POSIT: Flexible Shape-Guided Docking For Pose Prediction.

We present a new approach to structure-based drug design (POSIT) rigorously built on the simple concept that pose prediction is intimately coupled to the quality and availability of experimental structural data. We demonstrate the feasibility of the approach by performing retrospective analyses on three data sets designed to explore the strengths and weaknesses of POSIT relative to existing methods. We then present results documenting 2.5 years of prospective use of POSIT across a variety of structure-based industrial drug-discovery research projects. We find that POSIT is well-suited to guiding research decision making for structure-based design and, in particular, excels at enabling lead-optimization campaigns. We show that the POSIT framework can drive superior pose-prediction performance and generate results that naturally lend themselves to prospective decision making during lead optimization. We believe the results presented here are (1) the largest prospective validation of a pose prediction method reported to date (71 crystal structures); (2) provide an unprecedented look at the scope of impact of a computational tool; and (3) represent a first-of-its-kind analysis. We hope that this work inspires additional studies that look at the real impact and performance of computational research tools on prospective drug design.

ALOHA: a novel probability fusion approach for scoring multi-parameter drug-likeness during the lead optimization stage of drug discovery.

Automated lead optimization helper application (ALOHA) is a novel fitness scoring approach for small molecule lead optimization. ALOHA employs a series of generalized Bayesian models trained from public and proprietary pharmacokinetic, absorption, distribution, metabolism, and excretion, and toxicology data to determine regions of chemical space that are likely to have excellent drug-like properties. The input to ALOHA is a list of molecules, and the output is a set of individual probabilities as well as an overall probability that each of the molecules will pass a panel of user selected assays. In addition to providing a summary of how and when to apply ALOHA, this paper will discuss the validation of ALOHA's Bayesian models and probability fusion approach. Most notably, ALOHA is demonstrated to discriminate between members of the same chemical series with strong statistical significance, suggesting that ALOHA can be used effectively to select compound candidates for synthesis and progression at the lead optimization stage of drug discovery.

Frozen out: molecular modeling in the age of cryocrystallography.

As molecular modellers we need to remember that the flexibility of a protein is necessary for it to function. Unfortunately, this flexibility is not readily apparent from the seductive molecular graphics rendering of cryocrystallographic results.

Crystal structures of human adenosine kinase inhibitor complexes reveal two distinct binding modes.

Adenosine kinase (AK) is an enzyme responsible for converting endogenous adenosine (ADO) to adenosine monophosphate (AMP) in an adenosine triphosphate- (ATP-) dependent manner. The structure of AK consists of two domains, the first a large alpha/beta Rossmann-like nucleotide binding domain that forms the ATP binding site, and a smaller mixed alpha/beta domain, which, in combination with the larger domain, forms the ADO binding site and the site of phosphoryl transfer. AK inhibitors have been under investigation as antinociceptive, antiinflammatory, and anticonvulsant as well as antiinfective agents. In this work, we report the structures of AK in complex with two classes of inhibitors: the first, ADO-like, and the second, a novel alkynylpyrimidine series. The two classes of structures, which contain structurally similar substituents, reveal distinct binding modes in which the AK structure accommodates the inhibitor classes by a 30 degrees rotation of the small domain relative to the large domain. This change in binding mode stabilizes an open and a closed intermediate structural state and provide structural insight into the transition required for catalysis. This results in a significant rearrangement of both the protein active site and the orientation of the alkynylpyrimidine ligand when compared to the observed orientation of nucleosidic inhibitors or substrates.

Crystallography, NMR and virtual screening: integrated tools for drug discovery.

Crystallography, nuclear magnetic resonance and virtual ligand screening have become common tools in structural approaches to drug discovery. Appropriately used, these techniques are highly complementary and synergistic, significantly enhancing the pace of the discovery process and the quality of compounds selected for further development. The integration of these discovery tools will be discussed, and examples in which the combination of these technologies has impacted on the drug discovery cycle will be highlighted.

NMR-driven discovery of benzoylanthranilic acid inhibitors of far upstream element binding protein binding to the human oncogene c-myc promoter.

