Cheminformatics Perturbation-Theory Machine Learning Laboratory

Group leader
Humbert González-Díaz
+34 94 601 3547

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

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
Ikerbasque Research Professor

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

PTML Model of ChEMBL Compounds Assays for Vitamin Derivatives. ACS Comb Sci. 2020 Feb 13. PubMed PMID: 32011854.

Cabrera-Andrade A, López-Cortés A, Jaramillo-Koupermann G, Paz-Y-Miño C, Pérez-Castillo Y, Munteanu CR, González-Díaz H, Pazos A, Tejera E.

Gene Prioritization through Consensus Strategy, Enrichment Methodologies Analysis, and Networking for Osteosarcoma Pathogenesis. Int J Mol Sci. 2020 Feb 5;21(3). pii: E1053.

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.

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.

González-Durruthy M, Manske Nunes S, Ventura-Lima J, Gelesky MA, González-Díaz H, Monserrat JM, Concu R, Cordeiro MNDS.

MitoTarget Modeling Using ANN-Classification Models Based on Fractal SEM Nano-Descriptors: Carbon Nanotubes as Mitochondrial F0F1 ATPase Inhibitors. J Chem Inf Model. 2019 Jan 28;59(1):86-97.

Carracedo-Reboredo P, Corona R, Martinez-Nunes M, Fernandez-Lozano C, Tsiliki G, Sarimveis H, Aranzamendi E, Arrasate S, Sotomayor N, Lete E, Munteanu CR, González-Díaz H.

MCDCalc: Markov Chain Molecular Descriptors Calculator for Medicinal Chemistry. Curr Top Med Chem. 2019 Dec 25. PubMed PMID: 31878856.

Vásquez-Domínguez E, Armijos-Jaramillo VD, Tejera E, González-Díaz H.

Multioutput Perturbation-Theory Machine Learning (PTML) Model of ChEMBL Data for Antiretroviral Compounds. Mol Pharm. 2019 Oct 7;16(10):4200-4212.

Tenorio-Borroto E, Castañedo N, García-Mera X, Rivadeneira K, Vázquez Chagoyán JC, Barbabosa Pliego A, Munteanu CR, González-Díaz H.

Perturbation Theory Machine Learning Modeling of Immunotoxicity for Drugs Targeting Inflammatory Cytokines and Study of the Antimicrobial G1 Using Cytometric Bead Arrays. Chem Res Toxicol. 2019 Sep 16;32(9):1811-1823.

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.

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.

González-Durruthy M, Monserrat JM, Viera de Oliveira P, Fagan SB, Werhli AV, Machado K, Melo A, González-Díaz H, Concu R, D S Cordeiro MN.

Computational MitoTarget Scanning Based on Topological Vacancies of Single-Walled Carbon Nanotubes with the Human Mitochondrial Voltage-Dependent Anion Channel (hVDAC1). Chem Res Toxicol. 2019 Apr 15;32(4):566-577.

Nocedo-Mena D, Cornelio C, Camacho-Corona MDR, Garza-González E, Waksman de Torres N, Arrasate S, Sotomayor N, Lete E, González-Díaz H.

Modeling Antibacterial Activity with Machine Learning and Fusion of Chemical Structure Information with Microorganism Metabolic Networks. J Chem Inf Model. 2019 Mar 25;59(3):1109-1120.

López-Cortés A, Paz-Y-Miño C, Cabrera-Andrade A, Barigye SJ, Munteanu CR, González-Díaz H, Pazos A, Pérez-Castillo Y, Tejera E.

Gene prioritization, communality analysis, networking and metabolic integrated pathway to better understand breast cancer pathogenesis. Sci Rep. 2018 Nov 12;8(1):16679.

Bediaga H, Arrasate S, González-Díaz H.

PTML Combinatorial Model of ChEMBL Compounds Assays for Multiple Types of Cancer. ACS Comb Sci. 2018 Nov 12;20(11):621-632.

Blay V, Yokoi T, González-Díaz H.

Perturbation Theory-Machine Learning Study of Zeolite Materials Desilication. J Chem Inf Model. 2018 Dec 24;58(12):2414-2419.

Barreiro E, Munteanu CR, Cruz-Monteagudo M, Pazos A, González-Díaz H.

Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems. Sci Rep. 2018 Aug 17;8(1):12340.

Simón-Vidal L, García-Calvo O, Oteo U, Arrasate S, Lete E, Sotomayor N, González-Díaz H.

Perturbation-Theory and Machine Learning (PTML) Model for High-Throughput Screening of Parham Reactions: Experimental and Theoretical Studies. J Chem Inf Model. 2018 Jul 23;58(7):1384-1396.

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.

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.

Monteagudo MC, González-Díaz H.

New Experimental and Computational Tools for Drug Discovery: Medicinal Chemistry, Molecular Docking, and Machine Learning -Part-VI. Curr Top Med Chem. 2018;18(27):2325-2326.

Arrasate S, Duardo-Sánchez A, De Miguel Beriain I, Casabona CR, González-Díaz H.

New Experimental and Computational Tools for Drug Discovery: Medicinal Chemistry, Personalized Medicine, Ethical & Legal Issues - Part-V. Curr Top Med Chem. 2018;18(25):2141-2142.