GENERA: A Combined Genetic/Deep-Learning Algorithm for Multiobjective Target-Oriented De Novo Design.

This study introduces a new de novo design algorithm called GENERA that combines the capabilities of a deep-learning algorithm for automated drug-like analogue design, called DeLA-Drug, with a genetic algorithm for generating molecules with desired target-oriented properties. Specifically, GENERA was applied to the angiotensin-converting enzyme 2 (ACE2) target, which is implicated in many pathological conditions, including COVID-19. The ability of GENERA to de novo design promising candidates for a specific target was assessed using two docking programs, PLANTS and GLIDE. A fitness function based on the Pareto dominance resulting from computed PLANTS and GLIDE scores was applied to demonstrate the algorithm's ability to perform multiobjective optimizations effectively. GENERA can quickly generate focused libraries that produce better scores compared to a starting set of known ACE-2 binders. This study is the first to utilize a DL-based algorithm designed for analogue generation as a mutational operator within a GA framework, representing an innovative approach to target-oriented de novo design.

DeLA-Drug: A Deep Learning Algorithm for Automated Design of Druglike Analogues.

In this paper, we present a deep learning algorithm for automated design of druglike analogues (DeLA-Drug), a recurrent neural network (RNN) model composed of two long short-term memory (LSTM) layers and conceived for data-driven generation of similar-to-bioactive compounds. DeLA-Drug captures the syntax of SMILES strings of more than 1 million compounds belonging to the ChEMBL28 database and, by employing a new strategy called sampling with substitutions (SWS), generates molecules starting from a single user-defined query compound. Remarkably, the algorithm preserves druglikeness and synthetic accessibility of the known bioactive compounds present in the ChEMBL28 repository. The absence of any time-demanding fine-tuning procedure enables DeLA-Drug to perform a fast generation of focused libraries for further high-throughput screening and makes it a suitable tool for performing de novo design even in low-data regimes. To provide a concrete idea of its applicability, DeLA-Drug was applied to the cannabinoid receptor subtype 2 (CB2R), a known target involved in different pathological conditions such as cancer and neurodegeneration. DeLA-Drug, available as a free web platform (http://www.ba.ic.cnr.it/softwareic/deladrugportal/), can help medicinal chemists interested in generating analogues of compounds already available in their laboratories and, for this reason, good candidates for an easy and low-cost synthesis.

DeLA-DrugSelf: Empowering multi-objective de novo design through SELFIES molecular representation.

In this paper, we introduce DeLA-DrugSelf, an upgraded version of DeLA-Drug [J. Chem. Inf. Model. 62 (2022) 1411-1424], which incorporates essential advancements for automated multi-objective de novo design. Unlike its predecessor, which relies on SMILES notation for molecular representation, DeLA-DrugSelf employs a novel and robust molecular representation string named SELFIES (SELF-referencing Embedded String). The generation process in DeLA-DrugSelf not only involves substitutions to the initial string representing the starting query molecule but also incorporates insertions and deletions. This enhancement makes DeLA-DrugSelf significantly more adept at executing data-driven scaffold decoration and lead optimization strategies. Remarkably, DeLA-DrugSelf explicitly addresses the SELFIES-related collapse issue, considering only collapse-free compounds during generation. These compounds undergo a rigorous quality metrics evaluation, highlighting substantial advancements in terms of drug-likeness, uniqueness, and novelty compared to the molecules generated by the previous version of the algorithm. To evaluate the potential of DeLA-DrugSelf as a mutational operator within a genetic algorithm framework for multi-objective optimization, we employed a fitness function based on Pareto dominance. Our objectives focused on target-oriented properties aimed at optimizing known cannabinoid receptor 2 (CB2R) ligands. The results obtained indicate that DeLA-DrugSelf, available as a user-friendly web platform (https://www.ba.ic.cnr.it/softwareic/delaself/), can effectively contribute to the data-driven optimization of starting bioactive molecules based on user-defined parameters.

Multimerization of an apoptogenic TRAIL-mimicking peptide by using adamantane-based dendrons.

We have developed a straightforward strategy to multimerize an apoptogenic peptide that mimics the natural tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) by using adamantane-based dendrons as multivalent scaffolds. The selective binding affinity of the ligands to TRAIL receptor 2 (TR2) was studied by surface plasmon resonance, thus demonstrating that the trimeric and hexameric forms of the peptide exert an increased affinity of about 1500- and 20,000-fold, respectively, relative to the monomer. Moreover, only the trimeric and hexameric ligands were able to induce cell death in TR2 expressing cells (BJAB), thus confirming that a multivalent form of the peptide is necessary to trigger a substantial TR2-dependent apoptotic response in vitro. These results provide interesting insight into the multivalency effect on biological ligand/receptor interactions for future therapeutic applications.

Endocannabinoid Degradation Enzyme Inhibitors as Potential Antipsychotics: A Medicinal Chemistry Perspective.

