Presentation about "Predictive in silico models," given by Joerg Bentzien at the Open Source Pharma Conference. The event took place at Rockefeller Foundation Bellagio Center in July 2014.
Joerg Bentzien Bio:
http://www.opensourcepharma.net/participants/jorg-bentzien
Conference Agenda (see Day 1, Session 2):
http://www.opensourcepharma.net/agenda.html
Medical Imaging Seminar Company PresentationsSpace IDEAS Hub
Medical Imaging - Opportunities for Business Seminar
24/01/12
Short Company Presentations
14 companies took the opportunity to present a short sales pitch of their work and interests to the audience.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
or
call us at : 08263069601
Query Performance Prediction by Means of Intent-Aware Metrics in Systematic ...Giorgio Di Nunzio
London Information Retrieval Meetup September 2021 @ DESIRED 2021
Giorgio Maria Di Nunzio, Department of Information Engineering, University of Padova 15/09/2021
Intelligent Interactive Information Access (IIIA) Hub http://iiia.dei.unipd.it
High Performance IOL Power Calculation….at the push of a buttonMichael Mrochen
earSight Innovations is developing new technologies that allow accurate measurement of the eye with the ability to move cataract surgery outcomes to a new level. Cataract surgery is the most commonly performed surgery in the world with over 20 million surgeries conducted annually.
Medical Imaging Seminar Company PresentationsSpace IDEAS Hub
Medical Imaging - Opportunities for Business Seminar
24/01/12
Short Company Presentations
14 companies took the opportunity to present a short sales pitch of their work and interests to the audience.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
or
call us at : 08263069601
Query Performance Prediction by Means of Intent-Aware Metrics in Systematic ...Giorgio Di Nunzio
London Information Retrieval Meetup September 2021 @ DESIRED 2021
Giorgio Maria Di Nunzio, Department of Information Engineering, University of Padova 15/09/2021
Intelligent Interactive Information Access (IIIA) Hub http://iiia.dei.unipd.it
High Performance IOL Power Calculation….at the push of a buttonMichael Mrochen
earSight Innovations is developing new technologies that allow accurate measurement of the eye with the ability to move cataract surgery outcomes to a new level. Cataract surgery is the most commonly performed surgery in the world with over 20 million surgeries conducted annually.
Masayo Takahashi received her M.D. in 1986, and her Ph.D. in 1992 from Kyoto University. After an assistant professorship in the Department of Ophthalmology, Kyoto University Hospital, she moved to the Salk Institute in 1995, where she discovered the potential of stem cells as a tool for retinal therapy. She returned to Kyoto University Hospital in 1997, and was appointed associate professor at the Translational Research Center in the same hospital in 2001. She joined the RIKEN as a team leader of the Lab for Retinal Regeneration in 2006. In 2013, her team launched a pilot clinical study of autologous iPS cell-derived RPE cell sheets for exudative aged-related macular degeneration (AMD), and performed the first RPE cell sheet graft transplantation in Sept. 2014. In 2017, the team started using allogeneic iPS cells suspension in the clinical study.
Science-driven innovation is a cross-cutting key to success in today's business environment. In an increasingly crowded competitive landscape, decision makers need critical advice about markets, technologies, and strategy – both to position existing business for growth and to launch new businesses wisely. Emerging technology fields are fertile hunting grounds for new business opportunities, and corporate executives, investors, policymakers, and entrepreneurs across many industries look to these areas to drive growth.
Market Research plays a vital role for organizations that are looking to move confidently in a new direction. This presentation gives an overview of our unique approach towards delivering unparralled results.
Chris Scafario
Presented by Tomasz Sablinski at the Open Source Pharma Conference in July 2014 at Rockefeller Foundation Bellagio Center.
Tomasz Sablinski's bio: http://www.opensourcepharma.net/participants/tomasz-sablinski
Conference agenda: http://www.opensourcepharma.net/agenda.html
Not too long ago, Sales success was the result of a (rather mystical) combination of networking, people skills, intuition, and good ol’ cold calling.
