Dataledger(tm) is a research data platform that integrates data acquisition with machine learning pipelines for chemical processing and experimental studies. It supports various machine learning methods, including shallow and deep learning techniques, for tasks such as QSAR and inverse QSAR modeling, while offering free services for molecular representation and structure prediction. The platform emphasizes open data science principles, collaboration, and automation to aid in data science workflows and chemical property predictions.

1. Dataledger(TM) - research data
platform with data acquisition and
integrated machine learning
pipeline
SCIENCE DATA SOFTWARE, LLC, Rockville, Maryland, United States

2. Data, Information, Knowledge, Wisdom

3. Laboratory Data Acquisition
VirtualStandardFAIRDataBus
Other Registries
Other Registries
Other Registries

5. Open Data Science Platform – Dataledger(TM)
D
a
t
a
Data Lake
Social
Media
Electronic
Notebooks
Databases
Sensor Med
Dev
IoT
Curated
Repository
Models
Curation &
Integration
Validation
Decision
Support
Analysis &
Modeling
Open Data Science Platform
Mining
USERS
Model-driven experimental studies

6. Workflow
• Data acquisition and templated data entry
• Processing
• Curation
• Datasets management
• Descriptive analysis
• Modeling
• Validation
• Prediction
• Feedback cycle
• Automation

7. Open Data Science Platform

8. Chemical processing
● Support for chemical
formats
● Chemistry validation
and standardization
● Automatic processing
and visualization

10. FAIR Data Principles

11. Collaborative data authoring and curation
● Datacite.org
support
● Other formats
● Audit trail
● Notifications

12. Built-in Machine Learning
● Automated ML
pipeline
● Pre-built ML
modules
● Comparison
between different
ML algorithms
● NB, NN, RF, SVM, LR
● DNN

14. Train Models:
auto optimized fingerprints

15. MACCS – 166 public MACCS keys + 1 zero bit
AVALON – Avalon count FPs from Avalon cheminformatcis toolkit
ECFP – Extented Connectivity FingerPrint
FCFC – Feature Connectivity Fingerprint Count vector
DESCS – all of the RDKit supported descriptors
Automated optimization of molecular representation by combining
up to 4 different types of fingerprints
TOP-5 combinations (BioHC, VP, KM datasets):
• DESC, MACCS, ECFP (Radius: 3, Size: 512), FCFC (Radius: 2, Size: 128)
• DESC, AVALON Size: 128, ECFP (Radius: 2, Size: 128), FCFC (Radius: 2, Size: 128)
• DESC, MACCS, ECFP (Radius: 2, Size: 256), FCFC (Radius: 3, Size: 128)
• MACCS, AVALON (Size: 1024), Type: ECFP (Radius: 2, Size: 256), FCFC (Radius: 4, Size: 512)
• DESC, AVALON (Size: 128), ECFP (Radius: 3, Size: 512 )

16. Prediction

17. Sharing

18. OSDR Single Structure Prediction Free Service
https://ssp.dataledger.io/predict

19. https://ssp.dataledger.io/predict
OSDR Single Structure Prediction Free Service

20. https://ssp.dataledger.io/predict
OSDR Single Structure Prediction Free Service

21. Molecule representation
RDKit: Descriptors, Fingerprints; Autoencoder FP, CNN

22. Smart Molecule representation:
SMILES-based seq2seq encoders
CC(C)Cc1ccc(cc1)C(C)C(=O)O
CC(C)Cc1ccc(cc1)C(C)C(=O)O
• A powerful tool for data-driven features extraction
• Latent vector can be used as a QSAR descriptor of a molecule
32.4
24.2
…
24.2
32.1
Latent
vector
Initial SMILES
Reconstructed
SMILES
IGC50, R2 Solubility, R2
ENUM2CAN 0.7918 0.8954
CAN2CAN 0.7256 0.8289
ECFP4 0.562 0.6359
Trained Decoder combined with
Multiple Objective Optimization
algorithm can be used as a
specialized inverse QSAR - type
generative model
B. Sattarov et al, Ready for publication

