NANOIF.PTML models in nanotechnology

Classic Machine Learning (ML) models can be transformed into read-across multi-objective models (multi-output models able to optimize multiple properties (Zeta potential, IC50, Half time, etc.), multi-level (cells, organisms, tissues, etc.) and multi-label models (multi-species, cores, shapes, coats, techniques, sensors, etc.). One way to that is using Information Fusion Perturbation Theory Machine Learning (IFPTML) algorithms coined by us as (NANOIFPTML) See some papers from CHEMIF.PTML LAB in this area. Read more about our group in the CHEMIF.PTML LAB Newsletter: https://www.dhirubhai.net/newsletters/chem-ptml-lab-newsletter-6924816402038071296/

Paper 6.

Summary: We experimentally synthesized and characterized new Fe3O4 based hashtag#Magnetic hashtag#Nanoparticles (MNPs) functionalized with PMAO and PEG. However, many NP cores and coatings remain unexplored. Then, we developed hashtag#AI / hashtag#ML predictive algorithms for 14 output properties (CC50 (μM), EC50 (μM), etc.) for all combinations of 54 different NP cores classes vs. 15 different coats and vs. 41 different cell lines shortlisting best results for assays.

Reference: Shan He, Ander Barón Torre, Cristian R. Munteanu, Bego?a De Bilbao Gonzalez, Gerardo M. Casanola-Martin, Mariana Chelu, Adina Magdalena Musuc, Harbil Bediaga Ba?eres, Estefania Ascencio Medina, Idoia Castellanos Rubio, Sonia Arrasate Gil, Alejandro Pazos, Maite Insausti, Bakhtiyor Rasulev, Humbert G. Díaz, Prof. Drug Release Nanoparticle Systems Design:?Dataset Compilation and Machine Learning Modeling. ACS Applied Materials & Interfaces (2025) doi: 10.1021/acsami.4c16800, link: https://lnkd.in/dw8_Cqk5.

Institutions: (1) Department of Coatings and Polymer Materials, North Dakota State University, Fargo, ND, 58102, United States. (2) Department of Organic and Inorganic Chemistry, University of Basque Country, ZTF-FCT :: UPV/EHU, 48940 Leioa, Greater Bilbao, Basque Country, Spain. (3) IKERDATA S.L., UPVEHU ZITEK, Rectorate Building, 48940 Leioa, Basque Country, Spain. (4) Computer Science Faculty, Universidade da Coru?a, CITIC Centro de Investigacion TIC - Universidade da Coru?a, 15071 A Coru?a, Spain. (5) BCMATERIALS, BASQUE CENTER FOR MATERIALS, APPLICATIONS AND NANOSTRUCTURES, 48940 Leioa, Spain.(6) Ilie Murgulescu Institute of Physical Chemistry, 202 Spl. Independentei, 060021 Bucharest, Romania. (7) BIOFISIKA: Basque Center for Biophysics, CSIC-UPVEH, Fundación Biofísica Bizkaia / Biofisika Bizkaia Fundazioa 48940 Leioa, Spain. (8) Ikerbasque, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain.

Sponsors: The authors acknowledge financial support from National Science Foundation (NSF) Science Foundation NSFMRI award OAC-2019077. European Commission hashtag#NextGenerationEU Lanbide hashtag#Investigo Grant IKERDATA 2022/IKER/000040. Eusko Jaurlaritza - Gobierno Vasco hashtag#ELKARTEK SPRI Group 2022-2023, and IT1558-22, 2022-2025, GoverNPent / EuskoJaurlaritza, Ministry of Science and Innovation of Spain Grant PID2019-104148GB-I00, MCIN/AEI/10.13039/501100011033, Grant ED431C 2022/46 – Competitive Reference Groups (GRC) – funded by the EU and Xunta de Galicia (Spain).

Paper 5.

Linkedin Post: CHEM.PTML LAB NEWS: Springer Nature Group Published: NANO.PTML Model for read-across prediction of nanosystems in neurosciences. Computational model and experimental case of study. by: Shan He, Karam Nader Pisonero, Julen Segura Abarrategi, Harbil Bediaga Ba?eres, Deyani Nocedo Mena, Estefania Ascencio Medina, Gerardo M. Casanola-Martin, Idoia Castellanos Rubio*, Maite Insausti, Bakhtiyor Rasulev, Sonia Arrasate Gil*, and Humbert G. Díaz, Prof..

