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
in vitro experiments by predicting experimental data
Pharma's first AI safety prediction platform/
Biopharmaceutical R&D Solution
Biopharmaceutical R&D Solution
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 AI/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
BinomLabs applies AI/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
Accelerate discovery with connected, intelligent R&D
Reduce in vitro cost
phone nomber: 053-382-60-75
WhatsApp: +972-53-382-6075
WhatsApp: +972-53-382-6075
kulikov.kirill.g@gmail.com
koshlan.tetiana@gmail.com
Binomlabs Platform
Data prognosis
Data prognosis
$
$
No AI -prediction
No AI -prediction
$
$
$
$
$
$
$
$
$
$
$
$
lab $ costs for each in vitro test
Contacts for more detailed information:
for technical information:
Confidentiality & Intellectual Property
Your unpublished biochemical research is more than data — it is scientific intellectual property. BinomLabs treats project information, researcher identities, and laboratory affiliation as confidential research assets.
“
At BinomLabs, we understand that unpublished biochemical research is intellectual property. Experimental results, molecular targets, assay strategies, unpublished hypotheses, researcher names, and laboratory identity may represent years of scientific work, future publications, patent opportunities, and competitive value. That is why BinomLabs handles all shared materials with strict confidentiality and does not disclose, publish, reuse, or reference them without explicit written permission.
- Unpublished research is treated as confidential intellectual property
- Researcher names and laboratory identity remain private
- No public disclosure without written permission
- NDA-based collaboration available before detailed scientific review
- Access limited to authorized personnel involved in the pilot
“Intellectual Property” means
proprietary methods, discoveries, inventions, patents, trade secrets, copyrights, trademarks, service marks, trade dress, compositions , products, procedures, know how, data, reports, programs, processes, protocols, written or electronic writings, illustrations, images, and any other form of proprietary rights.
Fine-tuning an existing antibody to an antigen.
We will skip the intermediate experiments to determine the most effective modification using computational methods, where the calculation results provide a direct, visual correlation with experimental methods.
Next generation antibodies demand a modern digital solution
Order a free flow optimiztion antibody-to-antigen assay from BinomLabs
- Antibody R&D teams are investigating a variety of antibody formats, modernizing scientific workflows with NGS and CRISPR, and automating key workflows to bring the next wave of breakthrough antibodies to the market.
- The clinical need forces the industry to adopt advanced engineering platforms that can fine-tune binding affinity and reduce off-target toxicity.
- Custom monoclonal antibody projects and full antibody discovery runs commonly cost thousands to tens of thousands of dollars (typical service quotes range from ~USD 3,000 to >USD 15,000 depending on scope).
- modification of antibody flexible chains guided by AI,
- prediction of the affinity range (Kd, stability) based on structural input,
- identification of key binding residues for epitope mapping,
- stepwise affinity testing of each antibody–antigen pair,
Binom Labs introduces a high-throughput, AI-powered antibody affinity optimization service that accelerates the engineering of high-affinity binders at a fraction of the cost of conventional lab methods—offering universities and companies a rapid, scalable alternative to hybridoma, phage display, or SPR screening.
The industry requires a new kind of digital solution that is immediately ready to handle the complexity of modern antibody R&D.
It must provide adaptability to keep up with the frequent evolutions within the industry, and traceability to give R&D teams a complete view of their programs and candidates.
It must provide adaptability to keep up with the frequent evolutions within the industry, and traceability to give R&D teams a complete view of their programs and candidates.
Digitize your lab, automate workflows, and increase productivity with AI
Start your free pilot project today!
What do you need to order a high affinity antibody prognosis?
Below is an example of data for the production of high-affinity antibodies,
compared with two experimental data
compared with two experimental data
Input data for Binomlabs Calculations :
Explore the interactive demo to experience how BinomLab’s solution can support your antibody and protein research workflow.
- UNiProt
- PDB database
- Amino acid sequence
OutPut date after calculation by
the BinomLabs software package:
the BinomLabs software package:
- a set of thermodynamic parameters for each case of antibody chain modification
Both sets of data obtained in completely different ways will be compared on one graph for a visually understandable presentation, with a correlation coefficient between them
Experimental Improving antibody-antigen affinity was accomplished in several stages:
- Rough determination of the affinity of over 2,000 antibody modifications.
- Selection of the 20 most suitable.
- Determining affinity using Kd determination.
Correlation graphs between experimental Kd (less then 20 modif.) and calculated thermodynamic values
Correlation graphs between experimental rough ratio (2000 modifications) and calculated thermodynamic values
You can get a sample of highly effective antibody modifications without performing 2000 rough experimental measurements
Antibody and Protein Solution Demo
High-affinity antibodies were found after analyzing 2000 modifications using approximate experimental methods.
Finally, significant modifications were found!
Binom Labs AI SolutionPredictive Modeling:
- Use your antibody–antigen structural data in PDB or docking format
- AI Engine: Machine learning predicts binding affinities (Kd, ΔG), residue-wise contributions, and recommends optimal mutations
- High Coverage: Compute hundreds of variants concurrently
- Fast: Outcomes delivered in 7–10 business days
Reduce Antibody Discovery Costs Using Data Science
Protein Solutions to advance their biologics through the R&D lifecycle. Work across a broad range of antibody and protein discovery modalities (e.g. phage display, hybridoma, B-cell) with fit-for-purpose software designed for speed, collaboration, and insights.
Get the best from in silico molecular biology tools in one modern, digital platform designed for biologics R&D.
To assess antibody potency, we support:
AI-driven affinity prediction for rapid, scalable discovery campaigns.
Comparison of experimental and BinomLabs methods:
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%!
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!
Prices start from 5 Euros for the calculation of one mutant protein,
Below on the visual cards are presented the benefits and price comparisons
Below on the visual cards are presented the benefits and price comparisons
Collaboration options: Free Pilot Project/Approximate services price
Financial benefits/Practice Areas
BinomLabs Cost:
BinomLabs Cost:
Experimental cost
Experimental cost:
Experimental cost:
Application area:
Comparison of financial costs:
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.
- 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.
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
- monomers, dimers,
- trimers, tetramers,
- protein-protein interaction
- peptides,
- protein+inhibitors+atoms,
- Antibody-Antigen
- RNA molecules
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
| |
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 |
