Data driven life sciences The Pyramids meet the Tower of Babel

Data driven life sciences The Pyramids meet the Tower of Babel Rajarshi Guha NIH Chemical Genomics Center 2010 ACS Na;onal Mee;ng, Boston, MA

Characteris9cs •  Large sizes (but this is rela;ve) –  Chemistry datasets are not really that big •  Mul;‐dimensional •  Mul;ple sources (and hence, types) •  Challenges –  Handling and processing large datasets –  Integra;ng mul;ple data types / sources –  Get a coherent story out of it all

How Useful is More Data? •  Alterna;vely, can we stop doing science and just do paMern recogni;on on increasingly large datasets? •  According to Chris Anderson, yes. There is now a better way. Petabytes allow us to say: "Correlation is enough." We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot. hMp://www.wired.com/science/discoveries/magazine/16‐07/pb_theory

How Useful is More Data? •  The u;lity of more data is obvious in many scenarios –  Sta;s;cal models on 10 observa;ons is not a good idea •  But can there be such a thing as too much data? –  Sta;s;cal models on 106 observa;ons may not be a good idea

Big Data for Some Problems •  Halevy et al discuss the eﬀec;veness of extremely large datasets •  Their applica;on focuses on machine transla;on – see the Google n‐gram corpus •  They suggest that such extremely large datasets are useful because they eﬀec;vely encompass all n‐grams (phrases) commonly used •  Domain is rela;vely constrained Halevy et al, IEEE Intelligent Systems, 2009, 24, 8‐12

Google Scale in Chemistry? •  What would be the equivalent of an n‐gram corpus in chemistry? –  Fragments –  A more direct analogy can be made by using LINGO’s •  It is possible to generate arbitrarily large (virtual) compound and fragment collec;ons •  But would such a collec;on span all of “commonly used” chemistry? –  Depending on the ini;al compound set, yes –  But we’re also interested in going beyond such a “commonly used” set Fink T, Reymond JL, J Chem Inf Model, 2007, 47, 342

Fragment Diversity •  Consider a set of bioac;ves such as the LOPAC collec;on, 1280 compounds •  Using exhaus;ve fragmenta;on we get 40 2,460 unique fragments Percent of Total 30 •  On the MLSMR (~ 400K compounds), 20 we get 164,583 10 fragments 0 0 1 2 3 4 log Fragment Frequency

Fragment Diversity 6 All fragments 4 Fragments occurring in 5 to 50 molecules 4 2 2 PC 2 0 PC 2 0 -2 -2 -4 -4 -4 -2 0 2 -4 -2 0 2 4 PC 1 PC 1 •  Distribu;on of MLSMR fragments in BCUT space

What Do We Do with Fragments? •  Assuming we obtain fragments from a large enough collec;on what do we do? –  Learning from fragments – QSARs, genera;ve models –  Use fragments as ﬁlters, alterna;ve to clustering –  Explore chemotypes and ac;vity White, D and Wilson, RC, J Chem Inf Model, 2010, ASAP

Scaffold Ac9vity Diagrams •  Network oriented view of fragment (scaffold) collec;ons –  Similar in idea to Scaffold Hunter etc –  Not purely hierarchical •  Color by arbitrary proper;es •  Quickly assess u;lity of a scaffold •  Try it online

What Makes a Good Scaﬀold? •  What makes a good scaﬀold? –  Size, complexity, … –  Do the members represent an SAR or not? –  Intui;on and experience also play a role

Scaﬀold QSAR Fit PLS or ridge regression model 0 ! ! !! ! !2 ! ! ! ! Predicted ! !4 ! ! !! ! Evaluate topological ! ! and physicochemical ! !6 descriptors for the ! ! R‐groups !8 Characterize the !8 !6 !4 !2 0 Observed SAR landscape

Scaffold QSAR ‐ Drawbacks •  Many scaffolds have few (5 to 10) members •  Invariably, more features than observa;ons •  If the number of R‐groups is large, the feature matrix can be very sparse –  Less of a problem for combinatorial libraries •  A linear fit may not be the best approach to correla;ng R‐groups to the ac;vi;es –  Difficult to choose a model type a priori •  S;ll working on it …

Fragments for Automa9on •  What is the mo;va;on for scaﬀold QSAR? •  Automate a high throughput screen •  Try and develop heuris;cs to automa;cally push chemotypes into secondary screening

Big Data and Chemistry •  But in the end, the fundamental problem with big data is the issue of domain applicability •  Tradi;onal models are developed on small datasets and perform well within the training domain •  But models trained on very large datasets will not necessarily perform well, even though the training domain is now much larger Helgee et al, J Chem Inf Model, 2010, 50, 677‐689