Post link: https://www.dhirubhai.net/posts/humbertgdiaz_womens-nanobiotechnology-elkartek-activity-7211386427262062595-2c2K?utm_source=share&utm_medium=member_desktop

Citation: He, S., Nader, K., Abarrategi, J.S. et al. NANO.PTML model for read-across prediction of nanosystems in neurosciences. computational model and experimental case of study. J Nanobiotechnol (2024) 22, 435, doi: 10.1186/s12951-024-02660-9

Paper link: https://doi.org/10.1186/s12951-024-02660-9

Collection: This paper is intended to be published in the hashtag#Womens in hashtag#Nanobiotechnology (WiN) Collection. Guest Editors: Dr Zi (Sophia) Gu (UNSW Science University of?New South Wales, Australia),??Dr Wei Tao (Harvard Medical School, USA): https://lnkd.in/d3tz5rUw

Abstract: Neurodegenerative diseases involve progressive neuronal death. Traditional treatments often struggle due to solubility, bioavailability, and crossing the Blood-Brain Barrier (BBB). Nanoparticles (NPs) in biomedical field are garnering growing attention as neurodegenerative disease drugs (NDDs) carrier to the central nervous system. Here, we introduced computational and experimental analysis. In the computational study, a specific IFPTML technique was used, which combined Information Fusion (IF)?+?Perturbation Theory (PT)?+?Machine Learning (ML) to select the most promising Nanoparticle Neuronal Disease Drug Delivery (N2D3) systems. For the application of IFPTML model in the nanoscience, NANO.PTML is used. IF-process was carried out between 4403 NDDs assays and 260 cytotoxicity NP assays conducting a dataset of 500,000 cases. The optimal IFPTML was the Decision Tree (DT) algorithm which shown satisfactory performance with specificity values of 96.4% and 96.2%, and sensitivity values of 79.3% and 75.7% in the training (375k/75%) and validation (125k/25%) set. Moreover, the DT model obtained Area Under Receiver Operating Characteristic (AUROC) scores of 0.97 and 0.96 in the training and validation series, highlighting its effectiveness in classification tasks. In the experimental part, two samples of NPs (Fe3O4_A and Fe3O4_B) were synthesized by thermal decomposition of an iron(III) oleate (FeOl) precursor and structurally characterized by different methods. Additionally, in order to make the as-synthesized hydrophobic NPs (Fe3O4_A and Fe3O4_B) soluble in water the amphiphilic CTAB (Cetyl Trimethyl Ammonium Bromide) molecule was employed. Therefore, to conduct a study with a wider range of NP system variants, an experimental illustrative simulation experiment was performed using the IFPTML-DT model. For this, a set of 500,000 prediction dataset was created. The outcome of this experiment highlighted certain NANO.PTML systems as promising candidates for further investigation. The NANO.PTML approach holds potential to accelerate experimental investigations and offer initial insights into various NP and NDDs compounds, serving as an efficient alternative to time-consuming trial-and-error procedures.

Affiliations: 1 Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58108, USA. 2 Department of Organic and Inorganic Chemistry, Universidad del País Vasco/Euskal Herriko Unibertsitatea ZTF-FCT :: UPV/EHU, 48940 Leioa, Spain. 3 IKERDATA S.L., Zitek UPV/EHU, UPV/EHU, Rectorate Building, no6, 48940 Leioa, Basque Country, Spain. 4 Faculty of Physical Mathematical Sciences, Universidad Autónoma de Nuevo León, México. 5 BCMATERIALS, BASQUE CENTER FOR MATERIALS, APPLICATIONS AND NANOSTRUCTURES, 48940 Leioa, Spain. 6 BIOFISIKA: Basque Center for Biophysics CSIC, Fundación Biofísica Bizkaia / Biofisika Bizkaia Fundazioa, Basque Country, Spain. 7 IKERBASQUE (Ikerbasque), Basque Foundation for Science, 48011 Bilbao, Biscay, Spain.

Funding: Basque Government / Eusko Jaurlaritza - Gobierno Vasco (IT1558-22), SPRI Group hashtag#ELKARTEK AIMOFGIF (KK-2022/00032) grant and Lanbide, hashtag#INVESTIGO grant, IKERDATA S.L. 2022/IKER/000040 funded by hashtag#NextGenerationEU funds of European Commission. Ministry of Science and Innovation of Spain grant (PID2022-137365NB-I00), and grant No. PID2022- 136993OB-I00 (AEI/FEDER, UE), funded by MCIN/AEI/ 10.13039/ 501100011033, ERDF, European Union. U.S. Department of Energy (DOE) Grant DE-SC0022239. Center for Computationally-Assisted Science and Technology (CCAST) at North Dakota State University (Fargo, ND USA), U.S. National Science Foundation (NSF), MRI Award No. 2019077.

