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.

Making sense of chemical space network shows signs of criticality.

Chemical space modelling has great importance in unveiling and visualising latent information, which is critical in predictive toxicology related to drug discovery process. While the use of traditional molecular descriptors and fingerprints may suffer from the so-called curse of dimensionality, complex networks are devoid of the typical drawbacks of coordinate-based representations. Herein, we use chemical space networks (CSNs) to analyse the case of the developmental toxicity (Dev Tox), which remains a challenging endpoint for the difficulty of gathering enough reliable data despite very important for the protection of the maternal and child health. Our study proved that the Dev Tox CSN has a complex non-random organisation and can thus provide a wealth of meaningful information also for predictive purposes. At a phase transition, chemical similarities highlight well-established toxicophores, such as aryl derivatives, mostly neurotoxic hydantoins, barbiturates and amino alcohols, steroids, and volatile organic compounds ether-like chemicals, which are strongly suspected of the Dev Tox onset and can thus be employed as effective alerts for prioritising chemicals before testing.

Where developmental toxicity meets explainable artificial intelligence: state-of-the-art and perspectives.

INTRODUCTION: The application of Artificial Intelligence (AI) to predictive toxicology is rapidly increasing, particularly aiming to develop non-testing methods that effectively address ethical concerns and reduce economic costs. In this context, Developmental Toxicity (Dev Tox) stands as a key human health endpoint, especially significant for safeguarding maternal and child well-being. AREAS COVERED: This review outlines the existing methods employed in Dev Tox predictions and underscores the benefits of utilizing New Approach Methodologies (NAMs), specifically focusing on eXplainable Artificial Intelligence (XAI), which proves highly efficient in constructing reliable and transparent models aligned with recommendations from international regulatory bodies. EXPERT OPINION: The limited availability of high-quality data and the absence of dependable Dev Tox methodologies render XAI an appealing avenue for systematically developing interpretable and transparent models, which hold immense potential for both scientific evaluations and regulatory decision-making.

Growth hormone secretagogues modulate inflammation and fibrosis in mdx mouse model of Duchenne muscular dystrophy.

Introduction: Growth hormone secretagogues (GHSs) exert multiple actions, being able to activate GHS-receptor 1a, control inflammation and metabolism, to enhance GH/insulin-like growth factor-1 (IGF-1)-mediated myogenesis, and to inhibit angiotensin-converting enzyme. These mechanisms are of interest for potentially targeting multiple steps of pathogenic cascade in Duchenne muscular dystrophy (DMD). Methods: Here, we aimed to provide preclinical evidence for potential benefits of GHSs in DMD, via a multidisciplinary in vivo and ex vivo comparison in mdx mice, of two ad hoc synthesized compounds (EP80317 and JMV2894), with a wide but different profile. 4-week-old mdx mice were treated for 8 weeks with EP80317 or JMV2894 (320 µg/kg/d, s.c.). Results: In vivo, both GHSs increased mice forelimb force (recovery score, RS towards WT: 20% for EP80317 and 32% for JMV2894 at week 8). In parallel, GHSs also reduced diaphragm (DIA) and gastrocnemius (GC) ultrasound echodensity, a fibrosis-related parameter (RS: ranging between 26% and 75%). Ex vivo, both drugs ameliorated DIA isometric force and calcium-related indices (e.g., RS: 40% for tetanic force). Histological analysis highlighted a relevant reduction of fibrosis in GC and DIA muscles of treated mice, paralleled by a decrease in gene expression of TGF-ß1 and Col1a1. Also, decreased levels of pro-inflammatory genes (IL-6, CD68), accompanied by an increment in Sirt-1, PGC-1α and MEF2c expression, were observed in response to treatments, suggesting an overall improvement of myofiber metabolism. No detectable transcript levels of GHS receptor-1a, nor an increase of circulating IGF-1 were found, suggesting the presence of a novel receptor-independent mechanism in skeletal muscle. Preliminary docking studies revealed a potential binding capability of JMV2894 on metalloproteases involved in extracellular matrix remodeling and cytokine production, such as ADAMTS-5 and MMP-9, overactivated in DMD. Discussion: Our results support the interest of GHSs as modulators of pathology progression in mdx mice, disclosing a direct anti-fibrotic action that may prove beneficial to contrast pathological remodeling.

