Download multi-task deep convolutional network

How deep learning can help to
design better and safer medicine?
KinomeNet: multi-task deep
convolutional network
Olexandr Isayev, Ph.D.
University of North Carolina at Chapel Hill
@olexandr
http://olexandrisayev.com
About me
Ph.D. in Chemistry (computational)
Minor in CS
Worked in Federal research lab on HPC & GPU
computing to solve chemical problems
Now I am research faculty at the University of North
Carolina, Chapel Hill
http://olexandrisayev.com
And I am also Director of Drug Discovery at Atlas
Regeneration. We use AI & multi-omics for developing
regenerative medicine and stem cell differentiation
technologies.
http://atlasregeneration.com/
A public-private partnership
that supports the discovery
of new medicines through
open access research
www.thesgc.org
How drugs are discovered?
The Long and Winding Road to Drug Discovery
Data Science approaches
useful across the pipeline,
but
very different techniques
aim for success,
but if not:
fail early, fail cheap
Medicines Are Transforming the Treatment of Many Diseases
Robotic synthesis
Robotic biological tests (HTS)
Drowning in Data
but starving for Knowledge
The rapid growth of materials research has led to accumulation of vast amounts of data: For example, 160,000 entries in the Inorganic Crystal Structure Database (ICSD) Numerous commercial and open experimental databases NIST, MatWeb, MatBase etc.
Vast computational databases such as AFLOWLIB, Materials Project, and Harvard Clean Energy.
Decline in Pharmaceutical R&D efficiency
The cost of developing a new drug (~$2‐3B) roughly doubles every nine years.
Scannell et al. Nature Reviews Drug Discovery, 2012, 11, 191‐200
Why Drugs are failed?
Selectivity of Kinase inhibitors
All kinases bind ATP and therefore contain a conserved binding site
Most compounds inhibit more than one kinase
Why Don’t we Do Better?
A Couple of Observations
• Tykerb – Breast cancer
• Gleevac – Leukemia, GI
cancers
• Nexavar – Kidney and liver
cancer
• Staurosporine – natural product
– alkaloid – uses many e.g.,
antifungal antihypertensive
>40% of biologically active
compounds bind to more than
one target
Collins and Workman 2006 Nature Chemical Biology 2 689‐700
Virtual Screening
to identify potential hits
Empirical Rules/Filters
Similarity Search
ML or QSAR Models
Consensus
QSA Models
Structure-based
VIRTUAL
SCREENING
~102 – 103
molecules
~106 – 107
molecules
Candidate
molecules
Potential
Hits
Our vision for next-gen
cheminformatics platforms
• Scale up Machine Learning Methods with the Data
• Use all viraity of available data (-omics, sensors, etc)
• Take advantage of latest algorithmic developments –
Deep Learning
Human Kinase Inhibitor Data Collection
Collected all human kinase data from open sources
•
•
•
•
•
ChEMBL
PKIS
PubChem
Private datasets
Literature, patents, etc.
300,000+ Molecules
489 Targets >800,000 Experimental data points
Biggest target data: >25000 molecules Smallest target data: 1 Human Kinase IC50 Data Distribution “Popular” targets
“Rare” targets
Convolutional Neural Network (ConvNet)
Convolution Function (Filter)
Comes from Image and Signal Processing
The easiest way to understand a convolution is by thinking of it as a sliding window function applied to a matrix.
Groundbreaking results of DL are mostly based on networks with convolutional filters
• Image recognition
• Object detection
• Medical image processing Different Levels of Abstraction • Hierarchical Learning • Natural progression from low level to high level structure as seen in natural complexity • Easier to monitor what is being learnt and to guide the machine to better subspaces • A good lower level representation can be used for many distinct tasks KinomeNet: Convolutional Neural Network for QSAR
ABL1
ACVR1
…
ConvNet
ZAK
ZAP70
2D matrix of Descriptors
Multitask Learning
(253 targets)
Some Statistics & Performance Numbers
RF (Random Forest)
KinomeNet
Average AUC: 0.90
Average AUC: 0.96
Random Forest Models
N compounds
Active AUC
@1uM
TN
FP
TP
FN Sensitivity Specificity
MAP4K4 160
10
0.88
149
1
1
9
0.1
0.93
BMX
151
0.78
0
4
151 0
1.0
0.0
0.6
0.94
0.99
1.0
155
DL Model
MAP4K4 160
10
0.91
150
0
6
BMX
151
0.93
4
0
149 6
155
4
KinomeNet: “Deorphanizing” rare targets
ABL1
ACVR1
…
ConvNet
ZAK
ZAP70
2D matrix of Descriptors
Multitask Learning
(253 targets)
KinomeNet: “Deorphanizing” rare targets
“Frequent”
(253 targets)
ConvNet
ACVR1
…
2D matrix of Descriptors
“Rare” targets
(67 targets)
TYMS
Multitask Learning
(320 targets)
Why it Works: Transfer Learning
• Feature‐representation‐transfer
• To learn a “good” feature representation for the target domain. • The knowledge used to transfer across domains is encoded into the learned feature representation.
• With the new feature representation, the performance of the target task is expected to improve. Recovery of Kinase Similarity by the Network Atlas Regeneration
Young
dynamic startup company
(formed in 2015) in North Carolina
We
use AI to develop regenerative
medicine
Design
molecules to induce iPSC
stem cell differentiation
Tissue
and muscle regeneration,
fibrosis
AI Drug Discovery Platform
250M+ SCREENING
MOLECULES
o Integrated public data
(PubChem, ChEMBL, etc)
o Private datasets
o Literature and patents
o In vitro (HTS)
o In vivo (mouse, rats)
o Multi-omics
o Signaling Pathways
o Gene Expression
BIG CHEMICAL
DATA
FAST ARTIFICIAL
INTELLIGENCE
TOP HITS
TGF beta inhibitor (Fibrosis)
Large scale prediction of bioactivity with Deep Learning
200M+
of potential
candidates
Selectivity
Off target binding
Toxicity
Metabolic stability
Bioavailability
Solubility
etc.
FAST ARTIFICIAL
INTELLIGENCE
7
•
•
•
•
•
Good selectivity
Three novel scaffolds
Predicted potency 7 – 25 nM
Good synthetic accessibility
Good ADME/Tox properties
Conclusions
• Data availability is the biggest barrier
• Novel architecture for multitask‐QSAR
• Improvement over well converged RF models
• Convenience: 1 vs 320 models
• Training of 1 network is faster that 320 RF models
• Scalability of DL to “Big Data”
• DL benefits from transfer learning
• More tasks and more data – higher the benefit
• Transferability: KinomeNet ‐> GPCRNet