Accelerating Therapeutics for Opportunities in Medicine (ATOM)
Advanced Computing Solutions for Cancer (JDACS4C)

JDACS4C Cellular Level Pilot: Predictive Modeling for Pre-Clinical Screening
JDACS4C Molecular Level Pilot: Improving Outcomes for RAS-related Cancers
JDACS4C Molecular level Pilot: KRAS4b Protein Modeling using Multiscale Machine-Learned Modeling Infrastructure (MuMMI)
JDACS4C Population Level Pilot: Population Information Integration, Analysis, and Modeling for Precision Surveillance
Neurocrine Histamine H1 receptor Demonstration
Safety Screening

3k disordered 3-component-system (DPPC-DOPC-CHOL)
Autoencoder for MD Simulation Data (P2B1)
Cancer Type Classifier Based on Somatic Mutations (P1B2)
Combination Drug Response Predictor (Combo)
Drug MoA Information
Drug Molecular Descriptors
Gene Expression Autoencoder (P1B1)
H1 Selectivity Assay
Integrated DataFrames of Most Prevalent Cancer Types - TopN (Top6/Top21)
KRAS4b Simulation Data
ML Ready Pathology Reports
MultiTask Convolutional Neural Network (MT-CNN)
MuMMI Splash Run 2 for KRAS4b Protein Modeling
MuMMI Splash Run 4 for KRAS4b Protein Modeling
Normal-Tumor Pair Classifier Model (NT3)
Pathology Reports Hierarchical Self-Attention Network (HiSAN)
Pilot 1 Cancer Drug Response Prediction Dataset
RAS Protein Molecular Dynamics Simulations
RNA-Seq Latent Featurizer Using Center Loss Cost Function (CLRNA)
Single Drug Response Predictor (P1B3)
Tumor Classifier Model (TC1)
Unified Drug Response Predictor (Uno)