Targeting protein-protein interactions with low molecular weight and short peptide modulators: insights on disease pathways and starting points for drug discovery.

INTRODUCTION: Protein-protein interactions (PPIs) have been often considered undruggable targets although they are attractive for the discovery of new therapeutics. The spread of artificial intelligence and machine learning complemented with experimental methods is likely to change the perspectives of protein-protein modulator research. Noteworthy, some novel low molecular weight (LMW) and short peptide modulators of PPIs are already in clinical trials for the treatment of relevant diseases. AREAS COVERED: This review focuses on the main molecular properties of protein-protein interfaces and on key concepts pertaining to the modulation of PPIs. The authors survey recently reported state-of-the-art methods dealing with the rational design of PPI modulators and highlight the role of several computer-based approaches. EXPERT OPINION: Interfering specifically with large protein interfaces is still an open challenge. The initial concerns about the unfavorable physicochemical properties of many of these modulators are nowadays less acute with several molecules lying beyond the rule of 5, orally available and successful in clinical trials. As the cost of biologics interfering with PPIs is very high, it would seem reasonable to put more effort, both in academia and the private sectors, on actively developing novel low molecular weight compounds and short peptides to perform this task.

Exploration of a new class of monoamine oxidase B inhibitors by assembling benzyloxy pharmacophore on halogenated chalcones.

Eight derivatives of benzyloxy-derived halogenated chalcones (BB1-BB8) were synthesized and tested for their ability to inhibit monoamine oxidases (MAOs). MAO-A was less efficiently inhibited by all compounds than MAO-B. Additionally, the majority of the compounds displayed significant MAO-B inhibitory activities at 1 µM with residual activities of less than 50%. With an IC50 value of 0.062 µM, compound BB4 was the most effective in inhibiting MAO-B, followed by compound BB2 (IC50 = 0.093 µM). The lead molecules showed good activity than the reference MAO-B inhibitors (Lazabemide IC50 = 0.11 µM and Pargyline Pargyline IC50 = 0.14). The high selectivity index (SI) values for MAO-B were observed in compounds BB2 and BB4 (430.108 and 645.161, respectively). Kinetics and reversibility experiments revealed that BB2 and BB4 were reversible competitive MAO-B inhibitors with Ki values of 0.030 ± 0.014 and 0.011 ± 0.005 µM, respectively. Swiss target prediction confirmed the high probability in the targets of MAO-B for both compounds. Hypothetical binding mode revealed that the BB2 or BB4 is similarly oriented to the binding cavity of MAO-B. Based on the modelling results, BB4 showed a stable confirmation during the dynamic simulation. From these results, it was concluded that BB2 and BB4 were potent selective reversible MAO-B inhibitors and they can be considered drug candidates for treating related neurodegenerative diseases such as Parkinson's disease.

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.

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).

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.

Bcr-Abl Allosteric Inhibitors: Where We Are and Where We Are Going to.

The fusion oncoprotein Bcr-Abl is an aberrant tyrosine kinase responsible for chronic myeloid leukemia and acute lymphoblastic leukemia. The auto-inhibition regulatory module observed in the progenitor kinase c-Abl is lost in the aberrant Bcr-Abl, because of the lack of the N-myristoylated cap able to bind the myristoyl binding pocket also conserved in the Bcr-Abl kinase domain. A way to overcome the occurrence of resistance phenomena frequently observed for Bcr-Abl orthosteric drugs is the rational design of allosteric ligands approaching the so-called myristoyl binding pocket. The discovery of these allosteric inhibitors although very difficult and extremely challenging, represents a valuable option to minimize drug resistance, mostly due to the occurrence of mutations more frequently affecting orthosteric pockets, and to enhance target selectivity with lower off-target effects. In this perspective, we will elucidate at a molecular level the structural bases behind the Bcr-Abl allosteric control and will show how artificial intelligence can be effective to drive the automated de novo design towards off-patent regions of the chemical space.

