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22nd May 2017 @ 13:08

The final hidden test set for the PfATP4 competition has been released here.

All of the 400 compounds contained in the "MASTER SHEET" section of the spreadsheet were submitted to predictions using our earlier described PfATP4 Ion Regulation Activity classification model.

  1. The “Predicted PfATP4 Ion Regulation Activity Class” is the predicted class for the actual molecule (being 1.0 = “active”, 0.0 = “inactive”, and 0.5 = “partially active”).

  2. The “PfATP4 Active Class Probability” is the probability of the actual molecule to be active.

Molecules are reported below in rank order with the most probable to be active at the top.

 

ID

Predicted PfATP4 Ion Regulation Activity Class

PfATP4 Active Class Probability

MMV006239

1.0

0.733

MMV021057

1.0

0.667

MMV676186

1.0

0.533

MMV688550

1.0

0.533

MMV461553

1.0

0.467

MMV676571

0.0

0.467

MMV019551

0.0

0.467

MMV688350

0.0

0.467

MMV001493

0.0

0.467

MMV688509

0.0

0.467

MMV688756

0.0

0.467

MMV676603

0.0

0.467

MMV689000

0.0

0.467

MMV024829

0.0

0.4

MMV688270

0.0

0.4

MMV161996

0.0

0.4

MMV020520

0.0

0.4

MMV021660

0.0

0.4

MMV011903

0.0

0.4

MMV688362

0.0

0.4

MMV687699

0.0

0.4

MMV688754

0.0

0.4

MMV688508

0.0

0.4

MMV047015

0.0

0.4

MMV676008

0.0

0.4

MMV688327

0.0

0.4

MMV688703

0.0

0.4

MMV676604

0.0

0.4

MMV688775

0.0

0.4

MMV689437

0.0

0.4

MMV637953

0.0

0.333

MMV688313

0.0

0.333

MMV407539

0.0

0.333

MMV676162

0.0

0.333

MMV688845

0.0

0.333

MMV675968

0.0

0.333

MMV687703

0.0

0.333

MMV687812

0.0

0.333

MMV687803

0.0

0.333

MMV675997

0.0

0.333

MMV663250

0.0

0.333

MMV1088520

0.0

0.333

MMV676478

0.0

0.333

MMV021375

0.0

0.333

MMV688921

0.0

0.333

MMV687813

0.0

0.333

MMV000011

0.0

0.333

MMV688994

0.0

0.333

MMV020591

0.0

0.267

MMV020710

0.0

0.267

MMV003152

0.0

0.267

MMV676269

0.0

0.267

MMV676270

0.0

0.267

MMV202553

0.0

0.267

MMV675998

0.0

0.267

MMV689709

0.0

0.267

MMV611037

0.0

0.267

MMV019189

0.0

0.267

MMV687180

0.0

0.267

MMV090930

0.0

0.267

MMV688371

0.0

0.267

MMV687749

0.0

0.267

MMV668727

0.0

0.267

MMV688271

0.0

0.267

MMV676442

0.0

0.267

MMV688279

0.0

0.267

MMV687762

0.0

0.267

MMV676480

0.0

0.267

MMV687765

0.0

0.267

MMV000062

0.0

0.267

MMV1110498

0.0

0.267

MMV687700

0.0

0.267

MMV687798

0.0

0.267

MMV024114

0.0

0.267

MMV687796

0.0

0.267

MMV676526

0.0

0.2

MMV102872

0.0

0.2

MMV676386

0.0

0.2

MMV031011

0.0

0.2

MMV020517

0.0

0.2

MMV1030799

0.0

0.2

MMV011691

0.0

0.2

MMV675993

0.0

0.2

MMV676528

0.0

0.2

MMV688889

0.0

0.2

MMV019234

0.0

0.2

MMV688360

0.0

0.2

MMV676600

0.0

0.2

MMV690028

0.0

0.2

MMV688891

0.0

0.2

MMV687696

0.0

0.2

MMV676536

0.0

0.2

MMV671636

0.0

0.2

MMV688980

0.0

0.2

MMV020670

0.0

0.2

MMV687800

0.0

0.2

MMV000023

0.0

0.2

MMV019807

0.0

0.2

MMV024937

0.0

0.2

MMV020391

0.0

0.2

MMV688943

0.0

0.2

MMV676384

0.0

0.2

MMV687776

0.0

0.2

MMV022236

0.0

0.2

MMV024311

0.0

0.2

MMV099637

0.0

0.2

MMV676477

0.0

0.2

MMV688776

0.0

0.2

MMV023949

0.0

0.2

MMV688273

0.0

0.2

MMV688755

0.0

0.2

MMV676182

0.0

0.2

MMV024101

0.0

0.2

MMV676053

0.0

0.133

MMV146306

0.0

0.133

MMV1198433

0.0

0.133

MMV688793

0.0

0.133

MMV020321

0.0

0.133

MMV007133

0.0

0.133

MMV024406

0.0

0.133

MMV202458

