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Open Source Pharma: From philosophy to real time experience
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Transcript of Open Source Pharma: From philosophy to real time experience
JALEEL
From philosophy to real time experience
Toward a New, Open Source Pharmaceutical IndustryRockefeller Foundation Bellagio Center, July 2014
#opensourcepharma
Literature
Annotation Tools
Genomic Databases
Curated Annotations
Raw Annotations
OSDD C2DCommunity
800+ Student Researchers
Collaborative Curation
Pathway/Interactome | Gene Ontology | Protein Structure/Fold | Glycomics| Immunome
The “Connect to Decode” Programme
Cheminformatics OSDD experience
Curated molecule datasets
Cheminformatics Models
Data Mining and Analysis
HT Virtual screening
PubChem
ChEMBL
DrugBank
Experimental Assays
Community of About 400
Other Active Communities:• OSDD Women Scientists Forum• OSDD Junior Scientists Forum
Pubchem Bioassay data
(approx. 1 lakh molecules/
dataset
6000 descriptors/molecule
Successful Models
Screen PubChem
(30 million)
Data amplification in Drug discovery
Potential Hits
o Down sizing and random validation require multiple calculation for validation of resultso Cross validation up to 50+ time for each experiment
Current Challenges
Around 150 fellows - Open in blogs
TCOF1.3
Workflow
Workflow
TCOWF
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THE WORK PLAN
April 9, 2023 CSIR-OSDD Research Unit, Indian Institute of Science, Bengaluru
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MATERIALSSELECTION OF BIOASSAY
• Bioassay AID 434987 for Model.• Inconclusive Datasets.
PATTERN
SEARCH
• Descriptors by Power MV.• Random Forest Model by WEKA.
PROMSCUITY
• Non- specific Interactions.• 275 rules to conclude if a compound is promiscuous by Eli-Lilly.
NEURAL NETWORKS
• an unsupervised training.• SOM; Kohonen network is used.
LIGAND-
TARGET
INTERACTIONS
• Glide- Schrodinger.• Prioritization of the molecule.
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SCREENED RESULT IN JCHEM EXCELChemAxon (AID 895)
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LILLY MEDCHEM RULES CLASSIFIED MOLECULES
PubChem Dataset
AID
Number of
Inconclusive
Molecules
Lilly MedChem rules
classified molecules
895 795 75
906 30 03
928 870 80
942 18 01
1533 1058 111
1626 3501 262
1949 3175 366
434955 4331 414
• Reasons for rejection include reactivity, interference with assay measurements, activities that damage proteins, instability, and lack of druggability.
• The molecules which are found to be good are separated and saved as an SDF file in each case.
April 9, 2023 CSIR-OSDD Research Unit, Indian Institute of Science, Bengaluru
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HIERARCHICAL CLUSTERING
• Of aid 434987 actives and predicted actives from inconclusive datasets.
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SELF ORGAINIZING MAPS
• SOM uses neural network to perform virtual screening for compilation of thematic molecular libraries.
• Unsupervised training.
• A particular kind of SOM known as Kohonen network is used.
Aminobenzimidazole series
NH
NNH
OSO O
NH
NNH
OSO
NH
NN
OSO O
NH
NNH
OS
OO
NH
NH
OSO O
NH
N HN
SO O
O
NH
NNH
OSNH2
O O
NH
NNH
OHN
S
O
O
N
NNH
OSO O
O
NNH
OSO O
S
NNH
OSO O
N NH
NNH
OSO O
Thiazolylurea series
S
N
NH N
H
S
S
N
NH N
H
O
O
N
NH N
H
S
NH
N
NH N
H
S
S
NN
NH N
H
S
NH
NN
NH N
H
S
S
N
NH N
H
S
S
N
NH
S
S
N
NH
S
S
N
NH N
H
SHN
S
N
NH N
H
S
S
N
NH
SHN
Benzoylpyrrolidone series
O
N O
O
O
N O
O
O
N
NH
O
O
O
N
O
O
O
N O
O
O
N O
O
N O
Cl
O
N O
S
O
N O
S
O
O
N O
HN
O
N O
H2N
O
O
N
N
O
OO
NN O
O
O
NHN O
O
TCOF.1.1
Random forest in CUDA • User Manual or Documentation
http://ajaykmathias.files.wordpress.com/2014/07/virtualscreening.pdf
Installation Manualhttp://ajaykmathias.files.wordpress.com/2014/07/installation-manual.pdf
• Checklist for Virtual Modelling and Screening Processhttp://ajaykmathias.files.wordpress.com/2014/07/screen.xlsx
Mutagenicity studies
Various stages Various models
Open lab
Social networks
OSDD OPEN LAB
OSDD Research Communities
TATA FELOWSHIPS