Cheminformatics Perturbation-Theory Machine Learning Laboratory

Group leader
Humbert González-Díaz
Phone
+34 94 601 3547
Address

Dept. of Organic Chemistry II,
University of The Basque Country (UPV/EHU), 48940, Leioa
Basque Country (Spain)

Google Maps Get directions

Research goal

Our group 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, UPVEHU, 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 Perturbation-Theory Machine Learning (PTML) method developed and published by our group. Our partners/clients are mainly, but not limited to, Biophysics, Medicinal Chemistry, Pharmaceutical, Biotechnology, Nanotechnology, and Biomedical Engineering Industry and Research centres. 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

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,
10.1039/d1en00967b

Diéguez-Santana K, Nachimba-Mayanchi MM, Puris A, Gutiérrez RT, González-Díaz H.

Prediction of acute toxicity of pesticides for Americamysis bahia using linear and nonlinear QSTR modelling approaches
ENVIRON RES. 2022 Nov;214(Pt 3):113984
10.1016/j.envres.2022.113984

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
10.1021/acs.jcim.2c00731

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
10.1016/j.fuel.2021.122274

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.
10.1016/j.jacbts.2021.08.009

Diéguez-Santana K, González-Díaz H

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

Ortega-Tenezaca B, González-Díaz H

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

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
10.1016/j.ejmech.2021.113458

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
10.1021/acschemneuro.0c00687

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
10.1039/d0nr01849j

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.
10.1039/c9nr05070a

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
10.1021/acschemneuro.9b00302

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
10.1021/acs.jcim.9b00295

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.
10.1021/acs.jproteome.8b00949

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
10.1021/acschemneuro.8b00083

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.
10.1021/acs.jproteome.7b00861