23. Public available free service for molecular
representations calculations https://ssp.dataledger.io/features

24. AI and Machine Learning methods in OSDR for QSAR, QSPR, and material SPR
Shallow Machine Learning (SML) methods:
• Naive Bayes, k Nearest Neighbors, Linear Logistic Regression, AdaBoost Decision Tree, Random Forest, Elastic Net regression
(L1 and L2 regularizes), Support Vector Machine, XGBoost
• Clustering, Classification and Regression models
• Open source based advanced ML libraries such as scikit-learn (CPU for training and prediction) for training, tuning
(automated optimization), and validating all SML models
Deep Neural Networks (DNN) methods:
• Different complexity or feed-forward DNN (up to 6 hidden layers)
• Encoder-decoder NN for feature selection and clustering
• Long-Shot Term Memory (LSTM) NN for inverse QSAR (generate SMILES of molecules with desired properties)
• Convolutional Graph NN (Graph CNN)
• Clustering, Classification, Regression and Generative models, models optimization and tuning
• Open source Keras and Tensorflow (GPU training and CPU or GPU for prediction)
Datasets preparation:
• Dataset exploratory analysis (values distributions, average heavy atoms, rotational bonds, etc.)
• Converting SMILES to feature vector (fingerprints and descriptors alone and in combinations)
• Split into train and test datasets (80/20 is a default settings) maintaining equal proportions of active/inactive class or similar
train/test distributions for regression (stratified splitting)
• k-fold cross on train data for better model generalization

25. Models’ performance evaluation metrics
• Receiver Operating Characteristic (ROC) curve and the area under it (AUC) - is created by plotting the
true positive rate (TPR) against the false positive rate (FPR) at various threshold settings.
• F1-Score - the harmonic mean of the Recall and Precision:
𝐹𝐹1 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 = 2 ∗
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 ∗ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 + 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅
• Accuracy - the percentage of correctly identified labels out of the entire population:
𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 =
𝑇𝑇𝑇𝑇+𝑇𝑇𝑇𝑇
𝑇𝑇𝑇𝑇+𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹+𝐹𝐹𝐹𝐹
• Matthews correlation coefficient - is generally regarded as a balanced measure which can be used even
if the classes are of very different sizes:
𝑀𝑀𝑀𝑀𝑀𝑀 =
𝑇𝑇𝑇𝑇 � 𝑇𝑇𝑇𝑇 − 𝐹𝐹𝐹𝐹 � 𝐹𝐹𝐹𝐹
√(𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹)(𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹)(𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹)(𝑇𝑇𝑇𝑇 + 𝐹𝐹𝐹𝐹)
• Cohen’s Kappa coefficient - estimating overall model performance, attempts to leverage the Accuracy
by normalizing it to the probability that the classification would agree by chance (pe):
𝐶𝐶𝐶𝐶 =
𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴−𝑝𝑝𝑒𝑒
1−𝑝𝑝𝑒𝑒
, where
𝑝𝑝𝑒𝑒 = 𝑝𝑝𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 + 𝑝𝑝𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹, 𝑝𝑝𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 =
𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹
𝑇𝑇𝑇𝑇+𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹+𝐹𝐹𝐹𝐹
�
𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹
𝑇𝑇𝑇𝑇+𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹+𝐹𝐹𝐹𝐹
, 𝑝𝑝𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 =
𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹
𝑇𝑇𝑇𝑇+𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹+𝐹𝐹𝐹𝐹
�
𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹
𝑇𝑇𝑇𝑇+𝑇𝑇𝑇𝑇+𝐹𝐹𝐹𝐹+𝐹𝐹𝐹𝐹

26. OSDR models performance on publicly available datasets
OPERA: https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=337488
TEST: https://www.epa.gov/chemical-research/toxicity-estimation-software-
tool-test
EPA shallow ML vs OSDR deep models
Dataset
EPA OPERA,
R2 test
OSDR DNN,
R2 test
Octanol-water Partition
Coefficient
0.860 0.916
Melting Point 0.730 0.817
Water solubility 0.860 0.931
Vapor Pressure 0.920 0.944
Boiling Point 0.930 0.964
Fish Biotransformation Half-life 0.730 0.894
Biological Half-life 0.750 0.832
Bioconcentration Factor 0.830 0.775
Soil Adsorption Coefficient 0.710 0.785
Dataset
EPA TEST,
R2 test
OSDR
DNN, R2
test
BCF 0.758 0.743
BP 0.946 0.959
Density 0.955 0.968
FP 0.878 0.898
IGC50 0.763 0.855
LD50 0.624 0.681
LC50 0.729 0.760
LC50DM 0.657 0.716
MP 0.830 0.842
ST 0.898 0.938
TC 0.889 0.911
Viscosity 0.856 0.875
VP 0.953 0.953
WS 0.858 0.853

