MzDOCK: A free ready-to-use GUI-based pipeline for molecular docking simulations.

Molecular docking is by far the most preferred approach in structure-based drug design for its effectiveness to predict the scoring and posing of a given bioactive small molecule into the binding site of its pharmacological target. Herein, we present MzDOCK, a new GUI-based pipeline for Windows operating system, designed with the intent of making molecular docking easier to use and higher reproducible even for inexperienced people. By harmonic integration of python and batch scripts, which employs various open source packages such as Smina (docking engine), OpenBabel (file conversion) and PLIP (analysis), MzDOCK includes many practical options such as: binding site configuration based on co-crystallized ligands; generation of enantiomers from SMILES input; application of different force fields (MMFF94, MMFF94s, UFF, GAFF, Ghemical) for energy minimization; retention of selectable ions and cofactors; sidechain flexibility of selectable binding site residues; multiple input file format (SMILES, PDB, SDF, Mol2, Mol); generation of reports and of pictures for interactive visualization. Users can download for free MzDOCK at the following link: https://github.com/Muzatheking12/MzDOCK.

TISBE: A Public Web Platform for the Consensus-Based Explainable Prediction of Developmental Toxicity.

Despite being extremely relevant for the protection of prenatal and neonatal health, the developmental toxicity (Dev Tox) is a highly complex endpoint whose molecular rationale is still largely unknown. The lack of availability of high-quality data as well as robust nontesting methods makes its understanding even more difficult. Thus, the application of new explainable alternative methods is of utmost importance, with Dev Tox being one of the most animal-intensive research themes of regulatory toxicology. Descending from TIRESIA (Toxicology Intelligence and Regulatory Evaluations for Scientific and Industry Applications), the present work describes TISBE (TIRESIA Improved on Structure-Based Explainability), a new public web platform implementing four fundamental advancements for in silico analyses: a three times larger dataset, a transparent XAI (explainable artificial intelligence) framework employing a fragment-based fingerprint coding, a novel consensus classifier based on five independent machine learning models, and a new applicability domain (AD) method based on a double top-down approach for better estimating the prediction reliability. The training set (TS) includes as many as 1008 chemicals annotated with experimental toxicity values. Based on a 5-fold cross-validation, a median value of 0.410 for the Matthews correlation coefficient was calculated; TISBE was very effective, with a median value of sensitivity and specificity equal to 0.984 and 0.274, respectively. TISBE was applied on two external pools made of 1484 bioactive compounds and 85 pediatric drugs taken from ChEMBL (Chemical European Molecular Biology Laboratory) and TEDDY (Task-Force in Europe for Drug Development in the Young) repositories, respectively. Notably, TISBE gives users the option to clearly spot the molecular fragments responsible for the toxicity or the safety of a given chemical query and is available for free at https://prometheus.farmacia.uniba.it/tisbe.

Pleiotropic Effects of Direct Oral Anticoagulants in Chronic Heart Failure and Atrial Fibrillation: Machine Learning Analysis.

Oral anticoagulant therapy (OAT) for managing atrial fibrillation (AF) encompasses vitamin K antagonists (VKAs, such as warfarin), which was the mainstay of anticoagulation therapy before 2010, and direct-acting oral anticoagulants (DOACs, namely dabigatran etexilate, rivaroxaban, apixaban, edoxaban), approved for the prevention of AF stroke over the last thirteen years. Due to the lower risk of major bleeding associated with DOACs, anticoagulant switching is a common practice in AF patients. Nevertheless, there are issues related to OAT switching that still need to be fully understood, especially for patients in whom AF and heart failure (HF) coexist. Herein, the effective impact of the therapeutic switching from warfarin to DOACs in HF patients with AF, in terms of cardiac remodeling, clinical status, endothelial function and inflammatory biomarkers, was assessed by a machine learning (ML) analysis of a clinical database, which ultimately shed light on the real positive and pleiotropic effects mediated by DOACs in addition to their anticoagulant activity.

CIRCE: Web-Based Platform for the Prediction of Cannabinoid Receptor Ligands Using Explainable Machine Learning.

