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Peptide-activated G protein-coupled receptors (GPCRs) play crucial roles in numerous diseases, but remain difficult therapeutic targets due to the challenges in developing small-molecule drugs. Here, we explore structure-based strategies to identify small-molecule agonists of neurotensin (NTS) receptors, which hold promise for developing non-opioid analgesics. Chemical libraries of drug-like molecules are first designed based on a receptor-peptide complex, and then 14.5 million compounds are computationally docked to the orthosteric binding site of the NTS₁ receptor. A set of 39 top-ranked compounds is synthesized, and seven of these are experimentally confirmed to activate the NTS₁ receptor. Structure-guided optimization yields NTS₁ ligands with signaling signatures distinct from the endogenous peptide, and these compounds also exhibit high affinity for the NTS₂ receptor. High-resolution crystal structures of two agonists bound to the NTS₁ receptor confirm predicted binding modes and reveal key determinants of activation. In vivo, the compounds produce robust antinociception in rodents without inducing hypotension, consistent with a contribution of NTS₂ receptor activity. To facilitate broader application of our virtual screening approach to peptide-binding GPCRs, we provide access to tailored chemical libraries containing billions of readily synthesizable compounds.

Derivatives of natural amino acid hold promise as starting point for drug discovery. We synthesized five pyrimidine-containing derivatives of natural amino acids 1a-e using green condition and seven of their N-aryl amide adducts 2a-g using amide coupling. The synthesized compounds were screened for anticancer activity using MTT assay on HepG2 cell line. The results showed that fragment-like compounds 1a-c were more potent and reached low µM IC₅₀ of 18-25 µM. Moreover, the best compound 1a inhibited significantly 3D tumorsphere formation and cell migration. Docking studies of 1a-c suggest their HepG2 antiproliferative activity could be mediated via multikinase inhibition of p38α, VEGFR2 and FGFR-1. 1a-c is a good starting point to develop more potent compounds with excellent calculated physico chemical properties.

Allosteric modulation of G protein–coupled receptors (GPCRs) is an exciting strategy for developing new therapeutic agents, and it has several advantages over more commonly used orthosteric drugs. Recently determined GPCR structures have revealed allosteric pockets facing the lipid bilayer, enabling rational drug design. Here, we develop a virtual screening strategy to discover ligands of extrahelical binding pockets and apply this approach to the adenosine A₁ receptor (A₁R). The A₁R is a high-value therapeutic target for ischemia–reperfusion injury and chronic neuropathic pain. Developing effective A₁R therapeutics remains challenging due to high structural conservation across orthosteric binding sites and on-target unwanted effects stimulated by prototypical A₁R agonists, such as bradycardia and atrioventricular block. However, A₁R positive allosteric modulators (PAMs) acting through spatially distinct allosteric sites can fine-tune A₁R activity with high subtype selectivity and spatiotemporal specificity, thereby overcoming current limitations. A chemical library of 160 million compounds was computationally docked to the allosteric pocket identified in a cryo-EM structure of the A₁R, and a set of 26 top-ranked compounds were selected for experimental evaluation. Pharmacological evaluation of these, and structure-guided hit optimization, led to the discovery of subtype-selective A₁R PAMs. These compounds demonstrated minimal allosteric agonism and negligible impact on A₁R-mediated beat rate of an orthosteric agonist. The discovered PAMs pave the way for potential treatments for neuropathic pain and ischemia–reperfusion injury without accompanying side effects. Our results demonstrate the utility of a synergistic computational and experimental approach in GPCR drug discovery.

The orphan G protein-coupled receptor (GPCR) GPR139 attracts interest as a target for neuropsychiatric disorders. Whereas the physiological functions of GPR139 remain elusive, a high-resolution receptor structure is now available. To assess whether structural information enables ligand discovery, we computationally dock 235 million compounds to the GPR139 binding site. Of 68 top-ranked compounds evaluated experimentally, five are full agonists with potencies ranging from 160 nM to 3.6 µM. Structure-guided optimization identifies one of the most potent GPR139 agonists, and a cryo-EM structure of the receptor-ligand complex confirms the predicted binding mode. Functional characterization provides insights into GPR139 signalling, and one agonist elicits behavioural effects in mice. We also explore the potential to replace experimental structure determination with the deep-learning method AlphaFold3, revealing a limited capability of artificial intelligence to model receptor-ligand interactions for understudied GPCRs. The results demonstrate how high-resolution GPCR structures combined with large-library docking can accelerate drug discovery.

