A Platform For Real Impact In Immunology Medicines

We are opening a new landscape of novel biologics by applying machine learning specifically to immunology, guided by our team of experts in biologics discovery and development.

From Linear To Parallel Drug Development

To Parallelize is to optimize multiple relevant desired parameters simultaneously to rapidly modulate key drug-like attributes of protein therapeutics.

Traditional protein identification and optimization involves inefficient, independent sequential iteration and protracted combination steps.

Seismic’s IMPACT platform enables parallelized multi-property optimization by integrating machine learning with structural biology, protein engineering, and translational immunology for the accelerated generation of novel biologics.

Enabling The Unexpected

The IMPACT platform opens new protein sequence space, allows the simultaneous optimization of multiple drug-like properties, including immunogenicity, and reduces both the number and duration of design/test cycles.

New Sequence Space
The IMPACT platform allows us to find previously unexplored sequences and to design proteins with optimal drug-like properties.

Design Test Cycle
The IMPACT platform enables parallelization, thereby removing many cycles of iteration and mutation combinations and shortening the time of each cycle to rapidly identify top candidates.

The IMPACT Platform in Action

Our pipeline programs are derived from our IMPACT platform. Our proven team of integrated protein engineers and immunologists work collectively with our cutting-edge machine learning team to discover and develop novel enzyme and antibody drug candidates for autoimmune diseases.


The following explains how we’ve applied our IMPACT platform and integrated machine learning into our discovery efforts to make improved drug candidates.

Immunoglobulin Sculpting (IgSc) Enzymes

We leveraged the IMPACT platform to screen thousands of naturally occurring enzymes that modify pathogenic antibodies, and, driven by machine learning, rapidly engineered them into novel enzymes with ideal drug-like characteristics using multi-parameter optimization. Using this approach, we have discovered and developed invisibilized, developable, and functional novel pan-IgG proteases.

How we optimized our Ig sculpting enzymes

  • Using our Invisi-B and Invisi-T ML tools, remove B cell and T cell epitopes to create proteins with increased invisibility
  • Elucidate pairwise and higher order residue dependencies to optimize drug-like properties (i.e. stability)
  • Remove chemical and other manufacturing liabilities
  • Retain or augment enzymatic activity (i.e. cleavage function)
Dual-cell Bidirectional (DcB) Antibodies

We leveraged the IMPACT platform to discover and develop DcB antibodies that simultaneously engage multiple inhibitory pathways in more than one immune cell type to regulate both sides of the immune cell synapse. In particular, machine learning was critical to designing novel inhibitory Fc gamma receptor IIb (FcγRIIb)-selective Fc domains by predicting mutations that tune for the desired binding properties at the Fc/FcγRIIb interface.  Our ML approach was also instrumental in predicting antibody epitopes to support epitope binning and epitope/function relationship analysis.

How we optimized one of our DcB Antibodies

  • Map protein-protein interfaces to design FcγRIIb selective Fc domains to enhance checkpoint agonism
  • Predict epitopes recognized by our proprietary antibodies to enable in silico epitope binning and epitope/function relationship elucidation
  • Enable de novo paratope generation in silico for antibody generation/discovery
  • Optimize developability of antibodies
  • Map and remove immunogenic T-cell epitopes, using our Invisi-T ML tool, to create novel inhibitory receptor agonist antibodies with increased invisibility

The IMPACT Platform and Capabilities

Our platform integrates machine learning along with structural biology, protein engineering, and translational immunology. At the center of our ability to harness the power of machine learning is our team’s deep expertise in biologics discovery and development, and our commitment to integrating different disciplines. Together, our platform and our people bring the capabilities needed to drive innovation for immunology drug discovery.

About Us

What We Do

Our trailblazing team generates state-of-the-art proprietary machine learning algorithms and statistical models to systematically analyze and modify protein sequences.

Where It Leads

We generate novel biologic drug candidates on an unprecedented scale with optimized biologic activity and minimized immunogenicity .

What We Do

Our skilled team utilizes a variety of analytical techniques, such as x-ray crystallography and cryoEM to view proteins in 3D.

Where It Leads

3D models fuel our machine learning approaches and elucidate the assembly, function and interactions of our novel biologic drug candidate.

What We Do

Our proven team discovers antibody moieties and engineers natural proteins to create directed and optimized biologics using amino acid mutagenesis within protein sequences along with investigating and optimizing drug-like properties of biologics.

Where It Leads

We achieve improved functional activity, enhanced drug-like properties and good manufacturing attributes of our novel biologic drug candidates.

What We Do

Our experienced team investigates dysregulated immune mechanisms while effectively examining preclinical and potential clinical drug properties in novel in vitro human systems, and in vivo.

Where It Leads

We develope meaningful and precise therapeutics to alleviate dysregulated immune function in patients with autoimmune and inflammatory diseases.

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