0.0

0.133

MMV688553

0.0

0.133

MMV676472

0.0

0.133

MMV019790

0.0

0.133

MMV688274

0.0

0.133

MMV006901

0.0

0.133

MMV688410

0.0

0.133

MMV676589

0.0

0.133

MMV688471

0.0

0.133

MMV020165

0.0

0.133

MMV023985

0.0

0.133

MMV676204

0.0

0.133

MMV1236379

0.0

0.133

MMV676191

0.0

0.133

MMV688766

0.0

0.133

MMV676350

0.0

0.133

MMV676380

0.0

0.133

MMV009054

0.0

0.133

MMV688467

0.0

0.133

MMV019087

0.0

0.133

MMV024443

0.0

0.133

MMV688934

0.0

0.133

MMV688853

0.0

0.133

MMV690027

0.0

0.133

MMV020388

0.0

0.133

MMV1028806

0.0

0.133

MMV688557

0.0

0.133

MMV687254

0.0

0.133

MMV028694

0.0

0.133

MMV026490

0.0

0.133

MMV676406

0.0

0.133

MMV688466

0.0

0.133

MMV689061

0.0

0.133

MMV023388

0.0

0.133

MMV010576

0.0

0.133

MMV688179

0.0

0.133

MMV688852

0.0

0.133

MMV687730

0.0

0.133

MMV676439

0.0

0.133

MMV688936

0.0

0.133

MMV676159

0.0

0.133

MMV689758

0.0

0.133

MMV023969

0.0

0.133

MMV000063

0.0

0.133

MMV658993

0.0

0.133

MMV084603

0.0

0.133

MMV688411

0.0

0.133

MMV153413

0.0

0.133

MMV688417

0.0

0.133

MMV676382

0.0

0.133

MMV010545

0.0

0.133

MMV032967

0.0

0.133

MMV1029203

0.0

0.133

MMV010764

0.0

0.133

MMV687248

0.0

0.133

MMV006372

0.0

0.133

MMV688798

0.0

0.133

MMV689243

0.0

0.133

MMV690102

0.0

0.133

MMV687729

0.0

0.133

MMV676398

0.0

0.133

MMV023860

0.0

0.133

MMV688122

0.0

0.133

MMV690103

0.0

0.133

MMV030734

0.0

0.133

MMV688543

0.0

0.133

MMV676605

0.0

0.133

MMV688470

0.0

0.133

MMV676599

0.0

0.133

MMV676050

0.0

0.133

MMV023370

0.0

0.133

MMV688978

0.0

0.133

MMV688469

0.0

0.133

MMV001625

0.0

0.133

MMV687251

0.0

0.133

MMV687794

0.0

0.133

MMV687273

0.0

0.133

MMV687807

0.0

0.067

MMV228911

0.0

0.067

MMV200748

0.0

0.067

MMV676509

0.0

0.067

MMV688955

0.0

0.067

MMV676474

0.0

0.067

MMV676554

0.0

0.067

MMV687243

0.0

0.067

MMV688472

0.0

0.067

MMV020081

0.0

0.067

MMV676470

0.0

0.067

MMV687188

0.0

0.067

MMV407834

0.0

0.067

MMV688942

0.0

0.067

MMV020982

0.0

0.067

MMV023953

0.0

0.067

MMV007471

0.0

0.067

MMV188296

0.0

0.067

MMV688554

0.0

0.067

MMV026468

0.0

0.067

MMV007920

0.0

0.067

MMV016136

0.0

0.067

MMV022478

0.0

0.067

MMV676597

0.0

0.067

MMV024035

0.0

0.067

MMV053220

0.0

0.067

MMV676161

0.0

0.067

MMV688407

0.0

0.067

MMV006741

0.0

0.067

MMV019838

0.0

0.067

MMV676584

0.0

0.067

MMV007803

0.0

0.067

MMV688958

0.0

0.067

MMV676260

0.0

0.067

MMV688795

0.0

0.067

MMV688762

0.0

0.067

MMV676063

0.0

0.067

MMV676064

0.0

0.067

MMV019721

0.0

0.067

MMV020320

0.0

0.067

MMV020289

0.0

0.067

MMV688474

0.0

0.067

MMV032995

0.0

0.067

MMV688704

0.0

0.067

MMV688372

0.0

0.067

MMV688547

0.0

0.067

MMV022029

0.0

0.067

MMV687170

0.0

0.067

MMV019993

0.0

0.067

MMV687138

0.0

0.067

MMV676461

0.0

0.067

MMV676555

0.0

0.067

MMV688844

0.0

0.067

MMV688796

0.0

0.067

MMV012074

0.0

0.067

MMV676412

0.0

0.067

MMV688854

0.0

0.067

MMV676377

0.0

0.067

MMV688416

0.0

0.067

MMV688797

0.0

0.067

MMV676539

0.0

0.067

MMV688178

0.0

0.067

MMV495543

0.0

0.067

MMV676057

0.0

0.067

MMV676512

0.0

0.067

MMV023183

0.0

0.067

MMV688352

0.0

0.067

MMV667494

0.0

0.067

MMV688991

0.0

0.067

MMV009135

0.0

0.067

MMV069458

0.0

0.067

MMV1037162

0.0

0.067

MMV687706

0.0

0.067

MMV595321

0.0

0.067

