Year in Review: Our Key Achievements

January 2, 2019

ATOM was officially established in October 2017 by founding members, GSK, Lawrence Livermore National Laboratory, Frederick National Laboratory for Cancer Research, and the University of California, San Francisco. The ATOM Technical Team, comprised of experts in machine learning, data science, pharmaceutical sciences, cancer biology, biophysics, and engineering, has been working diligently to build our computational and experimental infrastructure, develop algorithms, and reach R&D milestones.

At the ATOM 1-Year Anniversary event this past October, over 60 delegates from ATOM member organizations convened at ATOM headquarters. Team leaders shared the major accomplishments achieved which ranged from benchmarking ATOM DeepChem models for demonstrated performance gains, to performing proof-of-principle multi-parameter simulations of the active learning process.

 
ATOM Co-leads  celebrating our one year anniversary. (L-R) Tom Rush, Ph.D., ATOM Head of Science; Stacie Calad-Thomson, Ph.D., ATOM Head of Operations; Jim Brase. M.S., ATOM Head of Technology.

ATOM Co-leads celebrating our one year anniversary. (L-R) Tom Rush, Ph.D., ATOM Head of Science; Stacie Calad-Thomson, Ph.D., ATOM Head of Operations; Jim Brase. M.S., ATOM Head of Technology.

 
 

Highlights from the Team’s Year-1 Achievements

 

Data and modeling:

  • Combined private and curated datasets, and  identified diversity and data gaps.

  • Made descriptor sets of chemical features for more than 2 million compounds

  • Automated a framework for model creation and tracking

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Novel hybrid model development:

  • Showed that hybrid models perform better than typical molecule descriptors

Pharmacokinetic (PK) and safety data-driven modeling:

  • Built baseline models for PK parameters and liability assays with a focus on heart and liver toxicity

  • Benchmarked ATOM DeepChem models and demonstrated performance gains

  • Generated safety data sets to fill data gaps

Active learning integrated loop:

  • Performed proof-of-principle multi-parameter simulations of active learning process

 
 

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