But the advancement in enterprise technology over the past two decades has changed all that, especially in the B2B environment. Today, networking takes place online, technical skills are becoming more valuable than people skills, data has replaced the ‘hunch’, and automation is threatening to shrink the job market.
But while technology seems to have made selling harder, it has certainly made buying easier. Which, when you think about it, is exactly what Sales and Marketing are trying to do.
Luckily, Sales professionals are known for their ability to adapt. The Sales department is often the first in a business to embrace new technology, find better ways of doing things, and cut unnecessary tasks.
It was Sales, after all, who first embraced cloud technology (CRM was the first viable cloud application for business). And Sales who played a huge role in the consumerization of IT, by insisting on bringing their own devices to work.
The internet, SEO and ferocious content marketing have put a wealth of information at the buyer’ fingertips. And in many cases, have made Sales all but redundant until the final stages of the buying process.
Now, Sales needs to lead the charge once again – accepting the changes that technology brings, and making the most of their new situation.
To find out how our proposal management software is a necessity for a sales professional of the future visit our website for more info: https://www.qorusdocs.com/proposal-software
This talk is presented at Bio-inspiring and evolutionary computation: Trends, applications and open issues workshop, 7 Nov. 2015 Faculty of Computers and Information, Cairo University
Masayo Takahashi received her M.D. in 1986, and her Ph.D. in 1992 from Kyoto University. After an assistant professorship in the Department of Ophthalmology, Kyoto University Hospital, she moved to the Salk Institute in 1995, where she discovered the potential of stem cells as a tool for retinal therapy. She returned to Kyoto University Hospital in 1997, and was appointed associate professor at the Translational Research Center in the same hospital in 2001. She joined the RIKEN as a team leader of the Lab for Retinal Regeneration in 2006. In 2013, her team launched a pilot clinical study of autologous iPS cell-derived RPE cell sheets for exudative aged-related macular degeneration (AMD), and performed the first RPE cell sheet graft transplantation in Sept. 2014. In 2017, the team started using allogeneic iPS cells suspension in the clinical study.
Science-driven innovation is a cross-cutting key to success in today's business environment. In an increasingly crowded competitive landscape, decision makers need critical advice about markets, technologies, and strategy – both to position existing business for growth and to launch new businesses wisely. Emerging technology fields are fertile hunting grounds for new business opportunities, and corporate executives, investors, policymakers, and entrepreneurs across many industries look to these areas to drive growth.
Market Research plays a vital role for organizations that are looking to move confidently in a new direction. This presentation gives an overview of our unique approach towards delivering unparralled results.
Chris Scafario
Presented by Tomasz Sablinski at the Open Source Pharma Conference in July 2014 at Rockefeller Foundation Bellagio Center.
Tomasz Sablinski's bio: http://www.opensourcepharma.net/participants/tomasz-sablinski
Conference agenda: http://www.opensourcepharma.net/agenda.html
Not too long ago, Sales success was the result of a (rather mystical) combination of networking, people skills, intuition, and good ol’ cold calling.
But the advancement in enterprise technology over the past two decades has changed all that, especially in the B2B environment. Today, networking takes place online, technical skills are becoming more valuable than people skills, data has replaced the ‘hunch’, and automation is threatening to shrink the job market.
But while technology seems to have made selling harder, it has certainly made buying easier. Which, when you think about it, is exactly what Sales and Marketing are trying to do.
Luckily, Sales professionals are known for their ability to adapt. The Sales department is often the first in a business to embrace new technology, find better ways of doing things, and cut unnecessary tasks.
It was Sales, after all, who first embraced cloud technology (CRM was the first viable cloud application for business). And Sales who played a huge role in the consumerization of IT, by insisting on bringing their own devices to work.
The internet, SEO and ferocious content marketing have put a wealth of information at the buyer’ fingertips. And in many cases, have made Sales all but redundant until the final stages of the buying process.