MMV688364

0.0

0.067

MMV024195

0.0

0.067

MMV020623

0.0

0.067

MMV085499

0.0

0.067

MMV1019989

0.0

0.067

MMV020136

0.0

0.067

MMV553002

0.0

0.067

MMV688761

0.0

0.067

MMV688941

0.0

0.067

MMV687775

0.0

0.067

MMV085071

0.0

0.067

MMV661713

0.0

0.067

MMV688180

0.0

0.067

MMV687172

0.0

0.067

MMV001059

0.0

0.067

MMV688124

0.0

0.067

MMV688514

0.0

0.067

MMV687145

0.0

0.067

MMV688361

0.0

0.067

MMV688548

0.0

0.067

MMV011765

0.0

0.067

MMV687246

0.0

0.067

MMV688415

0.0

0.067

MMV560185

0.0

0.067

MMV676358

0.0

0.067

MMV000016

0.0

0.067

MMV689255

0.0

0.067

MMV023233

0.0

0.067

MMV001499

0.0

0.067

MMV688990

0.0

0.067

MMV676379

0.0

0

MMV676449

0.0

0

MMV688938

0.0

0

MMV676445

0.0

0

MMV020152

0.0

0

MMV020512

0.0

0

MMV675996

0.0

0

MMV688771

0.0

0

MMV002529

0.0

0

MMV689029

0.0

0

MMV687189

0.0

0

MMV007638

0.0

0

MMV676431

0.0

0

MMV675994

0.0

0

MMV392832

0.0

0

MMV272144

0.0

0

MMV675995

0.0

0

MMV676395

0.0

0

MMV676388

0.0

0

MMV689028

0.0

0

MMV023227

0.0

0

MMV020120

0.0

0

MMV011511

0.0

0

MMV062221

0.0

0

MMV676411

0.0

0

MMV676444

0.0

0

MMV084864

0.0

0

MMV001561

0.0

0

MMV687747

0.0

0

MMV063404

0.0

0

MMV016838

0.0

0

MMV688552

0.0

0

MMV688330

0.0

0

MMV676468

0.0

0

MMV634140

0.0

0

MMV637229

0.0

0

MMV676383

0.0

0

MMV652003

0.0

0

MMV687239

0.0

0

MMV000907

0.0

0

MMV676877

0.0

0

MMV676520

0.0

0

MMV024397

0.0

0

MMV688774

0.0

0

MMV676048

0.0

0

MMV676389

0.0

0

MMV676409

0.0

0

MMV676492

0.0

0

MMV393144

0.0

0

MMV006833

0.0

0

MMV676524

0.0

0

MMV688768

0.0

0

MMV689244

0.0

0

MMV688773

0.0

0

MMV658988

0.0

0

MMV085210

0.0

0

MMV004168

0.0

0

MMV007625

0.0

0

MMV020537

0.0

0

MMV688763

0.0

0

MMV026550

0.0

0

MMV003270

0.0

0

MMV676476

0.0

0

MMV659010

0.0

0

MMV675969

0.0

0

MMV688939

0.0

0

MMV045105

0.0

0

MMV688125

0.0

0

MMV085230

0.0

0

MMV687146

0.0

0

MMV011229

0.0

0

MMV688555

0.0

0

MMV688888

0.0

0

MMV026020

0.0

0

MMV689060

0.0

0

MMV688846

0.0

0

MMV054312

0.0

0

MMV689480

0.0

0

MMV676881

0.0

0

MMV019742

0.0

0

MMV688283

0.0

0

MMV676501

0.0

0

MMV008439

0.0

0

MMV676558

0.0

0

MMV659004

0.0

0

MMV676602

0.0

0

MMV000858

0.0

0

MMV021013

0.0

0

MMV026356

0.0

0

MMV676401

0.0

0

MMV688345

0.0

0

MMV688262

0.0

0

MMV020291

0.0

0

MMV026313

0.0

0

MMV002816

0.0

0

MMV002817

0.0

0

MMV687801

0.0

0

MMV393995

0.0

0

MMV676588

0.0

0

30th March 2017 @ 17:26

If after the publishing of the OSM hidden test set our predictive model for PfATP4 Ion Regulation Activity results to be useful, it can be effectively and thoroughly exploited by anybody after Molomics provides it in Lead Designer, an Android app to easily and quickly access molecule properties important in drug discovery.
Lead Designer allows to easily sketch new molecules with an easy, fully automatized touchpad drawing mechanism. For each molecule, PfATP4 Ion Regulation Activity class and its associated prediction confidence can be instantaneously calculated on the fly. In this way all the people willing to participate in the OSM project, especially medicinal and synthetic chemists, can do design hypothesis for new active compounds and easily check in Real-Time if these compounds have high chances to be active or not (according to the provided prediction model). Each user can save her or his interesting molecules on the cloud to later access them from different devices through its own account.
