Recent Advances in In Silico Target Fishing

In silico and pharmacokinetic studies of glucomoringin from Moringa oleifera root for Alzheimer's disease like pathology

Aim: The aim of this research is to investigate the potential of glucomoringin, derived from Moringa oleifera, as a therapeutic agent for Alzheimer's disease through in silico analysis. Materials & methods: This study employs in silico or computational methodologies, including pkCSM, Swiss ADME, OSIRIS® property explorer, PASS online web resource and MOLINSPIRATION® software, to predict the pharmacokinetic characteristics and biological activity of glucomoringin. Results & conclusion: Molecular docking indicates strong binding to I-1β and the pharmacokinetic profile shows cytochrome P450 enzyme inhibition, prompting further research for dosing strategies. Toxicological predictions affirm safety, while bioactivity assessments demonstrate versatility in modulating essential pathways. glucomoringin's potential for Alzheimer's treatment, emphasizing the need for additional empirical research.

Berberine modulates cardiovascular diseases as a multitarget-mediated alkaloid with insights into its downstream signals using in silico prospective screening approaches

Atherosclerosis is potentially correlated with several cardiac disorders that are greatly associated with cellular oxidative stress generation, inflammation, endothelial cells dysfunction, and many cardiovascular complications. Berberine is a natural isoquinoline alkaloid compound that widely modulates pathogenesis of atherosclerosis through its different curative potentials. This in silico screening study was designed to confirm the potent restorative properties of berberine chloride as a multitarget-mediated alkaloid against the CVDs and their complications through screening, identifying, visualizing, and evaluating its binding models, affinities, and interactions toward several CVDs-related targets as direct and/or indirect-mediated signals via inhibiting cellular ER stress and apoptotic signals and activating autophagy pathway. The drug-likeness properties of berberine were predicted using the computational QSAR/ADMET and Lipinski's RO5 analyses as well as in silico molecular docking simulations. The potent berberine-binding modes, residues-interaction patterns, and free energies of binding scores towards several CVDs-related targets were estimated using molecular docking tools. Furthermore, the pharmacokinetic properties and toxicological features of berberine were clearly determined. According to this in silico virtual screening study, berberine chloride could restore cardiac function and improve pathogenic features of atherosclerotic CVDs through alleviating ER stress and apoptotic signals, activating autophagy, improving insulin sensitivity, decreasing hyperglycemia and dyslipidemia, increasing intracellular RCT signaling, attenuating oxidative stress and vascular inflammation, and upregulating cellular antioxidant defenses in many cardiovascular tissues. In this in silico study, berberine chloride greatly modulated several potent CVDs-related targets, including SIGMAR1, GRP78, CASP3, BECN1, PIK3C3, SQSTM1/p62, LC3B, GLUT3, INSR, LDLR, LXRα, PPARγ, IL1β, IFNγ, iNOS, COX-2, MCP-1, IL10, GPx1, and SOD3.

VenomPred 2.0: A Novel In Silico Platform for an Extended and Human Interpretable Toxicological Profiling of Small Molecules

The application of artificial intelligence and machine learning (ML) methods is becoming increasingly popular in computational toxicology and drug design; it is considered as a promising solution for assessing the safety profile of compounds, particularly in lead optimization and ADMET studies, and to meet the principles of the 3Rs, which calls for the replacement, reduction, and refinement of animal testing. In this context, we herein present the development of VenomPred 2.0 (http://www.mmvsl.it/wp/venompred2/), the new and improved version of our free of charge web tool for toxicological predictions, which now represents a powerful web-based platform for multifaceted and human-interpretable in silico toxicity profiling of chemicals. VenomPred 2.0 presents an extended set of toxicity endpoints (androgenicity, skin irritation, eye irritation, and acute oral toxicity, in addition to the already available carcinogenicity, mutagenicity, hepatotoxicity, and estrogenicity) that can be evaluated through an exhaustive consensus prediction strategy based on multiple ML models. Moreover, we also implemented a new utility based on the Shapley Additive exPlanations (SHAP) method that allows human interpretable toxicological profiling of small molecules, highlighting the features that strongly contribute to the toxicological predictions in order to derive structural toxicophores.

