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
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.
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
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!
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!
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!
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
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
The peculiarity of the pricing policy is high-flow calculations, which allow to reduce the cost of one calculation to 5 euros. For comparison, the cost of 10 milligrams of pure protein (purity 84%-98%) will be from 270 Euros and more, not counting a large number of associated costs.
With BinomLabs you can secure and get insurance for your expensive experimental results by pre-ordering calculations of the compounds you are interested in.
With BinomLabs you can secure and get insurance for your expensive experimental results by pre-ordering calculations of the compounds you are interested in.
Our best result of successful cooperation with the experimental laboratory is the reduction of the number of necessary
in vitro experiments from 10 quantities to 3 quantities.
Accordingly, the costs of conducting verification and clarifying experiments were reduced several times.
in vitro experiments from 10 quantities to 3 quantities.
Accordingly, the costs of conducting verification and clarifying experiments were reduced several times.
We have developed a biophysical and AI-supported antibody improoving method to dramatically reduce the cost of experiments in the following fields:
- antibody–antigen interaction,
- peptide–protein interaction,
- protein–protein interaction,
- using advanced data science and mass spectrometry-based analysis
BinomLabs proposes a novel computational approach for accurately estimating the spatial distance between antigens on the cell membrane. Specifically, we have developed algorithms based on quantum tunneling principles to predict the physical proximity and accessibility of epitopes targeted by antibody constructs.
Unlike conventional approaches that rely solely on structural modeling or classical distance measurements, our method leverages the quantum tunneling effect as a sensitive probe of nanoscale distances between membrane-anchored antigens.
This framework enables the evaluation of epitope combinations, which is critical for predicting and optimizing bispecific antibody functionality and binding sequence, as outlined in the problem statement.
• Bispecific antibodies are extremely specific and highly targeted, able to recognize two antigenic molecules simultaneously.
• Compared to combination therapy using two monoclonal antibodies, bispecific antibody drugs reduce the cost of development and clinical trials.
• Bispecific antibodies prove significantly more effective than combinations of two or more monospecific antibodies.
• In comparison to the combination of multiple monoclonal antibodies, bispecific antibodies yield fewer side effects and enhance drug safety and efficacy
Unlike conventional approaches that rely solely on structural modeling or classical distance measurements, our method leverages the quantum tunneling effect as a sensitive probe of nanoscale distances between membrane-anchored antigens.
This framework enables the evaluation of epitope combinations, which is critical for predicting and optimizing bispecific antibody functionality and binding sequence, as outlined in the problem statement.
• Bispecific antibodies are extremely specific and highly targeted, able to recognize two antigenic molecules simultaneously.
• Compared to combination therapy using two monoclonal antibodies, bispecific antibody drugs reduce the cost of development and clinical trials.
• Bispecific antibodies prove significantly more effective than combinations of two or more monospecific antibodies.
• In comparison to the combination of multiple monoclonal antibodies, bispecific antibodies yield fewer side effects and enhance drug safety and efficacy
- Despite the expanding applications of kinase inhibitors in oncology, their clinical success has a limited duration due to the development of resistance. Acquired resistance mechanisms are complex and diverse, including on-target mutations, the acquisition of ‘bypass’ signaling pathways, and tumor histological transformation. This presents significant challenges in kinase drug discovery: understanding the mechanisms of acquired resistance and developing strategies to overcome it.
- BinomLabs provides an extensive investigation of mutant kinases, covering on-target mutations - the most commonly encountered causes of acquired resistance to kinase inhibitor drugs. This calculation method is widely used in the exploration of mechanisms underlying resistance to kinase drugs and discovery of next-generation kinase inhibitors to tackle on-target resistance.
- BinomLabs has a large variety of methods for determining subtle differences between kinases. Developing these methods is therefore a key in the realm of kinase drug discovery. BinomLab's comprehensive methods of kinase investigation, along with kinase substrates and reagents, provides a robust toolbox to facilitate your assay development. Furthermore, we offer custom services to support your biochemical and cell-based assay needs
Adaptor Proteins |
Cell Cycle Proteins |
Fluorescent Proteins |
Transcription Factors |
Microtubule- and Actin-Associated Proteins |
Custom Protein Properties Calculation |
Tau Proteins |
Chaperones |
Regulatory Proteins |
Cytokines & Growth Factors |
BinimLabs provides a broad range of enzyme activators investigations and inhibitors for studying the regulatory roles of enzymes in key cell signaling pathways and enzymatic react.
