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.

Identification of New GSK3ß Inhibitors through a Consensus Machine Learning-Based Virtual Screening.

Glycogen synthase kinase-3 beta (GSK3ß) is a serine/threonine kinase that plays key roles in glycogen metabolism, Wnt/ß-catenin signaling cascade, synaptic modulation, and multiple autophagy-related signaling pathways. GSK3ß is an attractive target for drug discovery since its aberrant activity is involved in the development of neurodegenerative diseases such as Alzheimer's and Parkinson's disease. In the present study, multiple machine learning models aimed at identifying novel GSK3ß inhibitors were developed and evaluated for their predictive reliability. The most powerful models were combined in a consensus approach, which was used to screen about 2 million commercial compounds. Our consensus machine learning-based virtual screening led to the identification of compounds G1 and G4, which showed inhibitory activity against GSK3ß in the low-micromolar and sub-micromolar range, respectively. These results demonstrated the reliability of our virtual screening approach. Moreover, docking and molecular dynamics simulation studies were employed for predicting reliable binding modes for G1 and G4, which represent two valuable starting points for future hit-to-lead and lead optimization studies.

MolBook UNIPI─Create, Manage, Analyze, and Share Your Chemical Data for Free.

Here, we present MolBook UNIPI, freely available and user-friendly software specifically designed for medicinal chemists as a powerful tool for the easy management of virtual libraries of chemical compounds. With MolBook UNIPI, it is possible to create, store, handle, and share molecular databases in a very simple and intuitive way. The software allows users to rapidly generate libraries of bioactive ligands, building blocks, or commercial compounds by either manually creating single molecules or automatically importing compounds from public databases and pre-existing libraries. MolBook UNIPI databases can be enriched with all kinds of data and can be filtered based on molecular structures or properties, allowing the desired molecules, along with their structures and features, to be easily accessible in just a few clicks. Moreover, new molecular properties and potential toxicological effects of compounds can be rapidly and reliably predicted. Notably, all of these functions can be easily mastered even by inexperienced users, with no prior cheminformatics knowledge or programming skills, which makes MolBook UNIPI an invaluable tool for medicinal chemists. MolBook UNIPI can be downloaded free of charge from the project web page https://molbook.farm.unipi.it/.

Machine Learning-Based Virtual Screening for the Identification of Cdk5 Inhibitors.

Cyclin-dependent kinase 5 (Cdk5) is an atypical proline-directed serine/threonine protein kinase well-characterized for its role in the central nervous system rather than in the cell cycle. Indeed, its dysregulation has been strongly implicated in the progression of synaptic dysfunction and neurodegenerative diseases, such as Alzheimer's disease (AD) and Parkinson's disease (PD), and also in the development and progression of a variety of cancers. For this reason, Cdk5 is considered as a promising target for drug design, and the discovery of novel small-molecule Cdk5 inhibitors is of great interest in the medicinal chemistry field. In this context, we employed a machine learning-based virtual screening protocol with subsequent molecular docking, molecular dynamics simulations and binding free energy evaluations. Our virtual screening studies resulted in the identification of two novel Cdk5 inhibitors, highlighting an experimental hit rate of 50% and thus validating the reliability of the in silico workflow. Both identified ligands, compounds CPD1 and CPD4, showed a promising enzyme inhibitory activity and CPD1 also demonstrated a remarkable antiproliferative activity in ovarian and colon cancer cells. These ligands represent a valuable starting point for structure-based hit-optimization studies aimed at identifying new potent Cdk5 inhibitors.

Novel Potent and Selective Agonists of the GPR55 Receptor Based on the 3-Benzylquinolin-2(1H)-One Scaffold.

A growing body of evidence underlines the crucial role of GPR55 in physiological and pathological conditions. In fact, GPR55 has recently emerged as a therapeutic target for several diseases, including cancer and neurodegenerative and metabolic disorders. Several lines of evidence highlight GPR55's involvement in the regulation of microglia-mediated neuroinflammation, although the exact molecular mechanism has not been yet elucidated. Nevertheless, there are only a limited number of selective GPR55 ligands reported in the literature. In this work, we designed and synthesized a series of novel GPR55 ligands based on the 3-benzylquinolin-2(1H)-one scaffold, some of which showed excellent binding properties (with Ki values in the low nanomolar range) and almost complete selectivity over cannabinoid receptors. The full agonist profile of all the new derivatives was assessed using the p-ERK activation assay and a computational study was conducted to predict the key interactions with the binding site of the receptor. Our data outline a preliminary structure-activity relationship (SAR) for this class of molecules at GPR55. Some of our compounds are among the most potent GPR55 agonists developed to date and could be useful as tools to validate this receptor as a therapeutic target.

Reversible Monoacylglycerol Lipase Inhibitors: Discovery of a New Class of Benzylpiperidine Derivatives.