Data Availability: Figshare repository, doi:10.6084/m9.figshare.25450291. hashtag#Phyton code uploaded to GitHub repository: https://lnkd.in/dC7MYPJb.

Paper 4.

CHEM.PTML LAB NEWS: Royal Society of Chemistry Published our Work: IFPTML mapping of hashtag#nanoparticle hashtag#antibacterial activity: vs. hashtag#pathogen hashtag#metabolic hashtag#networks, by Bernabe Ortega-Tenezaca and Humbert G. Díaz, Prof. *

Linkedin post: https://www.dhirubhai.net/posts/humbertgdiaz_nanoparticle-antibacterial-pathogen-activity-6962443438927560704-gzx7?utm_source=share&utm_medium=member_desktop

Citation: Nanoscale, 2021,13, 1318-1330, doi: 10.1039/d0nr07588d.

Abstract: Nanoparticles are useful antimicrobial drug-release systems, but some nanoparticles also exhibit antibacterial activity. However, investigation of their antibacterial activity is a difficult and slow process due to the numerous combinations of nanoparticle size, shape, and composition vs. biological tests, assay organisms, and multiple activity parameters to be measured. Additionally, the overuse of antibiotics has led to the emergence of resistant bacterial strains with different metabolic networks. Computational models may speed up this process, but the models reported to date do not to consider all the previous factors, and the data sources are dispersed and not curated. Thus, herein, we used an information fusion, perturbation-theory machine learning (IFPTML) approach, which is introduced by us for the first time, to fit a model for the discovery of antibacterial nanoparticles. The dataset studied had 15 classes of nanoparticles (1–100 nm) with most cases in the range of 1–50 nm vs. >20 pathogenic bacteria species with different metabolic networks. The nanoparticles studied included metal nanoparticles of Au, Ag, and Cu; oxide nanoparticles of Zn, Cu, La, Al, Fe, Sn, Ti, Cd, and Si; and metal salt nanoparticles of CuI and CdS. We used the SOFT.PTML software (our own application) with a user-friendly interface for the IFPTML calculations and a control statistics package. Using SOFT.PTML, we found a linear logistic regression equation that could model 4 biological activity parameters using only 8 variables with χ2 = 2265.75, p-level <0.05, sensitivity, Sn = 79.4, and specificity, Sp = 99.3, for 3213 cases (nanoparticle-bacteria pairs) in the training series. The model had Sn = 80.8 and Sp = 99.3 for 2114 cases in the external validation series. We also developed a random forest non-linear model with higher values of Sn and Sp = 98–99% in the training/validation series, although it was more complicated to use. SOFT.PTML has been demonstrated to be a useful tool for the analysis of complex data in nanotechnology. We also introduced a new anabolism-catabolism unbalance index of metabolic networks to reveal the biological connotation of the IFPTML predictions for antibacterial nanoparticles. These new models open a new door for the discovery of NPs vs. new bacterial species and strains with different topological structures of their metabolic networks.

Affiliations: (1) Universidad de A Coruna, hashtag#Galicia, hashtag#Espa?a (2) Universidad Estatal Amazónica, hashtag#Ecuador, (3) UPV/EHU :: FCT-ZTF, Universidad del País Vasco/Euskal Herriko Unibertsitatea, Fundación Biofísica Bizkaia / Biofisika Bizkaia Fundazioa, Ikerbasque.Data Sources: European Bioinformatics Institute | EMBL-EBI hashtag#ChEMBL database.

Paper 3.

CHEM.PTML LAB PAPER: Royal Society of Chemistry Journal, Published our paper: Towards Rational hashtag#Nanomaterial Design by Prediction of hashtag#Drug-hashtag#Nanoparticle Systems Interaction vs. hashtag#Bacteria hashtag#Metabolic hashtag#Networks. by Karel Dieguez Santana, Bakhtiyor Rasulev, and Humbert G. Díaz, Prof..

