CHEMIF.PTML LAB | Biofisika

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 leader

Humbert González-Díaz

ORCID www.orcid.org/0000-0002-9392-2797

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)

Group members

Publications

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

Drug Release Nanoparticle Systems Design: Dataset Compilation and Machine Learning Modeling

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

IFPTML Multi-output Modelling for Anti-Retroviral Compounds Including Drug Structure and Target Protein Sequences Information

Machine Learning Approach for Predicting Drug-Like Molecules Targeting Calmodulin Pathway Proteins

PTML models of self assembled ligand free nanoparticle catalysts for cross coupling reactions

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

IFE.PTML: Advancing Neurotherapeutic Nanoparticle Release-System Design for Enhanced Brain Delivery

Implementation of IFPTML Computational Models in Drug Discovery Against Flaviviridae Family

OptiMo-LDLr: An Integrated In Silico Model with Enhanced Predictive Power for LDL Receptor Variants, Unraveling Hot Spot Pathogenic Residues

Identification of Riluzole derivatives as novel calmodulin inhibitors with neuroprotective activity by a joint synthesis, biosensor, and computational guided strategy.

MATEO: InterMolecular α-Amidoalkylation Theoretical Enantioselectivity Optimization. Online Tool for Selection and Design of Chiral Catalysts and Products

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

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

Multi-output chemometrics model for gasoline compounding

MLb-LDLr: A Machine Learning Model for Predicting the Pathogenicity of LDL receptor Missense Variants

Towards Machine Learning Discovery of Dual Antibacterial Drug-Nanoparticle Systems.

IFPTML mapping of nanoparticle antibacterial activity vs. pathogen metabolic networks.

Palladium-mediated Synthesis and Biological Evaluation of C-10b substituted Dihydropyrrolo[1,2-b]isoquinolines as Antileishmanial Agents.

Synthesis, Pharmacological, and Biological Evaluation of 2-Furoyl-Based MIF-1 Peptidomimetics and the Development of a General-Purpose Model for Allosteric Modulators (ALLOPTML)

Predicting coated-nanoparticle drug release systems with perturbation-theory machine learning (PTML) models

Designing nanoparticle release systems for drug-vitamin cancer co-therapy with multiplicative perturbation-theory machine learning (PTML) models

Big Data Challenges Targeting Proteins in GPCR Signaling Pathways; Combining PTML-ChEMBL Models and [35S]GTP?S Binding Assays

QSAR-Co: An Open Source Software for Developing Robust Multitasking or Multitarget Classification-Based QSAR Models

PTML Model of Enzyme Subclasses for Mining the Proteome of Biofuel Producing Microorganisms

Perturbation-Theory Machine Learning (PTML) Multilabel Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma Compounds

Chromosome Gene Orientation Inversion Networks (GOINs) of Plasmodium Proteome

Polymerase Chain Reaction. Perturbation Theory and Machine Learning Artificial Intelligence-Experimental Microbiome Analysis: Applications to Ancient DNA and Tree Soil Metagenomics Cases of Study