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

Neurodegenerative diseases are characterized by slowly progressing neuronal cell death. Conventional drug treatment strategies often fail because of poor solubility, low bioavailability, and the inability of the drugs to effectively cross the blood–brain barrier. Therefore, the development of new neurodegenerative disease drugs (NDDs) requires immediate attention. Nanoparticle (NP) systems are of increasing interest for transporting NDDs to the central nervous system. However, discovering effective nanoparticle neuronal disease drug delivery systems (N2D3Ss) is challenging because of the vast number of combinations of NP and NDD compounds, as well as the various assays involved. Artificial intelligence/machine learning (AI/ML) algorithms have the potential to accelerate this process by predicting the most promising NDD and NP candidates for assaying. Nevertheless, the relatively limited amount of reported data on N2D3S activity compared to assayed NDDs makes AI/ML analysis challenging. In this work, the IFPTML technique, which combines information fusion (IF), perturbation theory (PT), and machine learning (ML), was employed to address this challenge. Initially, we conducted the fusion into a unified dataset comprising 4403 NDD assays from ChEMBL and 260 NP cytotoxicity assays from journal articles. Through a resampling process, three new working datasets were generated, each containing 500,000 cases. We utilized linear discriminant analysis (LDA) along with artificial neural network (ANN) algorithms, such as multilayer perceptron (MLP) and deep learning networks (DLN), to construct linear and non-linear IFPTML models. The IFPTML-LDA models exhibited sensitivity (Sn) and specificity (Sp) values in the range of 70% to 73% (>375,000 training cases) and 70% to 80% (>125,000 validation cases), respectively. In contrast, the IFPTML-MLP and IFPTML-DLN achieved Sn and Sp values in the range of 85% to 86% for both training and validation series. Additionally, IFPTML-ANN models showed an area under the receiver operating curve (AUROC) of approximately 0.93 to 0.95. These results indicate that the IFPTML models could serve as valuable tools in the design of drug delivery systems for neurosciences.This work was funded by the grants AIMOFGIFT ELKARTEK project 2022 (KK-2022/00032) - 2022 – 2023 and grant (IT1045-16) - 2016 – 2021 of Basque Government and Grant IKERDATA 2022/IKER/000040 funded by NextGenerationEU funds of European Commission

Multi-output chemometrics model for gasoline compounding

Computational models may help to reduce research cost by predicting properties of alternative blends. Nowadays, most efforts focus on prediction of a few properties for sets of gasoline samples. However, there are no reports of models able for classification of gasoline samples with multiple output properties measured in real life refinery plants. In this work, Information Fusion (IF), Perturbation Theory (PT), and Machine Learning (ML) algorithm (IFPTML) was used to model real production data with >230,000 outcomes gathered from a petroleum refinery plant. IF-pre-processing phase assembled the working dataset with 44 physicochemical output properties vs. 574 input variables of 4 production lines distributed in 26 data blocks including 14 different streams and 23 operations carried out in the plant. PT-calculation phase quantifies the effect of perturbations (deviations) in all input variables using PT Operators. Last, in ML-analysis phase involved Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANN) models training. IFPTML-LDA model presented AUROC = 0.936 with overall Sensitivity Sn and Specificity Sp ≈ 84–91% for training and validation sets. In internal control experiment we obtained an IFPTML-FT-NIR model with similar Sn and Sp ≈ 86–97%, for >25,000 values of 16 properties measured FT-NIR technique; demonstrating the robustness of the algorithm to changes on the experimental techniques used. This model could be useful for the design of new alternatives blends (biofuels, refuse-derived fuels, etc.) with lower environmental impact.The authors acknowledge financial support from Basque government SPRI ELKARTEK program grant (KK-2019/00037). The authors also acknowledge partial financial support from research grants of Ministry of Economy and Competitiveness, MINECO, Spain (FEDER CTQ2016-74881-P) and Basque Government (Eusko Jaurlaritza) consolidation groups grant (IT1045-16). G.D.H. personally acknowledges the support of IKERBASQUE, Basque Foundation for Science. J.P.-M. thanks Petronor S.A. for their support.Peer reviewe

PTML Multi-Label Algorithms: Models, Software, and Applications

By combining Machine Learning (ML) methods with Perturbation Theory (PT), it is possible to develop predictive models for a variety of response targets. Such combination often known as Perturbation Theory Machine Learning (PTML) modeling comprises a set of techniques that can handle various physical, and chemical properties of different organisms, complex biological or material systems under multiple input conditions. In so doing, these techniques effectively integrate a manifold of diverse chemical and biological data into a single computational framework that can then be applied for screening lead chemicals as well as to find clues for improving the targeted response(s). PTML models have thus been extremely helpful in drug or material design efforts and found to be predictive and applicable across a broad space of systems. After a brief outline of the applied methodology, this work reviews the different uses of PTML in Medicinal Chemistry, as well as in other applications. Finally, we cover the development of software available nowadays for setting up PTML models from large datasets.H.G.-D. and S.A.G. acknowledge financial support from grants MINECO (CTQ2013-41229-P) - 2014−2016 and MINECO (CTQ2016-74881-P) - 2017−2019 of Spain government. H.G.-D. and S.A.G. also acknowledge financial support from ELKARTEK SPRI grants (KK-2019/00037 and KK-2020/00110) and grant (IT1045-16) - 2016−2021) and of Basque Government. M.N.D.S.C. thanks also the financial support of project UID/QUI/50006/2020 with funding from FCT/MCTES through national funds. This project was also supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. ED431G/01, ED431D 2017/16).Peer reviewe

