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Small-molecule antivirals that prevent the replication of the SARS-CoV-2 virus by blocking the enzymatic activity of its main protease (Mpro) are and will be a tenet of pandemic preparedness. However, the peptidic nature of such compounds often precludes the design of compounds within favorable physical property ranges, limiting cellular activity. Here we describe the discovery of peptide aldehyde Mpro inhibitors with potent enzymatic and cellular antiviral activity. This structure-activity relationship (SAR) exploration was guided by the use of calculated hydration site thermodynamic maps (WaterMap) to drive potency via displacement of waters from high-energy sites. Thousands of diverse compounds were designed to target these high-energy hydration sites and then prioritized for synthesis by physics- and structure-based Free-Energy Perturbation (FEP+) simulations, which accurately predicted biochemical potencies. This approach ultimately led to the rapid discovery of lead compounds with unique SAR that exhibited potent enzymatic and cellular activity with excellent pan-coronavirus coverage., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: all authors are current or former employees of Takeda Pharmaceuticals, Inc, Schrödinger, Inc, or Treeline Biosciences, Inc. (see affiliations) and may own shares of stock and/or stock options in these companies., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Understanding the determinants of α-conotoxin (α-CTX) selectivity for different nicotinic acetylcholine receptor (nAChR) subtypes is a prerequisite for the design of tool compounds to study nAChRs. However, selectivity optimization of these small, disulfide-rich peptides is difficult not only because of an absence of α-CTX/nAChR co-structures but also because it is challenging to predict how a mutation to an α-CTX will alter its potency and selectivity. As a prototypical system to investigate selectivity, we employed the α-CTX LvIA that is 25-fold selective for the α3β2 nAChR over the related α3β4 nAChR subtype, which is a target for nicotine addiction. Using two-electrode voltage clamp electrophysiology, we identified LvIA[D11R] that is 2-fold selective for the α3β4 nAChR, reversing the subtype preference. This effect is specifically due to the change in charge and not shape of LvIA[D11R], as substitution of D11 with citrulline retains selectivity for the α3β2 nAChR. Furthermore, LvIA[D11K] shows a stronger reversal, with 4-fold selectivity for the α3β4 nAChR. Motivated by these findings, using site-directed mutagenesis, we found that β2[K79A] (I79 on β4), but not β2[K78A] (N78 on β4), largely restores the potency of basic mutants at position 11. Finally, to understand the structural basis of this effect, we used AlphaFold2 to generate models of LvIA in complex with both nAChR subtypes. Both models confirm the plausibility of an electrostatic mechanism to explain the data and also reproduce a broad range of potency and selectivity structure-activity relationships for LvIA mutants, as measured using free energy perturbation simulations. Our work highlights how electrostatic interactions can drive α-CTX selectivity and may serve as a strategy for optimizing the selectivity of LvIA and other α-CTXs.

The lead optimization stage of a drug discovery program generally involves the design, synthesis, and assaying of hundreds to thousands of compounds. The design phase is usually carried out via traditional medicinal chemistry approaches and/or structure-based drug design (SBDD) when suitable structural information is available. Two of the major limitations of this approach are (1) difficulty in rapidly designing potent molecules that adhere to myriad project criteria, or the multiparameter optimization (MPO) problem, and (2) the relatively small number of molecules explored compared to the vast size of chemical space. To address these limitations, we have developed AutoDesigner, a de novo design algorithm. AutoDesigner employs a cloud-native, multistage search algorithm to carry out successive rounds of chemical space exploration and filtering. Millions to billions of virtual molecules are explored and optimized while adhering to a customizable set of project criteria such as physicochemical properties and potency. Additionally, the algorithm only requires a single ligand with measurable affinity and a putative binding model as a starting point, making it amenable to the early stages of an SBDD project where limited data are available. To assess the effectiveness of AutoDesigner, we applied it to the design of novel inhibitors of d-amino acid oxidase (DAO), a target for the treatment of schizophrenia. AutoDesigner was able to generate and efficiently explore over 1 billion molecules to successfully address a variety of project goals. The compounds generated by AutoDesigner that were synthesized and assayed (1) simultaneously met not only physicochemical criteria, clearance, and central nervous system (CNS) penetration (Kp,uu) cutoffs but also potency thresholds and (2) fully utilize structural data to discover and explore novel interactions and a previously unexplored subpocket in the DAO active site. The reported data demonstrate that AutoDesigner can play a key role in accelerating the discovery of novel, potent chemical matter within the constraints of a given drug discovery lead optimization campaign.

Protein-protein interactions (PPIs) are ubiquitous biomolecular processes that are central to virtually all aspects of cellular function. Identifying small molecules that modulate specific disease-related PPIs is a strategy with enormous promise for drug discovery. The design of drugs to disrupt PPIs is challenging, however, because many potential drug-binding sites at PPI interfaces are "cryptic": When unoccupied by a ligand, cryptic sites are often flat and featureless, and thus not readily recognizable in crystal structures, with the geometric and chemical characteristics of typical small-molecule binding sites only emerging upon ligand binding. The rational design of small molecules to inhibit specific PPIs would benefit from a better understanding of how such molecules bind at PPI interfaces. To this end, we have conducted unbiased, all-atom MD simulations of the binding of four small-molecule inhibitors (SP4206 and three SP4206 analogs) to interleukin 2 (IL2)-which performs its function by forming a PPI with its receptor-without incorporating any prior structural information about the ligands' binding. In multiple binding events, a small molecule settled into a stable binding pose at the PPI interface of IL2, resulting in a protein-small-molecule binding site and pose virtually identical to that observed in an existing crystal structure of the IL2-SP4206 complex. Binding of the small molecule stabilized the IL2 binding groove, which when the small molecule was not bound emerged only transiently and incompletely. Moreover, free energy perturbation (FEP) calculations successfully distinguished between the native and non-native IL2-small-molecule binding poses found in the simulations, suggesting that binding simulations in combination with FEP may provide an effective tool for identifying cryptic binding sites and determining the binding poses of small molecules designed to disrupt PPI interfaces by binding to such sites., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: YS, VPM, AEL, ETK, and SS are former paid employees of D. E. Shaw Research; DES is the sole beneficial owner and Chief Scientist of D. E. Shaw Research.