The endocannabinoid system, which includes cannabinoid receptor 1 and 2 subtypes (CB1R and CB2R, respectively), is responsible for the onset of various pathologies including neurodegeneration, cancer, neuropathic and inflammatory pain, obesity, and inflammatory bowel disease. Given the high similarity of CB1R and CB2R, generating subtype-selective ligands is still an open challenge. In this work, the Cannabinoid Iterative Revaluation for Classification and Explanation (CIRCE) compound prediction platform has been generated based on explainable machine learning to support the design of selective CB1R and CB2R ligands. Multilayer classifiers were combined with Shapley value analysis to facilitate explainable predictions. In test calculations, CIRCE predictions reached ∼80% accuracy and structural features determining ligand predictions were rationalized. CIRCE was designed as a web-based prediction platform that is made freely available as a part of our study.

TIRESIA: An eXplainable Artificial Intelligence Platform for Predicting Developmental Toxicity.

Herein, a robust and reproducible eXplainable Artificial Intelligence (XAI) approach is presented, which allows prediction of developmental toxicity, a challenging human-health endpoint in toxicology. The application of XAI as an alternative method is of the utmost importance with developmental toxicity being one of the most animal-intensive areas of regulatory toxicology. In this work, the established CAESAR (Computer Assisted Evaluation of industrial chemical Substances According to Regulations) training set made of 234 chemicals for model learning is employed. Two test sets, including as a whole 585 chemicals, were instead used for validation and generalization purposes. The proposed framework favorably compares with the state-of-the-art approaches in terms of accuracy, sensitivity, and specificity, thus resulting in a reliable support system for developmental toxicity ensuring informativeness, uncertainty estimation, generalization, and transparency. Based on the eXtreme Gradient Boosting (XGB) algorithm, our predictive model provides easy interpretative keys based on specific molecular descriptors and structural alerts enabling one to distinguish toxic and nontoxic chemicals. Inspired by the Organisation for Economic Co-operation and Development (OECD) principles for the validation of Quantitative Structure-Activity Relationships (QSARs) for regulatory purposes, the results are summarized in a standard report in portable document format, enclosing also details concerned with a density-based model applicability domain and SHAP (SHapley Additive exPlanations) explainability, the latter particularly useful to better understand the effective roles played by molecular features. Notably, our model has been implemented in TIRESIA (Toxicology Intelligence and Regulatory Evaluations for Scientific and Industry Applications), a free of charge web platform available at http://tiresia.uniba.it.

Artificial intelligence-driven identification of morin analogues acting as CaV1.2 channel blockers: Synthesis and biological evaluation.

Morin is a vasorelaxant flavonoid, whose activity is ascribable to CaV1.2 channel blockade that, however, is weak as compared to that of clinically used therapeutic agents. A conventional strategy to circumvent this drawback is to synthesize new derivatives differently decorated and, in this context, morin-derivatives able to interact with CaV1.2 channels were found by employing the potential of PLATO in target fishing and reverse screening. Three different derivatives (5a-c) were selected as promising tools, synthesized, and investigated in in vitro functional studies using rat aorta rings and rat tail artery myocytes. 5a-c were found more effective vasorelaxant agents than the naturally occurring parent compound and antagonized both electro- and pharmaco-mechanical coupling in an endothelium-independent manner. 5a, the series' most potent, reduced also Ca2+ mobilization from intracellular store sites. Furthermore, 5a≈5c > 5b inhibited Ba2+ current through CaV1.2 channels. However, compound 5a caused also a concentration-dependent inhibition of KCa1.1 channel currents.

Investigation on Novel E/Z 2-Benzylideneindan-1-One-Based Photoswitches with AChE and MAO-B Dual Inhibitory Activity.