Reversal of aberrant gene expression that is induced by the proto-oncogene c-myc is likely to be effective for treating a variety of tumors that rely on this pathway for growth. One strategy to down-regulate the c-myc pathway is to target transcription factors that regulate its own expression. A host of proteins act in coordination to regulate c-myc expression and any one of them are theoretical targets for small-molecule therapy. Experimentally, it has been shown that the far upstream element (FUSE) binding protein (FBP) is essential for c-myc expression, and reductions in FBP levels both reduce c-myc expression and correlate with slower cell growth. FBP binds to ssDNA by capturing exposed DNA bases in a hydrophobic pocket. This suggests that a small molecule could be designed to occupy this pocket and inhibit FBP function. Using a variety of screening methodologies, we have identified ligands that bind to the DNA binding pockets of the KH domains of FBP. Gel shift analyses using full length FBP and a related transcription factor confirm that a small-molecule lead compound inhibits DNA binding in a specific manner. The benzoylanthranilic acid compounds described here represent leads in the design of FBP inhibitors that can serve as useful tools in the study of c-myc regulation and in the development of therapeutics that target the c-myc pathway.

A unified, probabilistic framework for structure- and ligand-based virtual screening.

We present a probabilistic framework for interpreting structure-based virtual screening that returns a quantitative likelihood of observing bioactivity and can be quantitatively combined with ligand-based screening methods to yield a cumulative prediction that consistently outperforms any single screening metric. The approach has been developed and validated on more than 30 different protein targets. Transforming structure-based in silico screening results into robust probabilities of activity enables the general fusion of multiple structure- and ligand-based approaches and returns a quantitative expectation of success that can be used to prioritize (or deprioritize) further discovery activities. This unified probabilistic framework offers a paradigm shift in how docking and scoring results are interpreted, which can enhance early lead-finding efforts by maximizing the value of in silico computational tools.

Molecular shape and medicinal chemistry: a perspective.

The eight contributions here provide ample evidence that shape as a volume or as a surface is a vibrant and useful concept when applied to drug discovery. It provides a reliable scaffold for "decoration" with chemical intuition (or bias) for virtual screening and lead optimization but also has its unadorned uses, as in library design, ligand fitting, pose prediction, or active site description. Computing power has facilitated this evolution by allowing shape to be handled precisely without the need to reduce down to point descriptors or approximate metrics, and the diversity of resultant applications argues for this being an important step forward. Certainly, it is encouraging that as computation has enabled our intuition, molecular shape has consistently surprised us in its usefulness and adaptability. The first Aurelius question, "What is the essence of a thing?", seems well answered, however, the third, "What do molecules do?", only partly so. Are the topics covered here exhaustive, or is there more to come? To date, there has been little published on the use of the volumetric definition of shape described here as a QSAR variable, for instance, in the prediction or classification of activity, although other shape definitions have been successful applied, for instance, as embodied in the Compass program described above in "Shape from Surfaces". Crystal packing is a phenomenon much desired to be understood. Although powerful models have been applied to the problem, to what degree is this dominated purely by the shape of a molecule? The shape comparison described here is typically of a global nature, and yet some importance must surely be placed on partial shape matching, just as the substructure matching of chemical graphs has proved useful. The approach of using surfaces, as described here, offers some flavor of this, as does the use of metrics that penalize volume mismatch less than the Tanimoto, e.g., Tversky measures. As yet, there is little to go on as to how useful a paradigm this will be because there is less software and fewer concrete results.Finally, the distance between molecular shapes, or between any shapes defined as volumes or surfaces, is a metric property in the mathematical sense of the word. As yet, there has been little, if any, application of this observation. We cannot know what new application to the design and discovery of pharmaceuticals may yet arise from the simple concept of molecular shape, but it is fair to say that the progress so far is impressive.

Large-scale application of high-throughput molecular mechanics with Poisson-Boltzmann surface area for routine physics-based scoring of protein-ligand complexes.

We apply a high-throughput formulation of the molecular mechanics with Poisson-Boltzmann surface area (htMM-PBSA) to estimate relative binding potencies on a set of 308 small-molecule ligands in complex with the proteins urokinase, PTP-1B, and Chk-1. We observe statistically significant correlation to experimentally measured potencies and report correlation coefficients for the three proteins in the range 0.72-0.83. The htMM-PBSA calculations illustrate the feasibility of procedural automation of physics-based scoring calculations to produce rank-ordered binding-potency estimates for protein-ligand complexes, with sufficient throughput for realization of practical implementation into scientist workflows in an industrial drug discovery research setting.

