benefits/Practice Areas

Prices start from 5 Euros for the calculation of one mutant protein,
compared to the experimental cost of ordering 10 milligram of the same protein from 270 Euros+associated costs.
Below on the visual cards are presented the benefits and price comparisons
biochemical experiment automation protein mutation analysis laboratory assay automation
digital lab workflow predictive analytics for laboratory research
Reduce in vitro cost
phone nomber: 053-382-60-75
WhatsApp: +972-53-382-6075
kulikov.kirill.g@gmail.com
koshlan.tetiana@gmail.com

Binomlabs Platform
Data prognosis
$
$
No AI -prediction
No AI -prediction
$
$
$
$
$
$
$
$
$
$
$
$
lab $ costs for each in vitro test
Contacts for more detailed information:
for technical information:
AI-powered biochemical data analysis automated lab data processing in vitro experiment simulation
life science data interpretation, in vitro diagnostics workflow pharma data analytics research lab automation tools AI for drug discovery bioinformatics visualization platform

AI Powered Platform for Protein Analysis

Use AI to predict biochemical data:
  • Kd, IC50%, affinity prediction,
  • Alanine screening, mutagenesis,
  • Entropy change,
  • Big data analysis,
  • selection of flexible antibody-antigen chains
The aim of the AI platform is to replace verification and intermediate in vitro experiments by predicting experimental data
Pharma and biotech spend tens of billions each year on in‑vitro work, where variability and low first‑try success increse cost and timelines. BinomLabs applies state‑of‑the‑art ML to deliver experiment prognoses that visually align with lab data up to R = 0.93, demonstrated on real in‑vitro studies. With this guidance, teams typically reduce ~10 experiments to 3–4, lifting the proportion of successful first attempts and freeing capital for the highest‑value questions. We enhance expert judgment—turning every scientist into a faster, more precise decision‑maker.
Are you part of a biochemical or pharmacological laboratory and looking for highly effective results?

Join our free pilot program featuring an advanced AI platform that predicts experimental data with 70%-90% correlation, reducing the number of required experiments by 60%!
🚨 We’re Hiring Pilot Projects! 🚨 FREE PILOT project for biochemistry labs!
Benefits:
Save time and resources.
Enhance research efficiency.
Focus on breakthroughs while AI handles the heavy lifting.

📩 Apply now to explore this game-changing technology and collaborate with us!
  • Enzyme properties and the effect of substrate concentration
  • Protein assay and standard curve generation
  • Thin-layer chromatography
  • ELISA.
  • DNA/RNA Sequencing.
  • Nutrition.
  • Gel Electrophoresis.
  • Antibodies & Antigens.
  • Blotting Methods.
AI-powered biochemical data analysis, digital lab notebook automation,
Benefit from modern AI-BinomLab technologies:

AI Platform for Accurate Experimental Data Prediction:

  • activity of the complex,
  • cell growth,
  • affinity, IС50, Kd,
  • survival,
  • toxicity,
  • drug efficacy,
  • effect of protein modification and drug addition,
  • structural changes,
  • unfolding,
  • denaturation,
  • molecular weight of the complex, aggregation
  • entropy change,
  • enthalpy change,
  • numerical stability parameter,
  • potential energy,
  • calculated Kd,
  • heat maps,
  • numerical values
AI-powered biochemical data analysis, digital lab notebook automation,

AI Platform for Predicting Structure, Composition, and Chemical Reactions of Substances

Suitable for the following molecules:

You will receive the following set of calculated data:

records data, and studies the functions, chemical processes
New! Tubulin Inhibitors: Decades of controversy.
In the last decade, conflicting data on the effectiveness of Rigosertib have accumulated. We make a Comparative numerical analysis: Rigosertib/ ON01500?
Let us conduct a comparative computational analysis for tubulin tetramers containing small chemical molecules (inhibitors): GTP, Rig, oN015000
Arrows indicate the calculated values ​​of the movement of biochemical processes within the framework of the given formations.
The system is in search of molecular formations that meet the above conditions of thermodynamic equilibrium. Learn More
R&D automation platform, laboratory informatics solution, life science data interpretation

Comparative numerical analysis: Rigosertib/ ON01500?

R&D automation platform, laboratory informatics solution, life science data interpretation

Comparative numerical analysis: GTP/ GDP?

A step-by-step analysis of the effect of the addition
of GTP, GDP and Mg atoms to tubulin
Microtubules are unique among cytoskeletal filaments in that they exhibit a behavior known as “dynamic instability”, which is defined as stochastic switching between states polymerization, where heterodimers are added to the growing microtubule lattice, and depolymerization, where heterodimers leave the shrinking microtubule lattice. 
Despite the apparently clear link between tubulin conformation and polymerization state, a robust connection between tubulin nucleotide state and tubulin dimer conformation has been difficult to establish. There is a continuing controversy over whether different nucleotide-dependent curvature states exist for tubulin dimers in solution.

The switch-like behaviour of microtubules depends on GTP hydrolysis.
Most tubulin dimers in solution have GTP bound to their β-subunit, and the hydrolysis of this GTP to GDP is triggered by polymerization. GTP–tubulin forms stable filaments, as shown by the high stability of microtubules grown in the presence of the slowly hydrolysed GTP analogue GMPCPP11. Following hydrolysis, GDP–tubulincontaining microtubules are highly unstable and rapidly depolymerize. A cap of GtP–tubulin at the plus (+) end of GDP–tubulin-containing microtubules is thought to stabilize the growth phase .
How much does it cost to use AI in biochemical experiments?


Modern technologies will allow you to get a large set of preliminary data for the price of 2.49 Euros for each small protein or peptide when ordering a study on a hundred similar amino acid sequences.

Leave your request/questions and our specialists will contact you shortly on all issues

|

Meet the group, explore the school, and get a free consultation
I have a question about the study of molecules
list of molecules:
I would like to study the following properties of these molecules
List of properties:
Made on
Tilda