The multitarget therapeutic strategy, as opposed to the more traditional 'one disease-one target-one drug', may hold promise in treating multifactorial neurodegenerative syndromes, such as Alzheimer's disease (AD) and related dementias. Recently, combining a photopharmacology approach with the multitarget-directed ligand (MTDL) design strategy, we disclosed a novel donepezil-like compound, namely 2-(4-((diethylamino)methyl)benzylidene)-5-methoxy-2,3-dihydro-1H-inden-1-one (1a), which in the E isomeric form (and about tenfold less in the UV-B photo-induced isomer Z) showed the best activity as dual inhibitor of the AD-related targets acetylcholinesterase (AChE) and monoamine oxidase B (MAO-B). Herein, we investigated further photoisomerizable 2-benzylideneindan-1-one analogs 1b-h with the unconjugated tertiary amino moiety bearing alkyls of different bulkiness and lipophilicity. For each compound, the thermal stable E geometric isomer, along with the E/Z mixture as produced by UV-B light irradiation in the photostationary state (PSS, 75% Z), was investigated for the inhibition of human ChEs and MAOs. The pure E-isomer of the N-benzyl(ethyl)amino analog 1h achieved low nanomolar AChE and high nanomolar MAO-B inhibition potencies (IC50s 39 and 355 nM, respectively), whereas photoisomerization to the Z isomer (75% Z in the PSS mixture) resulted in a decrease (about 30%) of AChE inhibitory potency, and not in the MAO-B one. Molecular docking studies were performed to rationalize the different E/Z selectivity of 1h toward the two target enzymes.

Getting Insights into Structural and Energetic Properties of Reciprocal Peptide-Protein Interactions.

Peptide-protein interactions play a key role for many cellular and metabolic processes involved in the onset of largely spread diseases such as cancer and neurodegenerative pathologies. Despite the progress in the structural characterization of peptide-protein interfaces, the in-depth knowledge of the molecular details behind their interactions is still a daunting task. Here, we present the first comprehensive in silico morphological and energetic study of peptide binding sites by focusing on both peptide and protein standpoints. Starting from the PixelDB database, a nonredundant benchmark collection of high-quality 3D crystallographic structures of peptide-protein complexes, a classification analysis of the most representative categories based on the nature of each cocrystallized peptide has been carried out. Several interpretable geometrical and energetic descriptors have been computed both from peptide and target protein sides in the attempt to unveil physicochemical and structural causative correlations. Finally, we investigated the most frequent peptide-protein residue pairs at the binding interface and made extensive energetic analyses, based on GRID MIFs, with the aim to study the peptide affinity-enhancing interactions to be further exploited in rational drug design strategies.

An Integrated Machine Learning Model To Spot Peptide Binding Pockets in 3D Protein Screening.

The prediction of peptide-protein binding sites is of utmost importance to tackle the onset of severe neurodegenerative diseases and cancer. In this work, we detail a novel machine learning model based on Linear Discriminant Analysis (LDA) demonstrating to be highly predictive in detecting the putative protein binding regions of small peptides. Starting from 439 high-quality pockets derived from peptide-protein crystallographic complexes, three sets of well-established peptide-binding regions were first selected through a Partitioning Around Medoids (PAM) clustering algorithm based on morphological and energetic 3D GRID-MIF molecular descriptors. Next, the best combination between all the putative interacting peptide pockets and related GRID-MIF scores was automatically explored by using the LDA-based protocol implemented in BioGPS. This approach proved successful to recognize the actual interacting peptide regions (that is, AUC = 0.86 and partial ROC enrichment at 5% of 0.48) from all the other pockets of the protein. Validated on two external collections sets, including 445 and 347 crystallographic peptide-protein complexes, our LDA-based model could be effective to further run peptide-protein virtual screening campaigns.

PLATO: A Predictive Drug Discovery Web Platform for Efficient Target Fishing and Bioactivity Profiling of Small Molecules.

PLATO (Polypharmacology pLATform predictiOn) is an easy-to-use drug discovery web platform, which has been designed with a two-fold objective: to fish putative protein drug targets and to compute bioactivity values of small molecules. Predictions are based on the similarity principle, through a reverse ligand-based screening, based on a collection of 632,119 compounds known to be experimentally active on 6004 protein targets. An efficient backend implementation allows to speed-up the process that returns results for query in less than 20 s. The graphical user interface is intuitive to give practitioners easy input and transparent output, which is available as a standard report in portable document format. PLATO has been validated on thousands of external data, with performances better than those of other parallel approaches. PLATO is available free of charge (http://plato.uniba.it/ accessed on 13 April 2022).

Assessing the Role of a Malonamide Linker in the Design of Potent Dual Inhibitors of Factor Xa and Cholinesterases.

