CHEMIF.PTML LAB

Group leader
Humbert González-Díaz
Phone
+34 946 01 3547
Address

Dept. of Organic and Inorganic Chemistry
Faculty of Science and Technology
University of The Basque Country (EHU)
Barrio Sarriena s/n. 48940 Leioa, Bizkaia (Spain)

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Research goal

The CHEMIF.PTML LAB lead, by Prof Humbert G Díaz (PI) and Prof Sonia Arrasate (Co-PI), offers Cheminformatics Expert Consulting, User-Friendly Software Development, and Tailored Data Analysis solutions. Our algorithms/software may reduce partners/clients experimental research and/or production costs in terms of material resources, laboratory animals, and time. Our team include researchers and students from IKERBASQUE, UPV/EHU, and Biofisika, Bilbao, and UDC Coruña. The principal tools used in our group are the result of combining Cheminformatics, Artificial Intelligence (AI), Machine Learning (ML) algorithms. We put special emphasis on the use of Cheminformatics Information Fusion and Perturbation-Theory Machine Learning (CHEMIF.PTML) method developed
and published by our group. Our partners/clients are mainly, but not limited to, Biophysics, Organic Chemistry, Medicinal Chemistry, Pharmaceutical, Biotechnology, Nanotechnology, and Biomedical Engineering Industry and Research centers. We can work with experimental research and industrial partner consortia or clients to detect their data analysis problem and formulate the problem in cheminformatics data analysis terms in order to train and validate an AI/ML predictive model. Next, we can develop and release (transference) a user-friendly AI/ML software tailored for the client necessities. Some of our previous partners/clients are Repsol-Petronor, Tecnalia, Gaiker, Tekniker, Biodonostia, DIPC, etc. We have published more than 200 JCR research papers, supervised more than 10 PhD theses, edited more than 20 Journals/Special issues, and developed more than 10 research software packages. We have also given consulting services (contract or pro-bono).

Group members
Publications

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Diéguez-Santana K, Casanola-Martin GM, Torres-Gutiérrez R, Rasulev B, González-Díaz H.

AQUA Tox: A web tool for predicting aquatic toxicity in rotifer species using intrinsic explainable models
J Hazard Mater. 2025 Jul 15;492:138050.
0.1016/j.jhazmat.2025.138050

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He S, Barón A, Munteanu CR, de Bilbao B, Casañola-Martin GM, Chelu M, Musuc AM, Bediaga H, Ascencio E, Castellanos-Rubio I, Arrasate S, Pazos A, Insausti M, Rasulev B, Gonzalez-Diaz H. 

Drug Release Nanoparticle System Design: Data set Compilation and Machine Learning Modeling
ACS Applied Materials and Interfaces. Volume 17, Issue 3, Pages 5290 - 5306
10.1021/acsami.4c16800

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Dieguez-Santana K., Casanola-Martin G.M., Torres-Gutiérrez R., Rasulev B., Gonzalez-Díaz H

First report on Quantitative Structure-Toxicity Relationship modeling approaches for predicting acute toxicity of organic chemicals against rotifer species
Sci Total Environ. 2025 May 15;977:179350. Epub 2025 Apr 10. PMID: 40215635.
10.1016/j.scitotenv.2025.179350

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Vásquez-Domínguez E, He S, Santolaria C, Arrasate S, González-Díaz H.

IFPTML Multi-output Modelling for Anti-Retroviral Compounds Including Drug Structure and Target Protein Sequences Information
J Chem Inf Model. 2025 May 12;65(9):4353-4366. Epub 2025 Apr 28. PMID: 40293160.
10.1021/acs.jcim.5c00242.

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Baltasar-Marchueta M, López N, Alicante S, Barbolla I, Garcia Ibarluzea M, Ramis R, Salomon AM, Muguruza-Montero A, Nuñez Viadero E, Leonardo A, Arrasate S, Sotomayor N, Montemore MM, Villarroel A, Bergara A, Lete E, González-Díaz H. 