Prediction of compound-target interaction using several artificial intelligence algorithms and comparison with a consensus-based strategy

For understanding a chemical compound's mechanism of action and its side effects, as well as for drug discovery, it is crucial to predict its possible protein targets. This study examines 15 developed target-centric models (TCM) employing different molecular descriptions and machine learning algorithms. They were contrasted with 17 third-party models implemented as web tools (WTCM). In both sets of models, consensus strategies were implemented as potential improvement over individual predictions. The findings indicate that TCM reach f1-score values greater than 0.8. Comparing both approaches, the best TCM achieves values of 0.75, 0.61, 0.25 and 0.38 for true positive/negative rates (TPR, TNR) and false negative/positive rates (FNR, FPR); outperforming the best WTCM. Moreover, the consensus strategy proves to have the most relevant results in the top 20 % of target profiles. TCM consensus reach TPR and FNR values of 0.98 and 0; while on WTCM reach values of 0.75 and 0.24. The implemented computational tool with the TCM and their consensus strategy at: https://bioquimio.udla.edu.ec/tidentification01/ . Scientific Contribution: We compare and discuss the performances of 17 public compound-target interaction prediction models and 15 new constructions. We also explore a compound-target interaction prioritization strategy using a consensus approach, and we analyzed the challenging involved in interactions modeling.

CODD-Pred: A Web Server for Efficient Target Identification and Bioactivity Prediction of Small Molecules

Target identification and bioactivity prediction are critical steps in the drug discovery process. Here we introduce CODD-Pred (COmprehensive Drug Design Predictor), an online web server with well-curated data sets from the GOSTAR database, which is designed with a dual purpose of predicting potential protein drug targets and computing bioactivity values of small molecules. We first designed a double molecular graph perception (DMGP) framework for target prediction based on a large library of 646 498 small molecules interacting with 640 human targets. The framework achieved a top-5 accuracy of over 80% for hitting at least one target on both external validation sets. Additionally, its performance on the external validation set comprising 200 molecules surpassed that of four existing target prediction servers. Second, we collected 56 targets closely related to the occurrence and development of cancer, metabolic diseases, and inflammatory immune diseases and developed a multi-model self-validation activity prediction (MSAP) framework that enables accurate bioactivity quantification predictions for small-molecule ligands of these 56 targets. CODD-Pred is a handy tool for rapid evaluation and optimization of small molecules with specific target activity. CODD-Pred is freely accessible at http://codd.iddd.group/.

ReverseDock: a web server for blind docking of a single ligand to multiple protein targets using AutoDock Vina

Several platforms exist to perform molecular docking to computationally predict binders to a specific protein target from a library of ligands. The reverse, that is, docking a single ligand to various protein targets, can currently be done by very few web servers, which limits the search to a small set of pre-selected human proteins. However, the possibility to in silico predict which targets a compound identified in a high-throughput drug screen bind would help optimize and reduce the costs of the experimental workflow needed to reveal the molecular mechanism of action of a ligand. Here, we present ReverseDock, a blind docking web server based on AutoDock Vina specifically designed to allow users with no computational expertise to dock a ligand to 100 protein structures of their choice. ReverseDock increases the number and type of proteins a ligand can be docked to, making the task of in silico docking of a ligand to entire families of proteins straightforward. We envision ReverseDock will support researchers by providing the possibility to apply inverse docking computations using web browser. ReverseDock is available at: https://reversedock.biologie.uni-freiburg.de/.

Antiviral evaluation of 1,4-disubstituted-1,2,3-triazole derivatives against Chikungunya virus

Aim

This work aimed to investigate the antiviral activity of two 1,4-disubstituted-1,2,3-triazole derivatives (1 and 2) against Chikungunya virus (CHIKV) replication.