27. SDS trained public available data sets used by EPA
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45
EPA
OSDR
OSDR vs EPA predictions consensus
Deviatons from experimental values
10.00% 25.00% 50.00% Out of 50%
Tested 40 structures from PHYSPROP and DrugBank, 9 properties:
Water Solubility, Melting Poing, Vapor Pressure, Boiling Point, LogP, LogBCF, LC50, LD50, ICG50
Trained Models are available in OSDR for a Single Structure Prediction as a Free Service
https://ssp.dataledger.io/predict

28. Deep models showed overall better ability to learn representations and great generality of the deep
algorithms vs shallow approaches.

29. In progress: Materials

30. In progress: Graph convolutional neural networks as
"general-purpose" property predictors
V. Korolev et al, Ready for publication

31. In progress: Deep Learning approach for Ln(III)
complexation
models and model analysis
0
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La Ce Pr Nd Pm Sm Eu Gd Tb Dy Ho Er Tm Yb Lu
Train Validation Test
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La Ce Pr Nd Pm Sm Eu Gd Tb Dy Ho Er Tm Yb Lu
Train Validation Test
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Impact,[logK]
Bit number
A. Mitrofanov, Ready for submission

32. Reaction 1: compounds and solutions
Diisobutylaluminium hydride (1.1 M in cyclohexane,
2.93 mL, 3.23 mmol) was added dropwise to the
solution of 9 (500 mg, 1.29 mmol) and
dichloromethane (20 mL) at −78 °C. The reaction
mixture was stirred at −78 °C for another 2 h, warmed
up to rt, quenched with methanol (3 mL) and citric
acid (aq) (w/w, 10%, 5 mL), concentrated. The residue
was added with water (10 mL) and extracted with
dichloromethane (12 mL × 3). The organic layers were
combined, dried over Na2SO4, filtered and
concentrated. The crude product was further purified
by column chromatography (SiO2, EtOAc–hexanes,
1 : 7; Rf 0.33) to give 10 (308 mg, 1.02 mmol, 79%) as a
colourless liquid.
Solutions
• Diisobutylaluminium hydride
Compounds
• 9
• dichloromethane
• methanol
• citric acid
• water
• Dichloromethane
• Na2SO4
• 10
Ignored for now (only the name was
extracted in this pass) – in time
“Substances”
• SiO2
• EtOAc–hexanes
Reaction components: reactant , solvent, product
Other compound/substance used in procedure

33. Reaction 2: Planned reaction runReaction
• Reaction Run: Reaction SJH-01-227 dated 2/12/2014; FailedReaction: false; Experiment Stage: Planned
• Stoichiometry Table:
Label Reaction Component Substance Amounts Comments
SJH-01-223 Role: Limiting Reactant
Compound
Molecular Mass: 756.95
State: Solid Equivalence: 1
Moles: 0.132 mMol
Mass: 0.1 g
benzaldehyde Role: Reactant
Compound
Molecular Mass: 206.24
State: Solid Equivalence: 6
Moles: 0.293 mMol
Mass: 0.163 g
Cu(OTf)2 Role: Reactant
Compound
Molecular Mass: 361.67
State: Liquid
Purity: 98%
Source: 283673-5G, Sigma Aldrich
Equivalence: 0.1
Moles: 0.013 mMoles
Mass: 0.005 g
DCE Role: Solvent
Compound
State: Liquid
Purity: 99-100%
Source: 283673-5G, Sigma Aldrich
Volume: 1.321 mL Concentration in
line 1: 0.1 M
TFA Role: Solvent
Compound
Molecular Mass: 114.02
Density: 1.49 g/ml
State: Liquid
Purity: 99%
Source: T6508-500mL, Sigma Aldrich
Equivalence: 3
Moles: 0.396 mMol
Mass: 0.045 g
Volume: 0.030 mL
SJH_01_227 Role: Product
Compound
State: Solid Equivalence: 1
Moles: 0.132 mMol

34. S88 process standard approach
Process
Process
Stage
Process
Stage
Process
Stage
Process
Operation
Process
Actions
Experiment Synthesis stage Preparation / Reaction /
Work up / Isolation
Heat / Cool /
Dose / Stir etc.
S88 allows procedure steps (process actions) to be grouped
into “process operations”:
We allow “Procedure Steps” to be nested and have seeded the following
procedure step types to assign to procedure steps for these parent operations:
S88 process operation/Procedure. StepTypes.Title
Preparation
Reaction
S88 process operation/Procedure. StepTypes.Title
Work up
Isolation