Machine Learning Approach for Predicting Drug-Like Molecules Targeting Calmodulin Pathway Proteins
J Chem Inf Model. 2025 Nov 10;65(21):11892-11907. Epub 2025 Oct 22. PMID: 41125631; PMCID: PMC12606648.
10.1021/acs.jcim.5c02111

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Ruiz-Escudero A, Serna-Burgos Z, Arrasate S, González-Díaz H

PTML models of self assembled ligand free nanoparticle catalysts for cross coupling reactions
Sci Rep. 2025 Aug 14;15(1):29895. PMID: 40813882; PMCID: PMC12354725.
10.1038/s41598-025-14080-2

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Bediaga-Bañeres H, Moreno-Benítez I, Arrasate S, Pérez-Álvarez L, Halder AK, Cordeiro MNDS, González-Díaz H, Vilas-Vilela JL

Artificial Intelligence-Driven Modeling for Hydrogel Three-Dimensional Printing: Computational and Experimental Cases of Study
Polymers (Basel). 2025 Jan 6;17(1):121. PMID: 39795524; PMCID: PMC11723248.
10.3390/polym17010121

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Ruiz-Escudero A, Belii M, Nader K, Insausti M,  Castellanos-Rubio I,  Arrasate S, Montemore MM, González-Díaz H

IFE.PTML: Advancing Neurotherapeutic Nanoparticle Release-System Design for Enhanced Brain Delivery
Mach. Learn.: Sci. Technol. 2025 6 035065.
10.1088/2632-2153/ae038a

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Velásquez-López Y, Ruiz-Escudero A, Arrasate S, González-Díaz H

Implementation of IFPTML Computational Models in Drug Discovery Against Flaviviridae Family
J Chem Inf Model. 2024 Mar 25;64(6):1841-1852
doi: 10.1021/acs.jcim.3c01796

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Larrea-Sebal A, Sasiain I, Jebari-Benslaiman S, Galicia-Garcia U, Uribe KB, Benito-Vicente A, Gracia-Rubio I, Bediaga-Bañeres H, Arrasate S, Cenarro A, Civeira F, González-Díaz H, Martín C.

OptiMo-LDLr: An Integrated In Silico Model with Enhanced Predictive Power for LDL Receptor Variants, Unraveling Hot Spot Pathogenic Residues
Adv Sci (Weinh). 2024 Apr;11(13):e2305177
10.1002/advs.202305177

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Baltasar-Marchueta M, Llona L, M-Alicante S, Barbolla I, Ibarluzea MG, Ramis R, Salomon AM, Fundora B, Araujo A, Muguruza-Montero A, Nuñez E, Pérez-Olea S, Villanueva C, Leonardo A, Arrasate S, Sotomayor N, Villarroel A, Bergara A, Lete E, González-Díaz H.

Identification of Riluzole derivatives as novel calmodulin inhibitors with neuroprotective activity by a joint synthesis, biosensor, and computational guided strategy
Biomed Pharmacother. 2024 Apr 17;174:116602.
10.1016/j.biopha.2024.116602

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Carracedo-Reboredo P, Aranzamendi E, He S, Arrasate S, Munteanu CR, Fernandez-Lozano C, Sotomayor N, Lete E, González-Díaz H.

MATEO: intermolecular α-amidoalkylation theoretical enantioselectivity optimization. Online tool for selection and design of chiral catalysts and products
J Cheminform. 2024 Jan 23;16(1):9
doi: 10.1186/s13321-024-00802-7

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Diéguez-Santana K, Rasulev B,  González-Díaz H.

Towards rational nanomaterial design by predicting drug–nanoparticle system interaction vs. bacterial metabolic networks
ENVIRON. SCI.: NANO, 2022 Feb 03,9, 1391-1413,
DOI: 10.1039/d1en00967b

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Santiago C, Ortega-Tenezaca B, Barbolla I, Fundora-Ortiz B, Arrasate S, Dea-Ayuela MA, González-Díaz H, Sotomayor N, Lete E.

Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2-Acylpyrrole Derivatives
J CHEM INF MODEL. 2022 Aug 22;62(16):3928-3940
DOI: 10.1021/acs.jcim.2c00731

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Bediaga H, Moreno MI, Arrasate S, Vilas JL, Orbe L, Unzueta E, Mercader JP, Gonzalez-Diaz H.