Now, Sales needs to lead the charge once again – accepting the changes that technology brings, and making the most of their new situation.
To find out how our proposal management software is a necessity for a sales professional of the future visit our website for more info: https://www.qorusdocs.com/proposal-software
This talk is presented at Bio-inspiring and evolutionary computation: Trends, applications and open issues workshop, 7 Nov. 2015 Faculty of Computers and Information, Cairo University
Lung cancer is a significant public health issue. So Early detection and diagnosis of lung cancer can significantly improve the survival rates of patients. In this presentation, we will discuss the development of a neural network for the prediction of lung cancer.
ReComp and P4@NU: Reproducible Data Science for HealthPaolo Missier
brief overview of the ReComp project (http://recomp.org.uk) on Selective recurring re-computation of complex analytics, and a brief outlook for the P4@NU project on seeking digital biomarkers for age-0related metabolic diseases
The Cold Start Problem and Per-Group Personalization in Real-Life Emotion Rec...Maciej Behnke
Emotion recognition in real life from physiological signals provided by wrist worn devices still remains a great challenge especially due to difficulties with gathering annotated emotional events. For that purpose, we suggest building pretrained machine learning models capable of detecting intense emotional states. This work aims to explore the cold start problem, where no data from the target subjects (users) are available at the beginning of the experiment to train the reasoning model. To address this issue, we investigate the potential of pergroup personalization and the amount of data needed to perform it. Our results on real-life data indicate that even a week’s worth of personalized data improves the model performance.
Friday, October 15th, 2021, Sapporo, Hokkaido, Japan.
Hokkaido University ICReDD - Faculty of Medicine Joint Symposium
https://www.icredd.hokudai.ac.jp/event/5993
ICReDD (Institute for Chemical Reaction Design and Discovery)
https://www.icredd.hokudai.ac.jp
BioAssay Express: Creating and exploiting assay metadataPhilip Cheung
The challenge of accurately characterizing bioassays is a real pain point for many drug discovery organizations. Research has shown that some organizations have legacy assay collections exceeding 20,000 protocols, the great majority of which are not accurately characterized. This problem is compounded by the fact that many new protocol registrations are still not following FAIR (Findability, Accessibility, Interoperability, and Reusability) Data principles.
BioAssay Express is a tool focused on transforming the traditional protocol description from an unstructured free form text into a well-curated data store based upon FAIR Data principles. By using well-defined annotations for assays, the tool enables precise ontology based searches without having to resort to imprecise keyword searches.
This talk explores a number of new important features designed to help scientists accelerate the drug discovery process. Some example use-cases include: enabling drug repositioning projects; improving SAR models; identifying appropriate machine learning data sets; fine-tuning integrative-omic pathways;
An aspirational goal for our team is to build a metadata schema based on semantic web vocabularies that is comprehensive to the extent that the text description becomes optional. One of the many possibilities is to take the initial prospective ELN entry for a bioassay protocol and feed it directly to an automated instrument. While there are many challenges involved in creating the ELN-to-robot loop, we will provide some insights into our collaborations with UCSF automation experts.
In summary, the ability to quickly and accurately search or analyze bioassay data (public or internal) is a rate limiting problem in drug discovery. We will present the latest developments toward removing this bottleneck.
https://plan.core-apps.com/acs_sd2019/abstract/6f58993d-a716-49ad-9b09-609edde5a3f4
Workflows supporting drug discovery against malariaBarry Hardy
The goal of Scientists Against Malaria (SAM) is the discovery of novel anti-malarial compounds. SAM supports virtual drug discovery organizational structures collaborating on target selection and modeling, protein expression and assay development, computational drug design, and screening. A combination of interoperable information systems, ontologies and web services were designed and deployed to manage the data, documents, computational and assay results, activity and toxicology predictions, as well as dashboards to track project progress and to support decision making. Workflows were developed for consensus virtual screening of candidate malarial kinase inhibitors including docking, pharmacophore-based screening and free energy-based molecular simulations. The models were applied to the discovery of active ligands against a novel target with previously unknown structure or ligands. The workflows were extended to include OpenTox model web services to prioritize drug candidates according to their predicted toxicities, supporting a weight of evidence categorization of candidate molecules according to their activity and toxicity profiles.