Linkedin post: https://www.dhirubhai.net/posts/humbertgdiaz_nanomaterial-drug-nanoparticle-activity-6891468221174611968-NB5n?utm_source=share&utm_medium=member_desktop

Citation: Environm. Sci. Nano, (2022) 9, 1391-1413, doi: 10.1039/d1en00967b, https://doi.org/10.1039/D1NR04178A

Abstract: The emergence of multidrug-resistant (MDR) strains with perturbed metabolic networks (MNs) pushes researchers to improve antibacterial drugs (ADs). Certain nanoparticles (NPs) may present antibacterial activity along with acting as delivery systems. Thus, developing dual antibacterial drug–nanoparticle (DADNP) systems becomes an option. However, testing DADNPs vs. strains with different MNs is a hard and costly task. Artificial intelligence (AI) or machine learning (ML) could accelerate this by predicting bacterial sensitivity. In this work, we used an information fusion perturbation-theory machine learning (IFPTML) analysis and mapping of DADNP (AD + NP) systems vs. MNs of pathogenic bacterial species as a new application of AI/ML methods. Furthermore, most existing AI/ML models do not use cj of experimental conditions of assays (i.e., bacteria species, strain, NP shape, etc.) as input vectors. A working solution may be the use of an AI/ML method with an information fusion (IF) additive approach. Additive IF uses the sets of vectors Ddk, Dnk, Dmk and cdk, cnk, csk as inputs with information about AD, NP, and MN structure and assays separately. Accordingly, the IFPTML algorithm was selected to seek predictive models based on a ChEMBL dataset of >160?000 AD assays enriched with 300 NP assays and >25 MNs of different bacterial species. IFPTML uses the IF process to join the three datasets, PT operators (PTOs) to codify Ddk, Dnk, Dsk and cdk, cnk, csk vector information, and ML algorithms to train the model. The IFPTML linear discriminant analysis (LDA) model with Sp ≈ 90% and Sn ≈ 80% and the best artificial neural network (ANN) model found with Sp ≈ Sn ≈ 95% in the training/validation series presented good results. This kind of model could be useful for DADNP system discovery. We also ran a simulation with >140?000 points of putative DADNP systems vs. wild type and knockout (KO) computationally generated bacterial strains. The linear and additive IFPTML model was able to predict 102 experimental cases of complex DADNPs with a high degree of structural and biological variety. This led us to introduce the concept of MDR computational surveillance that could help to detect new strains of MDR bacteria.

Affiliations: North Dakota State University, Universidad del País Vasco/Euskal Herriko Unibertsitatea, Fundación Biofísica Bizkaia / Biofisika Bizkaia Fundazioa, Ikerbasque.Data Sources: European Bioinformatics Institute | EMBL-EBI hashtag#ChEMBL database.

Paper 2.

CHEM.PTML LAB NEWS: Royal Society of Chemistry Journal Published: hashtag#MachineLearning hashtag#Discovery of Dual hashtag#Antibacterial hashtag#Drug-hashtag#Nanoparticles (hashtag#DADNP) hashtag#Systems. by Karel Dieguez Santana & Humbert G. Díaz, Prof.

Linkedin post: https://www.dhirubhai.net/posts/humbertgdiaz_machinelearning-discovery-antibacterial-activity-6854851458978877441-iOi_?utm_source=share&utm_medium=member_desktop

Citation: Nanoscale, 2021, 13, 17854-17870, doi: 10.1039/d1nr04178a, https://lnkd.in/dTYiYy3F

Abstract: Artificial Intelligence/Machine Learning (AI/ML) algorithms may speed up the design of DADNP systems formed by Antibacterial Drugs (AD) and Nanoparticles (NP). In this work, we used IFPTML = Information Fusion (IF) + Perturbation-Theory (PT) + Machine Learning (ML) algorithm for the first time to study of a large dataset of putative DADNP systems composed by >165?000 ChEMBL AD assays and 300 NP assays vs. multiple bacteria species. We trained alternative models with Linear Discriminant Analysis (LDA), Artificial Neural Networks (ANN), Bayesian Networks (BNN), K-Nearest Neighbour (KNN) and other algorithms. IFPTML-LDA model was simpler with values of Sp ≈ 90% and Sn ≈ 74% in both training (>124 K cases) and validation (>41 K cases) series. IFPTML-ANN and KNN models are notably more complicated even when they are more balanced Sn ≈ Sp ≈ 88.5%–99.0% and AUROC ≈ 0.94–0.99 in both series. We also carried out a simulation (>1900 calculations) of the expected behavior for putative DADNPs in 72 different biological assays. The putative DADNPs studied are formed by 27 different drugs with multiple classes of NP and types of coats. In addition, we tested the validity of our additive model with 80 DADNP complexes experimentally synthetized and biologically tested (reported in >45 papers). All these DADNPs show values of MIC < 50 μg mL?1 (cutoff used) better that MIC of AD and NP alone (synergistic or additive effect). The assays involve DADNP complexes with 10 types of NP, 6 coating materials, NP size range 5–100 nm vs. 15 different antibiotics, and 12 bacteria species. The IFPTML-LDA model classified correctly 100% (80 out of 80) DADNP complexes as biologically active. IFPMTL additive strategy may become a useful tool to assist the design of DADNP systems for antibacterial therapy taking into consideration only information about AD and NP components by separate.