Materials & methods

Cytotoxicity was analyzed using colorimetric assays and the antiviral potential was evaluated using plaque assays and computational tools.

Results

Compound 2 showed antiviral activity against CHIKV 181-25 in BHK-21 and Vero cells. Also, this compound presented a higher activity against CHIKV BRA/RJ/18 in Vero cells, like compound 1. Compound 2 exhibited virucidal activity and inhibited virus entry while compound 1 inhibited virus release. Molecular docking suggested that these derivatives inhibit nsP1 protein while compound 1 may also target capsid protein.

Conclusion

Both compounds exhibit promising antiviral activity against CHIKV by blocking different steps of virus replication.

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.

Thiazolidinedione-triazole conjugates: design, synthesis and probing of the α-amylase inhibitory potential

Aim: The primary objective of this investigation was the synthesis, spectral interpretation and evaluation of the α-amylase inhibition of rationally designed thiazolidinedione-triazole conjugates (7a-7aa). Materials & methods: The designed compounds were synthesized by stirring a mixture of thiazolidine-2,4-dione, propargyl bromide, cinnamaldehyde and azide derivatives in polyethylene glycol-400. The α-amylase inhibitory activity of the synthesized conjugates was examined by integrating in vitro and in silico studies. Results: The investigated derivatives exhibited promising α-amylase inhibitory activity, with IC50 values ranging between 0.028 and 0.088 μmol ml-1. Various computational approaches were employed to get detailed information about the inhibition mechanism. Conclusion: The thiazolidinedione-triazole conjugate 7p, with IC50 = 0.028 μmol ml-1, was identified as the best hit for inhibiting α-amylase.

Scaffold repositioning of spiro-acridine derivatives as fungi chitinase inhibitor by target fishing and in vitro studies

The concept of "one target, one drug, one disease" is not always true, as compounds with previously described therapeutic applications can be useful to treat other maladies. For example, acridine derivatives have several potential therapeutic applications. In this way, identifying new potential targets for available drugs is crucial for the rational management of diseases. Computational methodologies are interesting tools in this field, as they use rational and direct methods. Thus, this study focused on identifying other rational targets for acridine derivatives by employing inverse virtual screening (IVS). This analysis revealed that chitinase enzymes can be potential targets for these compounds. Subsequently, we coupled molecular docking consensus analysis to screen the best chitinase inhibitor among acridine derivatives. We observed that 3 compounds displayed potential enhanced activity as fungal chitinase inhibitors, showing that compound 5 is the most active molecule, with an IC₅₀ of 0.6 ng/µL. In addition, this compound demonstrated a good interaction with the active site of chitinases from Aspergillus fumigatus and Trichoderma harzianum. Additionally, molecular dynamics and free energy demonstrated complex stability for compound 5. Therefore, this study recommends IVS as a powerful tool for drug development. The potential applications are highlighted as this is the first report of spiro-acridine derivatives acting as chitinase inhibitors that can be potentially used as antifungal and antibacterial candidates.

Target identification by structure-based computational approaches: Recent advances and perspectives

The use of computational techniques in the early stages of drug discovery has recently experienced a boost, especially in the target identification step. Finding the biological partner(s) for new or existing synthetic and/or natural compounds by "wet" approaches may be challenging; therefore, preliminary in silico screening is even more recommended. After a brief overview of some of the most known target identification techniques, recent advances in structure-based computational approaches for target identification are reported in this digest, focusing on Inverse Virtual Screening and its recent applications. Moreover, future perspectives concerning the use of such methodologies, coupled or not with other approaches, are analyzed.