Processing Large Datasets •  Most cheminforma;cs tasks are not algorithmically parallel •  Rather, they are applied to large numbers of inputs and hence embarrassingly parallel –  Start up lots of jobs •  Hadoop is useful technology for those problems that follow the map/reduce paradigm –  Not aware of cheminforma;cs methods that work in this manner –  But can also be used like a job submission system

Common HTS Analysis Tasks •  Analysis of Ac;vity –  Concentra;on response across mul;ple phenotypes, mul;ple assays –  Assay interference (differen;a;ng ac;vity from ar;facts) –  Assay ontology (biological rela;onships, assay plaqorms) –  Compound annota;ons, known ligand‐target network, prior art assessment –  Profile data (PubChem, BindingDB, ChEMBL, PDSP, etc, physical proper;es) •  Iden;fica;on of Series and Singletons –  Clustering of ac;ves, iden;fica;on of top scaffolds –  Profiling of series across all assays –  Series and singleton priori;za;on •  Compound Selec;on for Followup –  Assessment of structure ac;vity rela;onships –  Rapid iden;fica;on of key compounds to confirm, new compounds to test –  Mining of commercially available chemical libraries How do we beMer automate such tasks?

A Smorgasbord of Data

Data Integra9on •  It’s nice to simplify data, but we can s;ll be faced with a mul;tude of data types •  We want to explore these data in a linked fashion •  How we explore and what we explore is generally inﬂuenced by the task at hand •  At one point, make inferences over all the data

Data Integra9on User’s Network Content: ‐ Drugs ‐ Compounds ‐ Scaﬀolds ‐ Assays ‐ Genes ‐ Targets ‐ Pathways ‐ Diseases ‐ Clinical Trials ‐ Documents Links: Network of Public Data ‐Manually curated ‐Derived from algorithms

Record View of an Assay

Access Disease Hierarchy & Network

Ar9cles, Patents, Drug Labels, …

Going Beyond Explora9on? •  Simply being able to explore data in an integrated manner is useful as an idea generator •  Can we integrate heterogenous data types & sources to get a systems level view? –  Current research problem in genomics and systems biology –  Some aMempts have been made to merge chemical data with other data types Young, D.W. et al, Nat. Chem. Biol., 2008, 4, 59‐68

RNAi & Compound Screens What targets mediate ac;vity of siRNA and compound Pathway elucida;on, iden;ﬁca;on •  Reuse pre‐exis;ng MLI data of interac;ons •  Develop new annotated libraries CAGCATGAGTACTACAGGCCA TACGGGAACTACCATAATTTA Target ID and valida;on Link RNAi generated pathway peturba;ons to small molecule ac;vi;es. Could provide insight into polypharmacology •  Run parallel RNAi screen Goal: Develop systems level view of small molecule acDvity

Small Molecule HTS Summary •  2,899 FDA‐approved ! Most Potent AcDves ! ! ! Proscillaridin A compounds screened 0 ! ! !20 Activity •  55 compounds retested ac;ve ! !40 ! ! ! ! ! ! ! !60 ! !9 !8 !7 !6 !5 •  Which components of the NF‐ log Concentration (uM) ! ! Trabec;din 0 ! ! ! !20 κB pathway do they hit? ! Activity !60 ! –  17 molecules have target/ !100 ! ! ! ! ! ! ! ! !9 !8 !7 !6 !5 pathway informa;on in GeneGO log Concentration (uM) ! ! ! Digoxin 0 ! ! –  Literature searches list a few ! !20 Activity more !40 ! ! ! ! ! ! !60 ! ! ! !9 !8 !7 !6 !5 log Concentration (uM) Miller, S.C. et al, Biochem. Pharmacol., 2010, ASAP

RNAi HTS Summary •  Qiagen HDG library – 6886 genes, 4 siRNA’s per gene •  A total of 567 genes were knocked down by 1 or more siRNA’s –  We consider >= 2 as a “reliable” hit –  16 reliable hits –  Added in 66 genes for follow up via triage procedure

RNAi & Small Molecule •  Based on reporter assays, the only conclusions one can draw are the obvious ones •  Limited by 1‐D signal •  Going to high content gives us much richer data, but more complexity –  Shown to be useful for compounds –  Much more diﬃcult when the phenotypic parameters come from diﬀerent systems

Summary •  Mul;ple data types are probably the most challenging aspect of data driven discovery •  Size issues can be addressed with more hardware or wai;ng (a bit) longer •  Integra;on issues require new approaches both at the presenta;on & algorithmic levels

Acknowledgements •  Ruili Huang •  Ajit Jadhav •  Trung Ngyuen •  Noel Southall

Job Openings at NCGC/NCTT •  Sowware development (focusing on Tripod) –  Java, Swing UI, algorithms •  Research Informa;cs Scien;st –  Generalist, cheminforma;cs, comp chem, med chem •  Collaborate with chemists, biologists •  Cuxng edge problems •  Lots of fresh data •  Fun!

Data driven life sciences The Pyramids meet the Tower of Babel

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