If the current proposal is of interest, especially to medicinal and synthetic chemists involved in the project, Lead Designer could be used for the design of new active compounds of OSM Series-4. All the molecules designed for the project through Lead Designer are automatically collected on the cloud and then provided to the OSM consortium for possible synthesis and testing. As Lead Designer can involve an arbitrary large number of participants spread around the globe, this project can result in the World's First Crowd Sourced Drug Design Campaign, which can be interesting also for publication purposes.
Please, let us know whether you would be interested in this proposal.

28th March 2017 @ 14:39

We developed several PfATP4 Ion Regulation Activity classification models using different strategies for modeling set sampling, different machine learning methods and different descriptors. Here we report the best performing one.

Data and approach 

The total set of 455 compounds with experimental PfATP4 Ion Regulation Activity was submitted to Molomics standard chemical structure curation protocol, similar to the one described by Fourches et Al.1A curated set of 445 different molecules was obtained.

For the model development, validation and exploitation we followed an internal protocol considering QSAR best practices as defined in literature2,3. The final curated set was split into:

  • a modeling set containing 150 compounds that was subsequently split for internal validation into multiple randomly-chosen, response-stratified training and test sets. The internal validation used a 10-folds cross validation procedure.

  • an external validation set containing 295 compounds.

The OSM competition set consists of 35 compounds obtained from the original data file provided by OSM consortium for this competition. The 35 compounds are those where the Ion Regulation Test Set column is equal to “A,B”, “B” and “C”. Predictions for these compounds were extracted from the test and external validation sets.

The molecules were described with 23 non-highly-correlated (property-based) molecular descriptors and ECFC4 structural fingerprints hashed in 1024-bytes vectors. The machine learning technique used to build the model was an ensemble (Random Forest-like) decision-tree model. The best resulting model uses 15 trees (average tree depth = 15.3; average number of nodes = 47.9).

Results

Results were analyzed considering standard assessment metrics generally used in virtual screening reported for 3 compounds sets: OSM competition, internal validation and external validation sets.

  • confusion matrix (counting correct and wrong classified molecules)

  • accuracy = (TP+TN)/N

  • sensitivity of active molecules. Sensitivity = TP/(TP+FN)

  • specificity of active molecules. Specificity = TN/(TN+FP)

  • balanced accuracy of active molecules. This is very important when the compounds activity is distributed in heavily unbalanced classes, as in the case of OSM. Balanced accuracy = (sensitivity+specificity)/2

  • precision of active molecules. Precision = TP/(TP+FP)

  • Area Under the Curve (AUC) of active molecules

Where TP, TN, FP and FN are True Positives, True Negatives, False Positives and False Negatives, respectively. Active molecules are those with Ion Regulation Activity class = 1.

