AI prediction for in-vitro experiments for Free!

protein-protein interaction prediction
antibody–antigen interaction prediction
therapeutic antibody optimization
binding affinity prediction: Kd, IC50, ΔG
protein stability prediction

Apply for free Pilot

AI prediction for in-vitro experiments for Free!

protein-protein interaction prediction
antibody–antigen interaction prediction
therapeutic antibody optimization
binding affinity prediction: Kd, IC50, ΔG
protein stability prediction

Apply for Free Pilot!

Predict In-Vitro Outcomes Before You Pay for the Next Experiment

BinomLabs helps biochemical laboratories and pharmaceutical R&D teams reduce exploratory wet-lab costs by using AI-driven prediction to prioritize the most informative experiments before they are performed.

Predict In-Vitro Outcomes Before You Pay for the Next Experiment

BinomLabs helps biochemical laboratories and pharmaceutical R&D teams reduce exploratory wet-lab costs by using AI-driven prediction to prioritize the most informative experiments before they are performed.

Problem:

Pharma, biotech, and academic laboratories spend significant time and budget on in-vitro experiments, protein assays, antibody testing, and biochemical validation.
Low first-try success, experimental variability, and repeated intermediate assays increase R&D cost and slow down decision-making.

Reduce In-Vitro R&D Cost With AI-Guided Experiment Prediction

Solution:
The implementation of the BinomLabs predictive software in a biochemical laboratory allows typically reduce ~10 experiments to 3–4, lifting the proportion of successful first attempts and freeing capital for the highest‑value questions.

Proof of Value:

BinomLabs applies AI/ML to deliver experiment prognoses align with biochemestry lab data up to R = 0.93,

demonstrated on real in‑vitro studies in biochemestry lab.

BinomLabs Founder Message: A New Model for Pharma R&D/From 10 In Vitro Experiments to 3

Our predictive platform supports antibody optimization, protein-protein interaction prediction, mutation prioritization, binding-site interpretation, and affinity-related ranking of candidate variants.
Designed for teams working with protein assays, ELISA, DSC, ITC, SPR, biosensors, and other biochemical validation workflows.
Pilot Project: https://lnkd.in/djmVxJrR
PDF file: https://lnkd.in/d4ti2E6F
PITCH PDF file: https://lnkd.in/d4KY5MWM

That alone tells us something very important: the system is still spending massive amounts of money before it has enough confidence that it is pursuing the right biological path.

Tatiana Koshlan
Founder of BinomLabs

More than 90% of drug candidates fail before final approval.

Pharma and biotech spend tens of billions each year on in‑vitro work, where variability and low first‑try success increse cost and timelines.

In biochemical research, first-try success is often limited, which increases cost, time, and uncertainty.

For high-precision assays such as Isothermal titration calorimetry and surface plasmon resonance methods to probe protein–protein interactions, and FRET, success on the first attempt is often uncommon.

BinomLabs helps R&D teams reduce broad trial-and-error screening by selecting the most informative experiments before the full lab series begins.

BinomLabs is especially useful when experimental measurements can be directly compared with calculated thermodynamic, kinetic, or interaction-based parameters.
This makes the platform relevant for laboratories using:

Thermodynamic Stability & Interaction Studies
Differential Scanning Calorimetry — DSC
Isothermal Titration Calorimetry — ITC
Surface Plasmon Resonance — SPR
Interferometry-Based Biosensors
Fluorescence Correlation Spectroscopy — FCS

how many in vitro experiments actually achieve their goal on the first attempt?

Problem Description:

The key question is simple:

Cost Saving Analyses without AI and using AI

Without AI prediction, researchers may need around 10 experiments to identify a successful result. With BinomLabs AI prognosis, the number of necessary experiments can often be reduced to 3–4, lowering cost and accelerating decision-making.

BinomLabs helps R&D teams reduce broad trial-and-error screening by selecting the most informative experiments before the full lab series begins.

Tatiana Koshlan
Founder of BinomLabs

What allows me, as the Founder of BinomLabs, to be absolutely confident in the successful implementation of our software suite by biochemical laboratories?

A thorough understanding of biochemical processes in the laboratory, specialized education, and work as a biochemist in the field of macromolecular spectroscopy.

A brilliantly completed proof-of-concept stage in a real biochemical laboratory, professional and academic publications, and successful completion of blind peer reviews.

Application of immutable physical laws in quantum mechanics and thermodynamics, development of a new sustainability paradigm.

Long-term testing of the developed software suite and the involvement of a highly qualified team of dedicated researchers in its development.

Thus, as a result of many years of work, our key benefits are:

High correlation rate with experimental data.
Clear presentation of the obtained data.

Cut In-Vitro Costs Before the Wet-Lab Run

The platform compares calculated thermodynamic and interaction parameters with real laboratory readouts, helping scientists see which molecular variants, binding conditions, or protein states deserve priority testing.