The rational discovery of new peptidomimetic inhibitors of the coagulation factor Xa (fXa) could help set more effective therapeutic options (to prevent atrial fibrillation). In this respect, we explored the conformational impact on the enzyme inhibition potency of the malonamide bridge, compared to the glycinamide one, as a linker connecting the P1 benzamidine anchoring moiety to the P4 aryl group of novel selective fXa inhibitors. We carried out structure-activity relationship (SAR) studies aimed at investigating para- or meta-benzamidine as the P1 basic group as well as diversely decorated aryl moieties as P4 fragments. To this end, twenty-three malonamide derivatives were synthesized and tested as inhibitors of fXa and thrombin (thr); the molecular determinants behind potency and selectivity were also studied by employing molecular docking. The malonamide linker, compared to the glycinamide one, does significantly increase anti-fXa potency and selectivity. The meta-benzamidine (P1) derivatives bearing 2',4'-difluoro-biphenyl as the P4 moiety proved to be highly potent reversible fXa-selective inhibitors, achieving inhibition constants (Ki) in the low nanomolar range. The most active compounds were also tested against cholinesterase (ChE) isoforms (acetyl- or butyrylcholinesterase, AChE, and BChE), and some of them returned single-digit micromolar inhibition potency against AChE and/or BChE, both being drug targets for symptomatic treatment of mild-to-moderate Alzheimer's disease. Compounds 19h and 22b were selected as selective fXa inhibitors with potential as multimodal neuroprotective agents.

Development of a Novel Class of Pyridazinone Derivatives as Selective MAO-B Inhibitors.

Sixteen compounds (TR1-TR16) were synthesized and evaluated for their inhibitory activities against monoamine oxidase A and B (MAOs). Most of the derivatives showed potent and highly selective MAO-B inhibition. Compound TR16 was the most potent inhibitor against MAO-B with an IC50 value of 0.17 µM, followed by TR2 (IC50 = 0.27 µM). TR2 and TR16 selectivity index (SI) values for MAO-B versus MAO-A were 84.96 and higher than 235.29, respectively. Compared to the basic structures, the para-chloro substituent in TR2 and TR16 increased the inhibitory activity of MAO-B. TR2 and TR16 were reversible MAO-B inhibitors that were competitive, with Ki values of 0.230 ± 0.004 and 0.149 ± 0.016 µM, respectively. The PAMPA method indicated that compounds TR2 and TR16 had the tendency to traverse the blood-brain barrier. Docking investigations revealed that lead compounds were beneficial for MAO-B inhibition via association with key as well as selective E84 or Y326 residues, but not for MAO-A inhibition via interaction primarily driven by hydrophobic contacts. In conclusion, TR2 and TR16 are therapeutic prospects for the management of multiple neurodegenerative diseases.

Quantitative Polypharmacology Profiling Based on a Multifingerprint Similarity Predictive Approach.

We present a new quantitative ligand-based bioactivity prediction approach employing a multifingerprint similarity search algorithm, enabling the polypharmacological profiling of small molecules. Quantitative bioactivity predictions are made on the basis of the statistical distributions of multiple Tanimoto similarity θ values, calculated through 13 different molecular fingerprints, and of the variation of the measured biological activity, reported as ΔpIC50, for all of the ligands sharing a given protein drug target. The application data set comprises as much as 4241 protein drug targets as well as 418 485 ligands selected from ChEMBL (release 25) by employing a set of well-defined filtering rules. Several large internal and external validation studies were carried out to demonstrate the robustness and the predictive potential of the herein proposed method. Additional comparative studies, carried out on two freely available and well-known ligand-target prediction platforms, demonstrated the reliability of our proposed approach for accurate ligand-target matching. Moreover, two applicative cases were also discussed to practically describe how to use our predictive algorithm, which is freely available as a user-friendly web platform. The user can screen single or multiple queries at a time and retrieve the output as a terse html table or as a json file including all of the information concerning the explored similarities to obtain a deeper understanding of the results. High-throughput virtual reverse screening campaigns, allowing for a given query compound the quick detection of the potential drug target from a large collection of them, can be carried out in batch on demand.

Morpholine-based chalcones as dual-acting monoamine oxidase-B and acetylcholinesterase inhibitors: synthesis and biochemical investigations.