 

 

OSM competition compounds general results

Confusion matrix:

 

Predicted class

Experimental class

Inactive (0)

Active (1)

Partial (0.5)

Inactive (0)

10

2

1

Active (1)

3

15

0

Partial (0.5)

1

3

0


Assessment metrics:

Assessment metrics

Value

Correct classified

25

Wrong classified

10

Accuracy

0.714

Sensitivity of actives

0.833

Specificity of actives

0.706

Balanced accuracy of actives

0.770

Precision of actives

0.75

AUC

0.810


 

OSM competition compounds individual results

Here we report the individual prediction class for each OSM competition test compound and the class prediction probability for the 3 model classes (i.e. 0, 0.5 and 1).  

Molecule_ID

Ion Regulation Activity class

Prediction (Ion Regulation Activity class)

P(class=0.0)

P(class=1.0)

P(class=0.5)

OSM-S-218

1

1

0

1

0

OSM-S-378

1

1

0

1

0

OSM-S-373

0

0

0.933

0.067

0

OSM-S-372

0

0

0.867

0.133

0

OSM-S-390

1

1

0.067

0.867

0.067

OSM-S-370

1

1

0.2

0.8

0

OSM-S-254

0.5

0

0.733

0.2

0.067

OSM-S-385

1

1

0.267

0.733

0

OSM-S-375

0

0

0.667

0.267

0.067

OSM-S-388

0

0

0.667

0.333

0

OSM-S-382

0

0

0.667

0.333

0

OSM-S-387

0

0

0.667

0.333

0

OSM-S-278

0.5

1

0.333

0.667

0

OSM-S-389

1

1

0.267

0.6

0.133

OSM-S-374

0

1

0.4

0.6

0

OSM-S-204

0.5

1

0.333

0.6

0.067

OSM-S-279

1

1

0.267

0.533

0.2

OSM-S-383

1

1

0.4

0.533

0.067

OSM-S-371

1

0

0.533

0.4

0.067

OSM-S-201

0

0.5

0.267

0.2

0.533

OSM-S-379

1

1

0.4

0.533

0.067

OSM-S-369

1

1

0.333

0.533

0.133

OSM-S-175

1

1

0.4

0.533

0.067

OSM-S-272

1

1

0.467

0.533

0

OSM-S-380

1

0

0.533

0.467

0

OSM-S-363

0

0

0.533

0.4

0.067

OSM-S-353

1

1

0.467

0.533

0

OSM-S-376

1

1

0.133

0.533

0.333

OSM-S-364

0

0

0.533

0.4

0.067

OSM-S-384

1

1

0.467

0.533

0

OSM-S-368

0.5

1

0.4

0.533

0.067

OSM-S-386

0

0

0.533

0.467

0

OSM-S-366

0

1

0.333

0.533

0.133

OSM-S-367

0

0

0.467

0.467

0.067

OSM-S-293

1

0

0.467

0.467

0.067



Internal validation compounds general results

Confusion matrix:

 

Predicted class

Experimental class

Inactive (0)

Active (1)

Partial (0.5)

Inactive (0)

107

3

1

Active (1)

13

22

0

Partial (0.5)

1

3

0

 

Assessment metrics:

Assessment metrics

Value

Correct classified

129

Wrong classified

21

Accuracy

0.860

Sensitivity of actives

0.629

Specificity of actives

0.948

Balanced accuracy of actives

0.788

Precision of actives

0.786

AUC

0.860

 



External validation compounds general results

 Confusion matrix:

 

Predicted class

Experimental class

Inactive (0)

Active (1)

Partial (0.5)

Inactive (0)

272

3

0

Active (1)

12

8

0

Partial (0.5)

0

0

0

 

Assessment metrics:

Assessment metrics

Value

Correct classified

280

Wrong classified

15

Accuracy

0.949

Sensitivity of actives

0.400

Specificity of actives

0.989

Balanced accuracy of actives

0.695

Precision of actives

0.727

AUC

0.835

 

Model statistical significance

In order to asses the statistical significance of the model performance, we developed 100 similar models using a bootstrapped sampling of the modeling set and 100 response-permuted models where the compound response (i.e. the Ion Regulation Activity class) has been randomly permuted for all the compounds. The balanced accuracy distribution of the 100 bootstrapped models is shown in figure 3, while that of the Y-randomized model is shown in figure 4. Where the balanced accuracy is calculated for active molecules (i.e. Ion Regulation Activity = 1).

