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.

Structural basis for specific inhibition of salicylate synthase from Mycobacterium abscessus.

Blocking iron uptake and metabolism has been emerging as a promising therapeutic strategy for the development of novel antimicrobial compounds. Like all mycobacteria, M. abscessus (Mab) has evolved several countermeasures to scavenge iron from host carrier proteins, including the production of siderophores, which play a crucial role in these processes. In this study, we solved, for the first time, the crystal structure of Mab-SaS, the first enzyme involved in the biosynthesis of siderophores. Moreover, we screened a small, focused library and identified a compound exhibiting a potent inhibitory effect against Mab-SaS (IC50 ≈ 2 µM). Its binding mode was investigated by means of Induced Fit Docking simulations, performed on the crystal structure presented herein. Furthermore, cytotoxicity data and pharmacokinetic predictions revealed the safety and drug-likeness of this class of compounds. Finally, the crystallographic data were used to optimize the model for future virtual screening campaigns. Taken together, the findings of our study pave the way for the identification of potent Mab-SaS inhibitors, based on both established and unexplored chemotypes.

AMALPHI: A Machine Learning Platform for Predicting Drug-Induced PhospholIpidosis.

Drug-induced phospholipidosis (PLD) involves the accumulation of phospholipids in cells of multiple tissues, particularly within lysosomes, and it is associated with prolonged exposure to druglike compounds, predominantly cationic amphiphilic drugs (CADs). PLD affects a significant portion of drugs currently in development and has recently been proven to be responsible for confounding antiviral data during drug repurposing for SARS-CoV-2. In these scenarios, it has become crucial to identify potential safe drug candidates in advance and distinguish them from those that may lead to false in vitro antiviral activity. In this work, we developed a series of machine learning classifiers with the aim of predicting the PLD-inducing potential of drug candidates. The models were built on a high-quality chemical collection comprising 545 curated small molecules extracted from ChEMBL v30. The most effective model, obtained using the balanced random forest algorithm, achieved high performance, including an AUC value computed in validation as high as 0.90. The model was made freely available through a user-friendly web platform named AMALPHI (https://www.ba.ic.cnr.it/softwareic/amalphiportal/), which can represent a valuable tool for medicinal chemists interested in conducting an early evaluation of PLD inducer potential.

ALPACA: A machine Learning Platform for Affinity and selectivity profiling of CAnnabinoids receptors modulators.

The development of small molecules that selectively target the cannabinoid receptor subtype 2 (CB2R) is emerging as an intriguing therapeutic strategy to treat neurodegeneration, as well as to contrast the onset and progression of cancer. In this context, in-silico tools able to predict CB2R affinity and selectivity with respect to the subtype 1 (CB1R), whose modulation is responsible for undesired psychotropic effects, are highly desirable. In this work, we developed a series of machine learning classifiers trained on high-quality bioactivity data of small molecules acting on CB2R and/or CB1R extracted from ChEMBL v30. Our classifiers showed strong predictive power in accurately determining CB2R affinity, CB1R affinity, and CB2R/CB1R selectivity. Among the built models, those obtained using random forest as algorithm proved to be the top-performing ones (AUC in validation ≥0.96) and were made freely accessible through a user-friendly web platform developed ad hoc and called ALPACA (https://www.ba.ic.cnr.it/softwareic/alpaca/). Due to its user-friendly interface and robust predictive power, ALPACA can be a valuable tool in saving both time and resources involved in the design of selective CB2R modulators.

A conformational rearrangement of the SARS-CoV-2 host protein sigma-1 is required for antiviral activity: insights from a combined in-silico/in-vitro approach.

