Comprehensive 3D-QSAR Model Predicts Binding Affinity of Structurally Diverse Sigma 1 Receptor Ligands

Machine learning models to predict ligand binding affinity for the orexin 1 receptor

The orexin 1 receptor (OX1R) is a G-protein coupled receptor that regulates a variety of physiological processes through interactions with the neuropeptides orexin A and B. Selective OX1R antagonists exhibit therapeutic effects in preclinical models of several behavioral disorders, including drug seeking and overeating. However, currently there are no selective OX1R antagonists approved for clinical use, fueling demand for novel compounds that act at this target. In this study, we meticulously curated a dataset comprising over 1300 OX1R ligands using a stringent filter and criteria cascade. Subsequently, we developed highly predictive quantitative structure-activity relationship (QSAR) models employing the optimized hyper-parameters for the random forest machine learning algorithm and twelve 2D molecular descriptors selected by recursive feature elimination with a 5-fold cross-validation process. The predictive capacity of the QSAR model was further assessed using an external test set and enrichment study, confirming its high predictivity. The practical applicability of our final QSAR model was demonstrated through virtual screening of the DrugBank database. This revealed two FDA-approved drugs (isavuconazole and cabozantinib) as potential OX1R ligands, confirmed by radiolabeled OX1R binding assays. To our best knowledge, this study represents the first report of highly predictive QSAR models on a large comprehensive dataset of diverse OX1R ligands, which should prove useful for the discovery and design of new compounds targeting this receptor.

MCPNET: Development of an interpretable deep learning model based on multiple conformations of the compound for predicting developmental toxicity

The development of deep learning models for predicting toxicological endpoints has shown great promise, but one of the challenges in the field is the accuracy and interpretability of these models. The bioactive conformation of a compound plays a critical role for it to bind in the target. It is a big issue to figure out the bioactive conformation in deep learning without the co-crystal structure or highly precise molecular simulations. In this study, we developed a deep learning framework of Multi-Conformation Point Network (MCPNET) to construct classification and regression models, respectively, based on electrostatic potential distributions on vdW surfaces around multiple conformations of the compound using a dataset of compounds with developmental toxicity in zebrafish embryo. MCPNET applied 3D multi-conformational surface point cloud to extract the molecular features for model training, which may be critical for capturing the structural diversity of compounds. The models achieved an accuracy of 85 % on the classification task and R2 of 0.66 on the regression task, outperforming traditional machine learning models and other deep learning models. The key feature of our model is its interpretability with the component visualization to identify the factors contributing to the prediction and to understand the compound action mechanism. MCPNET may predict the conformation quietly close to the bioactive conformation of a compound by attention-based multi-conformation pooling mechanism. Our results demonstrated the potential of deep learning based on 3D molecular representations in accurately predicting developmental toxicity. The source code is publicly available at https://github.com/Superlit-CC/MCPNET.

Computer-Aided Drug Design towards New Psychotropic and Neurological Drugs

Central nervous system (CNS) disorders are a therapeutic area in drug discovery where demand for new treatments greatly exceeds approved treatment options. This is complicated by the high failure rate in late-stage clinical trials, resulting in exorbitant costs associated with bringing new CNS drugs to market. Computer-aided drug design (CADD) techniques minimise the time and cost burdens associated with drug research and development by ensuring an advantageous starting point for pre-clinical and clinical assessments. The key elements of CADD are divided into ligand-based and structure-based methods. Ligand-based methods encompass techniques including pharmacophore modelling and quantitative structure activity relationships (QSARs), which use the relationship between biological activity and chemical structure to ascertain suitable lead molecules. In contrast, structure-based methods use information about the binding site architecture from an established protein structure to select suitable molecules for further investigation. In recent years, deep learning techniques have been applied in drug design and present an exciting addition to CADD workflows. Despite the difficulties associated with CNS drug discovery, advances towards new pharmaceutical treatments continue to be made, and CADD has supported these findings. This review explores various CADD techniques and discusses applications in CNS drug discovery from 2018 to November 2022.

Deciphering the competitive inhibition of dihydropteroate synthase by 8 marcaptoguanine analogs: enhanced potency in phenylsulfonyl fragments

The emergence of sulfa-drug resistance and reduced efficacy of pterin-based analogs towards Dihydropteroate synthase (DHPS) inhibition dictate a pressing need of developing novel antimicrobial agents for immune-compromised patients. Recently, a series of 8-Marcaptoguanin (8-MG) derivatives synthesized for 6-Hydroxymethyl-7,8-dihydropterin pyrophosphokinase (experimental K_D ∼ 100-.0.36) showed remarkable homology with the pteroic-acid and serve as a template for product antagonism in DHPS. The present work integrates ligand-based drug discovery techniques with structure-based docking, enhanced MD simulation, and MM/PBSA techniques to demonstrate the essential features of 8-MG analogs which make it a potent inhibitor for DHPS. The delicate balance in hydrophilic, hydrophobic substitutions on the 8-MG core is the crucial signature for DHPS inhibition. It is found that the dynamic interactions of active compounds are mainly dominated by consistent hydrogen bonding network with Asp 96, Asn 115, Asp 185, Ser 222, Arg 255 and π-π stacking, π-cation interactions with Phe 190, Lys 221. Further, two new 8-MG compounds containing N-phenylacetamide (compound S1, ΔG_bind-eff = -62.03 kJ/mol) and phenylsulfonyl (compound S3, ΔG_bind-eff = -71.29 kJ/mol) fragments were found to be the most potent inhibitor of DHPS, which stabilize the flexible pABA binding loop, thereby increasing their binding affinity. MM/PBSA calculation shows electrostatic energy contribution to be the principal component in stabilizing the inhibitors in the binding pocket. This fact is further confirmed by the higher energy barrier obtained in umbrella sampling for this class of inhibitors.