Combining the In Silico and In Vitro Assays to Identify Strobilanthes cusia Kuntze Bioactives against Penicillin-Resistant Streptococcus pneumoniae

Leaves of Strobilanthes cusia Kuntze (S. cusia) are a widely used alexipharmic Traditional Chinese Medicine (TCM) in southern China for the prevention of cold and respiratory tract infectious diseases. One of the most common bacterial pathogens in the respiratory tract is the gram-positive bacterium Streptococcus pneumoniae. The antibiotic resistance of colonized S. pneumoniae makes it a more serious threat to public health. In this study, the leaves of S. cusia were found to perform antibacterial effects on the penicillin-resistant S. pneumoniae (PRSP). Confocal assay and Transmission Electron Microscopy (TEM) monitored the diminished cell wall integrity and capsule thickness of the PRSP with treatment. The following comparative proteomics analysis revealed that the glycometabolism-related pathways were enriched for the differentially expressed proteins between the samples with treatment and the control. To further delve into the specific single effective compound, the bio-active contents of leaves of S. cusia were analyzed by UPLC-UV-ESI-Q-TOF/MS, and 23 compounds were isolated for anti-PRSP screening. Among them, Tryptanthrin demonstrated the most promising effect, and it possibly inhibited the N-glycan degradation proteins, as suggested by reverse docking analysis in silico and further experimental verification by the surface plasmon resonance assay (SPR). Our study provided a research foundation for applications of the leaves of S. cusia as a TCM, and supplied a bio-active compound Tryptanthrin as a candidate drug skeleton for infectious diseases caused by the PRSP.

Computational Approaches to Designing Antiviral Drugs against COVID-19: A Comprehensive Review

The global impact of the COVID-19 pandemic caused by SARS-CoV-2 necessitates innovative strategies for the rapid development of effective treatments. Computational methodologies, such as molecular modelling, molecular dynamics simulations, and artificial intelligence, have emerged as indispensable tools in the drug discovery process. This review aimed to provide a comprehensive overview of these computational approaches and their application in the design of antiviral agents for COVID-19. Starting with an examination of ligand-based and structure-based drug discovery, the review has delved into the intricate ways through which molecular modelling can accelerate the identification of potential therapies. Additionally, the investigation extends to phytochemicals sourced from nature, which have shown promise as potential antiviral agents. Noteworthy compounds, including gallic acid, naringin, hesperidin, Tinospora cordifolia, curcumin, nimbin, azadironic acid, nimbionone, nimbionol, and nimocinol, have exhibited high affinity for COVID-19 Mpro and favourable binding energy profiles compared to current drugs. Although these compounds hold potential, their further validation through in vitro and in vivo experimentation is imperative. Throughout this exploration, the review has emphasized the pivotal role of computational biologists, bioinformaticians, and biotechnologists in driving rapid advancements in clinical research and therapeutic development. By combining state-of-the-art computational techniques with insights from structural and molecular biology, the search for potent antiviral agents has been accelerated. The collaboration between these disciplines holds immense promise in addressing the transmissibility and virulence of SARS-CoV-2.

Development of machine learning classifiers to predict compound activity on prostate cancer cell lines

Prostate cancer is the most common type of cancer in men. The disease presents good survival rates if treated at the early stages. However, the evolution of the disease in its most aggressive variant remains without effective therapeutic answers. Therefore, the identification of novel effective therapeutics is urgently needed. On these premises, we developed a series of machine learning models, based on compounds with reported highly homogeneous cell-based antiproliferative assay data, able to predict the activity of ligands towards the PC-3 and DU-145 prostate cancer cell lines. The data employed in the development of the computational models was finely-tuned according to a series of thresholds for the classification of active/inactive compounds, to the number of features to be implemented, and by using 10 different machine learning algorithms. Models’ evaluation allowed us to identify the best combination of activity thresholds and ML algorithms for the classification of active compounds, achieving prediction performances with MCC values above 0.60 for PC-3 and DU-145 cells. Moreover, in silico models based on the combination of PC-3 and DU-145 data were also developed, demonstrating excellent precision performances. Finally, an analysis of the activity annotations reported for the ligands in the curated datasets were conducted, suggesting associations between cellular activity and biological targets that might be explored in the future for the design of more effective prostate cancer antiproliferative agents.