Multi-output chemometrics model for gasoline compounding
FUEL, 2021 Oct 08, PartA, 310, 122274
DOI: 10.1016/j.fuel.2021.122274

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Larrea-Sebal A, Benito-Vicente A, Fernandez-Higuero JA, Jebari-Benslaiman S, Galicia-Garcia U, Uribe KB, Cenarro A, Ostolaza H, Civeira F, Arrasate S, González-Díaz H, Martín C

MLb-LDLr: A Machine Learning Model for Predicting the Pathogenicity of LDLr Missense Variants
JACC BASIC TRANSL SCI. 2021 Nov 22;6(11):815-827.
DOI: 10.1016/j.jacbts.2021.08.009

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Diéguez-Santana K, González-Díaz H

Towards machine learning discovery of dual antibacterial drug-nanoparticle systems
NANOSCALE. 2021 Nov 4;13(42):17854-17870
DOI: 10.1039/d1nr04178a

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Ortega-Tenezaca B, González-Díaz H

IFPTML mapping of nanoparticle antibacterial activity vs.pathogen metabolic networks
NANOSCALE. 2021 Jan 21;13(2):1318-1330
DOI: 10.1039/d0nr07588d

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Barbolla I, Hernández-Suárez L, Quevedo-Tumailli V, Nocedo-Mena D, Arrasate S, Dea-Ayuela MA, González-Díaz H, Sotomayor N, Lete E

Palladium-mediated synthesis and biological evaluation of C-10b substituted Dihydropyrrolo[1,2-b]isoquinolines as antileishmanial agents
EUR J MED CHEM. 2021 Aug 5;220:113458
DOI: 10.1016/j.ejmech.2021.113458

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Sampaio-Dias IE, Rodríguez-Borges JE, Yáñez-Pérez V, Arrasate S, Llorente J, Brea JM, Bediaga H, Viña D, Loza MI, Caamaño O, García-Mera X, González-Díaz H

Synthesis, Pharmacological, and Biological Evaluation of 2-Furoyl-Based MIF-1 Peptidomimetics and the Development of a General-Purpose Model for Allosteric Modulators (ALLOPTML)
ACS CHEM NEUROSCI. 2021 Jan 6;12(1):203-215
DOI: 10.1021/acschemneuro.0c00687

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Santana R, Zuluaga R, Gañán P, Arrasate S, Onieva E, González-Díaz H

Predicting coated-nanoparticle drug release systems with perturbation-theory machine learning (PTML) models
NANOSCALE. 2020 Jul 2;12(25):13471-13483
DOI: 10.1039/d0nr01849j

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Santana R, Zuluaga R, Gañán P, Arrasate S, Onieva E, González-Díaz H.

Designing nanoparticle release systems for drug-vitamin cancer co-therapy with multiplicative perturbation-theory machine learning (PTML) models
Nanoscale. 2019 Nov 21;11(45):21811-21823.
DOI: 10.1039/c9nr05070a

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Diez-Alarcia R, Yáñez-Pérez V, Muneta-Arrate I, Arrasate S, Lete E, Meana JJ, González-Díaz H.

Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [(35)S]GTPγS Binding Assays
ACS Chem Neurosci. 2019 Nov 20;10(11):4476-4491
DOI: 10.1021/acschemneuro.9b00302

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Ambure P, Halder AK, González Díaz H, Cordeiro MNDS.

QSAR-Co: An Open Source Software for Developing Robust Multitasking or Multitarget Classification-Based QSAR Models
J Chem Inf Model. 2019 Jun 24;59(6):2538-2544
DOI: 10.1021/acs.jcim.9b00295

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Concu R, D S Cordeiro MN, Munteanu CR, González-Díaz H.

PTML Model of Enzyme Subclasses for Mining the Proteome of Biofuel Producing Microorganisms
J Proteome Res. 2019 Jul 5;18(7):2735-2746.
DOI: 10.1021/acs.jproteome.8b00949

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Ferreira da Costa J, Silva D, Caamaño O, Brea JM, Loza MI, Munteanu CR, Pazos A, García-Mera X, González-Díaz H.

Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-Prolyl-l-leucyl-glycinamide Peptidomimetics
ACS CHEM NEUROSCI. 2018 Nov 21;9(11):2572-2587
DOI: 10.1021/acschemneuro.8b00083

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Quevedo-Tumailli VF, Ortega-Tenezaca B, González-Díaz H.

Chromosome Gene Orientation Inversion Networks (GOINs) of Plasmodium Proteome.
J Proteome Res. 2018 Mar 2;17(3):1258-1268.
DOI: 10.1021/acs.jproteome.7b00861