RBFE Atomic Decomposition BACE

Best-in-Class Small Molecules

We are leveraging our proprietary QUAISAR platform to design best-in-class small molecule therapeutics. Our computation-first approach, focused on protein targets with strong biological and genetic validation, is used to design molecules that rapidly reach the desired target product profile (TPP) for disease areas where effective therapeutic solutions are lacking.

Our computation-first approach to molecular design begins by computationally enumerating and analyzing billions of synthetically tractable design ideas based on physics-driven structural hypotheses. The vast chemical space of virtual molecules is first triaged using property prediction models and structural filters to retain only molecules with desirable drug-like properties. Top molecules are then subjected to accurate in silico assays for binding, selectivity and ADME properties to choose candidate molecules for synthesis and in vitro assay.

Biophysical and structural biology studies are also performed in our laboratory to characterize the most promising protein-ligand complexes. As data accumulates on our projects, we integrate machine learning to improve the accuracy and applicability of the property prediction models. Our integrated multi-disciplinary drug discovery team focuses on the key properties of our molecules to systematically engineer, in an atom-by-atom fashion, lead candidates that achieve the desired TPP, overcoming limitations in existing chemical matter and delivering best-in-class medicines to patients. It is the predictive power of QUAISAR, coupled with our multi-disciplinary team, that propels our projects rapidly toward the clinic.

Our differentiating capabilities to design best-in-class therapeutics rest on five cornerstones:

  • Computation-First Design

    Our proprietary QUAISAR platform searches vast chemical space to identify regions of novel intellectual property (IP) and desirable drug-like properties. Bespoke methods are implemented to overcome target-specific challenges that have stymied others, resulting in best-in-class drugs that overcome shortcomings of earlier therapies.

  • Validated Approach

    We have validated our QUAISAR-driven computation-first approach via Stimulator of Interferon Genes (STING), where we uniquely solved the problem of designing a small molecule with drug-like properties for a large, charged, undrugged binding site. SNX281 is currently in clinical development at a spinout company for patients with advanced solid tumors and lymphoma.

  • Physics-Driven Simulations

    We leverage our leading physics-based algorithms and accurate in-house force field to gain unique design insights, even in the absence of data. Accurate in silico assays for project-critical properties such as binding affinity, selectivity, permeability and solubility allow us to more efficiently design molecules with desirable properties by computationally triaging the vast virtual chemical space.

  • Machine Learning Models

    We build accurate property prediction models based on curated literature data. As data accumulates over the course of a project, we augment our models with a bias toward the local chemical space of interest. We also leverage machine learning to rapidly explore chemical space through generative models and reaction-based enumerations, which are filtered through our physics-based computational assays to select top molecules for synthesis.

  • Integrated Team, Rapid Iterations

    We minimize the design cycle time by incorporating knowledge about the difficulty of chemical synthesis and availability of chemical building blocks. We perform simulations on massive internal high-performance computing (HPC) resources to deliver decision-making results in a timely fashion. Our nimble project teams with deep domain expertise work in unison to make data-driven decisions that drive our projects toward the desired TPP.