Discovery of a Novel Class of d-Amino Acid Oxidase Inhibitors Using the Schrödinger Computational Platform.

d-Serine is a coagonist of the N-methyl d-aspartate (NMDA) receptor, a key excitatory neurotransmitter receptor. In the brain, d-serine is synthesized from its l-isomer by serine racemase and is metabolized by the D-amino acid oxidase (DAO, DAAO). Many studies have linked decreased d-serine concentration and/or increased DAO expression and enzyme activity to NMDA dysfunction and schizophrenia. Thus, it is feasible to employ DAO inhibitors for the treatment of schizophrenia and other indications. Powered by the Schrödinger computational modeling platform, we initiated a research program to identify novel DAO inhibitors with the best-in-class properties. The program execution leveraged an hDAO FEP+ model to prospectively predict compound potency. A new class of DAO inhibitors with desirable properties has been discovered from this endeavor. Our modeling technology on this program has not only enhanced the efficiency of structure-activity relationship development but also helped to identify a previously unexplored subpocket for further optimization.

OPLS4: Improving Force Field Accuracy on Challenging Regimes of Chemical Space.

We report on the development and validation of the OPLS4 force field. OPLS4 builds upon our previous work with OPLS3e to improve model accuracy on challenging regimes of drug-like chemical space that includes molecular ions and sulfur-containing moieties. A novel parametrization strategy for charged species, which can be extended to other systems, is introduced. OPLS4 leads to improved accuracy on benchmarks that assess small-molecule solvation and protein-ligand binding.

Reaction-Based Enumeration, Active Learning, and Free Energy Calculations To Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin-Dependent Kinase 2 Inhibitors.

The hit-to-lead and lead optimization processes usually involve the design, synthesis, and profiling of thousands of analogs prior to clinical candidate nomination. A hit finding campaign may begin with a virtual screen that explores millions of compounds, if not more. However, this scale of computational profiling is not frequently performed in the hit-to-lead or lead optimization phases of drug discovery. This is likely due to the lack of appropriate computational tools to generate synthetically tractable lead-like compounds in silico, and a lack of computational methods to accurately profile compounds prospectively on a large scale. Recent advances in computational power and methods provide the ability to profile much larger libraries of ligands than previously possible. Herein, we report a new computational technique, referred to as "PathFinder", that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. In this work, the integration of PathFinder-driven compound generation, cloud-based FEP simulations, and active learning are used to rapidly optimize R-groups, and generate new cores for inhibitors of cyclin-dependent kinase 2 (CDK2). Using this approach, we explored >300 000 ideas, performed >5000 FEP simulations, and identified >100 ligands with a predicted IC50 < 100 nM, including four unique cores. To our knowledge, this is the largest set of FEP calculations disclosed in the literature to date. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

OPLS3e: Extending Force Field Coverage for Drug-Like Small Molecules.

Building upon the OPLS3 force field we report on an enhanced model, OPLS3e, that further extends its coverage of medicinally relevant chemical space by addressing limitations in chemotype transferability. OPLS3e accomplishes this by incorporating new parameter types that recognize moieties with greater chemical specificity and integrating an on-the-fly parametrization approach to the assignment of partial charges. As a consequence, OPLS3e leads to greater accuracy against performance benchmarks that assess small molecule conformational propensities, solvation, and protein-ligand binding.

OPLS3: A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins.

The parametrization and validation of the OPLS3 force field for small molecules and proteins are reported. Enhancements with respect to the previous version (OPLS2.1) include the addition of off-atom charge sites to represent halogen bonding and aryl nitrogen lone pairs as well as a complete refit of peptide dihedral parameters to better model the native structure of proteins. To adequately cover medicinal chemical space, OPLS3 employs over an order of magnitude more reference data and associated parameter types relative to other commonly used small molecule force fields (e.g., MMFF and OPLS_2005). As a consequence, OPLS3 achieves a high level of accuracy across performance benchmarks that assess small molecule conformational propensities and solvation. The newly fitted peptide dihedrals lead to significant improvements in the representation of secondary structure elements in simulated peptides and native structure stability over a number of proteins. Together, the improvements made to both the small molecule and protein force field lead to a high level of accuracy in predicting protein-ligand binding measured over a wide range of targets and ligands (less than 1 kcal/mol RMS error) representing a 30% improvement over earlier variants of the OPLS force field.

Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field.

Designing tight-binding ligands is a primary objective of small-molecule drug discovery. Over the past few decades, free-energy calculations have benefited from improved force fields and sampling algorithms, as well as the advent of low-cost parallel computing. However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (∼5× in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread commercial application of free-energy simulations has been limited due to the lack of large-scale validation coupled with the technical challenges traditionally associated with running these types of calculations. Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes and ligands, with retrospective results encompassing 200 ligands and a wide variety of chemical perturbations, many of which involve significant changes in ligand chemical structures. In addition, we have applied the method in prospective drug discovery projects and found a significant improvement in the quality of the compounds synthesized that have been predicted to be potent. Compounds predicted to be potent by this approach have a substantial reduction in false positives relative to compounds synthesized on the basis of other computational or medicinal chemistry approaches. Furthermore, the results are consistent with those obtained from our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.

Illuminating HIV gp120-Ligand Recognition through Computationally-Driven Optimization of Antibody-Recruiting Molecules.

Here we report on the structure-based optimization of antibody-recruiting molecules targeting HIV gp120 (ARM-H). These studies have leveraged a combination of medicinal chemistry, biochemical and cellular assay analysis, and computation. Our findings have afforded an optimized analog of ARM-H, which is ~1000 fold more potent in gp120-binding and MT-2 antiviral assays than our previously reported derivative. Furthermore, computational analysis, taken together with experimental data, provides evidence that azaindole- and indole-based attachment inhibitors bind gp120 at an accessory hydrophobic pocket beneath the CD4-binding site and can also adopt multiple unique binding modes in interacting with gp120. These results are likely to prove highly enabling in the development of novel HIV attachment inhibitors, and more broadly, they suggest novel applications for ARMs as probes of conformationally flexible systems.

Virtual screening reveals allosteric inhibitors of the Toxoplasma gondii thymidylate synthase-dihydrofolate reductase.

The parasite Toxoplasma gondii can lead to toxoplasmosis in those who are immunocompromised. To combat the infection, the enzyme responsible for nucleotide synthesis thymidylate synthase-dihydrofolate reductase (TS-DHFR) is a suitable drug target. We have used virtual screening to determine novel allosteric inhibitors at the interface between the two TS domains. Selected compounds from virtual screening inhibited TS activity. Thus, these results show that allosteric inhibition by small drug-like molecules can occur in T. gondii TS-DHFR and pave the way for new and potent species-specific inhibitors.

Characterization of biaryl torsional energetics and its treatment in OPLS all-atom force fields.

The frequency of biaryl substructures in a database of approved oral drugs has been analyzed. This led to designation of 20 prototypical biaryls plus 10 arylpyridinones for parametrization in the OPLS all-atom force fields. Bond stretching, angle-bending, and torsional parameters were developed to reproduce the MP2 geometries and torsional energy profiles. The transferability of the new parameters was tested through their application to three additional biaryls. The torsional energetics for the 33 biaryl molecules are analyzed and factors leading to preferences for planar and nonplanar geometries are identified. For liquid biphenyl, the computed density and heat of vaporization at the boiling point (255 °C) are also reported.

Virtual screening and optimization yield low-nanomolar inhibitors of the tautomerase activity of Plasmodium falciparum macrophage migration inhibitory factor.

The Plasmodium falciparum orthologue of the human cytokine, macrophage migratory inhibitory factor (PfMIF), is produced by the parasite during malaria infection and modulates the host's immune response. As for other MIF orthologues, PfMIF has tautomerase activity, whose inhibition may influence the cytokine activity. To identify small-molecule inhibitors of the tautomerase activity of PfMIF, virtual screening has been performed by docking 2.1 million compounds into the enzymatic site. Assaying of 17 compounds identified four as active. Substructure search for the most potent of these compounds, a 4-phenoxypyridine analogue, identified four additional compounds that were purchased and also shown to be active. Thirty-one additional analogues were then designed, synthesized, and assayed. Three were found to be potent PfMIF tautomerase inhibitors with K(i) of ∼40 nM; they are also highly selective with K(i) > 100 µM for human MIF.

Pre-clinical pharmacokinetics and anti-chlamydial activity of salicylidene acylhydrazide inhibitors of bacterial type III secretion.

