Current technologies used for separation of trivalent lanthanides and actinides are based on multistage extraction cascade with relatively low efficiency at each stage. Difficulties of separation process arises from similarity of trivalent lanthanides and actinides properties and liquid-liquid extraction is used either as a basic separation technique or as a model experiment for more complicated system. Significant improvement can be reached by development selective and effective extractants, that could form a stable complex with a single ion (or at least with a single group of ions) in presence of competing ones. Now we know several groups of such ligands with satisfactory selectivity and other properties (as solubility in target diluent, nitric acid resistance, etc) and a process of development is usually devoted to modifications of these basic frameworks. On the other hand, a process of selective ligand development is very similar to drug design process, where we try to find a molecule with the best affinity to the biological target. In the case of drug design, using computational models, especially based on machine learning is an essential part of development process. Here we would like to present an example of adopting drug design approaches to lanthanides and actinides complexation problem. We have collected databases containing complex stability constants for trivalent f-elements. For each element we built models based on neural network architectures predicting target constants (R2~0.8, RMSE~2[logK]). We also ‘unboxed’ these models to determine key fragments of molecule with the most significant influence on stability constant value. This approach can ‘offer’ molecular fragments increasing or decreasing constant value. Finally, we combined models with genetic algorithm available not only to predict values of a drawn molecule, but to construct new molecules with increasing target property.

1. De novo design of ligands for
selective trivalent f-elements
complexation
Artem Mitrofanov, Boris Sattarov, Valery Tkachenko, Stepan Kalmykov
Science Data
Software, LLC
Lomonosov
Moscow State University

2. An/Ln separation
2
DAqueous phase DOrganic phase
System

3. An/Ln separation
Extractants
3
• Diglycolamides
• Pyridines
• Bipyridines
• Phenanthrolines
• …?

4. Computational approaches
Overview
4
• Ab initio: 4C, X2C
• DFT + A2C (+ECP)
• Semi-empirical
• Molecular dynamics

5. QSPR
Overview
5
Quantitative Structure-Property Relationship
• Database
• Molecule vectorization
• Machine learning

6. Deep learning
Outline
6

7. Datasets
Selection rules
7
• Ionic strength of 0.1-0.3M and at temperatures of 20-25 °C.
• Aqueous solutions
• The preference was given to constants measured with the
potentiometric titration.
• If the values of the constants obtained by one method and
under the same conditions differ greatly from one another, we
took into account a constant from the work, where the
experimental conditions were described in a more accurate
way

8. Datasets
8
Am(III)
Eu(III)

9. Deep learning
Learning
9
• Stratified shuffle train and test sets (20%
for test)
• 10x k-fold cross validation
• DNN with 1-5 hidden layers of 256-4096
nodes
• MinMax scaling of feature vectors
(separately for train and test)
• MACCS, ECFP(2-8), ECFC(2-8), FCFP(2-8),
FCFC(2-8), Avalon FP, RDkit descriptors
set (DESC) and their combinations.
Feature vector length ~200-4000
Eu(III)
Am(III)

10. Deep learning
Results
10
Eu(III) Am(III)
R^2 0.95 0.90
RMSE 1.36 1.61

11. 11
0
1
2
3
4
5
La Ce Pr Nd Pm Sm Eu Gd Tb Dy Ho Er Tm Yb Lu
Train Validation Test
0
0.2
0.4
0.6
0.8
1
La Ce Pr Nd Pm Sm Eu Gd Tb Dy Ho Er Tm Yb Lu
Train Validation Test
Deep learning
Results

12. Deep learning
Sensitivity analysis
12
+1.0±0.1 +0.8±0.2 +1.1±0.7 -1.2

13. Preliminary conclusions
13
• We build predictive models with RMSE
comparable to experimental one on relatively
small datasets
• We developed a method for ‘unboxing’ neural
network model
• … is it de novo design?

14. 14
Further development
Genetic algorithm
from towardsdatascience.com

15. Conclusions
…and plans
15
• We build predictive models with RMSE
comparable to experimental one on relatively
small datasets
• We developed a method for ‘unboxing’ neural
network model
• Work in progress: generative model

16. 16
Acknowledgements
• Boris Sattarov
• Kristina Yakubova
• Alexandru Korotcov
• Valery Tkachenko
• Petr Matveev
• Vladimir Petrov
• Stepan Kalmykov
Science Data
Software, LLC
Lomonosov
Moscow State University

17. Thank you!
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Slides:
https://www.slideshare.net/valerytkachenko16
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