AI in Biochemical experiments

12/10/2025

❗ What we’re looking for:
Experimental partners with any of the following: ITC/SPR/BLI data, binding/affinity measurements, mutant panels, inhibitor titrations, or biophysical readouts (Tm, DSF, etc.).
https://binomlabs.com/

At BinomLabs (computational lab), we’re launching a collaboration with an Experimental Laboratory to uncover the step-by-step formation order of biochemical complexes—including the effects of small molecules/inhibitors and protein mutations.
⏺️ What we’ll do together
✔️ Map the assembly pathway: identify intermediates from monomer → oligomer → final complex.
✔️ Quantify thermodynamics at each transition node: tracking how they change as the structure becomes more complex.
✔️ Estimate energy barriers between intermediates to pinpoint rate-limiting steps.
✔️ Validate against experiment: predicted formation order will be qualitatively (ideally quantitatively) consistent with measured KdK_dKd​ or other affinity readouts.
✔️ Detect “dead-end” complexes: we flag intermediates separated by large energy barriers from all other states—useful for understanding failed assembly or off-pathway traps.

⏺️ What we provide
✅ A computational pipeline (thermo + ML) to infer intermediate states, barriers, and formation order, plus clear, publication-ready figures/tables.
✅ Rapid pilot assessment and a joint plan for deeper validation.
If you’re interested in co-developing this with us (co-authorships welcome), DM me or email koshlan.tetiana@gmail.com

AI methods for preliminary research molecular solutions

11/08/2025

BinomLabs is a predictive analysis platform for biochemical experiments. Our free pilot program lets real teams test the software on real problems—so you get signal before you scale, and we learn from diverse, messy, real‑world data.

Why run a free pilot with us?
https://binomlabs.com/
De‑risk your next round: Prioritize experiments with the highest expected payoff and cut low‑yield branches early.

Use your own context: Bring your assay types, constraints, and endpoints—no toy demos.

Move faster: Turn past results into next‑step recommendations so you spend less time guessing and more time validating.

Low lift: Quick kickoff, clear success criteria, and a short timeline to a go/no‑go decision.

How it works

Share a brief problem statement and sample data.

We set objectives and metrics together.

Run the pilot on your questions.

Review impact and decide next steps.

Learn more or apply here: https://binomlabs.com/
(Use the contact form on the site and mention “Free Pilot.”)

If you’re in biotech R&D, pharma, or an academic lab and want to reduce cycles between ideas and results, this is for you.

02/08/2025

AI-Powered Linear Docking of Nap1 and Mdm2: Visual Workflow Explained
Are you working on protein–protein interactions or validating binding sites in the lab?https://binomlabs.com/linea
FREE PILOT project: https://binomlabs.com/pilot
Free AI-platform: https://binomlabs.com/server_calc
🔬 What you’ll learn: How to predict binding sites between proteins(e.g., Mdm2, Nap1)
How to use domain-level decomposition (alpha-helix, loops) to simplify docking Interpretation of lg(cond(W)) plots to rank binding probabilities
How to correlate predicted sites with experimental affinity shift. Visualization of docking-ready 3D domains in Nap1 structures Exportable results for Schrödinger, blind/hard docking tools, or mutagenesis planning

02/08/2025

🚀 Announcing Our Cost-Saving AI Platform for Biochemical Experiments! 🚀
We’re excited to share our latest results:
Our automated platform predicts the outcomes of key biochemical experiments with up to 91% accuracy, enabling scientists to reduce the number of in vitro experiments by more than half and dramatically cut costs.
What’s inside our platform?
Predicts results for affinity electrophoresis, isothermal calorimetry, gel electrophoresis, surface plasmon resonance, and spectroscopic assays
Enables custom experimental design: choose temperature, salt, and acid concentrations
Calculates advanced biochemical parameters (affinity, IC50%, Kd, condition number, Tm, and more)
Supports complex biomolecular systems: dimers, trimers, tetramers, mutants, and inhibitors
Peer-reviewed, published results with proven correlation to real lab data
Results:
Up to 91% correlation between calculated and experimental data,
Cuts unnecessary experiments from 10 to as few as 3–4 per research objective,
Collaborations with top academic labs (e.g., University of Würzburg)
We are looking for new partners!
If you’re interested in optimizing your lab workflow and saving R&D budget, check out the PDF below and connect with us for a free pilot project.
🔗 binomlabs.com
📄 Download the project overview https://lnkd.in/d9pKgCB4

