Marrying physics and data-driven approaches to obtain novel solutions to difficult problems. We believe that optimum solutions to multi-variable design problems can be obtained when we harness the advantages offered by physics, structure, and data.
Our design engine Sq-Suite contains three primary modules, namely, Sq-SYNT (small-molecule design), Sq PROT (peptide and protein design), Sq-AIMM (Immunogenicity assessment)
Marrying physics and data-driven approaches to obtain novel solutions to difficult problems. We believe that optimum solutions to multi-variable design problems can be obtained when we harness the advantages offered by physics, structure, and data.
Our design engine Sq-Suite contains three primary modules, namely, Sq-SYNT (small-molecule design), Sq PROT (peptide and protein design), Sq-AIMM (Immunogenicity assessment)
Leverages millions of years of evolutionary information, which is combined with powerful data and ML-driven assessments to yield a focused mutational space. Complemented by a powerful physics-based, MD-FEP driven engine to provide a small-sized, high-confidence libraries of novel biologics. The workflow is now complemented by cutting-edge conjugate design.
The requirement for novel interventions and first-in-class therapeutics still pose significant difficulties for in silico design approaches. Hence, we created Sq-SYNT – a flexible, workflow that utilizes a “best-of-all-worlds” approach. The problem-specific protocols provides efficient navigation of chemical space to yield molecules with the desired properties.
Leverages millions of years of evolutionary information, which is combined with powerful data and ML-driven assessments to yield a focused mutational space. Complemented by a powerful physics-based, MD-FEP driven engine to provide a small-sized, high-confidence libraries of novel biologics. The workflow is now complemented by cutting-edge conjugate design.
The requirement for novel interventions and first-in-class therapeutics still pose significant difficulties for in silico design approaches. Hence, we created Sq-SYNT – a flexible, workflow that utilizes a “best-of-all-worlds” approach. The problem-specific protocols provides efficient navigation of chemical space to yield molecules with the desired properties.
Was initially developed to complement Sq PROT and highlight potential regions for deimmunization within a novel biologic. The tool has evolved to identify immunogens for vaccine design. Sq-AIMM is largely a data-driven workflow, with support from structure, and evolutionary data. The output is a list of regions with high propensity to trigger cell-mediated or humoral responses.