Nine compounds (MO1-MO9) containing the morpholine moiety were assessed for their inhibitory activities against monoamine oxidases (MAOs) and acetylcholinesterase (AChE). Most of the compounds potently inhibited MAO-B; MO1 most potently inhibited with an IC50 value of 0.030 µM, followed by MO7 (0.25 µM). MO5 most potently inhibited AChE (IC50 = 6.1 µM), followed by MO9 (IC50 = 12.01 µM) and MO7 most potently inhibited MAO-A (IC50 = 7.1 µM). MO1 was a reversible mixed-type inhibitor of MAO-B (Ki = 0.018 µM); MO5 reversibly competitively inhibited AChE (Ki = 2.52 µM); and MO9 reversibly noncompetitively inhibited AChE (Ki = 7.04 µM). MO1, MO5 and MO9 crossed the blood-brain barrier, and were non-toxic to normal VERO cells. These results show that MO1 is a selective inhibitor of MAO-B and that MO5 is a dual-acting inhibitor of AChE and MAO-B, and that both should be considered candidates for the treatment of Alzheimer's disease.

Synthesis and biological evaluation of 3,5-diaryl-pyrazole derivatives as potential antiprostate cancer agents.

Prostate cancer is the most frequently diagnosed tumor in men and the second leading cause of cancer-associated mortality in most developed countries. 3,5-Diaryl substituted pyrazole derivatives (20-28) were prepared starting from related chalcones and biologically evaluated for in vitro growth inhibition activity against PC3 and DU145 human prostate cancer cell lines. Compounds 23, 26, and 28 were found to be more potent as compared to the other halogen-substituted derivatives. Especially, the 2-bromo-substituted pyrazole derivative (26) was found to be more potent against PC3 and DU145 cells. Epidermal growth factor receptor (EGFR) and vascular endothelial growth factor receptor 2 (VEGFR2) are known to be expressed in DU145 and PC3 cancer cells. The binding mode of the most selective compound 26 toward EGFR and VEGFR2 was investigated by employing docking simulations based on GLIDE standard precision (-5.912 and -6.949 kcal/mol, respectively).

First-in-Class Isonipecotamide-Based Thrombin and Cholinesterase Dual Inhibitors with Potential for Alzheimer Disease.

Recently, the direct thrombin (thr) inhibitor dabigatran has proven to be beneficial in animal models of Alzheimer's disease (AD). Aiming at discovering novel multimodal agents addressing thr and AD-related targets, a selection of previously and newly synthesized potent thr and factor Xa (fXa) inhibitors were virtually screened by the Multi-fingerprint Similarity Searching aLgorithm (MuSSeL) web server. The N-phenyl-1-(pyridin-4-yl)piperidine-4-carboxamide derivative 1, which has already been experimentally shown to inhibit thr with a Ki value of 6 nM, has been flagged by a new, upcoming release of MuSSeL as a binder of cholinesterase (ChE) isoforms (acetyl- and butyrylcholinesterase, AChE and BChE), as well as thr, fXa, and other enzymes and receptors. Interestingly, the inhibition potency of 1 was predicted by the MuSSeL platform to fall within the low-to-submicromolar range and this was confirmed by experimental Ki values, which were found equal to 0.058 and 6.95 µM for eeAChE and eqBChE, respectively. Thirty analogs of 1 were then assayed as inhibitors of thr, fXa, AChE, and BChE to increase our knowledge of their structure-activity relationships, while the molecular determinants responsible for the multiple activities towards the target enzymes were rationally investigated by molecular cross-docking screening.

Selected Class of Enamides Bearing Nitro Functionality as Dual-Acting with Highly Selective Monoamine Oxidase-B and BACE1 Inhibitors.

A small series of nitro group-bearing enamides was designed, synthesized (NEA1-NEA5), and evaluated for their inhibitory profiles of monoamine oxidases (MAOs) and ß-site amyloid precursor protein cleaving enzyme 1 (ß-secretase, BACE1). Compounds NEA3 and NEA1 exhibited a more potent MAO-B inhibition (IC50 value = 0.0092 and 0.016 µM, respectively) than the standards (IC50 value = 0.11 and 0.14 µM, respectively, for lazabemide and pargyline). Moreover, NEA3 and NEA1 showed greater selectivity index (SI) values toward MAO-B over MAO-A (SI of >1652.2 and >2500.0, respectively). The inhibition and kinetics studies suggested that NEA3 and NEA1 are reversible and competitive inhibitors with Ki values of 0.013 ± 0.005 and 0.0049 ± 0.0002 µM, respectively, for MAO-B. In addition, both NEA3 and NEA1 showed efficient BACE1 inhibitions with IC50 values of 8.02 ± 0.13 and 8.21 ± 0.03 µM better than the standard quercetin value (13.40 ± 0.04 µM). The parallel artificial membrane permeability assay (PAMPA) method demonstrated that all the synthesized derivatives can cross the blood-brain barrier (BBB) successfully. Docking analyses were performed by employing an induced-fit docking approach in the GLIDE module of Schrodinger, and the results were in agreement with their in vitro inhibitory activities. The present study resulted in the discovery of potent dual inhibitors toward MAO-B and BACE1, and these lead compounds can be fruitfully explored for the generation of newer, clinically active agents for the treatment of neurodegenerative disorders.