Distribution of balanced accuracy for active molecules in bootsrapped samples

Figure 3



Distribution of balanced accuracy for active molecules in response-randomized samples

Figure 4 

It can be seen from the figures (figure 3 and figure 4) that the resulting balanced accuracy distributions in the 2 experiment sets are completely non-overlapped. This suggests that the statistical significance of the model is reliable.

 

References

1 Denis Fourches, Eugene Muratov, Alexander Tropsha “Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research” J. Chem. Inf. Model. 2010, 50, 1189-1204.

 

2 Alexander Tropsha “Best Practices for QSAR Model Development, Validation, and Exploitation” Mol. Inf. 2010 Volume 29, Issue 6-7, Pages 476–488.

 

3 Lennart Eriksson, Joanna Jaworska, Andrew P Worth, Mark T D Cronin, Robert M McDowell, Paola Gramatica “Methods for Reliability and Uncertainty Assessment and for Applicability Evaluations of Classification- and Regression-Based QSARs”, Environ. Health Perspect. 2003, 111(10): 1361–1375.


9th March 2017 @ 11:37

A unique id was assigned to any compound using the IDs available in the data file with the following priority order: OSM Code, MMV, Internal OSM, PubChem, Chembl, Commercial, Other.

The PvsP pEC50, corresponding to -log10(EC50(M)) was calculated from the provided Potency vs Parasite EC50 (uM).

In the provided data file are present:

  1. 601 compounds with PvsP values, of which 569 have quantitative values and can be used for regression modeling, and 32 have a qualitative value (meaning they have an associated potency qualifier) and cannot be used in regression modeling.

  2. 455 compounds with IRA values; this activity is mainly binary discriminate active (activity = 1) and inactive (activity = 0) compounds. Nevertheless 5 molecules are tagged as slightly active (activity = 0.5).

  3. 370 compounds with both quantitative PvsP and IRA values.

Using the 370 compounds of point 3 the correlation between PvsP pEC50 and IRA was analyzed. Results are reported in the following table and figure:

Ion Regulation Activity (IRA)

PvsP pEC50 mean – 1St. Dev.

PvsP pEC50 mean

PvsP pEC50 + 1St. Dev.

0

5.7

6.2

6.7

0.5

5.5

5.8

6.2

1

5.8

6.6

7.3

Table 1 

 

Potency vs Parasite - Ion Regulation Activity correlation for all available compounds

Figure 1

As can be seen from the table and figure, if all the molecules are considered it seems that there is no correlation between IRA class (X-axis of figure) and PvsP pEC50 (Y-axis).

The same analysis was repeated using only the compounds of Open Source Malaria series 4 (OSM-S4) (whose origin column in original file is tagged as “OSM S4”). This is a quite smaller set as it contains 32 compounds with both quantitative PvsP and IRA values.

Ion Regulation Activity (IRA)

PvsP pEC50 mean – 1St. Dev.

PvsP pEC50 mean

PvsP pEC50 + 1St. Dev

0

4.8

5.3

5.8

0.5

5.4

5.8

6.1

1

6.0

6.5

7.0

Table 2

 

 

Potency vs Parasite - Ion Regulation Activity correlation for OSM-S4 compounds

Figure 2

As can be seen from the table and figure, if we consider only OSM-S4 compounds it seems there is a correlation between IRA class (X-axis of figure) and PvsP pEC50 (Y-axis).

The cause of the difference in the 2 trends (whether considering all the compounds or only OSM-S4 series ones) is unknown. The correlation, which we see for the OSM-4S IRA class (X-axis of figure 2) and PvsP pEC50 (Y-axis) data, may suggest that the two screens give similar results for specific molecule chemotype series. Still, we believe that both activities should be predicted independently and new molecules should preferentially be selected for synthesis on basis of positive results in both models.

We plan to develop different models based on the previously described observation.

9th March 2017 @ 07:59

In the framework of Open Source Malaria (OSM) project arose the necessity to develop a predictive model for PfATP4 (a sodium pump found in the membrane of the malaria parasite). A modeling competition was launched in order to promote the modeling of such target that is the putative target for the lead series.

The provided data file contains 2 activity types:

  1. Potency vs Parasite (uM): the potency of compounds in whole-cell assays expressed as EC50 (uM). From now on referred as PvsP.

  2. Ion Regulation Activity: PfATP4 target assay activity determining which compounds blocks the malaria parasite ion pumps. It binary discriminate active and inactive compounds. From now on referred as IRA.

The objective of the competition is to develop a computational model that predicts which molecules will block the malaria parasite's ion pump, PfATP4, especially focusing on OSM series 4 (OSM-S4) compounds.