Drug & Target Discovery Platforms

From in vitro high throughput screening platforms to artificial intelligence in silico screening platforms, this portfolio of Stanford technologies will accelerate your therapeutic pipeline.

Computational Therapeutic Discovery Platforms

S22-119
Sequential fragment-based ligand generation guided by geometric deep learning on protein-ligand structures
S21-250
EternaFold2: A general method for end-to-end nucleic acid structure inference
- Hannah Wayment-Steele
- Rhiju Das
S13-394
Machine learning features for ligand-based drug discovery.
- Steven Kearnes
- Vijay Pande
S14-069
FragFEATURE: Knowledge-based Fragment Binding Prediction using Unsupervised Machine Learning
- Grace Tang
- Russ Altman
S14-077
DrugFEATURE: Identifying Druggable Targets by Protein Microenvironments Matching
- Tianyun Liu
- Russ Altman

Computational Target Discovery Platforms

S21-177
Sparse Gaussian Mixture Model for Learning Gene Regulatory Networks
- Shaimaa Bakr
- Olivier Gevaert
S20-292
OmiXLearn: Machine learning analysis for metabolomics diagnostics and biomarker discovery
S19-340
Mr. Rogers Algorithm for Protein Pathway Discovery
- Jennifer Wilson
S19-020
MARIA-Neural network for accurate prediction of HLA class II antigen presentation
S16-250
MACHETE – algorithm for automated detection of RNA gene fusions
- Julia Salzman

In Vitro Drug & Target Screening Platforms

S22-065
Improved screening of integrin drugs via cell culture model of integrin-mediated adhesion
- Bianxiao Cui
- Wei Zhang
S21-317
DNA Element Responsive to Extrachromosomal DNA in Cancer Cells
S21-419
An integrated preclinical screening platform to identify novel empathogenic drugs and compounds
- Boris Heifets
- Robert Malenka
S13-389
ASAP1 and ASAP2: Fluorescent voltage sensor with fast kinetics for imaging high-frequency neuronal electrical activity
- Michael Lin
- Francois St-Pierre
S07-229
Light Activated Inducers of GPCR Activity for Screening Novel Therapeutics
- Raag Airan
- Karl Deisseroth
S05-206
Fluorescent Cell Barcoding (FCB) in Flow Cytometry to Enable High-Throughput Drug Screening and Large-Scale Signaling Profiling
- Peter Krutzik
- Garry Nolan
S12-039
Screening for compounds to treat obesity and metabolic disease by generating brown fat
- Brian Feldman
S19-375
Tissue engineered 3D models for cancer metastasis and drug screening
- Fan Yang
- Eva Carolina Diaz

Computational Therapeutic Discovery Platforms

Sequential fragment-based ligand generation guided by geometric deep learning on protein-ligand structures

EternaFold2: A general method for end-to-end nucleic acid structure inference

Machine learning features for ligand-based drug discovery.

FragFEATURE: Knowledge-based Fragment Binding Prediction using Unsupervised Machine Learning

DrugFEATURE: Identifying Druggable Targets by Protein Microenvironments Matching

Computational Target Discovery Platforms

Sparse Gaussian Mixture Model for Learning Gene Regulatory Networks

OmiXLearn: Machine learning analysis for metabolomics diagnostics and biomarker discovery

Mr. Rogers Algorithm for Protein Pathway Discovery

MARIA-Neural network for accurate prediction of HLA class II antigen presentation

MACHETE – algorithm for automated detection of RNA gene fusions

In Vitro Drug & Target Screening Platforms

Improved screening of integrin drugs via cell culture model of integrin-mediated adhesion

DNA Element Responsive to Extrachromosomal DNA in Cancer Cells

An integrated preclinical screening platform to identify novel empathogenic drugs and compounds

ASAP1 and ASAP2: Fluorescent voltage sensor with fast kinetics for imaging high-frequency neuronal electrical activity

Light Activated Inducers of GPCR Activity for Screening Novel Therapeutics

Fluorescent Cell Barcoding (FCB) in Flow Cytometry to Enable High-Throughput Drug Screening and Large-Scale Signaling Profiling

Screening for compounds to treat obesity and metabolic disease by generating brown fat

Tissue engineered 3D models for cancer metastasis and drug screening

Life Sciences

Physical Sciences

Sustainability