35. S88-style procedures
• Type of actions which can be assigned to procedure steps
Action Types
Add Synthesize Wait Degass
Yield Wash Unknown Irradiate
Stir Extract Precipitate Mill
Remove Filter Partition Sample
Heat Concentrate Quench Reflux
Dry Cool Apparatus Action Transfer
Purify Dissolve Recover

36. Reaction 1: procedure steps
Diisobutylaluminium hydride (1.1 M in cyclohexane,
2.93 mL, 3.23 mmol) was added dropwise to the
solution of 9 (500 mg, 1.29 mmol) and
dichloromethane (20 mL) at −78 °C. The reaction
mixture was stirred at −78 °C for another 2 h,
warmed up to rt, quenched with methanol (3 mL)
and citric acid (aq) (w/w, 10%, 5 mL), concentrated.
The residue was added with water (10 mL) and
extracted with dichloromethane (12 mL × 3). The
organic layers were combined, dried over Na2SO4,
filtered and concentrated. The crude product was
further purified by column chromatography (SiO2,
EtOAc–hexanes, 1 : 7; Rf 0.33) to give 10 (308 mg,
1.02 mmol, 79%) as a colourless liquid.
Text mining breaks down procedure summary into steps:
<dl:reactionActionList/dl:reactionActions> dl:phraseTexts
• action="Add“: Diisobutylaluminium hydride (1.1 M in
cyclohexane, 2.93 mL, 3.23 mmol) was added dropwise to the
solution of 9 (500 mg, 1.29 mmol) and dichloromethane (20 mL)
at −78 °C
• action=" Stir“: The reaction mixture was stirred at −78 °C for
another 2 h
• action="Heat“: warmed up to rt
• action="Quench“: quenched with methanol (3 mL) and citric
acid(aq) (w/w, 10%, 5 mL)
• action="Concentrate“: concentrated
• action="Add“: The residue was added with water (10 mL)
• action="Extract“: extracted with dichloromethane (12 mL × 3)
• action="Dry“: dried over Na2SO4
• action="Filter“: filtered
• action="Concentrate“: concentrated
• action="Purify“: The crude product was further purified by
column chromatography (SiO2, EtOAc–hexanes, 1 : 7; Rf 0.33)
• action="Yield“: to give 10 (308 mg, 1.02 mmol, 79%) as a
colourless liquid

37. Extensible micro-service based architecture

38. Technologies
● Mix of technologies connected
through microservices architecture
● Open source toolkits and libraries
with permissive licenses
● NoSQL Databases
● Containerization
● Leading practices in CI/CD
● Automated testing, rapid
development

39. OSDR CLI - https://github.com/scidatasoft/osdr-cli

40. Summary
• A Machine Learning toolkit with simple user interface have been
developed for the Dataledger(TM) software.
• Chemical datasets exploratory analysis, wide range of descriptors and
fingerprints selections, dataset preparation and validation for QSAR/QSPR
readiness for organic and inorganic materials
• Intelligent Machine Learning for QSAR and inverse QSAR, QSPR, and
material SPR
• Shallow Machine Learning methods (Naive Bayes, k Nearest Neighbors,
Linear Logistic Regression, AdaBoost Decision Tree, Random Forest, Elastic
Net regression (L1 and L2 regularized), Kernel Ridge Regression, Support
Vector Machine, XGBoost) for classification and regression problems
solving in SAR and SPR

41. Summary
• Deep Learning Methods (Feed-Forward Deep Neural Networks (NN),
Encoder-Decoder NN, Long-Short Term Memory NN, Convolutional Graph
NN) and their applications for drug discovery and pharmaceutical research
• Clustering, Classification, Regression and Generative models, models
optimization and tuning, models interpretation and applicability domain
estimation, prediction properties and screening chemical compounds for
leads search and hits optimization
• Free public Feature Vector Computation service:
https://ssp.dataledger.io/features
• Free public Single Structure Prediction service (multiple properties,
multiple models): https://ssp.dataledger.io/predict

42. Thank you!
On Web:
scidatasoft.com
Slides:
https://www.slideshare.net/valerytkachenko16
Contact us:
info@scidatasoft.com