Anti-Inflammatory Mechanisms of Total Flavonoids from Mosla scabra against Influenza A Virus-Induced Pneumonia by Integrating Network Pharmacology and Experimental Verification

Influenza virus is one of the most common infectious pathogens that could cause high morbidity and mortality in humans. However, the occurrence of drug resistance and serious complications extremely complicated the clinic therapy. Mosla scabra is a natural medicinal plant used for treating various lung and gastrointestinal diseases, including viral infection, cough, chronic obstructive pulmonary disease, acute gastroenteritis, and diarrhoea. But the therapeutic effects of this herbal medicine had not been expounded clearly. In this study, a network pharmacology approach was employed to investigate the protective mechanism of total flavonoids from M. scabra (MSTF) against influenza A virus- (IAV-) induced acute lung damage and inflammation. The active compounds of MSTF were analyzed by LC-MS/MS and then evaluated according to their oral bioavailability and drug-likeness index. The potential targets of each active compound in MSTF were identified by using PharmMapper Server, whereas the potential genes involved in IAV infection were obtained from GeneGards. The results showed that luteoloside, apigenin, kaempherol, luteolin, mosloflavone I, and mosloflavone II were the main bioactive compounds found in MSTF. Primarily, 23 genes were identified as the targets of those five active compounds, which contributed to the inactivation of chemical carcinogenesis ROS, lipid and atherosclerosis, MAPK signaling pathway, pathways in cancer, PI3K-AKT signaling pathway, proteoglycans in cancer, and viral carcinogenesis. Finally, the animal experiments validated that MSTF improved IAV-induced acute lung inflammation via inhibiting MAPK, PI3K-AKT, and oxidant stress pathways. Therefore, our study demonstrated the potential inhibition of MSTF on viral pneumonia in mice and provided a strategy to characterize the molecular mechanism of traditional Chinese medicine by a combinative method using network pharmacology and experimental validation.

Design, Synthesis, Bioactivity Evaluation, Crystal Structures, and In Silico Studies of New α-Amino Amide Derivatives as Potential Histone Deacetylase 6 Inhibitors

Hydroxamate, as a zinc-binding group (ZBG), prevails in the design of histone deacetylase 6(HDAC6) inhibitors due to its remarkable zinc-chelating capability. However, hydroxamate-associated genotoxicity and mutagenicity have limited the widespread application of corresponding HDAC6 inhibitors in the treatment of human diseases. To avoid such side effects, researchers are searching for novel ZBGs that may be used for the synthesis of HDAC6 inhibitors. In this study, a series of stereoisomeric compounds were designed and synthesized to discover non-hydroxamate HDAC6 inhibitors using α-amino amide as zinc-ion-chelating groups, along with a pair of enantiomeric isomers with inverted L-shaped vertical structure as cap structures. The anti-proliferative activities were determined against HL-60, Hela, and RPMI 8226 cells, and 7a and its stereoisomer 13a exhibited excellent activities against Hela cells with IC₅₀ = 0.31 µM and IC₅₀ = 5.19 µM, respectively. Interestingly, there is a significant difference between the two stereoisomers. Moreover, an evaluation of cytotoxicity toward human normal liver cells HL-7702 indicated its safety for normal cells. X-ray single crystal diffraction was employed to increase insights into molecule structure and activities. It was found that the carbonyl of the amide bond is on the different side from the amino and pyridine nitrogen atoms. To identify possible protein targets to clarify the mechanism of action and biological activity of 7a, a small-scale virtual screen using reverse docking for HDAC isoforms (1–10) was performed and the results showed that HDAC6 was the best receptor for 7a, suggesting that HDAC6 may be a potential target for 7a. The interaction pattern analysis showed that the α-amino amide moiety of 7a coordinated with the zinc ion of HDAC6 in a bidentate chelate manner, which is similar to the chelation pattern of hydroxamic acid. Finally, the molecular dynamics simulation approaches were used to assess the docked complex’s conformational stability. In this work, we identified 7a as a potential HDAC6 inhibitor and provide some references for the discovery of non-hydroxamic acid HDAC6 inhibitors.

