Harnessing biomolecular motion to design novel therapeutics that precisely reprogram protein function

Expand the boundaries of
drug discovery

Target insights

Modeling biomolecular motion uncovers novel therapeutic approaches

Considering the dynamic behavior of proteins expands the design space of therapeutics. Here, we simulate the dynamic protein-protein interface to pinpoint optimal strategies for designing molecules that maximize the intended conformational and therapeutic effect.

Generative design

Computational models generate diverse molecular designs aligned with the therapeutic hypothesis

Here, we search the design space for molecular glues that will reprogram protein function by stabilizing the desired protein-protein interface.

Computational assay

Predictive models enable accurate and high-throughput evaluation of therapeutic potency of molecular designs

Computational models forecast not only the affinities of the designed molecules for the target proteins but also their effects on functions. Here, we interrogate the efficacy of therapeutic degraders in tagging a protein for degradation. The therapeutic degraders induce transfer of ubiquitin to the surface lysines on the target protein.

CASE STUDY 01
Computation-driven optimization of mutant KRAS inhibitors

Accelerating discovery

Accurate binding affinity predictions speed up drug discovery
Consideration of binding pocket flexibility achieved valuable target insights leading to highly accurate predictions of molecular binding
Our binding free energy calculations enabled discovery of twice as many bioactive molecules from half as many synthesized.
Target insights
Elucidation of multiple pocket conformations
Generative design
Autonomous generation of hundreds of pocket-compatible molecular designs
Computational assay
Prediction of KRAS inhibition
CASE STUDY 02
SMARCA2 degrader design based on novel protein-protein interfaces

Expanding the design space

The first successful design of potent degraders based on computationally modeled protein-protein interfaces
Accurate models of protein-protein interface and computational assays resulted in superior rates of successful designs and unique degrader molecules.
Target insights
Discovery of a new protein-protein interface
Generative design
Enumeration of easy-to-synthesize degrader designs
Computational assay
Prediction of SMARCA2 degradation
CASE STUDY 03
Mutant-selective degraders for a biologically validated target

First-in-class molecules

Atommap degraders utilize novel mechanism of action to specifically target a mutant oncogenic protein
Target insights
Exploitation of the conformational differences between mutant and wild-type proteins
Generative design
Virtually creating over 500,000 PROTACs and more than 4.5 million molecular glues
Computational assay
Prediction of mutant vs wild-type degradation
Team
Pioneers of computation-driven drug discovery

Our leadership team has been instrumental in many of the most impactful developments in computational drug discovery:

  • The most widely used software for predicting protein-ligand affinities (FEP+)
  • The fastest molecular dynamics simulation software (Desmond)
  • A proprietary toolkit for rational design of degraders and molecular glues
  • A computational molecular design platform that produced drug molecules advanced into clinic (STING agonist)

Huafeng Xu, PhD

Founder and CEO

Chief Technology Officer of Silicon Therapeutics and Roivant Discovery

Led platform development at D. E. Shaw Research as the founding developer of the fastest molecular dynamics simulation code, DESMOND, and the most widely used binding free energy prediction software, FEP+. Ph.D. from Columbia University

Jesus Izaguirre, PhD

Co-founder and Chief Computational Scientist

VP of advanced simulations in Silicon Therapeutics and Roivant Discovery leading platform development of targeted protein degradation

Scientist at D. E. Shaw Research. Former professor of CS and Engineering and co-director of Computer-Aided Drug Design Core at the University of Notre Dame. Ph.D. from University of Illinois at Urbana-Champaign

Yujie Wu, PhD

Co-founder and Chief Technology Officer

Director of Free Energy Methods at Roivant Discovery

15 years at Schrodinger where he led FEP+ development and engineering teams, and contributed to other molecule dynamics based products. Ph.D. from University of Utah

Cheryl Koh, PhD

Head of Discovery Biology

Scientific leader and key contributor in drug discovery programs at AstraZeneca and DeepCure

Core team member for development of first clinical candidate (systemic STING agonist) at Silicon Therapeutics. Ph.D. from Johns Hopkins School of Medicine

Mandana Honu, PhD

Head of Business Development

Chief Scientific Officer and Head of Business Development of Kaleidoscope

Spent 16 years in cancer research before shifting focus to company building, including key roles in therapeutics and platform investing, new company creation, and corporate BD at Resilience. Led product development and sales at Kaleidoscope. Ph.D. from Duke University

Arie Zask, PhD

Head of Chemistry

Medicinal chemistry team leader at Wyeth and Pfizer

Over 30 years developing novel therapeutics in the pharmaceutical industry and academia with multiple clinical compounds in oncology, inflammation, and neurodegenerative disease. Ph.D. from Princeton University

Partner
Working with Atommap

Discovery

We deliver promising molecules, development candidates, virtual screening solutions, and generative designs based on unique target insights.

Hit discovery
  • Finding initial hits
  • Scaffold hopping (structural variations)
Optimization
  • Hit-to-lead development
  • Lead compound optimization
Engineering
  • Selectivity enhancement
  • Target specificity
Emerging modalities
  • Molecular glue
  • Chemically induced proximity

Computational modeling

We deliver accurate models and predictions.

Target protein insights 
  • Highly accurate structural and dynamic models
  • Druggable pocket identification
Computational assays
  • Binding affinity prediction
  • Customized functional potency predictions

Out-licensing Atommap molecules

We collaborate with partners to co-develop our drug candidates.

Mutant-selective degraders
  • The only mutant-specific degraders against a mutant oncogenic protein
  • Tackling a clear unmet medical need
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