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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.

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/.

In recent years, human dihydroorotate dehydrogenase inhibitors have been associated with acute myelogenous leukemia as well as studied as potent host targeting antivirals. Starting from MEDS433 (IC 50 1.2 nM), we kept improving the structure-activity relationship of this class of compounds characterized by 2-hydroxypyrazolo[1,5- a ]pyridine scaffold. Using an in silico/crystallography supported design, we identified compound 4 (IC 50 7.2 nM), characterized by the presence of a decorated aryloxyaryl moiety that replaced the biphenyl scaffold, with potent inhibition and pro-differentiating abilities on AML THP1 cells (EC 50 74 nM), superior to those of brequinar (EC 50 249 nM) and boosted when in combination with dipyridamole. Finally, compound 4 has an extremely low cytotoxicity on non-AML cells as well as MEDS433; it has shown a significant antileukemic activity in vivo in a xenograft mouse model of AML.

Two series of saccharin/isoxazole and saccharin/isoxazoline hybrids were synthesized by 1,3-dipolar cycloaddition. The new compounds showed to be endowed with potent and selective inhibitory activity against the cancer-related human carbonic anhydrase (hCA) IX and XII isoforms in the nanomolar range, while no affinity was encountered for off-targets, such as hCA I and II. Successive enantioseparation on a milligram scale of the most representative compounds led to the discovery that (S)-isomers were more potent than their corresponding (R)-enantiomers. Lastly, molecular modeling studies were conducted to define those structural requirements that were responsible for the discrimination among selected human isoforms of carbonic anhydrases. Two nanomolar hCA IX and XII inhibitors were also screened for their selective toxicity against non tumoral primary cells (fibroblasts) and against a breast adenocarcinoma cell line (MCF7) in hypoxic environment. The efficacious combination of these compounds with doxorubicin on MCF7 cells was demonstrated after 72 h of treatment.

Monoacylglycerol lipase (MAGL) is the enzyme degrading the endocannabinoid 2-arachidonoylglycerol, and it is involved in several physiological and pathological processes. The therapeutic potential of MAGL is linked to several diseases, including cancer. The development of MAGL inhibitors has been greatly limited by the side effects associated with the prolonged MAGL inactivation. Importantly, it could be preferable to use reversible MAGL inhibitors in vivo, but nowadays only few reversible compounds have been developed. In the present study, structural optimization of a previously developed class of MAGL inhibitors led to the identification of compound 23, which proved to be a very potent reversible MAGL inhibitor (IC 50 = 80 nM), selective for MAGL over the other main components of the endocannabinoid system, endowed of a promising antiproliferative activity in a series of cancer cell lines and able to block MAGL both in cell-based as well as in vivo assays.

STARD3 is a cellular protein that represents an attractive target for cancer therapy, being overexpressed in breast cancer and implied in the development of colorectal, gastric, and prostate cancers. Unfortunately, no STARD3 inhibitor has been identified yet. In this work, an in silico strategy was applied to predict a reliable binding mode of cholesterol into STARD3 and to develop a pharmacophore-based virtual screening protocol that allowed the identification of the first STARD3 inhibitor ever reported. The identified compound VS1 binds STARD3 with micromolar affinity (IC 50 = 35 μM) and shows antiproliferative activity in breast (MCF7 and MDA- MB-231) and colon (HCT-116) cancer cell lines in the same concentration range (IC 50 = 49.7-105.5 μM). Although VS1 has a moderate potency, we demonstrated that it specifically targets STARD3 in the cells and induces its degradation. Overall, the results confirm the reliability of the computational strategies herein applied and the identification of the first hit compound for the development of novel potent STARD3 inhibitors., Competing Interests: The authors declare no competing financial interest.

Molecular docking strategies are one of the most widely used techniques for predicting the binding mode of a ligand and for obtaining new hits in virtual screening studies. In order to improve the accuracy of this approach, we tested the reliability of applying a consensus docking protocol by combining ten different docking procedures. The analysis was carried out in terms of consensus cross-docking and by using an enriched database. The results highlight that from a qualitative point of view consensus docking is able to predict the ligand binding pose better than the single docking programs and is also able to give hints concerning the reliability of the docking pose. With regard to the virtual screening studies, consensus docking was evaluated for three different targets of the Directory of Useful Decoys (DUD), and the obtained results suggest that this approach performs as well as the best available methods found in the literature, therefore supporting the idea that this procedure can be profitably applied for the identification of new hits.

The abnormal activity of Fyn tyrosine kinase has been shown to be related to various human cancers. Furthermore, its involvement in signaling pathways that lead to severe pathologies, such as Alzheimer's and Parkinson's diseases, has also been demonstrated, thus making Fyn an attractive target for the discovery of potential novel therapeutics for brain pathologies and tumors. In this study we evaluated the reliability of various screening approaches based on the FLAP software. By the application of the best procedure, the virtual screening workflow was used to filter the Gold and Platinum database from Asinex to identify new Fyn inhibitors. Enzymatic assays revealed that among the eight top-scoring compounds five proved to efficiently inhibit Fyn activity with IC50 values in the micromolar range. These results demonstrate the validity of the methodologies we followed. Furthermore, the five active compounds herein described may be considered as interesting leads for the development of new and more efficient Fyn inhibitors.