Healthy skepticism: assessing realistic model performance.

Although the development of computational models to aid drug discovery has become an integral part of pharmaceutical research, the application of these models often fails to produce the expected impact on productivity. One reason for this may be that the expected performance of many models is simply not supported by the underlying data, because of often neglected effects of assay and prediction errors on the reliability of the predicted outcome. Another significant challenge to realizing the full potential of computational models is their integration into prospective medicinal chemistry campaigns. This article will analyze the impact of assay and prediction error on model quality, and explore scenarios where computational models can expect to have a significant influence on drug discovery research.

Synthesis and evaluation of benzothiazole-based analogues as novel, potent, and selective fatty acid amide hydrolase inhibitors.

High-throughput screening (HTS) identified benzothiazole analogue 3 as a potent fatty acid amide hydrolase (FAAH) inhibitor. Structure-activity relationship (SAR) studies indicated that the sulfonyl group, the piperidine ring and benzothiazole were the key components to their activity, with 16j being the most potent analogue in this series. Time-dependent preincubation study of compound 3 was consistent with it being a reversible inhibitor. Activity-based protein-profiling (ABPP) evaluation of 3 in rat tissues revealed that it had exceptional selectivity and no off-target activity with respect to other serine hydrolases. Molecular shape overlay of 3 with a known FAAH inhibitor indicated that these compounds might act as transition-state analogues, forming putative hydrogen bonds with catalytic residues and mimicking the charge distribution of the tetrahedral transition state. The modeling study also indicated that hydrophobic interactions of the benzothiazole ring with the enzyme contributed to its extraordinary potency. These compounds may provide useful tools for the study of FAAH and the endocannabinoid system.

Application of belief theory to similarity data fusion for use in analog searching and lead hopping.

A wide variety of computational algorithms have been developed that strive to capture the chemical similarity between two compounds for use in virtual screening and lead discovery. One limitation of such approaches is that, while a returned similarity value reflects the perceived degree of relatedness between any two compounds, there is no direct correlation between this value and the expectation or confidence that any two molecules will in fact be equally active. A lack of a common framework for interpretation of similarity measures also confounds the reliable fusion of information from different algorithms. Here, we present a probabilistic framework for interpreting similarity measures that directly correlates the similarity value to a quantitative expectation that two molecules will in fact be equipotent. The approach is based on extensive benchmarking of 10 different similarity methods (MACCS keys, Daylight fingerprints, maximum common subgraphs, rapid overlay of chemical structures (ROCS) shape similarity, and six connectivity-based fingerprints) against a database of more than 150,000 compounds with activity data against 23 protein targets. Given this unified and probabilistic framework for interpreting chemical similarity, principles derived from decision theory can then be applied to combine the evidence from different similarity measures in such a way that both capitalizes on the strengths of the individual approaches and maintains a quantitative estimate of the likelihood that any two molecules will exhibit similar biological activity.

Rapid estimation of relative protein-ligand binding affinities using a high-throughput version of MM-PBSA.

By employing a modified protocol of the Molecular Mechanics with Poisson-Boltzmann Surface Area (MM-PBSA) methodology we substantially decrease the required computation time for calculating relative estimates of protein-ligand binding affinities. The modified method uses a generalized Born implicit solvation model during molecular dynamics to enhance conformational sampling as well as a very efficient Poisson-Boltzmann solver and a computational design based on a distributed-computing paradigm. This construction allows for reduction of the computational cost of the calculations by roughly 2 orders of magnitude compared to the traditional formulation of MM-PBSA. With this high-throughput version of MM-PBSA we show that one can produce efficient physics-based estimates of relative binding free energies with reasonable correlation to experimental data and a total computation time that is sufficiently low such that an industrially relevant throughput can be realized given currently accessible computing resources. We demonstrate this approach by performing a comparison of different MM-PBSA implementations on a set of 18 ligands for the protein target urokinase.

4-amino-5-aryl-6-arylethynylpyrimidines: structure-activity relationships of non-nucleoside adenosine kinase inhibitors.