The development of effective drugs to treat coronavirus infections remains a significant challenge for the scientific community. Recent evidence reports on the sigma-1 receptor (S1R) as a key druggable host protein in the SARS-CoV-1 and SARS-CoV-2 interactomes and shows a potent antiviral activity against SARS-CoV-2 for the S1R antagonist PB28. To improve PB28 activity, we designed and tested a series of its analogues and identified a compound that is fourfold more potent against SARS-CoV-2 than PB28 itself. Interestingly, we found no direct correlation between S1R affinity and SARS-CoV-2 antiviral activity. Building on this, we employed comparative induced fit docking and molecular dynamics simulations to gain insights into the possible mechanism that occurs when specific ligand-protein interactions take place and that may be responsible for the observed antiviral activity. Our findings offer a possible explanation for the experimental observations, provide insights into the S1R conformational changes upon ligand binding and lay the foundation for the rational design of new S1R ligands with potent antiviral activity against SARS-CoV-2 and likely other viruses.

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.

Development of Riluzole Analogs with Improved Use-Dependent Inhibition of Skeletal Muscle Sodium Channels.

Several commercially available and newly synthesized riluzole analogs were evaluated in vitro as voltage-gated skeletal muscle sodium-channel blockers. Data obtained from the patch-clamp technique demonstrated that potency is well correlated with lipophilicity and the introduction of a protonatable amino function in the benzothiazole 2-position enhances the use-dependent behavior. The most interesting compound, the 2-piperazine analog of riluzole (14), although slightly less potent than the parent compound in the patch-clamp assay as well as in an in vitro model of myotonia, showed greater use-dependent Nav1.4 blocking activity. Docking studies allowed the identification of the key interactions that 14 makes with the amino acids of the local anesthetic binding site within the pore of the channel. The reported results pave the way for the identification of novel compounds useful in the treatment of cell excitability disorders.

hERG stereoselective modulation by mexiletine-derived ureas: Molecular docking study, synthesis, and biological evaluation.

Long QT syndrome (LQTS) is a disorder of cardiac electrophysiology resulting in life-threatening arrhythmias; nowadays, only a few drugs are available for the management of LQTS. Focusing our attention on LQT2, one of the most common subtypes of LQTS caused by mutations in the human ether-à-go-go-related gene (hERG), in the present work, the stereoselectivity of the recently discovered mexiletine-derived urea 8 was investigated on the hERG potassium channel. According to preliminary in silico predictions, in vitro studies revealed a stereoselective behavior, with the meso form showing the greatest hERG opening activity. In addition, functional studies on guinea pig isolated left atria, aorta, and ileum demonstrated that 8 does not present any cardiac or intestinal liability in our ex vivo studies. Due to its overall profile, (R,S)-8 paves the way for the design and development of a new series of compounds potentially useful in the treatment of both congenital and drug-induced forms of LQTS.

Bioisosteric replacement based on 1,2,4-oxadiazoles in the discovery of 1H-indazole-bearing neuroprotective MAO B inhibitors.

Following a hybridization strategy, a series of 5-substituted-1H-indazoles were designed and evaluated in vitro as inhibitors of human monoamine oxidase (hMAO) A and B. Among structural modifications, the bioisostere-based introduction of 1,2,4-oxadiazole ring returned the most potent and selective human MAO B inhibitor (compound 20, IC50 = 52 nM, SI > 192). The most promising inhibitors were studied in cell-based neuroprotection models of SH-SY5Y and astrocytes line against H2O2. Moreover, preliminary drug-like features (aqueous solubility at pH 7.4; hydrolytic stability at acidic and neutral pH) were assessed for selected 1,2,4-oxadiazoles and compared to amide analogues through RP-HPLC methods. Molecular docking simulations highlighted the crucial role of molecular flexibility in providing a better shape complementarity for compound 20 within MAO B enzymatic cleft than rigid analogue 18. Enzymatic kinetics analysis along with thermal stability curves (Tm shift = +2.9 °C) provided clues of a tight-binding mechanism for hMAO B inhibition by 20.

Development of Fluorescent 4-[4-(3H-Spiro[isobenzofuran-1,4'-piperidin]-1'-yl)butyl]indolyl Derivatives as High-Affinity Probes to Enable the Study of σ Receptors via Fluorescence-Based Techniques.