02/08/2025

🚀 Exciting News from BinomLabs! 🚀
We’re thrilled to share our latest Pitch deck, showcasing our AI platform for simulating biochemical in vitro experiments.
Our mission: replace costly and time-consuming verification experiments with advanced predictive analytics—helping the pharmaceutical and biotech industry accelerate R&D and save resources.
Key milestones achieved:
Identified and solved major pain points in biochemical data analysis
Built and validated our software in real laboratory settings
Published and peer-reviewed results in leading scientific journals
Gained recognition among industry, academia, and end-users
Now launching:
🧬 We are recruiting partner laboratories for free pilot projects! Join us to experience the benefits of automated biochemical analysis, contribute feedback, and help shape the future of lab data science.
🔗 Learn more and connect: binomlabs.com
📩 Download the full pitch: https://lnkd.in/d-tVcgWA

Let’s build the next generation of lab automation together!
Хэштег Хэштег Хэштег Хэштег Хэштег Хэштег Хэштег Хэштег

07/06/2025

Trastuzumab Mutation Profile

28/01/2025

Biochemical pathway of complex biochemical formation, taking into account thermodynamic parameters.
https://binomlabs.com/server_calc
Identify viral protein interactions order during replication and transcription processes.
In this work, we assume that the co-direction of changes in thermodynamic quantities as the final molecular formation is achieved signals a higher affinity of molecules among themselves than for a biochemical formation, which is characterized by the lack of coordination of the biochemical pathway directions of the final molecular compound. the studied molecular complexes, we took[LGP2-8dsRNA-LGP2].
Determining what biological formations exist in a solution during the formation of a hexamer (oligomers) from various proteins, and quantifying their abundance, involves analyzing the assembly pathway and equilibrium between monomers, dimers, trimers, tetramers, and higher-order oligomers. Determining the sequence or molecules queues entering chemical reactions involves understanding the order in which reactants interact to form products. This process is crucial in reaction kinetics, enzymatic pathways, and drug design. Below are the main approaches to determine these molecular queues.

19/01/2025

🚨 We’re Hiring Pilot Projects! 🚨 FREE PILOT project for biochemistry labs!
https://binomlabs.com/pilot
Are you part of a biochemical or pharmacological laboratory looking to innovate? 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%!

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!

Who Should Apply?
Biotech companies.
Pharmaceutical companies.
Academic researchers.
Laboratories focused on protein-protein interactions, binding affinity studies, enzyme kinetics, and similar areas.

If you're eager to explore cutting-edge technology that transforms how biochemical experiments are planned and executed, this is your chance!

Free pilot biochemestry project

19/01/2025

🚨 We’re Hiring Pilot Projects! 🚨 FREE PILOT project! Join our free pilot program featuring an advanced AI platform that predicts experimental data with 70%-90% correlation!
https://binomlabs.com/pilot

In vitro studies cost thousands of dollars, and the efficiency ranges from 20% to 70%.
Increase your productivity by obtaining a forecast of experimental data using our AI platform. Since we are an early startup, at the moment we are running free pilot projects to polish the software and collect feedback from "wet" biochemical and pharmacological labs. With a successful collaboration, your experimentalist will have to perform instead of 10 experiments to achieve the result, only 3-4 experiments, and sometimes only ONE experiment is enough to get the best result after receiving a forecast of experimental data.

Хэштег Хэштег Хэштег Хэштег Хэштег Хэштег

18/11/2024

Still getting ambiguous results in the biochemistry lab and don't understand how to interpret them? Use the online platform to get preliminary results in biochemical research.
Get a full set of calculated thermodynamic parameters using a ready-made software algorithm. You don't need to be able to program, just list the molecules whose interaction you want to study.
Our menu includes studies such as the selection of flexible antibody chains to antigen, fine-tuning of interacting amino acid chains.
The effects of oncogenic and other protein modifications on drug resistance.
Obtaining correlation dependencies between the experimental dissociation constant and calculated parameters.
Explanation of previously obtained results, finding the causes of the obtained results, their explanation and interpretation.
Confidentiality of received and sent data.
Accounting for protein aggregation regions.
Finding the order of formation of complex biological complexes.
Still getting ambiguous results in the biochemistry lab and don't understand how to interpret them? Use the online platform to get preliminary results in biochemical research.
Get a full set of calculated thermodynamic parameters using a ready-made software algorithm. You don't need to be able to program, just list the molecules whose interaction you want to study.
Our menu includes studies such as the selection of flexible antibody chains to antigen, fine-tuning of interacting amino acid chains.
The effects of oncogenic and other protein modifications on drug resistance.
Obtaining correlation dependencies between the experimental dissociation constant and calculated parameters.
Explanation of previously obtained results, finding the causes of the obtained results, their explanation and interpretation.
Confidentiality of received and sent data.
Accounting for protein aggregation regions.
Finding the order of formation of complex biological complexes.