Accelerating Drug Discovery by Early Protein Drug Target Prediction Based on a Multi-Fingerprint Similarity Search.

In this continuing work, we have updated our recently proposed Multi-fingerprint Similarity Search algorithm (MuSSel) by enabling the generation of dominant ionized species at a physiological pH and the exploration of a larger data domain, which included more than half a million high-quality small molecules extracted from the latest release of ChEMBL (version 24.1, at the time of writing). Provided with a high biological assay confidence score, these selected compounds explored up to 2822 protein drug targets. To improve the data accuracy, samples marked as prodrugs or with equivocal biological annotations were not considered. Notably, MuSSel performances were overall improved by using an object-relational database management system based on PostgreSQL. In order to challenge the real effectiveness of MuSSel in predicting relevant therapeutic drug targets, we analyzed a pool of 36 external bioactive compounds published in the Journal of Medicinal Chemistry from October to December 2018. This study demonstrates that the use of highly curated chemical and biological experimental data on one side, and a powerful multi-fingerprint search algorithm on the other, can be of the utmost importance in addressing the fate of newly conceived small molecules, by strongly reducing the attrition of early phases of drug discovery programs.

A New Approach for Drug Target and Bioactivity Prediction: The Multifingerprint Similarity Search Algorithm (MuSSeL).

We present MuSSeL, a multifingerprint similarity search algorithm, able to predict putative drug targets for a given query small molecule as well as to return a quantitative assessment of its bioactivity in terms of Ki or IC50 values. Predictions are automatically made exploiting a large collection of high quality experimental bioactivity data available from ChEMBL (version 22.1) combining, in a consensus-like approach, predictions resulting from a similarity search performed using 13 different fingerprint definitions. Importantly, the herein proposed algorithm is also effective in detecting and handling activity cliffs. A calibration set including small molecules present in the last updated version of ChEMBL (version 23) was employed to properly tune the algorithm parameters. Three randomly built external sets were instead challenged for model performances. The potential use of MuSSeL was also challenged by a prospective exercise for the prediction of five bioactive compounds taken from articles published in the Journal of Medicinal Chemistry just few months ago. The paper emphasizes the importance of implementing multifingerprint consensus strategies to increase the confidence in prediction of similarity search algorithms and provides a fast and easy-to-run tool for drug target and bioactivity prediction.

Analysis of solvent-exposed and buried co-crystallized ligands: a case study to support the design of novel protein-protein interaction inhibitors.

Molecular descriptors have been used to characterize and predict the functions of small molecules, including inhibitors of protein-protein interactions (iPPIs). Such molecules are valuable to investigate disease pathways and as starting points for drug discovery endeavors. iPPIs tend to bind at the surface of macromolecules and the design of such compounds remains challenging. Here, we report on our investigation of a pool of interpretable molecular descriptors for solvent-exposed and buried co-crystallized ligands. Several descriptors were found to be significantly different between the two classes and were further exploited using machine-learning approaches. This work could open new perspectives for the rational design of focused libraries enriched in new types of small drug-like molecules that could be used to prevent PPIs.

Prediction of Acute Oral Systemic Toxicity Using a Multifingerprint Similarity Approach.

The implementation of nonanimal approaches is of particular importance to regulatory agencies for the prediction of potential hazards associated with acute exposures to chemicals. This work was carried out in the framework of an international modeling initiative organized by the Acute Toxicity Workgroup (ATWG) of the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) with the participation of 32 international groups across government, industry, and academia. Our contribution was to develop a multifingerprints similarity approach for predicting five relevant toxicology endpoints related to the acute oral systemic toxicity that are: the median lethal dose (LD50) point prediction, the "nontoxic" (LD50 > 2000 mg/kg) and "very toxic" (LD50<50 mg/kg) binary classification, and the multiclass categorization of chemicals based on the United States Environmental Protection Agency and Globally Harmonized System of Classification and Labeling of Chemicals schemes. Provided by the ICCVAM's ATWG, the training set used to develop the models consisted of 8944 chemicals having high-quality rat acute oral lethality data. The proposed approach integrates the results coming from a similarity search based on 19 different fingerprint definitions to return a consensus prediction value. Moreover, the herein described algorithm is tailored to properly tackling the so-called toxicity cliffs alerting that a large gap in LD50 values exists despite a high structural similarity for a given molecular pair. An external validation set made available by ICCVAM and consisting in 2896 chemicals was employed to further evaluate the selected models. This work returned high-accuracy predictions based on the evaluations conducted by ICCVAM's ATWG.