Healthcare is undergoing a technological transformation, and it is imperative for the industry to leverage new technologies to generate, collect, and track novel data. Panel chaired by Ralf Reilmann of the George Huntington Institut, Muenster.
Simplified Knowledge Prediction: Application of Machine Learning in Real LifePeea Bal Chakraborty
Machine learning is the scientific study of algorithms and statistical models that is used by the machines to perform a specific task depending on patterns and inference rather than explicit instructions. This research and analysis aims to observe how precisely a machine can predict that a patient suspected of breast cancer is having malignant or benign cancer.In this paper the classification of cancer type and prediction of risk levels is done by various model of machine learning and is pictorially depicted by various tools of visual analytics.
Innovative Strategies For Successful Trial Design - Webinar SlidesnQuery
Full webinar available here: https://www.statsols.com/webinar/innovative-strategies-for-successful-trial-design
[Webinar] Innovative Strategies For Successful Trial Design- In this free webinar, you will learn about:
- The challenges facing your trials
- How to calculate the correct sample size
- Worked examples including Mixed/Hierarchical Models
- Posterior Error
- Adaptive Designs For Survival
www.statsols.com
Open Source Pharma: Crowdsourcing wet labs with undergraduatesOpen Source Pharma
Presented by Urmi Bajpai at Rockefeller Foundation Bellagio Center at the Open Source Pharma Conference in July 2014.
Conference Agenda (see Day 1, Session 4):
http://www.opensourcepharma.net/agenda.html
Open Source Pharma: From philosophy to real time experienceOpen Source Pharma
Presentation by Jaleel UC about structuring massive research collaborations. Given at the Rockefeller Foundation Bellagio Center conference about open source pharmaceuticals.
Jaleel's Bio:
http://www.opensourcepharma.net/participants/uc-jaleel
Conference Agenda (see Day 1, Session 4):
http://www.opensourcepharma.net/agenda.html
Presentation by Piero Olliaro about "Anti-TB Drug R&D: Peculiarities, Pipeline and Initiatives."
Piero Olliaro Bio:
http://www.opensourcepharma.net/participants/piero-olliaro
Conference Agenda (see Day 1, Session 1):
http://www.opensourcepharma.net/agenda.html
Open Source Pharma: OSDD: An innovative model for distributed co-creationOpen Source Pharma
Presentation given by Samir Brahmachari about the open research model used by Open Source Drug Discovery. The talk was given at the Open Source Pharma Conference at Rockefeller Foundation Bellagio Center in July 2014.
Samir Brahmachari Bio:
http://www.opensourcepharma.net/participants/samir-brahmachari
Conference Agenda (see Day 1, Session 4):
http://www.opensourcepharma.net/agenda.html
Presentation about the Open Source Malaria group, given by Matthew Todd a the Open Source Pharma Conference, which took place at Rockefeller Foundation Bellagio Center in July 2014.
Open Source Pharma: Game changing for innovative medicineOpen Source Pharma
Presentation given by Dimitrios Tzalis, of the European Lead Factory, at the Open Source Pharma Conference at Rockefeller Foundation Bellagio Center, July 2014.
Dimitrios Tzalis Bio:
http://www.opensourcepharma.net/participants/dimitrios-tzalis
Conference Agenda (See Day 1, Session 2):
http://www.opensourcepharma.net/agenda.html
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
ANAMOLOUS SECONDARY GROWTH IN DICOT ROOTS.pptxRASHMI M G
Abnormal or anomalous secondary growth in plants. It defines secondary growth as an increase in plant girth due to vascular cambium or cork cambium. Anomalous secondary growth does not follow the normal pattern of a single vascular cambium producing xylem internally and phloem externally.
