Designing nanoparticle release systems for drug–vitamin cancer co-therapy with multiplicative perturbation-theory machine learning (PTML) models

Nanoscale ◽  
2019 ◽  
Vol 11 (45) ◽  
pp. 21811-21823 ◽  
Author(s):  
Ricardo Santana ◽  
Robin Zuluaga ◽  
Piedad Gañán ◽  
Sonia Arrasate ◽  
Enrique Onieva ◽  
...  
Keyword(s):  
Machine Learning ◽  
Perturbation Theory ◽  
Multiplicative Perturbation ◽  
Nanoparticle Release

Perturbation Theory Machine Learning (PTML) models are presented to predict biological of Nano-systems for cancer co-therapy including vitamins or vitamins derivatives.

Predicting coated-nanoparticle drug release systems with perturbation-theory machine learning (PTML) models

Nanoscale ◽  
2020 ◽  
Vol 12 (25) ◽  
pp. 13471-13483
Author(s):  
Ricardo Santana ◽  
Robin Zuluaga ◽  
Piedad Gañán ◽  
Sonia Arrasate ◽  
Enrique Onieva ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Delivery ◽  
Perturbation Theory ◽  
Drug Release ◽  
Drug Delivery Systems ◽  
Delivery Systems ◽  
Nanoparticle Drug Delivery ◽  
Coated Nanoparticle

We combine Perturbation Theory and Machine Learning (PTML algorithm) to train a model able to predicting the best components for Nanoparticle Drug Delivery Systems (DDNS).

Perturbation Theory Machine Learning Modeling of Immunotoxicity for Drugs Targeting Inflammatory Cytokines and Study of the Antimicrobial G1 Using Cytometric Bead Arrays

2019 ◽  
Vol 32 (9) ◽  
pp. 1811-1823 ◽  
Author(s):  
Esvieta Tenorio-Borroto ◽  
Nilo Castañedo ◽  
Xerardo García-Mera ◽  
Kenneth Rivadeneira ◽  
Juan Carlos Vázquez Chagoyán ◽  
...  
Keyword(s):  
Machine Learning ◽  
Perturbation Theory ◽  
Inflammatory Cytokines ◽  
Bead Arrays

Ab Initio Free Energy Calculations at Multiple Electronic Structure Levels Made Affordable: An Effective Combination of Perturbation Theory and Machine Learning

2020 ◽  
Author(s):  
Tomas Bucko ◽  
Monika Gešvandtnerová ◽  
Dario Rocca
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Perturbation Theory ◽  
Ab Initio ◽  
Single Point ◽  
Free Energy Calculations ◽  
Thermodynamic Quantities ◽  
Free Energies ◽  
Energy Calculations ◽  
Ab Initio Theory

<div>While free energies are fundamental thermodynamic quantities to characterize chemical reactions, their calculation based on ab initio theory is usually limited by the high computational cost. This is particularly true if multiple levels of theory have to be tested to establish their relative accuracy, if highly expensive quantum mechanical approximations are of interest, and also if several different temperatures have to be considered. We present an ab initio approach that effectively couples perturbation theory and machine learning to make ab initio free energy calculations more affordable. Starting from results based on a certain production ab initio theory, perturbation theory is applied to obtain free energies. The large number of single point calculations required by a brute force application of this approach are here significantly decreased by applying machine learning techniques. Importantly, the </div><div>training of the machine learning model requires only a small amount of data and does not need to be </div><div>performed again when the temperature is decreased.</div><div>The accuracy and efficiency of this method is demonstrated by computing the free energy of activation of the </div><div>proton exchange reaction in the zeolite chabazite. Starting from an ab initio calculation based on a semilocal</div><div>approximation of density functional theory, free energies based on significantly </div><div>more expensive non-local van der Waals and hybrid functionals are obtained with only a few tens</div><div>of additional single point calculations. In this way this work paves the route to</div><div>quick free energy calculations using different levels of theory or approximations that would be</div><div>too computationally expensive to be directly employed in molecular dynamics or Monte Carlo simulations.</div>

Predicting Metabolic Reaction Networks with Perturbation-Theory Machine Learning (PTML) Models

2021 ◽  
Vol 21 ◽  
Author(s):  
Karel Diéguez-Santana ◽  
Gerardo M. Casañola-Martin ◽  
James R. Green ◽  
Bakhtiyor Rasulev ◽  
Humberto González-Díaz
Keyword(s):  
Machine Learning ◽  
Perturbation Theory ◽  
External Validation ◽  
Machine Learning Techniques ◽  
Reaction Networks ◽  
Data Series ◽  
Metabolic Reaction ◽  
Large Set ◽  
Validation Data ◽  
Specificity And Sensitivity

Background: Checking the connectivity (structure) of complex Metabolic Reaction Networks (MRNs) models proposed for new microorganisms with promising properties is an important goal for chemical biology. Objective: In principle, we can perform a hand-on checking (Manual Curation). However, this is a hard task due to the high number of combinations of pairs of nodes (possible metabolic reactions). Method: In this work, we used Combinatorial, Perturbation Theory, and Machine Learning, techniques to seek a CPTML model for MRNs >40 organisms compiled by Barabasis’ group. First, we quantified the local structure of a very large set of nodes in each MRN using a new class of node index called Markov linear indices fk. Next, we calculated CPT operators for 150000 combinations of query and reference nodes of MRNs. Last, we used these CPT operators as inputs of different ML algorithms. Results: The CPTML linear model obtained using LDA algorithm is able to discriminate nodes (metabolites) with correct assignation of reactions from not correct nodes with values of accuracy, specificity, and sensitivity in the range of 85-100% in both training and external validation data series. Conclusion: Meanwhile, PTML models based on Bayesian network, J48-Decision Tree and Random Forest algorithms were identified as the three best non-linear models with accuracy greater than 97.5%. The present work opens a door to the study of MRNs of multiple organisms using PTML models.

Perturbation-Theory Machine Learning (PTML) Models for Predicting Metabolic Reaction Networks

2021 ◽  
Author(s):  
Karel Diéguez-Santana ◽  
Gerardo Casañola-Martin ◽  
James Green ◽  
Bakhtiyor Rasulev
Keyword(s):  
Machine Learning ◽  
Perturbation Theory ◽  
Reaction Networks ◽  
Metabolic Reaction

scholarly journals Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of Newl-Prolyl-l-leucyl-glycinamide Peptidomimetics

2018 ◽  
Vol 9 (11) ◽  
pp. 2572-2587 ◽  
Author(s):  
Joana Ferreira da Costa ◽  
David Silva ◽  
Olga Caamaño ◽  
José M. Brea ◽  
Maria Isabel Loza ◽  
...  
Keyword(s):  
Machine Learning ◽  
Perturbation Theory ◽  
Learning Model ◽  
Machine Learning Model
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