Sigma (σ) receptor subtypes, σ1 and σ2, are targets of wide pharmaceutical interest. The σ2 receptor holds promise for the development of diagnostics and therapeutics against cancer and Alzheimer's disease. Nevertheless, little is known about the mechanisms activated by the σ2 receptor. To contribute to the exploitation of its therapeutic potential, we developed novel specific fluorescent ligands. Indole derivatives bearing the N-butyl-3H-spiro[isobenzofuran-1,4'-piperidine] portion were functionalized with fluorescent tags. Nanomolar-affinity fluorescent σ ligands, spanning from green to red to near-infrared emission, were obtained. Compounds 19 (σ pan affinity) and 29 (σ2 selective), which displayed the best compromise between pharmacodynamic and photophysical properties, were investigated in flow cytometry, confocal, and live cell microscopy, demonstrating their specificity for the σ2 receptor. To the best of our knowledge, these are the first red-emitting fluorescent σ2 ligands, validated as powerful tools for the study of σ2 receptors via fluorescence-based techniques.

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.

Targeting Siderophore-Mediated Iron Uptake in M. abscessus: A New Strategy to Limit the Virulence of Non-Tuberculous Mycobacteria.

Targeting pathogenic mechanisms, rather than essential processes, represents a very attractive approach for the development of new antimycobacterial drugs. In this context, iron acquisition routes have recently emerged as potentially druggable pathways. However, the importance of siderophore biosynthesis in the virulence and pathogenicity of M. abscessus (Mab) is still poorly understood. In this study, we investigated the Salicylate Synthase (SaS) of Mab as an innovative molecular target for the development of inhibitors of siderophore production. Notably, Mab-SaS does not have any counterpart in human cells, making it an interesting candidate for drug discovery. Starting from the analysis of the binding of a series of furan-based derivatives, previously identified by our group as inhibitors of MbtI from M. tuberculosis (Mtb), we successfully selected the lead compound 1, exhibiting a strong activity against Mab-SaS (IC50 ≈ 5 µM). Computational studies characterized the key interactions between 1 and the enzyme, highlighting the important roles of Y387, G421, and K207, the latter being one of the residues involved in the first step of the catalytic reaction. These results support the hypothesis that 5-phenylfuran-2-carboxylic acids are also a promising class of Mab-SaS inhibitors, paving the way for the optimization and rational design of more potent derivatives.

N-adamantyl-anthranil amide derivatives: New selective ligands for the cannabinoid receptor subtype 2 (CB2R).

Cannabinoid type 2 receptor (CB2R) is a G-protein-coupled receptor that, together with Cannabinoid type 1 receptor (CB1R), endogenous cannabinoids and enzymes responsible for their synthesis and degradation, forms the EndoCannabinoid System (ECS). In the last decade, several studies have shown that CB2R is overexpressed in activated central nervous system (CNS) microglia cells, in disorders based on an inflammatory state, such as neurodegenerative diseases, neuropathic pain, and cancer. For this reason, the anti-inflammatory and immune-modulatory potentials of CB2R ligands are emerging as a novel therapeutic approach. The design of selective ligands is however hampered by the high sequence homology of transmembrane domains of CB1R and CB2R. Based on a recent three-arm pharmacophore hypothesis and latest CB2R crystal structures, we designed, synthesized, and evaluated a series of new N-adamantyl-anthranil amide derivatives as CB2R selective ligands. Interestingly, this new class of compounds displayed a high affinity for human CB2R along with an excellent selectivity respect to CB1R. In this respect, compounds exhibiting the best pharmacodynamic profile in terms of CB2R affinity were also evaluated for the functional behavior and molecular docking simulations provided a sound rationale by highlighting the relevance of the arm 1 substitution to prompt CB2R action. Moreover, the modulation of the pro- and anti-inflammatory cytokines production was also investigated to exert the ability of the best compounds to modulate the inflammatory cascade.