Salas, V. (2024) "John of St. Thomas (Poinsot) on the Science of Sacred Theol...Studia Poinsotiana
I Introduction
II Subalternation and Theology
III Theology and Dogmatic Declarations
IV The Mixed Principles of Theology
V Virtual Revelation: The Unity of Theology
VI Theology as a Natural Science
VII Theology’s Certitude
VIII Conclusion
Notes
Bibliography
All the contents are fully attributable to the author, Doctor Victor Salas. Should you wish to get this text republished, get in touch with the author or the editorial committee of the Studia Poinsotiana. Insofar as possible, we will be happy to broker your contact.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...Wasswaderrick3
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
What is greenhouse gasses and how many gasses are there to affect the Earth.
Open Source Pharma: Crowd computing: A new approach to predictive modeling
1. Predictive in silico models
Crowd computing:
A new approach to predictive modeling
Jörg Bentzien
Open-Source Pharma Bellagio, Italy 7/16/2014 – 7/18/2014
2. Open-Source Pharma
Bellagio, Italy 7/16/2014 – 7/18/2014 2
Introduction
Ph.D. In Chemistry, Univ. Münster, Germany, Prof Martin Klessinger
Photochemical [2+2] Cycloaddition reactions
Post-Doctoral Studies at USC, Los Angeles, CA,
Nobel Laureate Prof Arieh Warshel
Enzymatic Reactions
Xencor, Monrovia, CA
Protein Design
Boehringer Ingelheim Pharmaceuticals, Ridgefield,CT
ComputationalChemist, Small Molecule Drug Design
ADMETModeling
Crowdsourcing with Kaggle, 2012
Bentzien et al. Drug DiscoveryToday (2013), 18, 472 - 478.
Bentzien et al. J PhysChem B (1998), 102, 2293 - 2301
Hayes et al. J PNAS (2002), 99, 15926 - 15931
Bentzien, Klessinger
J OrgChem (1994), 59, 4887 - 4894
3. 3
Why are we building predictive in silico Models?
We cannot make and test every compound.
• Reduce drug failure rates, de-risk compounds
• Select and prioritize compounds before synthesis
Predictive in silico models could help to achieve this task.
Lack of efficacy and safety/toxicity are the main reasons why drugs fail in the clinic.
Toxicity is the main reason for attrition in early drug development.
Reasons for attrition in clinical trials:
Arrowsmith,
Nat. Rev. Drug
Disc. 2013,
12, 569
Open-Source Pharma
Bellagio, Italy 7/16/2014 – 7/18/2014
Efficacy
Safety
4. 4
Principal of in silico modeling
N
S
O
N
H
N
S
O O
OH
Prediction of (ADMET)
Observable
In vivo effect
In vitro effect
Code in Machine
readable form
Calculate Descriptors:
Physical Chemical Descriptors
Molecular Properties
Fingerprints
Substructure Counts
etc.
Generate predictive Model:
Random Forest
SVM
PLS
CoMFA
etc.
Select:
Training Set
Test Set
Validation Set
Pi = f(x1,x2,x3,x4, ….)x1,x2,x3,x4, ….
N
N
H
O
N N
O
N
+
O
O
OH
S
SH
H
Br
0
5
10
0 5 10
pIC50precited
IC50 exp
Positive
Predicted
Negative
Predicted
Positive
Exper.i
True
Positive
(TP)
False
Negative
(FN)
Negative
Experi.
False
Positive
(FP)
True
Negative
(TN)
Regression:
Classification:
Open-Source Pharma Bellagio, Italy 7/16/2014 – 7/18/2014
Find a relation between the chemical structure and the observable, Pi (e.g.
genotoxicity), by first calculating descriptors, xi (e.g. physchem properties), and then
using a mathematical algorithm that calculates the observable Pi for each structure.
