Identification of Human Dihydroorotate Dehydrogenase Inhibitor by a Pharmacophore-Based Virtual Screening Study

Watermelon: setup and validation of an in silico fragment-based approach

We present a new computational approach, named Watermelon, designed for the development of pharmacophore models based on receptor structures. The methodology involves the sampling of potential hotspots for ligand interactions within a protein target's binding site, utilising molecular fragments as probes. By employing docking and molecular dynamics (MD) simulations, the most significant interactions formed by these probes within distinct regions of the binding site are identified. These interactions are subsequently transformed into pharmacophore features that delineates key anchoring sites for potential ligands. The reliability of the approach was experimentally validated using the monoacylglycerol lipase (MAGL) enzyme. The generated pharmacophore model captured features representing ligand-MAGL interactions observed in various X-ray co-crystal structures and was employed to screen a database of commercially available compounds, in combination with consensus docking and MD simulations. The screening successfully identified two new MAGL inhibitors with micromolar potency, thus confirming the reliability of the Watermelon approach.

Development of a machine learning-based target-specific scoring function for structure-based binding affinity prediction for human dihydroorotate dehydrogenase inhibitors

Human dihydroorotate dehydrogenase (hDHODH) is a flavin mononucleotide-dependent enzyme that can limit de novo pyrimidine synthesis, making it a therapeutic target for diseases such as autoimmune disorders and cancer. In this study, using the docking structures of complexes generated by AutoDock Vina, we integrate interaction features and ligand features, and employ support vector regression to develop a target-specific scoring function for hDHODH (TSSF-hDHODH). The Pearson correlation coefficient values of TSSF-hDHODH in the cross-validation and external validation are 0.86 and 0.74, respectively, both of which are far superior to those of classic scoring function AutoDock Vina and random forest (RF) based generic scoring function RF-Score. TSSF-hDHODH is further used for the virtual screening of potential inhibitors in the FDA-Approved & Pharmacopeia Drug Library. In conjunction with the results from molecular dynamics simulations, crizotinib is identified as a candidate for subsequent structural optimization. This study can be useful for the discovery of hDHODH inhibitors and the development of scoring functions for additional targets.

Discovery of a Novel Chemo-Type for TAAR1 Agonism via Molecular Modeling

The search for novel effective TAAR1 ligands continues to draw great attention due to the wide range of pharmacological applications related to TAAR1 targeting. Herein, molecular docking studies of known TAAR1 ligands, characterized by an oxazoline core, have been performed in order to identify novel promising chemo-types for the discovery of more active TAAR1 agonists. In particular, the oxazoline-based compound S18616 has been taken as a reference compound for the computational study, leading to the development of quite flat and conformationally locked ligands. The choice of a "Y-shape" conformation was suggested for the design of TAAR1 ligands, interacting with the protein cavity delimited by ASP103 and aromatic residues such as PHE186, PHE195, PHE268, and PHE267. The obtained results allowed us to preliminary in silico screen an in-house series of pyrimidinone-benzimidazoles (1a-10a) as a novel scaffold to target TAAR1. Combined ligand-based (LBCM) and structure based (SBCM) computational methods suggested the biological evaluation of compounds 1a-10a, leading to the identification of derivatives 1a-3a (hTAAR1 EC50 = 526.3-657.4 nM) as promising novel TAAR1 agonists.

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.

Discovery of Nanomolar Inhibitors for Human Dihydroorotate Dehydrogenase Using Structure-Based Drug Discovery Methods

We used a structure-based drug discovery approach to identify novel inhibitors of human dihydroorotate dehydrogenase (DHODH), which is a therapeutic target for treating cancer and autoimmune and inflammatory diseases. In the case of acute myeloid leukemia, no previously discovered DHODH inhibitors have yet succeeded in this clinical application. Thus, there remains a strong need for new inhibitors that could be used as alternatives to the current standard-of-care. Our goal was to identify novel inhibitors of DHODH. We implemented prefiltering steps to omit PAINS and Lipinski violators at the earliest stages of this project. This enriched compounds in the data set that had a higher potential of favorable oral druggability. Guided by Glide SP docking scores, we found 20 structurally unique compounds from the ChemBridge EXPRESS-pick library that inhibited DHODH with IC50, DHODH values between 91 nM and 2.7 μM. Ten of these compounds reduced MOLM-13 cell viability with IC50, MOLM-13 values between 2.3 and 50.6 μM. Compound 16 (IC50, DHODH = 91 nM) inhibited DHODH more potently than the known DHODH inhibitor, teriflunomide (IC50, DHODH = 130 nM), during biochemical characterizations and presented a promising scaffold for future hit-to-lead optimization efforts. Compound 17 (IC50, MOLM-13 = 2.3 μM) was most successful at reducing survival in MOLM-13 cell lines compared with our other hits. The discovered compounds represent excellent starting points for the development and optimization of novel DHODH inhibitors.

Discovery a series of novel inhibitors of human dihydroorotate dehydrogenase: Biological activity evaluation and molecular docking

Human dihydroorotate dehydrogenase (hDHODH) is a key enzyme that catalyzes the de novo synthesis of pyrimidine. In recent years, various studies have shown that inhibiting this enzyme can treat autoimmune diseases such as rheumatoid arthritis (RA) and cancer. This study designed and synthesized a series of novel thiazolidone hDHODH inhibitors. Through biological activity evaluation, Compound 14 was found to have high inhibitory activity, with an IC50 value reaching nanomolar level. Preliminary structure-activity relationship studies found that the carboxyl group in R1 and the naphthalene in R2 are key factors in improving activity. Through molecular docking, the binding mode between inhibitors and proteins was elucidated. This study provides an important reference for further optimizing hDHODH inhibitors.