Association of sigma-1 receptor with dopamine transporter attenuates the binding of methamphetamine via distinct helix-helix interactions

Dopamine transporter (DAT) and sigma-1 receptor (σ1R) are potential therapeutic targets to reduce the psychostimulant effects induced by methamphetamine (METH). Interaction of σ1R with DAT could modulate the binding of METH, but the molecular basis of the association of the two transmembrane proteins and how their interactions mediate the binding of METH to DAT or σ1R remain unclear. Here, we characterize the protein-ligand and protein-protein interactions at a molecular level by various theoretical approaches. The present results show that METH adopts a different binding pose in the binding pocket of σ1R and is more likely to act as an agonist. The relatively lower binding affinity of METH to σ1R supports the role of antagonists as inhibitors that protect against METH-induced effects. We demonstrate that σ1R could bind to Drosophila melanogaster DAT (dDAT) through interactions with either the transmembrane helix α12 or α5 of dDAT. Our results showed that the truncated σ1R displays stronger association with dDAT than the full-length σ1R. Although different helix-helix interactions between σ1R and dDAT lead to distinct effects on the dynamics of individual protein, both associations attenuate the binding affinity of METH to dDAT, particularly in the interactions with the helix α5 of dDAT. Together, the present study provides the first computational investigation on the molecular mechanism of coupling METH binding and the association of σ1R with dDAT.

Novel Sigma 1 Receptor Antagonists as Potential Therapeutics for Pain Management

The sigma 1 receptor (S1R) is a molecular chaperone protein located in the endoplasmic reticulum and plasma membranes and has been shown to play important roles in various pathological disorders including pain and, as recently discovered, COVID-19. Employing structure- and QSAR-based drug design strategies, we rationally designed, synthesized, and biologically evaluated a series of novel triazole-based S1R antagonists. Compound 10 exhibited potent binding affinity for S1R, high selectivity over S2R and 87 other human targets, acceptable in vitro metabolic stability, slow clearance in liver microsomes, and excellent blood-brain barrier permeability in rats. Further in vivo studies in rats showed that 10 exhibited negligible acute toxicity in the rotarod test and statistically significant analgesic effects in the formalin test for acute inflammatory pain and paclitaxel-induced neuropathic pain models during cancer chemotherapy. These encouraging results promote further development of our triazole-based S1R antagonists as novel treatments for pain of different etiologies.

Development of potential therapeutics for pain treatment by inducing Sigma 1 receptor antagonism – in silico approach

QSAR modeling with computer-aided drug design were used for the in silico development of novel therapeutics for pain treatment.

Virtual Screening for Ligand Discovery at the σ1 Receptor

The σ₁ receptor is a transmembrane protein implicated in several pathophysiological conditions, including neurodegenerative disease (J. Pharmacol. Sci.2015127 (1), 1729), drug addiction (Behav. Pharmacol.201627 (2-3 Spec Issue), 10015), cancer (Handb. Exp. Pharmacol.2017244237308), and pain (Neural Regener. Res.201813 (5), 775778). However, there are no high-throughput functional assays for σ₁ receptor drug discovery. Here, we assessed high-throughput structure-based computational docking for discovery of novel ligands of the σ₁ receptor. We screened a library of over 6 million compounds using the Schrödinger Glide package, followed by experimental characterization of top-scoring candidates. 77% of tested candidates bound σ₁ with high affinity (K_D < 1 μM). These include compounds with high selectivity for the σ₁ receptor compared to the genetically unrelated but pharmacologically similar σ₂ receptor, as well as compounds with substantial crossreactivity between the two receptors. These results establish structure-based virtual screening as a highly effective platform for σ₁ receptor ligand discovery and provide compounds to prioritize in studies of σ₁ biology.

A New Pharmacophore Model for the Design of Sigma-1 Ligands Validated on a Large Experimental Dataset

The recent publication of the σ1R crystal structure is an important cornerstone for the derivation of more accurate activity prediction models. We report here a comparative study involving a set of more than 25,000 structures from our internal database that had been screened for σ1R affinity. Using the recently published crystal structure, 5HK1, two new pharmacophore models were generated. The first one, 5HK1-Ph.A, was obtained by an algorithm that identifies the most important receptor-ligand interactions including volume restrictions enforced by the atomic structure of the recognition site. The second, 5HK1-Ph.B, resulted from a manual edition of the first one by the fusion of two hydrophobic (HYD) features. Finally, we also docked the database using a high throughput docking technique and scored the resulting poses with seven different scoring functions. Statistical performance measures were obtained for the two models, comparing them with previously published σ1R pharmacophores (Hit Rate, sensitivity, specificity, and Receiver Operator Characteristic) and 5HK1-Ph.B emerged as the best one in discriminating between active and inactive compounds, with a ROC-AUC value above 0.8 and enrichment values above 3 at different fractions of screened samples. 5HK1-Ph.B also showed better results than the direct docking, which may be due to the rigidity of the crystal structure in the docking process (i.e., feature tolerances in the pharmacophore model). Additionally, the impact of the HYD interactions and the penalty for desolvating ligands with polar atoms may be not adequately captured by scoring functions, whereas HYD groups filling up such regions of the binding site are entailed in the pharmacophore model. Altogether, using annotated data from a large and diverse compound collection together with crystal structure information provides a sound basis for the generation and validation of predictive models to design new molecules.