Homobivalent Lamellarin-Like Schiff Bases: In Vitro Evaluation of Their Cancer Cell Cytotoxicity and Multitargeting Anti-Alzheimer's Disease Potential.

Marine alkaloids belonging to the lamellarins family, which incorporate a 5,6-dihydro-1-phenylpyrrolo[2,1-a]isoquinoline (DHPPIQ) moiety, possess various biological activities, spanning from antiviral and antibiotic activities to cytotoxicity against tumor cells and the reversal of multidrug resistance. Expanding a series of previously reported imino adducts of DHPPIQ 2-carbaldehyde, novel aliphatic and aromatic Schiff bases were synthesized and evaluated herein for their cytotoxicity in five diverse tumor cell lines. Most of the newly synthesized compounds were found noncytotoxic in the low micromolar range (<30 µM). Based on a Multi-fingerprint Similarity Search aLgorithm (MuSSeL), mainly conceived for making protein drug target prediction, some DHPPIQ derivatives, especially bis-DHPPIQ Schiff bases linked by a phenylene bridge, were prioritized as potential hits addressing Alzheimer's disease-related target proteins, such as cholinesterases (ChEs) and monoamine oxidases (MAOs). In agreement with MuSSeL predictions, homobivalent para-phenylene DHPPIQ Schiff base 14 exhibited a noncompetitive/mixed inhibition of human acetylcholinesterase (AChE) with Ki in the low micromolar range (4.69 µM). Interestingly, besides a certain inhibition of MAO A (50% inhibition of the cell population growth (IC50) = 12 µM), the bis-DHPPIQ 14 showed a good inhibitory activity on self-induced ß-amyloid (Aß)1-40 aggregation (IC50 = 13 µM), which resulted 3.5-fold stronger than the respective mono-DHPPIQ Schiff base 9.

Design, synthesis and biological evaluation of 3,5-diaryl isoxazole derivatives as potential anticancer agents.

The present study was carried out in the attempt to synthesize a new class of potential anticancer agents comprising eleven compounds (24-34) sharing the 3,5-diarylisoxazole as a core. The chemical structure of the new synthesized compounds was established by IR, 1H NMR, 13C NMR and elemental analysis. Their biological potential towards prostate cancer was evaluated by using cancer PC3 cells and non-tumorigenic PNT1a cells. Interestingly, compound 26 distinguished from others with a quite high selectivity value that is comparable to 5-FU. The binding mode of 26 towards Ribosomal protein S6 kinase beta-1 (S6K1) was investigated at a molecular level of detail by employing docking simulations based on GLIDE standard precision as well as MM-GBSA calculations.

Bcr-Abl Tyrosine Kinase Inhibitors in the Treatment of Pediatric CML.

The therapeutic approach to Chronic Myeloid Leukemia (CML) has changed since the advent of the tyrosine kinase inhibitor (TKI) imatinib, which was then followed by the second generation TKIs dasatinib, nilotinib, and, finally, by ponatinib, a third-generation drug. At present, these therapeutic options represent the first-line treatment for adults. Based on clinical experience, imatinb, dasatinib, and nilotinib have been approved for children even though the studies that were concerned with efficacy and safety toward pediatric patients are still awaiting more specific and high-quality data. In this scenario, it is of utmost importance to prospectively validate data extrapolated from adult studies to set a standard therapeutic management for pediatric CML by employing appropriate formulations on the basis of pediatric clinical trials, which allow a careful monitoring of TKI-induced adverse effects especially in growing children exposed to long-term therapy.