Structure-based virtual screening of vast chemical space as a starting point for drug discovery.

Structure-based virtual screening aims to find molecules forming favorable interactions with a biological macromolecule using computational models of complexes. The recent surge of commercially available chemical space provides the opportunity to search for ligands of therapeutic targets among billions of compounds. This review offers a compact overview of structure-based virtual screens of vast chemical spaces, highlighting successful applications in early drug discovery for therapeutically important targets such as G protein-coupled receptors and viral enzymes. Emphasis is placed on strategies to explore ultra-large chemical libraries and synergies with emerging machine learning techniques. The current opportunities and future challenges of virtual screening are discussed, indicating that this approach will play an important role in the next-generation drug discovery pipeline.

Structure-based virtual screening discovers potent and selective adenosine A1 receptor antagonists.

Development of subtype-selective leads is essential in drug discovery campaigns targeting G protein-coupled receptors (GPCRs). Herein, a structure-based virtual screening approach to rationally design subtype-selective ligands was applied to the A1 and A2A adenosine receptors (A1R and A2AR). Crystal structures of these closely related subtypes revealed a non-conserved subpocket in the binding sites that could be exploited to identify A1R selective ligands. A library of 4.6 million compounds was screened computationally against both receptors using molecular docking and 20 A1R selective ligands were predicted. Of these, seven antagonized the A1R with micromolar activities and several compounds displayed slight selectivity for this subtype. Twenty-seven analogs of two discovered scaffolds were designed, resulting in antagonists with nanomolar potency and up to 76-fold A1R-selectivity. Our results show the potential of structure-based virtual screening to guide discovery and optimization of subtype-selective ligands, which could facilitate the development of safer drugs.

Design of Drug Efficacy Guided by Free Energy Simulations of the ß2 -Adrenoceptor.

G-protein-coupled receptors (GPCRs) play important roles in physiological processes and are modulated by drugs that either activate or block signaling. Rational design of the pharmacological efficacy profiles of GPCR ligands could enable the development of more efficient drugs, but is challenging even if high-resolution receptor structures are available. We performed molecular dynamics simulations of the ß2 adrenergic receptor in active and inactive conformations to assess if binding free energy calculations can predict differences in ligand efficacy for closely related compounds. Previously identified ligands were successfully classified into groups with comparable efficacy profiles based on the calculated shift in ligand affinity upon activation. A series of ligands were then predicted and synthesized, leading to the discovery of partial agonists with nanomolar potencies and novel scaffolds. Our results demonstrate that free energy simulations enable design of ligand efficacy and the same approach can be applied to other GPCR drug targets.

Ultralarge Virtual Screening Identifies SARS-CoV-2 Main Protease Inhibitors with Broad-Spectrum Activity against Coronaviruses.

Drugs targeting SARS-CoV-2 could have saved millions of lives during the COVID-19 pandemic, and it is now crucial to develop inhibitors of coronavirus replication in preparation for future outbreaks. We explored two virtual screening strategies to find inhibitors of the SARS-CoV-2 main protease in ultralarge chemical libraries. First, structure-based docking was used to screen a diverse library of 235 million virtual compounds against the active site. One hundred top-ranked compounds were tested in binding and enzymatic assays. Second, a fragment discovered by crystallographic screening was optimized guided by docking of millions of elaborated molecules and experimental testing of 93 compounds. Three inhibitors were identified in the first library screen, and five of the selected fragment elaborations showed inhibitory effects. Crystal structures of target-inhibitor complexes confirmed docking predictions and guided hit-to-lead optimization, resulting in a noncovalent main protease inhibitor with nanomolar affinity, a promising in vitro pharmacokinetic profile, and broad-spectrum antiviral effect in infected cells.

Importance of Binding Site Hydration and Flexibility Revealed When Optimizing a Macrocyclic Inhibitor of the Keap1-Nrf2 Protein-Protein Interaction.

Upregulation of the transcription factor Nrf2 by inhibition of the interaction with its negative regulator Keap1 constitutes an opportunity for the treatment of disease caused by oxidative stress. We report a structurally unique series of nanomolar Keap1 inhibitors obtained from a natural product-derived macrocyclic lead. Initial exploration of the structure-activity relationship of the lead, followed by structure-guided optimization, resulted in a 100-fold improvement in inhibitory potency. The macrocyclic core of the nanomolar inhibitors positions three pharmacophore units for productive interactions with key residues of Keap1, including R415, R483, and Y572. Ligand optimization resulted in the displacement of a coordinated water molecule from the Keap1 binding site and a significantly altered thermodynamic profile. In addition, minor reorganizations of R415 and R483 were accompanied by major differences in affinity between ligands. This study therefore indicates the importance of accounting both for the hydration and flexibility of the Keap1 binding site when designing high-affinity ligands.

A practical guide to large-scale docking.

Structure-based docking screens of large compound libraries have become common in early drug and probe discovery. As computer efficiency has improved and compound libraries have grown, the ability to screen hundreds of millions, and even billions, of compounds has become feasible for modest-sized computer clusters. This allows the rapid and cost-effective exploration and categorization of vast chemical space into a subset enriched with potential hits for a given target. To accomplish this goal at speed, approximations are used that result in undersampling of possible configurations and inaccurate predictions of absolute binding energies. Accordingly, it is important to establish controls, as are common in other fields, to enhance the likelihood of success in spite of these challenges. Here we outline best practices and control docking calculations that help evaluate docking parameters for a given target prior to undertaking a large-scale prospective screen, with exemplification in one particular target, the melatonin receptor, where following this procedure led to direct docking hits with activities in the subnanomolar range. Additional controls are suggested to ensure specific activity for experimentally validated hit compounds. These guidelines should be useful regardless of the docking software used. Docking software described in the outlined protocol (DOCK3.7) is made freely available for academic research to explore new hits for a range of targets.

Docking Finds GPCR Ligands in Dark Chemical Matter.

High-throughput screening has revealed dark chemical matter, a set of drug-like compounds that has never shown bioactivity despite being extensively assayed. If dark molecules are found active at a therapeutic target, their extraordinary selectivity profiles make excellent starting points for drug development. We explored if ligands of therapeutically relevant G-protein-coupled receptors could be discovered by structure-based virtual screening of the dark chemical matter. Molecular docking screens against crystal structures of the A2A adenosine and the D4 dopamine receptors were carried out, and 53 top-ranked molecules were evaluated experimentally. Two ligands of each receptor were discovered, and the most potent had sub-micromolar affinities. Analysis of bioactivity data showed that the ligands lacked activity at hundreds of off-targets, including several that are associated with adverse effects. Our results demonstrate that virtual screening provides an efficient means to mine the dark chemical space, which could contribute to development of drugs with improved safety profiles.