Affiliations: Ikerbasque Universidad del País Vasco/Euskal Herriko Unibertsitatea, Fundación Biofísica Bizkaia / Biofisika Bizkaia Fundazioa. Data Sources: European Bioinformatics Institute | EMBL-EBI hashtag#ChEMBL database.

Paper 1.

Royal Society of Chemistry published: Designing nanoparticle release systems for drug-vitamin cancer co-therapy with multiplicative perturbation-theory machine learning (PTML) models. by Ricardo S. ana 1, Robin Zuluaga , Piedad Ga?án, Sonia Arrasate Gil , Enrique Onieva , Humbert G. Díaz, Prof.

Citation: Nanoscale, 2019,11, 21811-21823. https://doi.org/10.1039/C9NR05070A

Abstract: Nano-systems for cancer co-therapy including vitamins or vitamin derivatives have showed adequate results to continue with further research studies to better understand them. However, the number of different combinations of drugs, vitamins, nanoparticle types, coating agents, synthesis conditions, and system types (nanocapsules, micelles, etc.) to be tested is very large generating a high cost in experimentations. In this context, there are reports of large datasets of preclinical assays of compounds (like in the ChEMBL database) and increasing but yet limited reports of experimental measurements of nano-systems per se. On the other hand, Machine Learning is gaining momentum in Nanotechnology and Pharmaceutical Sciences as a tool for rational design of new drugs and drug-release nano-systems. In this work, we propose to combine Perturbation Theory principles and Machine Learning to develop a PTML model for rational selection of the components of cancer co-therapy drug–vitamin release nano-systems (DVRNs). In doing so, we apply information fusion techniques with 2 data sets: (1) a large ChEMBL dataset of >36?000 preclinical assays of vitamin derivatives and a new dataset of >1000 outcomes of DVRNs, collected herein from the literature for the first time. The ChEMBL dataset used covers a considerable number of assay conditions (cjvit) each one with multiple levels. These conditions included >504 biological activity parameters (c0vit), >340 types of proteins (c1vit), >650 types of cells (c2vit), >120 assay organisms (c3vit), >60 assay strains (c4vit). Regarding the DVRNs, there are 25 different types of nano-systems (njn), with up to 16 conditions (cjn) including also different levels such as 8 biological activity parameters (c0n), 9 raw nanomaterials (c4n), 15 assay cells (c11n), etc. In the first stage, we used Moving Average operators to quantify the perturbations (deviations) in all input variables with respect to the conditions. After that, we used multiplicative PT operators to carry out data fusion, and dimension reduction, and Linear Discriminant Analysis (LDA) to seek the PTML model. The best PTML model found showed values of specificity, sensitivity, and accuracy in the range of 83–88% in training and external validation series for >130?000 cases (DVRNs vs. ChEMBL data pairs) formed after data fusion. To the best of our knowledge, this is the first general purpose model for the rational design of DVRNs for cancer co-therapy.

Affiliations: a DeustoTech - Deusto Institute of Technology Universidad de Deusto , Bilbao, Spain. b New Materials Research Group, Universidad Pontificia Bolivariana UPB, Medellín, Colombia. cAgroindustrial Engineering College, Universidad Pontificia Bolivariana, Medellín, Colombia dChemical Engineering College, Universidad Pontificia Bolivariana, Medellín, Colombia. eDepartment of Organic Chemistry II, University of Basque Country UPV/EHU, 48940, Leioa, Spain. fBiofisika Institue CSIC-UPVEHU, University of Basque Country UPV/EHU, 48940, Leioa, Spain. g IKERBASQUE, Basque Foundation for Science, Bilbao, Spain