Monoacylglycerol lipase (MAGL) is the enzyme responsible for the metabolism of 2-arachidonoylglycerol in the brain and the hydrolysis of peripheral monoacylglycerols. Many studies demonstrated beneficial effects deriving from MAGL inhibition for neurodegenerative diseases, inflammatory pathologies, and cancer. MAGL expression is increased in invasive tumors, furnishing free fatty acids as pro-tumorigenic signals and for tumor cell growth. Here, a new class of benzylpiperidine-based MAGL inhibitors was synthesized, leading to the identification of 13, which showed potent reversible and selective MAGL inhibition. Associated with MAGL overexpression and the prognostic role in pancreatic cancer, derivative 13 showed antiproliferative activity and apoptosis induction, as well as the ability to reduce cell migration in primary pancreatic cancer cultures, and displayed a synergistic interaction with the chemotherapeutic drug gemcitabine. These results suggest that the class of benzylpiperidine-based MAGL inhibitors have potential as a new class of therapeutic agents and MAGL could play a role in pancreatic cancer.

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.

New PIN1 inhibitors identified through a pharmacophore-driven, hierarchical consensus docking strategy.

PIN1 is considered as a therapeutic target for a wide variety of tumours. However, most of known inhibitors are devoid of cellular activity despite their good enzyme inhibitory profile. Hence, the lack of effective compounds for the clinic makes the identification of novel PIN1 inhibitors a hot topic in the medicinal chemistry field. In this work, we reported a virtual screening study for the identification of new promising PIN1 inhibitors. A receptor-based procedure was applied to screen different chemical databases of commercial compounds. Based on the whole workflow, two compounds were selected and biologically evaluated. Both ligands, compounds VS1 and VS2, showed a good enzyme inhibitory activity and VS2 also demonstrated a promising antitumoral activity in ovarian cancer cells. These results confirmed the reliability of our in silico protocol and provided a structurally novel ligand as a valuable starting point for the development of new PIN1 inhibitors.

Recent Advances in In Silico Target Fishing.

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.

Co-Inhibition of P-gp and Hsp90 by an Isatin-Derived Compound Contributes to the Increase of the Chemosensitivity of MCF7/ADR-Resistant Cells to Doxorubicin.

Breast cancer is a complex and multi-drug resistant (MDR) disease, which could result in the failure of many chemotherapeutic clinical agents. Discovering effective molecules from natural products or by derivatization from known compounds is the interest of many research studies. The first objective of the present study is to investigate the cytotoxic combinatorial, chemosensitizing, and apoptotic effects of an isatin derived compound (5,5-diphenylimidazolidine-2,4-dione conjugated with 5-substituted isatin, named HAA2021 in the present study) against breast cancer cells (MCF7) and breast cancer cells resistant to doxorubicin (MCF7/ADR) when combined with doxorubicin. The second objective is to investigate the binding mode of HAA2021 withP-glycoprotein (P-gp) and heat shock protein 90 (Hsp90), and to determine whether their co-inhibition by HAA2021 contribute to the increase of the chemosensitization of MCF7/ADR cells to doxorubicin. The combination of HAA2021, at non-toxic doses, with doxorubicin synergistically inhibited the proliferation while inducing significant apoptosis in MCF7 cells. Moreover, HAA2021 increased the chemosensitization of MCF7/ADR cells to doxorubicin, resulting in increased cytotoxicity/selectivity and apoptosis-inducing efficiency compared with the effect of doxorubicin or HAA2021 alone against MCF7/ADR cells. Molecular modeling showed that two molecules of HAA2021 bind to P-gp at the same time, causing P-gp inhibitory effect of the MDR efflux pump, and accumulation of Rhodamine-123 (Rho123) in MCF7/ADR cells. Furthermore, HAA2021 stably interacted with Hsp90α more efficiently compared with 17-N-allylamino-17-demethoxygeldanamycin (17-AAG), which was confirmed with the surface plasmon resonance (SPR) and molecular modeling studies. Additionally, HAA2021 showed multi-target effects via the inhibition of Hsp90 and nuclear factor kappa B (NF-𝜅B) proteins in MCF7 and MCF7/ADR cells. Results of real time-PCR also confirmed the synergistic co-inhibition of P-gp/Hsp90α genes in MCF7/ADR cells. Further pharmacokinetic and in vivo studies are warranted for HAA2021 to confirm its anticancer capabilities.

Discovery of Monoacylglycerol Lipase (MAGL) Inhibitors Based on a Pharmacophore-Guided Virtual Screening Study.

Monoacylglycerol lipase (MAGL) is an important enzyme of the endocannabinoid system that catalyzes the degradation of the major endocannabinoid 2-arachidonoylglycerol (2-AG). MAGL is associated with pathological conditions such as pain, inflammation and neurodegenerative diseases like Parkinson's and Alzheimer's disease. Furthermore, elevated levels of MAGL have been found in aggressive breast, ovarian and melanoma cancer cells. Due to its different potential therapeutic implications, MAGL is considered as a promising target for drug design and the discovery of novel small-molecule MAGL inhibitors is of great interest in the medicinal chemistry field. In this context, we developed a pharmacophore-based virtual screening protocol combined with molecular docking and molecular dynamics simulations, which showed a final hit rate of 50% validating the reliability of the in silico workflow and led to the identification of two promising and structurally different reversible MAGL inhibitors, VS1 and VS2. These ligands represent a valuable starting point for structure-based hit-optimization studies aimed at identifying new potent MAGL inhibitors.