The endocannabinoid system (ECS) plays a very important role in numerous physiological and pharmacological processes, such as those related to the central nervous system (CNS), including learning, memory, emotional processing, as well pain control, inflammatory and immune response, and as a biomarker in certain psychiatric disorders. Unfortunately, the half-life of the natural ligands responsible for these effects is very short. This perspective describes the potential role of the inhibitors of the enzymes fatty acid amide hydrolase (FAAH) and monoacylglycerol lipase (MGL), which are mainly responsible for the degradation of endogenous ligands in psychic disorders and related pathologies. The examination was carried out considering both the impact that the classical exogenous ligands such as Δ9-tetrahydrocannabinol (THC) and (-)-trans-cannabidiol (CBD) have on the ECS and through an analysis focused on the possibility of predicting the potential toxicity of the inhibitors before they are subjected to clinical studies. In particular, cardiotoxicity (hERG liability), probably the worst early adverse reaction studied during clinical studies focused on acute toxicity, was predicted, and some of the most used and robust metrics available were considered to select which of the analyzed compounds could be repositioned as possible oral antipsychotics.

Ligand-based prediction of hERG-mediated cardiotoxicity based on the integration of different machine learning techniques.

Drug-induced cardiotoxicity is a common side effect of drugs in clinical use or under postmarket surveillance and is commonly due to off-target interactions with the cardiac human-ether-a-go-go-related (hERG) potassium channel. Therefore, prioritizing drug candidates based on their hERG blocking potential is a mandatory step in the early preclinical stage of a drug discovery program. Herein, we trained and properly validated 30 ligand-based classifiers of hERG-related cardiotoxicity based on 7,963 curated compounds extracted by the freely accessible repository ChEMBL (version 25). Different machine learning algorithms were tested, namely, random forest, K-nearest neighbors, gradient boosting, extreme gradient boosting, multilayer perceptron, and support vector machine. The application of 1) the best practices for data curation, 2) the feature selection method VSURF, and 3) the synthetic minority oversampling technique (SMOTE) to properly handle the unbalanced data, allowed for the development of highly predictive models (BAMAX = 0.91, AUCMAX = 0.95). Remarkably, the undertaken temporal validation approach not only supported the predictivity of the herein presented classifiers but also suggested their ability to outperform those models commonly used in the literature. From a more methodological point of view, the study put forward a new computational workflow, freely available in the GitHub repository (https://github.com/PDelre93/hERG-QSAR), as valuable for building highly predictive models of hERG-mediated cardiotoxicity.

Formation of efficient catalytic silver nanoparticles on carbon nanotubes by adenine functionalization.

Stuck together: adenine/carbon nanotube hybrids trigger the formation of controlled-size catalytic silver nanoparticles on the nanotube surface. The catalytic efficiency of the resulting species was assessed in the oxidation of 2-methylhydroquinone to its corresponding benzoquinone, with complete recovery and without loss of activity of the catalyst.

Damage Detection in Flat Panels by Guided Waves Based Artificial Neural Network Trained through Finite Element Method.

Artificial Neural Networks (ANNs) have rapidly emerged as a promising tool to solve damage identification and localization problem, according to a Structural Health Monitoring approach. Finite Element (FE) Analysis can be extremely helpful, especially for reducing the laborious experimental campaign costs for the ANN development and training phases. The aim of the present work is to propose a guided wave-based ANN, developed through the use of the Finite Element Method, to determine the position of damages. The paper first addresses the development and assessment of the modeling technique. The FE model accuracy was proven through the comparison of the predicted results with experimental and analytical data. Then, the ANN was developed and trained on an aluminum plate and subsequently verified in a composite plate, as well as under different damage configurations. According to the results herein proposed, the ANN allowed to detect and localize damages with a high level of accuracy in all cases of study.

Numerical Investigation on Guided Waves Dispersion and Scattering Phenomena in Stiffened Panels.

The aim of this work is to propose a numerical methodology based on the finite element (FE) method to investigate the dispersive behavior of guided waves transmitted, converted, and reflected by reinforced aluminum and composite structures, highlighting their differences. The dispersion curves of such modes can help designers in improving the damage detection sensitivity of Lamb wave based structural health monitoring (SHM) systems. A preliminary phase has been carried out to assess the reliability of the modelling technique. The accuracy of the results has been demonstrated for aluminum and composite flat panels by comparing them against experimental tests and semi-analytical data, respectively. Since the good agreement, the FE method has been used to analyze the phenomena of dispersion, scattering, and mode conversion in aluminum and composite panels characterized by a structural discontinuity, as a stiffener. The research activity allowed emphasizing modes conversion at the stiffener, offering new observations with respect to state of the art. Converted modes propagate with a slightly slower speed than the incident ones. Reflected waves, instead, have been found to travel with the same velocity of the incident ones. Moreover, waves reflected in the composite stiffened plate appeared different from those that occurred in the aluminum one for the aspects herein discussed.

Influence of PCL on mechanical properties and bioactivity of ZrO2-based hybrid coatings synthesized by sol-gel dip coating technique.

