About Us

EinNext employs Artificial Intelligence to revolutionize Antibody drug discovery and Enzyme engineering. With high tech efficiency in machine learning models followed by the meticulous molecular dynamics simulation-based validation approaches, EinNext’s computational protocol strikes the industry standard in the modern era of AI assisted computation-aided antibody/enzyme Engineering.

The company’s hybrid-based approach, which uses AI modelling of Antibody/Enzyme mutational modelling followed by thorough investigation of the predicted model using in-silico molecular dynamics simulation and free energy estimation strategy gains more confidence to come up with the precise target approach which greatly reduces time and cost when compared to directed evolutionary approaches. EinNext is bound to formulate strong partnerships, and collaborates along the academia and pharmaceutical industry to disentangle multiple interdisciplinary problems. The company was established in 2015 which is mapped in Chennai, India.

EinNext’s Services:

  • ML models using the principle of sequence and function modelling approaches for Antibody drugs and enzymes modelling.
  • ML algorithms are incorporated in our workflow commencing from Simple linear regression models, Support vector regression, Neural network, Deep neural network, Convolutional neural network and finally develop a consensus prediction models.
  • Accurate test of ML models using molecular dynamics simulation and molecular docking approaches.
  • Training dataset for ML models to engineer antibody/enzyme by literature data and simulation data.

The company’s DNA

We strongly believe that the future of healthcare is antibody-based drug and the antibody design will be automated by Artificial Intelligence that helps to accelerate drug discovery pipeline and aids in developing many precise and tailor-made medicine to cure diseases for which the drugs are yet to be found.

R&D Services


Employ Artificial Intelligence to Revolutionize Antibody/Enzyme Engineering

  • What is our philosophy?
    • To derive a data-driven principle that has the power of explaining physical and chemical nature of protein systems, which harness to accelerate Antibody/Enzyme engineering.
  • What have we done?
    • We have developed various ML models for Antibody/Enzyme properties prediction using a data-driven amino acid sequence-based approach, the ML learned from more than 200 single mutants of the antibody to predict accurately the binding affinity of the combination mutants using Support vector regression, Deep learning and convolution Neural network approaches.
  • Why is our work impactful?
    • We try to jot down the current standard in computational protein engineering using conventional approaches which demand the 3D structures of the proteins, instead we adopt a sequence-based approach which curtails the timeline of mutant prediction of all positions of the protein and also computationally less expensive without compromising on the accuracy.

In-Silico Based Protein Engineering


ML For Enzyme/Antibody Engineering

We develop ML algorithms to solve enzyme/antibody engineering problem using amino acid sequence information and the fitness of mutants using wet-lab experimental data. ML approaches outscore the accuracy of prediction of other forms of computational modelling.

Quick and Accurate Solutions

Since our approach uses only sequence information for modelling, our algorithms provide quick solutions without compromising on accuracy.

Focus on novelty and patentability

The robustness of our algorithms helps analyse all theoretical combinatorial possibilities of a mutant sequence. This enables us to explore functional mutants in untested regions of the enzyme/antibody ensuring novelty of mutants.

Additivity of Mutants for Property Improvement on a large scale

A unique feature of our ML algorithm is that it provides a reliable solution for the additivity of single mutants (epistasis). An accurate prediction of epistatic of mutants in our approach, that would otherwise impair prediction in other approaches, leads to property improvement of the enzyme/antibody on a large scale.

Cutting-Edge Enzyme/Antibody Engineering Algorithm and New Training Data

Our IT and ML experts are relentlessly working on the integration of novel ML approaches into our existing methods and addition of new data from our own research which will continue to strengthen the accuracy of our ML model prediction.



  • Complete Image-to-Computation studies
  • We perform segmentation of DICOM images obtained from standard imaging methods
  • Hemodynamic & FSI analysis in Intracranial aneurysms
  • Computational fluid dynamics has proved beneficial in analysing the hemodynamic in the aneurysm before and after treatment. We learn the interaction of stent with blood through strongly coupled fluid-structure interaction studies
  • Intracranial Aneurysm rupture prediction
  • Since a while, computational fluid dynamics has proved its mettle in predicting the rupture status of intracranial aneurysm. We use the association between aneurysm rupture sites and hemodynamic features to conclude its rupture state
  • Rupture mechanism of cerebral aneurysm
  • We research on the rupture mechanism of intracranial aneurysm. The study of mechanism of rupture is immensely valuable as it could save many lives.


  • Myocardial Ischemia prediction through hemodynamic
  • We predict myocardial stenosis through computational hemodynamic with the understanding of reserve volume of heart
  • Structural validation of stents - bending behaviour, recoiling, dog-boning & fore-shortening
  • Stent migration and FSI studies
  • Virtual testing of stents includes its structural analysis through computational methods and the results are matched with the FDA standards
  • Virtual Fatigue testing & Failure analysis of stents
  • We analyse the long run effects of stents from its fatigue and durability testing. Strain-based and Stress-based life predictions are made using non-linear finite element studies taking into consideration the effects of cardiac pulsatile loading and stent-vessel oversizing
  • Stent migration and FSI studies
  • The bio-mechanical interaction between a balloon-expandable stent and a stenotic artery gives a better understanding on the possible areas of artery injury during the stent deployment and areas of non-uniform contact pressure after the stent apposition, due to a non-uniform stent expansion
  • Design optimization & evaluation of vascular stents
  • Better optimized stent designs could improve the efficacy of stents. Each design variant undergoes strict evaluation through finite element analysis.