Artificial intelligence-assisted drug repurposing via "chemical-induced gene expression ranking"

Drug repurposing using artificial intelligence algorithms is a powerful technique that leverages existing datasets to find new medical applications for approved drugs. Pham et al. developed CIGER, a deep learning framework to overcome unreliable data in the datasets and present repositioned drugs against pancreatic cancer.

Isovitexin protects against acute liver injury by targeting PTEN, PI3K and BiP via modification of m6A

Isovitexin (IVT) has been shown to have a potential therapeutic effect on acute liver injury (ALI), but its underlying mechanisms especially the targets remain unclear, which was investigated in the present study. Briefly, the targets of IVT were predicted by bioinformatics and then were verified by multiple examinations using molecular docking, cellular thermal shift assay (CETSA), and Lipopolysaccharide/D-Galactosamine (LPS/D-GalN)-induced ALI animal model. The bioinformatic analysis predicted that the target genes of IVT against ALI were enriched into the PI3K/Akt and ERS-related pathways, in which, molecular docking and CETSA examination verified that the binding sites of IVT likely were PTEN, PI3K and BiP. Furthermore, the possible targets were also verified by animal experiments. The results revealed that IVT significantly ameliorated the hepatic injury, as evidenced by the attenuation of histopathological changes and the reduction in serum aminotransferase and total bilirubin activities. In addition, IVT treatment led to the reduction of PTEN, BiP and ERS-related targets expressions, as well as the elevation of PI3K, Akt and mTOR expressions. Notably, IVT significantly decreased total hepatic m6A level and m6A enrichment of PTEN and BiP, suggesting IVT regulated PTEN and BiP by modulating m6A modification. To sum up, the results indicate that IVT significantly ameliorates ALI, which is attributed to its ability to regulate the PI3K/Akt pathway and ERS by targeting PTEN, PI3K and BiP via modification of m6A. Our finding demonstrates that IVT may be a promising natural medicine for the treatment of ALI.

VenomPred: A Machine Learning Based Platform for Molecular Toxicity Predictions

The use of in silico toxicity prediction methods plays an important role in the selection of lead compounds and in ADMET studies since in vitro and in vivo methods are often limited by ethics, time, budget and other resources. In this context, we present our new web tool VenomPred, a user-friendly platform for evaluating the potential mutagenic, hepatotoxic, carcinogenic and estrogenic effects of small molecules. VenomPred platform employs several in-house Machine Learning (ML) models developed with datasets derived from VEGA QSAR, a software that includes a comprehensive collection of different toxicity models and has been used as a reference for building and evaluating our ML models. The results showed that our models achieved equal or better performance than those obtained with the reference models included in VEGA QSAR. In order to improve the predictive performance of our platform, we adopted a consensus approach combining the results of different ML models, which was able to predict chemical toxicity better than the single models. This improved method was thus implemented in the VenomPred platform, a freely accessible webserver that takes the SMILES (Simplified Molecular-Input Line-Entry System) strings of the compounds as input and sends the prediction results providing a probability score about their potential toxicity.

Target Characterization of Kaempferol against Myocardial Infarction Using Novel In Silico Docking and DARTS Prediction Strategy

Target identification is a crucial process for advancing natural products and drug leads development, which is often the most challenging and time-consuming step. However, the putative biological targets of natural products obtained from traditional prediction studies are also informatively redundant. Thus, how to precisely identify the target of natural products is still one of the major challenges. Given the shortcomings of current target identification methodologies, herein, a novel in silico docking and DARTS prediction strategy was proposed. Concretely, the possible molecular weight was detected by DARTS method through examining the protected band in SDS-PAGE. Then, the potential targets were obtained from screening and identification through the PharmMapper Server and TargetHunter method. In addition, the candidate target Src was further validated by surface plasmon resonance assay, and the anti-apoptosis effects of kaempferol against myocardial infarction were further confirmed by in vitro and in vivo assays. Collectively, these results demonstrated that the integrated strategy could efficiently characterize the targets, which may shed a new light on target identification of natural products.

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.