Salicylidene acylhydrazides belong to a class of compounds shown to inhibit bacterial type III secretion (T3S) in pathogenic Gram-negative bacteria. This class of compounds also inhibits growth and replication of Chlamydiae, strict intracellular bacteria that possess a T3S system. In this study a library of 58 salicylidene acylhydrazides was screened to identify inhibitors of Chlamydia growth. Compounds inhibiting growth of both Chlamydia trachomatis and Chlamydophila pneumoniae were tested for cell toxicity and seven compounds were selected for preliminary pharmacokinetic analysis in mice using cassette dosing. Two compounds, ME0177 and ME0192, were further investigated by individual pharmacokinetic analysis. Compound ME0177 had a relatively high peak plasma concentration (C(max)) and area under curve and therefore may be considered for systemic treatment of Chlamydia infections. The other compound, ME0192, had poor pharmacokinetic properties but the highest anti-chlamydial activity in vitro and therefore was tested for topical treatment in a mouse vaginal infection model. ME0192 administered vaginally significantly reduced the infectious burden of C. trachomatis and the number of infected mice.

Synthesis of [4-(2-hydroxyphenyl)thiazol-2-yl]methanones as potential bioisosteres of salicylidene acylhydrazides.

A focused library of [4-(2-hydroxyphenyl)thiazol-2-yl]methanones was prepared in a four-step synthesis with the aim to obtain potent inhibitors of type III secretion in Gram-negative bacteria. The compounds are potential bioisosteres of salicylidene acylhydrazides that are a known class of type III secretion inhibitors.

Synthesis of 2-(2-aminopyrimidine)-2,2-difluoroethanols as potential bioisosters of salicylidene acylhydrazides.

Salicylidene acylhydrazides are inhibitors of type III secretion in several gram-negative pathogens. To further develop the salicylidene acylhydrazides, scaffold hopping was applied to replace the core fragment of the compounds. The novel 2-(2-amino-pyrimidine)-2,2-difluoroethanol scaffold was identified as a possible analog to the salicylidene acylhydrazide core structure. The synthesis of a library of 2-(2-amino-pyrimidine)-2,2-difluoro-ethanols is described in this paper.

Statistical molecular design of a focused salicylidene acylhydrazide library and multivariate QSAR of inhibition of type III secretion in the Gram-negative bacterium Yersinia.

A combined application of statistical molecular design (SMD), quantitative structure-activity relationship (QSAR) modeling and prediction of new active compounds was effectively used to develop salicylidene acylhydrazides as inhibitors of type III secretion (T3S) in the Gram-negative pathogen Yersinia pseudotuberculosis. SMD and subsequent synthesis furnished 50 salicylidene acylhydrazides in high purity. Based on data from biological evaluation in T3S linked assays 18 compounds were classified as active and 25 compounds showed a dose-dependent inhibition. The 25 compounds were used to compute two multivariate QSAR models and two multivariate discriminant analysis models were computed from both active and inactive compounds. Three of the models were used to predict 4416 virtual compounds in consensus and eight new compounds were selected as an external test set. Synthesis and biological evaluation of the test set in Y. pseudotuberculosis and the intracellular pathogen Chlamydia trachomatis validated the models. Y. pseudotuberculosis and C. trachomatis cell-based infection models showed that compounds identified in this study are selective and non-toxic inhibitors of T3S dependent virulence.

Design, synthesis, and multivariate quantitative structure-activity relationship of salicylanilides--potent inhibitors of type III secretion in Yersinia.

Analogues to the salicylanilide N-(4-Chlorophenyl)-2-acetoxy-3,5-diiodobenzamide, 1a, an inhibitor of type III secretion (T3S) in Yersinia, were selected, synthesized, and biologically evaluated in three cycles. First, a set of analogues with variations in the salicylic acid ring moiety was synthesized to probe possible structural variation. A basic structure-activity relationship was established and then used to cherry-pick compounds from a principal component analysis score plot of salicylanilides to generate a second set. A third set with increased likelihood of biological activity was designed using D-optimal onion design. A quantitative structure-activity relationship model using hierarchical partial least-square regression to latent structures (Hi-PLS) was computed using PLS score vectors of building blocks correlated to the % inhibition of T3S as a response. A PLS discriminant analysis (PLS-DA) model was derived using the same descriptor set as that for the Hi-PLS model. Both models were validated with an external test set.