A series of non-nucleoside adenosine kinase (AK) inhibitors is reported. These inhibitors originated from the modification of 5-(3-bromophenyl)-7-(6-morpholin-4-ylpyridin-3-yl)pyrido[2,3-d]pyrimidin-4-ylamine (ABT-702). The identification of a linker that would approximate the spatial arrangement found between the pyrimidine ring and the aryl group at C(7) in ABT-702 was a key element in this modification. A search of potential linkers led to the discovery of an acetylene moiety as a suitable scaffold. It was hypothesized that the aryl acetylenes, ABT-702, and adenosine bound to the active site of AK (closed form) in a similar manner with respect to the orientation of the heterocyclic base. Although potent acetylene analogs were discovered based on this assumption, an X-ray crystal structure of 5-(4-dimethylaminophenyl)-6-(6-morpholin-4-ylpyridin-3-ylethynyl)pyrimidin-4-ylamine (16a) revealed a binding orientation contrary to adenosine. In addition, this compound bound tightly to a unique open conformation of AK. The structure-activity relationships and unique ligand orientation and protein conformation are discussed.

High-throughput calculation of protein-ligand binding affinities: modification and adaptation of the MM-PBSA protocol to enterprise grid computing.

We have developed a system for performing computations on an enterprise grid using a freely available package for grid computing that allows us to harvest unused CPU cycles off of employee desktop computers. By modifying the traditional formulation of Molecular Mechanics with Poisson-Boltzmann Surface Area (MM-PBSA) methodology, in combination with a coarse-grain parallelized implementation suitable for deployment onto our enterprise grid, we show that it is possible to produce rapid physics-based estimates of protein-ligand binding affinities that have good correlation to experimental data. This is demonstrated by examining the correlation of our calculated binding affinities to experimental data and also by comparison to the correlation obtained from the binding-affinity calculations using traditional MM-PBSA that are reported in the literature.

The use of three-dimensional shape and electrostatic similarity searching in the identification of a melanin-concentrating hormone receptor 1 antagonist.

To aid in the identification of novel melanin-concentrating hormone receptor 1 (MCHr1) antagonists, a unique virtual library of readily synthesized molecules was designed and assembled based on 323 monomer compounds containing reactive moieties in combination with 30 core molecules having diamine functionality. The resulting library of 3,129,870 molecules was searched using three-dimensional shape similarity measures that were complimented with a novel electrostatic distribution similarity-matching algorithm. One of the top scoring hits in this library was synthesized and characterized for MCHr1 antagonism, where it exhibited at least a threefold improvement in binding affinity and cellular potency relative to the parent MCHr1 ligand. This work demonstrates that the use of shape and electrostatic similarity matching in combination with a well-designed virtual library can be used to augment standard medicinal chemistry techniques.

A highly potent and selective farnesyltransferase inhibitor ABT-100 in preclinical studies.

Ras mutation has been detected in approximately 20-30% of all human carcinomas, primarily in pancreatic, colorectal, lung and bladder carcinomas. The indirect inhibition of Ras activity by inhibiting farnesyltransferase (FTase) function is one therapeutic intervention to control tumor growth. Here we report the preclinical anti-tumor activity of our most advanced FTase inhibitor (FTI), ABT-100, and a direct comparison with the current clinical candidates. ABT-100 is a highly selective, potent and orally bioavailable FTI. It broadly inhibits the growth of solid tumors in preclinical animal models. Thus, ABT-100 is an attractive candidate for further clinical evaluation. In addition, our results provide plausible insights to explain the impressive potency and selectivity of ABT-100. Finally, we have demonstrated that ABT-100 significantly suppresses the expression of vascular endothelial growth factor (VEGF) mRNA and secretion of VEGF protein, as well as inhibiting angiogenesis in the animal model.

Novel and selective imidazole-containing biphenyl inhibitors of protein farnesyltransferase.

A series of imidazole-containing biphenyls was prepared and evaluated in vitro for inhibition of FTase and cellular Ras processing. Several of these analogues, such as 21, are potent inhibitors of FTase (<1nM), FTase/GGTase selective (>300-fold) and cellularly active (

Design, synthesis, and activity of 4-quinolone and pyridone compounds as nonthiol-containing farnesyltransferase inhibitors.

As a part of our efforts to identify potent inhibitors of farnesyltransferase (FTase), modification of the structure of tipifarnib through structure-based design was undertaken by replacing the 2-quinolones with 4-quinolones and pyridones, and subsequent relocation of the D-ring to the N-methyl group on the imidazole ring. This study has yielded a novel series of potent and selective FTase inhibitors. The X-ray structure of tipifarnib (1) in complex with FTase was described.