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

This work describes the synthesis and pharmacological evaluation of 2-furoyl-based Melanostatin (MIF-1) peptidomimetics as dopamine D2 modulating agents. Eight novel peptidomimetics were tested for their ability to enhance the maximal effect of tritiated N-propylapomorphine ([3H]-NPA) at D2 receptors (D2R). In this series, 2-furoyl-l-leucylglycinamide (6a) produced a statistically significant increase in the maximal [3H]-NPA response at 10 pM (11 ± 1%), comparable to the effect of MIF-1 (18 ± 9%) at the same concentration. This result supports previous evidence that the replacement of proline residue by heteroaromatic scaffolds are tolerated at the allosteric binding site of MIF-1. Biological assays performed for peptidomimetic 6a using cortex neurons from 19-day-old Wistar-Kyoto rat embryos suggest that 6a displays no neurotoxicity up to 100 μM. Overall, the pharmacological and toxicological profile and the structural simplicity of 6a makes this peptidomimetic a potential lead compound for further development and optimization, paving the way for the development of novel modulating agents of D2R suitable for the treatment of CNS-related diseases. Additionally, the pharmacological and biological data herein reported, along with >20â000 outcomes of preclinical assays, was used to seek a general model to predict the allosteric modulatory potential of molecular candidates for a myriad of target receptors, organisms, cell lines, and biological activity parameters based on perturbation theory (PT) ideas and machine learning (ML) techniques, abbreviated as ALLOPTML. By doing so, ALLOPTML shows high specificity Sp = 89.2/89.4%, sensitivity Sn = 71.3/72.2%, and accuracy Ac = 86.1%/86.4% in training/validation series, respectively. To the best of our knowledge, ALLOPTML is the first general-purpose chemoinformatic tool using a PTML-based model for the multioutput and multicondition prediction of allosteric compounds, which is expected to save both time and resources during the early drug discovery of allosteric modulators.This research was funded by Fundação para a Ciência e Tecnologia (FCT, Portugal), through grants UIDB/50006/2020 (to LAQV-REQUIMTE Research Unit) and for project grants PTDC/BIA-MIB/29059/2017 and PEst-OE/QUI/UI0674/2013. This work was also supported by the Collaborative Project of Genomic Data Integration (CICLOGEN).The support of Ikerbasque, Basque Foundation for Science and the research grants from Ministry of Economy and Competitiveness, MINECO, Spain (FEDER CTQ2016–74881-P), and Basque government (IT1045–16) are also acknowledged. The financial support (ED431G 2019/02) from the Xunta de Galicia (Centro singular de investigación de Galicia accreditation 2019–2022) and the European Union (European Regional Development Fund—ERDF) is gratefully acknowledged. I.E.S.-D. thanks FCT for the doctoral grant SFRH/BD/93632/2013. X.G.-M. thanks Xunta de Galicia for financial support (GPC2014/003).Peer reviewe

OptiMo-LDLr: an integrated In silico model with enhanced predictive power for LDL receptor variants, unraveling hot spot pathogenic residues

Familial hypercholesterolemia (FH) is an inherited metabolic disease affecting cholesterol metabolism, with 90% of cases caused by mutations in the LDL receptor gene (LDLR), primarily missense mutations. This study aims to integrate six commonly used predictive software to create a new model for predicting LDLR mutation pathogenicity and mapping hot spot residues. Six predictive-software are selected: Polyphen-2, SIFT, MutationTaster, REVEL, VARITY, and MLb-LDLr. Software accuracy is tested with the characterized variants annotated in ClinVar and, by bioinformatic and machine learning techniques all models are integrated into a more accurate one. The resulting optimized model presents a specificity of 96.71% and a sensitivity of 98.36%. Hot spot residues with high potential of pathogenicity appear across all domains except for the signal peptide and the O-linked domain. In addition, translating this information into 3D structure of the LDLr highlights potentially pathogenic clusters within the different domains, which may be related to specific biological function. The results of this work provide a powerful tool to classify LDLR pathogenic variants. Moreover, an open-access guide user interface (OptiMo-LDLr) is provided to the scientific community. This study shows that combination of several predictive software results in a more accurate prediction to help clinicians in FH diagnosis.This research was funded by the Grupos Consolidados Gobierno Vasco 2021, grant number 449IT1720-22 and Proyectos de Generación de Conocimiento from the Ministerio de Ciencia, Innovación y Universidades, under the grant PID2022-136788OB-I00. A.L.-S. was supported by a grant PIF (2019–2020), Gobierno Vasco, and partially supported by Fundación Biofísica Bizkaia. S.J-B. was supported by a Margarita Salas Grant 2022 from the University of the Basque Country