5. BI 621,079
hCB2 cAMP EC50 = 1.6 nM
O
N
H
S
Cl
O
O N N
Open-Source Pharma
Bellagio, Italy 7/16/2014 – 7/18/2014
5
Crowd-Sourcing applied to in silico Modeling:
The general idea
Traditional Model Building The KAGGLE approach
Ames Positive
predicted
AM1
Ames Negative
predicted
AM1
Ames
Positive
experimental
167 (183) 16
Ames
Negative
experimental
21 53 (74)
Potent Ames negative
compound
O
F
F
F
N
N
H
Single
Expert
Modeller
3. Generate a Model
4. Find a solution
3. Generate a Model
Taking advantage of the “crowd”
one vs. many
Potent Ames positive compound
O
N
H
1. Define the problem
2. Prepare the Data
6. Open-Source Pharma Bellagio, Italy 7/16/2014 – 7/18/2014 6
The Kaggle Challenge:The Data Set
Predicting a Biological Response 3/16/2012 – 6/15/2012
Data Set of 6512 compounds from Literature
CADD-BI performed:
Data Set Clean-Up (6252: 3401p/2851n)
Random split into:
Training Set (3751: 2034p/1717n)
PublicTest Set (625: 329p/296n)
Private Validation Set (1876: 1038p/838n)
Pre-calculated Descriptors (1776)
Participants had no knowledge of
• the modeled endpoint
• the descriptor types
• the chemical structures
BI offered $20,000 for the best three models
Participants could use any technology they wanted
BI will get the models
Objectives:
• Response to competition
• Quality of the algorithms/models
• Model transfer
Task:
Generate an Ames Classification model
1 = Ames positive
0 = Ames negative
This Challenge does NOT test all aspects of predictive
in silico modeling
Important aspects, e.g. data set selection, descriptor
selection/design, are missing
Study is a machine learning exercise, a proof of concept
Advantage: We know exactly what to expect,
comparative benchmarks available
7. Open-Source Pharma
Bellagio, Italy 7/16/2014 – 7/18/2014
7
The Kaggle Challenge:The Competition
Predicting a Biological Response 3/16/2012 – 6/15/2012
Overwhelming response to competition!
Best models perform better than
standard benchmarks:
Rank Log Loss
Best Model 1 0.37356
Random Forest 352 0.41540
SVM 541 0.49503
Each Class Predicted with
Probability 0.5
599 0.69250
On average 88 entries per day!
Optimal model
generated
after ~20 Days
796 players (487 first time participants)
703 teams
8841 models submitted
9. Open-Source Pharma
Bellagio, Italy 7/16/2014 – 7/18/2014
9
The Kaggle Challenge: Lessons learned
Technology aspects:
• 1st ranked team: R-software, blending of several different RandomForest models, with
special feature selection and weighting techniques. Final models were merged using other
machine learning techniques.
• 2nd ranked team: R-software, RandomForest, derived new response variable pending on
value and observed activity.This may lead to better separation between actives and
inactives.
• 3rd ranked team: R-software, RandomForest with special techniques to deal with
imbalanced data sets.
• The challenge was a success
• There was a great response
• Predictive in silico models were generated within a three months time frame
• Models were at least as good as the literature
• Social aspects of crowd-sourcing were observed
10. Open-Source Pharma
Bellagio, Italy 7/16/2014 – 7/18/2014
10
The Kaggle Challenge: Lessons learned (continued)
Performance aspects:
• Model performance on par with best literature models, reached maximum performance for
data set
• Top ranking models are not significantly different from Random Forest benchmark
• Quick turn-around (3 months), code made available
• Model performance plateaued after 20 days
A standard RandomForest model is a good starting point.
In-house technology performs as well as more complex approaches.
