Humberto González-Díaz

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University of the Basque Country

Spain

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Recent Grants

Grant: Close

BIOPHYS.PTML: Perturbation-Theory Machine Learning (PTML) Models for Biophysics

Open Date: 2019-09-01

Close Date: 2021-12-01

Grant: Close

BIOPHYS.PTML: Perturbation-Theory Machine Learning (PTML) Models for Biophysics

Open Date: 2019-09-01

Close Date: 2021-12-01

Grant: Close

CHAIN: Computational prediction of eco-friendly alternative building-blocks for fuel blends in petro-chemistry with Perturbation-Theory Machine Learning (TPML) models

Open Date: 2019-07-01

Close Date: 2020-07-01

Grant: Close

CHAIN: Computational prediction of eco-friendly alternative building-blocks for fuel blends in petro-chemistry with Perturbation-Theory Machine Learning (TPML) models

Open Date: 2019-07-01

Close Date: 2020-07-01

Grant: Close

PANELFIT: Participatory Approaches to a New Ethical and Legal Framework for ICT

Open Date: 2018-11-01

Close Date: 2021-10-01

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Collaborators (5)

Maria Natália Dias Soeiro Cordeiro

Associate Professor

Universidade do Porto Faculdade de Engenharia

PORTUGAL

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Isabel Moreno-Benítez

Universidad del País Vasco

SPAIN

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Bakhtiyor Rasulev

Associate Professor

North Dakota State University

UNITED STATES

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Juan Ruso

Full Professor

Universidade de Santiago de Compostela

SPAIN

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Leyre Pérez Álvarez

University of the Basque Country

SPAIN

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Humberto González-Díaz

Research Interests

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Recent Grants

Articles (21)

IFE.PTML: advancing neurotherapeutic nanoparticle release-system design for enhanced brain delivery

AQUA Tox: A web tool for predicting aquatic toxicity in rotifer species using intrinsic explainable models

IFPTML Multi-Output Model for Anti-Retroviral Compounds Including the Drug Structure and Target Protein Sequence Information

First report on Quantitative Structure-Toxicity Relationship modeling approaches for the prediction of acute toxicity of various organic chemicals against rotifer species

Artificial Intelligence-Driven Modeling for Hydrogel Three-Dimensional Printing: Computational and Experimental Cases of Study

Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling

Prediction of Dielectric Constant in Series of Polymers by Quantitative Structure-Property Relationship (QSPR)

Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling

On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems

On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems

Trends in Nanoparticles for Leishmania Treatment: A Bibliometric and Network Analysis

Machine learning in antibacterial discovery and development: A bibliometric and network analysis of research hotspots and trends

Multi-output chemometrics model for gasoline compounding

Multi-output chemometrics model for gasoline compounding

A Fuzzy System Classification Approach for QSAR Modeling of alpha-Amylase and alpha-Glucosidase Inhibitors

Machine Learning Study of Metabolic Networks vs ChEMBL Data of Antibacterial Compounds

Prediction of Antileishmanial Compounds: General Model, Preparation, and Evaluation of 2-Acylpyrrole Derivatives

Prediction of acute toxicity of pesticides for Americamysis bahia using linear and nonlinear QSTR modelling approaches

Towards rational nanomaterial design by predicting drug-nanoparticle system interaction vs. bacterial metabolic networks

IFPTML Mapping of Drug Graphs with Protein and Chromosome Structural Networks vs. Pre-Clinical Assay Information for Discovery of Antimalarial Compounds

Towards machine learning discovery of dual antibacterial drug-nanoparticle systems.

Collaborators (5)

Maria Natália Dias Soeiro Cordeiro

Isabel Moreno-Benítez

Bakhtiyor Rasulev

Juan Ruso

Leyre Pérez Álvarez

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