With BinomLabs you can secure and get insurance for your expensive experimental results by pre-ordering calculations of the Enzymes you are interested in.
With BinomLabs you can secure and get insurance for your expensive experimental results by pre-ordering calculations of the Enzymes you are interested in.
Kinase Inhibitors |
Epigenetic Enzyme Inhibitors |
Epigenetic Enzyme Activators |
Kinase Activators |
Phosphatase Inhibitors |
The immune system recognizes and kills pathogens and tumor cells to protect the host. The immune system is composed of two major subdivisions, the innate or non-specific immune system and the adaptive or specific immune system. Innate immunity that is constitutively present and is immediately mobilized upon infection acts as the first line of defense against invading organisms. It is non-specific and reacts equally well to a variety of organisms. While the adaptive immune system acts as a second line of defense, responding specifically and generating immunological memory.
With BinomLabs you can secure and get insurance for your expensive experimental results by pre-ordering calculations of the Proteins Related to Immunology you are interested in related fields:
Adaptive Immunity
Cluster of Differentiation (CD)
Cytokines & Growth Factors
Fc Receptors
Inflammation
Innate Immunity
Proteins Related to Immunology:
Alkaline Phosphatase/ALPL Proteins
Serum Amyloid P Proteins
Complement C5 Proteins
CTLA-4 Proteins
Cathepsin B Proteins
BCA-1/CXCL13 Proteins
Tissue Factor Proteins
FAM3C Proteins
CD23 Proteins
CD16b/FCGR3B/Fc gamma RIIIB Proteins
GranzymeA/GZMA Proteins
ICAM-1/CD54 Proteins
IGFBP5 Proteins
IL-4 Proteins
IL-7 Proteins
CD31/PECAM-1 Proteins
Chemerin/RARRES2 Proteins
Serpin A1/Alpha-1-antitrypsin Proteins
TNFR-2 / CD120b Proteins
With BinomLabs you can secure and get insurance for your expensive experimental results by pre-ordering calculations of the Proteins Related to Immunology you are interested in related fields:
Adaptive Immunity
Cluster of Differentiation (CD)
Cytokines & Growth Factors
Fc Receptors
Inflammation
Innate Immunity
Proteins Related to Immunology:
Alkaline Phosphatase/ALPL Proteins
Serum Amyloid P Proteins
Complement C5 Proteins
CTLA-4 Proteins
Cathepsin B Proteins
BCA-1/CXCL13 Proteins
Tissue Factor Proteins
FAM3C Proteins
CD23 Proteins
CD16b/FCGR3B/Fc gamma RIIIB Proteins
GranzymeA/GZMA Proteins
ICAM-1/CD54 Proteins
IGFBP5 Proteins
IL-4 Proteins
IL-7 Proteins
CD31/PECAM-1 Proteins
Chemerin/RARRES2 Proteins
Serpin A1/Alpha-1-antitrypsin Proteins
TNFR-2 / CD120b Proteins
Signal transduction is a mechanism that converts a stimulus to a cell into a specific cellular response, through which the cell makes modifications in either activity of enzymes or gene transcription. Signal transduction processes are often initiated through receptor ligation or stresses, and may involve a cascade of signals within the cell. With each step of the cascade, the signal can be amplified and eventually creates a change to the cell to achieve the desired cell response. Transmembrane receptors span the cell membrane, with part of the receptor outside and part inside the cell. The chemical signal binds to the outer portion of the receptor, changing its shape and conveying another signal inside the cell. The cascading series of intracellular biochemical events often involve phosphorylation cascade. Activated kinases can catalyze the transfer of a phosphate group to serine, threonine, or tyrosine residues on target proteins and regulate protein activity, localization, and protein-protein interactions. Activities modified by phosphorylation can in turn be terminated by the phosphatases.