Social aspects of competition:
• Very strong response: 703 teams, 8841 models submitted
• People from all over the world participated:
1st place team from US (Harvard,Travelers insurance)
2nd place team from Russia graduate student from Moscow
3rd place from China graduate student from Beijing
• Winning teams had no CompChem/Chemistry background
• Formation of teams occurred during competition
Bentzien at al. “Crowd
computing: Using competitive
dynamics to develop and refine
highly predictive models”, Drug
DiscoveryToday (2013), 18, 472
- 478.
11. Open-Source Pharma
Bellagio, Italy 7/16/2014 – 7/18/2014
11
The Kaggle Challenge: Lessons learned (continued)
Important aspects for successful crowdsourcing:
Design the Crowdsourcing Challenge:
Very clear defined task/objective
Predefined precise metric to measure entries
Provide adequate incentive/prize money for participants
Participants:
Hosting the challenge either through third party or self
Internal/Restricted/Open Challenge
Promote the crowd sourcing challenge among key expert leaders
The Challenge:
Right barrier for participation
Fast turn-around/feedback to participants
Gamification
can provide additional incentive to participants
can lead to synergies amongst participants
After the Challenge:
Clear follow-up of what to do with the results
Does the challenge benefit to your Network/Organization?
12. Crowd-Sourcing : Other examples
Open-Source Pharma
Bellagio, Italy 7/16/2014 – 7/18/2014
12
http://www.nytimes.com/2012/11/24/science
/scientists-see-advancesin-
deep-learning-a-part-of-artificial-
intelligence.html?_r=0
Lakhani et al., Nat Biotech,
2013, 31, 108-111.
www.innocentive.com
www.the-dream-project.com
Prill et al., ScienceSignaling, 2011, 4, 1-6
www.kaggle.com
www.topcoder.com
www.grants4targets.com
13. Open-Source Pharma
Bellagio, Italy 7/16/2014 – 7/18/2014
13
Crowd-Sourcing: A new way for solving problems(?)
Will crowd-sourcing solve all the problems? Likely not.
Crowd sourcing offers opportunities but it is not without risks.
For crowd sourcing to be successful/innovative the task needs to be structured right.
Murcko & Walters, “Alpha Shock” J Comput Aided Mol Des 2012, 26, 97-102
Kittur et al. “The Future of Crowd Work” 16th ACM Conference on Computer
Supported Cooperative Work (CSCW 2013)
Will crowd-sourcing be the future way of drug discovery?
Maybe, ….
Drug Discovery will definitely be different from what it is now.
Potential framework for future crowd
work.
Requires
• Intelligent work decomposition
• sophisticated workflow design
• high level of collaborative work
• quality assurance.
Simple crowd work
• tendency to be mechanical
• not innovative
• has exploitive tendency
Example:
Amazon MechanicalTurk
14. Open-Source Pharma
Bellagio, Italy 7/16/2014 – 7/18/2014
14
Acknowledgements
Business Partners and Collaborates
ADMET-WG:
Jan Kriegl, Bernd BeckStefan,
Scheuerer, Michael Durawa,
Pierre Bonneau, Sanjay Srivastava,
Michel Garneau, Hassan Kadhim,
Matthias Klemencic, Christian
Klein, Robert Happel, Gerald
Birringer, Dustin Smith, Scott
Oloff, Zheng Yang
Toxicology:
Warren Ku
Patricia Escobar
Ray Kemper
External Collaborators:
Ernst-Walter Knapp
Özgür Demir-Kamuk
AlexTropsha
Curt Breneman
John Pu
Andy Fant
Zhuo Zhen
Medicinal Chemistry:
Robert Hughes
In silico VPR-team
All the MedChem users
Research IS:
Scott Oloff
DavidThompson (PAC)
Zheng Yang
Scott Whalen
Cathy Farrell
MiguelTeodoro
IS-InnovationTeam
Alex Renner
Structural Research:
Sandy Farmer
Neil Farrow
Ingo Mügge
All CADD colleagues
SKD:
Will Loging
Kaggle:
KaggleTeam
Kaggle Challenge Participants