Development of N-(1-Adamantyl)benzamides as Novel Anti-Inflammatory Multitarget Agents Acting as Dual Modulators of the Cannabinoid CB2 Receptor and Fatty Acid Amide Hydrolase.

Cannabinoid type 2 receptor (CB2R), belonging to the endocannabinoid system, is overexpressed in pathologies characterized by inflammation, and its activation counteracts inflammatory states. Fatty acid amide hydrolase (FAAH) is an enzyme responsible for the degradation of the main endocannabinoid anandamide; thus, the simultaneous CB2R activation and FAAH inhibition may be a synergistic anti-inflammatory strategy. Encouraged by principal component analysis (PCA) data identifying a wide chemical space shared by CB2R and FAAH ligands, we designed a small library of adamantyl-benzamides, as potential dual agents, CB2R agonists, and FAAH inhibitors. The new compounds were tested for their CB2R affinity/selectivity and CB2R and FAAH activity. Derivatives 13, 26, and 27, displaying the best pharmacodynamic profile as CB2R full agonists and FAAH inhibitors, decreased pro-inflammatory and increased anti-inflammatory cytokines production. Molecular docking simulations complemented the experimental findings by providing a molecular rationale behind the observed activities. These multitarget ligands constitute promising anti-inflammatory agents.

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.

A Comparative Molecular Dynamics Study of Selected Point Mutations in the Shwachman-Bodian-Diamond Syndrome Protein SBDS.

The Shwachman-Diamond Syndrome (SDS) is an autosomal recessive disease whose majority of patients display mutations in a ribosome assembly protein named Shwachman-Bodian-Diamond Syndrome protein (SBDS). A specific therapy for treating this rare disease is missing, due to the lack of knowledge of the molecular mechanisms responsible for its pathogenesis. Starting from the observation that SBDS single-point mutations, localized in different domains of the proteins, are responsible for an SDS phenotype, we carried out the first comparative Molecular Dynamics simulations on three SBDS mutants, namely R19Q, R126T and I212T. The obtained 450-ns long trajectories were compared with those returned by both the open and closed forms of wild type SBDS and strongly indicated that two distinct conformations (open and closed) are both necessary for the proper SBDS function, in full agreement with recent experimental observations. Our study supports the hypothesis that the SBDS function is governed by an allosteric mechanism involving domains I and III and provides new insights into SDS pathogenesis, thus offering a possible starting point for a specific therapeutic option.

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.

Radiosynthesis and characterization of [18F]BS224: a next-generation TSPO PET ligand insensitive to the rs6971 polymorphism.

PURPOSE: Translocator protein 18-kDa (TSPO) positron emission tomography (PET) is a valuable tool to detect neuroinflammed areas in a broad spectrum of neurodegenerative diseases. However, the clinical application of second-generation TSPO ligands as biomarkers is limited because of the presence of human rs6971 polymorphism that affects their binding. Here, we describe the ability of a new TSPO ligand, [18F]BS224, to identify abnormal TSPO expression in neuroinflammation independent of the rs6971 polymorphism. METHODS: An in vitro competitive inhibition assay of BS224 was conducted with [3H]PK 11195 using membrane proteins isolated from 293FT cells expressing TSPO-wild type (WT) or TSPO-mutant A147T (Mut), corresponding to a high-affinity binder (HAB) and low-affinity binder (LAB), respectively. Molecular docking was performed to investigate the interaction of BS224 with the binding sites of rat TSPO-WT and TSPO-Mut. We synthesized a new 18F-labeled imidazopyridine acetamide ([18F]BS224) using boronic acid pinacol ester 6 or iodotoluene tosylate precursor 7, respectively, via aromatic 18F-fluorination. Dynamic PET scanning was performed up to 90 min after the injection of [18F]BS224 to healthy mice, and PET imaging data were obtained to estimate its absorbed doses in organs. To evaluate in vivo TSPO-specific uptake of [18F]BS224, lipopolysaccharide (LPS)-induced inflammatory and ischemic stroke rat models were used. RESULTS: BS224 exhibited a high affinity (Ki = 0.51 nM) and selectivity for TSPO. The ratio of IC50 values of BS224 for LAB to that for HAB indicated that the TSPO binding affinity of BS224 has low binding sensitivity to the rs6971 polymorphism and it was comparable to that of PK 11195, which is not sensitive to the polymorphism. Docking simulations showed that the binding mode of BS224 is not affected by the A147T mutation and consequently supported the observed in vitro selectivity of [18F]BS224 regardless of polymorphisms. With optimal radiochemical yield (39 ± 6.8%, decay-corrected) and purity (> 99%), [18F]BS224 provided a clear visible image of the inflammatory lesion with a high signal-to-background ratio in both animal models (BPND = 1.43 ± 0.17 and 1.57 ± 0.37 in the LPS-induced inflammatory and ischemic stroke rat models, respectively) without skull uptake. CONCLUSION: Our results suggest that [18F]BS224 may be a promising TSPO ligand to gauge neuroinflammatory disease-related areas in a broad range of patients irrespective of the common rs6971 polymorphism.