Molecular Docking for Predictive Toxicology.

Molecular docking is an in silico method widely applied in drug discovery programs to predict the binding mode of a given molecule interacting with a specific biological target. This computational technique is today emerging also in the field of predictive toxicology for regulatory purposes, being for instance successfully applied to develop classification models for the prediction of the endocrine disruptor potential of chemicals. Herein, we describe the protocol for adapting molecular docking to the purposes of predictive toxicology.

Design, Synthesis, and Biological Evaluation of Tetrahydro-ß-carboline Derivatives as Selective Sub-Nanomolar Gelatinase Inhibitors.

Targeting matrix metalloproteinases (MMPs) is a pursued strategy for treating several pathological conditions, such as multiple sclerosis and cancer. Herein, a series of novel tetrahydro-ß-carboline derivatives with outstanding inhibitory activity toward MMPs are present. In particular, compounds 9 f, 9 g, 9 h and 9 i show sub-nanomolar IC50 values. Interestingly, compounds 9 g and 9 i also provide remarkable selectivity toward gelatinases; IC50 =0.15 nm for both toward MMP-2 and IC50 =0.63 and 0.58 nm, respectively, toward MMP-9. Molecular docking simulations, performed by employing quantum mechanics based partial charges, shed light on the rationale behind binding involving specific interactions with key residues of S1' and S3' domains. Taken together, these studies indicate that tetrahydro-ß-carboline represents a promising scaffold for the design of novel inhibitors able to target MMPs and selectively bias gelatinases, over the desirable range of the pharmacokinetics spectrum.

Automated identification of structurally heterogeneous and patentable antiproliferative hits as potential tubulin inhibitors.

By employing a recently developed hierarchical computational platform, we identified 37 novel and structurally diverse tubulin targeting compounds. In particular, hierarchical molecular filters, based on molecular shape similarity, structure-based pharmacophore, and molecular docking, were applied on a large chemical collection of commercial compounds to identify unexplored and patentable microtubule-destabilizing candidates. The herein proposed 37 novel hits, showing new molecular scaffolds (such as 1,3,3a,4-tetraaza-1,2,3,4,5,6,7,7a-octahydroindene or dihydropyrrolidin-2-one fused to a chromen-4-one), are provided with antiproliferative activity in the µm range toward MCF-7 (human breast cancer lines). Importantly, there is a likely causative relationship between cytotoxicity and the inhibition of tubulin polymerization at the colchicine binding site, assessed through fluorescence polymerization assays.

Predictive Structure-Based Toxicology Approaches To Assess the Androgenic Potential of Chemicals.

We present a practical and easy-to-run in silico workflow exploiting a structure-based strategy making use of docking simulations to derive highly predictive classification models of the androgenic potential of chemicals. Models were trained on a high-quality chemical collection comprising 1689 curated compounds made available within the CoMPARA consortium from the US Environmental Protection Agency and were integrated with a two-step applicability domain whose implementation had the effect of improving both the confidence in prediction and statistics by reducing the number of false negatives. Among the nine androgen receptor X-ray solved structures, the crystal 2PNU (entry code from the Protein Data Bank) was associated with the best performing structure-based classification model. Three validation sets comprising each 2590 compounds extracted by the DUD-E collection were used to challenge model performance and the effectiveness of Applicability Domain implementation. Next, the 2PNU model was applied to screen and prioritize two collections of chemicals. The first is a small pool of 12 representative androgenic compounds that were accurately classified based on outstanding rationale at the molecular level. The second is a large external blind set of 55450 chemicals with potential for human exposure. We show how the use of molecular docking provides highly interpretable models and can represent a real-life option as an alternative nontesting method for predictive toxicology.