With BinomLabs you can secure and get insurance for your expensive experimental results by pre-ordering calculations of the Proteins Related to Signal Transduction you are interested in:
Adaptor Proteins
Akt Pathway
Autophagy
Calcium-binding Proteins and Related Molecules
Cellular Senescence and Pathways in Aging
Circadian Rhythm Molecules
G Protein-Coupled Receptors (GPCRs)
Intracellular Kinases
ITIM/ITAM Immunoreceptors and Related Molecules
Jak/STAT Signaling
Neurotransmitter Receptors, Transporters, and Ion Channels
NFkB Pathway
Notch Pathway
Phosphatases and Regulators
phospho-Serine/phospho-Threonine Binding Proteins
Receptor Tyrosine Kinases (RTKs)
Serine Proteases and Regulators
Ubiquitin and Ubiquitin-like Modifiers
Wnt Family
Proteins Related to Signal Transduction:
With BinomLabs you can secure and get insurance for your expensive experimental results by pre-ordering calculations of the Proteins Related to Signal Transduction you are interested in:
Adaptor Proteins
Akt Pathway
Autophagy
Calcium-binding Proteins and Related Molecules
Cellular Senescence and Pathways in Aging
Circadian Rhythm Molecules
G Protein-Coupled Receptors (GPCRs)
Intracellular Kinases
ITIM/ITAM Immunoreceptors and Related Molecules
Jak/STAT Signaling
Neurotransmitter Receptors, Transporters, and Ion Channels
NFkB Pathway
Notch Pathway
Phosphatases and Regulators
phospho-Serine/phospho-Threonine Binding Proteins
Receptor Tyrosine Kinases (RTKs)
Serine Proteases and Regulators
Ubiquitin and Ubiquitin-like Modifiers
Wnt Family
Proteins Related to Signal Transduction:
- BLNK Proteins
- BTLA Proteins
- CD44 Proteins
- CEACAM-3/CD66d Proteins
- Chk1 Proteins
- CD21 Proteins
- DLL4 Proteins
- FGFR2 Proteins
- GABARAP Proteins
- IL-32 Proteins
- Insulin Receptor Proteins
- MAP3K8 Proteins
- p38 Proteins
- NKp44/NCR2 Proteins
- NOTCH1 Proteins
- S100A6 Proteins
- S100P Proteins
- HAI-2/SPINT2 Proteins
SARS-CoV-2 Prefusion Trimeric Spike Variant Proteins
SARS-CoV-2 Antibodies
SARS-CoV-2 Omicron Antigens
A subset of mutations identified in the receptor-binding domain (RBD) of the spike protein has been identified across multiple variants (e.g., Alpha, Delta, Omicron). These convergent mutations (e.g., N501Y, E484K) are of significant scientific interest because they have been hypothesized to contribute to increased viral transmissibility.
Binomlabs has developed various methods for investagation alterations in recombinant spike trimers and RBD proteins of these variants, fully supporting antibody assessment and vaccine development.
The nucleocapsid (N) protein, a primary target in rapid antigen tests (RATs), also harbors mutations in emerging variants. These mutations may alter antibody-epitope binding due to structural changes in the N protein. It’s critical to assess whether the current commercial antigen tests can detect the mutated N proteins with the same sensitivity and specificity as their WT counterpart:
SARS-CoV-2 Antibodies
SARS-CoV-2 Omicron Antigens
A subset of mutations identified in the receptor-binding domain (RBD) of the spike protein has been identified across multiple variants (e.g., Alpha, Delta, Omicron). These convergent mutations (e.g., N501Y, E484K) are of significant scientific interest because they have been hypothesized to contribute to increased viral transmissibility.
Binomlabs has developed various methods for investagation alterations in recombinant spike trimers and RBD proteins of these variants, fully supporting antibody assessment and vaccine development.
The nucleocapsid (N) protein, a primary target in rapid antigen tests (RATs), also harbors mutations in emerging variants. These mutations may alter antibody-epitope binding due to structural changes in the N protein. It’s critical to assess whether the current commercial antigen tests can detect the mutated N proteins with the same sensitivity and specificity as their WT counterpart:
- Influenza HA Trimer Protein
- MPXV Antigens and Antibodies
- HPV Antigens and Antibodies
- Herpes Virus Antigens and Antibodies
- Antigens and Detection Reagents for Avian Influenza
- SARS-CoV-2 Variants
- Ebola Virus (EBOV) Reagents
- RSV Antigens and Antibodies
- RABV Antigens and Antibodies
- VZV Antigens and Antibodies
- 600+ Influenza Virus Antigen Bank
- SARS-CoV-2 Spike Trimer Protein
- SARS-CoV-2 Serological Analysis Kit
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
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lab $ costs for each in vitro test
Contacts for more detailed information:
for technical information:
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
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
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
Comparative numerical analysis: Rigosertib/ ON01500?
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 .
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.
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