Structure-Based Prediction of hERG-Related Cardiotoxicity: A Benchmark Study.

Drug-induced blockade of the human ether-à-go-go-related gene (hERG) channel is today considered the main cause of cardiotoxicity in postmarketing surveillance. Hence, several ligand-based approaches were developed in the last years and are currently employed in the early stages of a drug discovery process for in silico cardiac safety assessment of drug candidates. Herein, we present the first structure-based classifiers able to discern hERG binders from nonbinders. LASSO regularized support vector machines were applied to integrate docking scores and protein-ligand interaction fingerprints. A total of 396 models were trained and validated based on: (i) high-quality experimental bioactivity information returned by 8337 curated compounds extracted from ChEMBL (version 25) and (ii) structural predictor data. Molecular docking simulations were performed using GLIDE and GOLD software programs and four different hERG structural models, namely, the recently published structures obtained by cryoelectron microscopy (PDB codes: 5VA1 and 7CN1) and two published homology models selected for comparison. Interestingly, some classifiers return performances comparable to ligand-based models in terms of area under the ROC curve (AUCMAX = 0.86 ± 0.01) and negative predictive values (NPVMAX = 0.81 ± 0.01), thus putting forward the herein proposed computational workflow as a valuable tool for predicting hERG-related cardiotoxicity without the limitations of ligand-based models, typically affected by low interpretability and a limited applicability domain. From a methodological point of view, our study represents the first example of a successful integration of docking scores and protein-ligand interaction fingerprints (IFs) through a support vector machine (SVM) LASSO regularized strategy. Finally, the study highlights the importance of using hERG structural models accounting for ligand-induced fit effects and allowed us to select the best-performing protein conformation (made available in the Supporting Information, SI) to be employed for a reliable structure-based prediction of hERG-related cardiotoxicity.

More Is Always Better Than One: The N-Terminal Domain of the Spike Protein as Another Emerging Target for Hampering the SARS-CoV-2 Attachment to Host Cells.

Although the approved vaccines are proving to be of utmost importance in containing the Coronavirus disease 2019 (COVID-19) threat, they will hardly be resolutive as new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, a single-stranded RNA virus) variants might be insensitive to the immune response they induce. In this scenario, developing an effective therapy is still a dire need. Different targets for therapeutic antibodies and diagnostics have been identified, among which the SARS-CoV-2 spike (S) glycoprotein, particularly its receptor-binding domain, has been defined as crucial. In this context, we aim to focus attention also on the role played by the S N-terminal domain (S1-NTD) in the virus attachment, already recognized as a valuable target for neutralizing antibodies, in particular, building on a cavity mapping indicating the presence of two druggable pockets and on the recent literature hypothesizing the presence of a ganglioside-binding domain. In this perspective, we aim at proposing S1-NTD as a putative target for designing small molecules hopefully able to hamper the SARS-CoV-2 attachment to host cells.