The biological properties of medical implants can be enhanced through surface modifications such as to provide a firm attachment of the implant. In this study, organic-inorganic hybrid coatings have been synthesized via sol-gel dip coating. They consist of an inorganic ZrO2 matrix in which different amounts of poly(ε-caprolactone) have been entrapped to improve the mechanical properties of the films. The influence of the PCL amount on the microstructural, biological and mechanical properties of the coating has been investigated. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) analyses have shown that the hybrids used for the coating are homogenous and totally amorphous materials; Fourier transform infrared spectroscopy (FT-IR) has demonstrated that hydrogen bonds arise between the organic and inorganic phases. SEM and atomic force microscopy (AFM) have highlighted the nanostructured nature of the film. SEM and EDS analyses, after soaking the samples in a simulated body fluid (SBF), have pointed out the apatite formation on the coating surface, which proves the bone-bonding ability of the nanocomposite bioactive films. Scratch and nano-indentation tests have shown that the coating hardness, stiffness and Young's modulus decrease in the presence of large amounts of the organic phase.

Adamantane-based dendrons for trimerization of the therapeutic P140 peptide.

Dendrons constituted of an adamantane core, a focal point and three arms, were synthetized starting from a multifunctional adamantane derivative. Maleimido groups at the periphery of the scaffold were used to covalently attach the peptide called P140, a therapeutic phosphopeptide controlling disease activity in systemic lupus, both in mice and patients. Biotinylation of the trimers at the focal point was performed using click chemistry and the conjugates were studied in terms of solubility, binding affinity to its receptor, the HSPA8/HSC70 chaperone protein, effect on HSPA8 folding property and in vivo activity. The results showed that the trimerization of P140 peptide does not trigger aggregation or steric hindrances during the interaction with HSPA8 protein. Compared to the monomeric cognate peptide, the trivalent P140 peptide displayed the same capacity, in vitro, to down-regulate HSPA8 activity and, in vivo in MRL/lpr lupus-prone mice, to reduce abnormal blood hypercellularity. The control trimer synthesized with the same scaffold and a scrambled sequence of P140 showed no effect in vivo. This work reveals that adamantane-based scaffolds with a well-defined spatial conformation are promising trivalent systems for molecular recognition and for biomedical applications.

Endowing carbon nanotubes with superparamagnetic properties: applications for cell labeling, MRI cell tracking and magnetic manipulations.

Coating of carbon nanotubes (CNTs) with magnetic nanoparticles (NPs) imparts novel magnetic, optical, and thermal properties with potential applications in the biomedical domain. Multi-walled CNTs have been decorated with iron oxide superparamagnetic NPs. Two different approaches have been investigated based on ligand exchange or "click chemistry". The presence of the NPs on the nanotube surface allows conferring magnetic properties to CNTs. We have evaluated the potential of the NP/CNT hybrids as a contrast agent for magnetic resonance imaging (MRI) and their interactions with cells. The capacity of the hybrids to magnetically monitor and manipulate cells has also been investigated. The NP/CNTs can be manipulated by a remote magnetic field with enhanced contrast in MRI. They are internalized into tumor cells without showing cytotoxicity. The labeled cells can be magnetically manipulated as they display magnetic mobility and are detected at a single cell level through high resolution MRI.

Enhancement of anti-inflammatory drug activity by multivalent adamantane-based dendrons.

We have developed a straightforward method to prepare 1(st) and 2(nd) generation adamantane-based dendrons, previously called HYDRAmers, bearing at the periphery the anti-inflammatory drug, ibuprofen. The multivalency effect on the drug activity was studied, demonstrating that our multivalent ibuprofen-dendron conjugates exert an enhanced anti-inflammatory activity compared to free ibuprofen, in vitro. These results provide insights into the effect of HYDRAmer multivalency on biological interactions for therapeutic applications.

HYDRAmers: design, synthesis and characterization of different generation novel Hydra-like dendrons based on multifunctionalized adamantane.

In this communication we present a new synthetic strategy to different generation Hydra-like dendrons based on tetrafunctionalized adamantane as a building block. The novel dendrons, which we termed HYDRAmers, possess at the periphery and at the central core orthogonal protections that can be exploited for conjugation of targeting ligands, drugs and/or imaging probes.

Dendronized iron oxide nanoparticles for multimodal imaging.

The synthesis of small-size dendrons and their grafting at the surface of iron oxide nanoparticles were achieved with the double objective to obtain a good colloidal stability with a mean hydrodynamic diameter smaller than 100 nm and to ensure the possibility of tuning the organic coating characteristics including morphology, functionalities, physico-chemical properties, grafting of fluorescent or targeting molecules. Magnetic resonance and fluorescence imaging are then demonstrated to be simultaneously possible using such versatile superparamagnetic iron oxide nanocrystals covered by a dendritic shell displaying either carboxylate or ammonium groups at their periphery which could be further labelled with a fluorescent dye. The grafting conditions of these functionalized dendrons at the surface of SPIO NPs synthesized by co-precipitation have been optimized as a function of the nature of the peripheral functional group. The colloidal stability has been investigated in water and osmolar media, and in vitro and in vivo MRI and optical imaging measurements have been performed showing encouraging biodistribution.