Fragment-based screening can catalyze drug discovery by identifying novel scaffolds, but this approach is limited by the small chemical libraries studied by biophysical experiments and the challenging hit optimization step. In efforts to identify DNA repair inhibitors, we explored the use of structure-based virtual screening to access ultralarge fragment libraries that cover four orders of magnitude larger fractions of chemical space than traditional techniques. A set of 14 million fragments were docked to 8-oxoguanine DNA glycosylase (OGG1), a challenging drug target involved in cancer and inflammation. Of the 29 top-ranked fragments that were experimentally evaluated, four compounds were shown to bind to OGG1 and X-ray crystallography confirmed the predicted binding modes. Docking of readily synthesizable elaborations guided fragment optimization, leading to the discovery of submicromolar OGG1 inhibitors with anti-inflammatory and anti-cancer effects in cell models. Our results demonstrate that fragment-based virtual screening enables efficient exploration of vast chemical libraries.

Artificial intelligence is revolutionizing protein structure prediction, providing unprecedented opportunities for drug design. To assess the potential impact on ligand discovery, we compared virtual screens using protein structures generated by the AlphaFold machine learning method and traditional homology modeling. More than 16 million compounds were docked to models of the trace amine-associated receptor 1 (TAAR1), a G protein-coupled receptor of unknown structure and target for treating neuropsychiatric disorders. Sets of 30 and 32 highly ranked compounds from the AlphaFold and homology model screens, respectively, were experimentally evaluated. Of these, 25 were TAAR1 agonists with potencies ranging from 12 to 0.03 mu M. The AlphaFold screen yielded a more than twofold higher hit rate (60%) than the homology model and discovered the most potent agonists. A TAAR1 agonist with a promising selectivity profile and drug-like properties showed physiological and antipsychotic-like effects in wild-type but not in TAAR1 knockout mice. These results demonstrate that AlphaFold structures can accelerate drug discovery.

The SARS-CoV-2 main protease, a vital enzyme for virus replication, is a potential target for developing drugs in COVID-19 treatment. Until now, three SARS-CoV-2 main protease inhibitors have been approved for COVID-19 treatment. This study explored the inhibitory potency of asymmetric imidazole-4,5-dicarboxamide derivatives against the SARS-CoV-2 main protease. Fourteen derivatives were designed based on the structure of the SARS-CoV-2 main protease active site, the hydrolysis mechanism, and the experience gained from the reported inhibitor structures. They were synthesized through a four-step procedure from benzimidazole and 2-methylbenzimidazole. SARS-CoV-2 main protease inhibition was evaluated in vitro by fluorogenic assay with lopinavir, ritonavir, and ebselen as positive references. N-(4-Chlorophenyl)-2-methyl-4-(morpholine-4-carbonyl)-1H-imidazole-5-carboxamide (5a2) exhibited the highest potency against the SARS-CoV-2 main protease with an IC₅₀ of 4.79 ± 1.37 μM relative to ebselen with an IC₅₀ of 0.04 ± 0.013 μM. Enzyme kinetic and molecular docking studies were carried out to clarify the inhibitory mechanism and to prove that the compound interacts at the active site. We also performed cytotoxicity assay to confirm that these compounds are not toxic to human cells.

Orally bioavailable, high molecular weight macrocyclic peptides that inhibit difficult-to-drug protein-protein interactions are of high therapeutic value, and rules for their design were proposed recently. Here, we emphasize the danger of rules that provide a false impression of the lipophilicity required of a clinical candidate.

Macrocycles are prominent among drugs for treatment of infectious disease, with many originating from natural products. Herein we report on the discovery of a series of macrocycles structurally related to the natural product hymenocardine. Members of this series were found to inhibit the growth of Plasmodium falciparum, the parasite responsible for most malaria cases, and of four kinetoplastid parasites. Notably, macrocycles more potent than miltefosine, the only oral drug used for the treatment of the neglected tropical disease visceral leishmaniasis, were identified in a phenotypic screen of Leishmania infantum. In vitro profiling highlighted that potent inhibitors had satisfactory cell permeability with a low efflux ratio, indicating their potential for oral administration, but low solubility and metabolic stability. Analysis of predicted crystal structures suggests that optimization should focus on the reduction of pi-pi crystal packing interactions to reduce the strong crystalline interactions and improve the solubility of the most potent lead.

Two series of N-(heteroaryl)thiophene sulfonamides, encompassing either a methylene imidazole group or a tertbutylimidazolylacetyl group in the meta position of the benzene ring, have been synthesized. An AT(2)R selective ligand with a Ki of 42 nM was identified in the first series and in the second series, six AT(2)R selective ligands with significantly improved binding affinities and Ki values of <5 nM were discovered. The binding modes to AT(2)R were explored by docking calculations combined with molecular dynamics simulations. Although some of the high affinity ligands exhibited fair stability in human liver microsomes, comparable to that observed with C21 undergoing clinical trials, most ligands displayed a very low metabolic stability with t(1/2) of less than 10 min in human liver microsomes. The most promising ligand, with an AT(2)R K-i value of 4.9 nM and with intermediate stability in human hepatocytes (t(1/2) = 77 min) caused a concentration-dependent vasorelaxation of pre-contracted mouse aorta.