  • Study the airway resistance and airflow through the tracheobronchial tree
  • The CT scan image obtained is rendered to generate the airflow model and computational fluid dynamics performed in the model gives the airflow resistance and velocity in every location. This gives us an understanding of the pathophysiology of lower airways that is useful in obstructive lung diseases
  • Identify the relationship between total cross-sectional area and airflow velocity in tracheobronchial tree
  • The air velocity tends to vary according to the total cross-sectional area of flow. We take into consideration the relation to identify the areas of constricted airflow in the airway tract. The associated recirculation and vorticity are also evaluated using the approach.
  • Analyse the pulmonary drug delivery mechanism
  • The CT scan image obtained is rendered to generate the design of an effective inhaler which requires the knowledge of drug particle deposition in the airway tract on using the inhaler. With the help of CFD analysis we identify the locations of excess deposition and the locations devoid of drug that could help in optimizing the design of asthma inhalers.


  • Design of orthopaedic implants
  • To obtain clinically acceptable design is very essential as it serves in terms of both patient safety and optimal performance of implants. We model the implants ensuring accuracy and safety
  • Screening of orthopaedic implant & prosthesis designs
  • We explore performance of various orthopaedic implants and their significance based on design changes through finite element analysis
  • Knee & Hip Implant performance evaluation
  • We work to ensure proper range of motion and synchronic performance of implants. The most important movements of joint are measured to meet the ideal levels
  • Durability assessment of Prostheses & Orthoses
  • Having knowledge of the implant durability is very essential. With this thought we develop methods for testing prostheses with repetitive load cycles to assess their performance and life. We also work on simulating different ranges of motions with different load cycles
  • Human gait analysis
  • The most popular and a systemic process is human gait or walking cycle. We track the movements of joints on each step of gait cycle to ensure that the implant is properly patent to the joint. Study of different types of gait and its interaction with joint and implant is quantified i.e., percentage of abnormal deviation from a normal gait


  • Material characterization of dental fillers
  • Different filling materials have varied interactions with the dental tissues. We use finite element approach to identify the change in stiffness due to various fillers and the resulting stress concentrations that may cause an undesirable break of the loaded tooth
  • Study the cement flow pattern in a system
  • Through computational fluid dynamics we analyse the cement flow pattern, optimum amount of cement required and the speed of crown seating while joining crown and implants using cement. This reduces the cement overflow and its related side effects for the patient
  • We re-design implant to retain the excess cement within the implant abutment and study various implant designs using CFD to understand the cement overflow.
  • Assessment of patient-specific dental implants for better fit
  • We carry out the 3D analysis to evaluate the biomechanical behaviour of patient-specific dental implants and its appropriate redesigning under different loading conditions. It helps to improve preparation designs, indicates the right material or combination of materials to be used in various load and stress conditions in order to reduce material and/or tooth failure in clinical practice.

Executive board

Technical board

Case Study


Virtual Stent Implantation

Atherosclerosis or arteriosclerotic vascular disease (ASVD), in which plaque builds up inside arteries,the most common cause for mortality in the developed world.Endovascular procedures like angioplasty for atherosclerosis treatment provide an alternative to open-surgery and require minimal invasion into the human body Angioplasty is used extensively for the treatment of peripheral vascular disease to restore correct blood flow and for the treatment of coronary heart disease and involves stent insertion.However, in-stent restenosis, a repeated narrowing of artery post stent implantation, limits the clinical success of angioplasty, which is caused by mechanical factors, such as wall strain distribution and blood flow induced wall shear stress and local arterial wall stress. Considering the huge expense for the experimental evaluation of stent deployment, we used an alternative route of computational numerical methods to understand the mechanical behavior of stent implantation.

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Aneurysm Rupture Prediction

According to a recent survey conducted among 400 adult volunteers who underwent clinical and radiological evaluations, Dr.Nakagawa reported the incidence of un-ruptured Intracranial Aneurysms (IA’s) to be as high as 7%. Brain aneurysms are often discovered when they rupture, causing bleeding into the brain or the space closely surrounding the brain called the subarachnoid space causing a Subarachnoid Hemorrhage (SAH). SAH can lead to hemorrhagic stroke, brain damage and death. Hence, it is required to determine whether the particular aneurysm has a high risk of rupture so that it can be treated before bleeding occurs. There are certain cases where neurosurgeons fail to judge the risk of rupture even with their profound experience and decide not to meddle with it. Such was a problem that was shared with us by a group of doctors wherein we are supposed to predict the rupture status of IA’s.

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Dental Implantants

In the current fast growing busy life, dental ailments have become a certainty in every common man’s life. Almost 5 out of 10 people have dental issues and end up in dental implants in some or the other stage of their life. The most undesirable part of this is the trauma and swelling suffered by the patients post dental implantation. Repetitive treatments on such affected areas are never welcoming by patients. Even expert dentists may not succeed in providing happy smiles even after proper fixation of such artificial tooth. We took up this challenge and initiated an effort in this regard to evaluate dental implant designs for efficient and pain-free fixation.

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EinNext Transformative Technologies

AI and Predictive Informatics Solutions



For more information and support, Please contact us!

4, Soundarya Nagar, Gowrivakkam, Chennai - 600073, Tamil Nadu, India


+91 (44) 2278 2028