GRAM: A True Null Model for Relative Binding Affinity Predictions.

Relative binding affinity prediction is a critical component in computer aided drug design. A significant amount of effort has been dedicated to developing rapid and reliable in silico methods. However, robust assessment of their performance is still a complicated issue, as it requires a performance measure applicable in the prospective setting and more importantly a true null model that defines the expected performance of being random in an objective manner. Although many performance metrics, such as the Pearson correlation coefficient (r), mean unsigned error (MUE), and root-mean-square error (RMSE), are frequently used in the literature, a true and nontrivial null model has yet been identified. To address this problem, here we introduce an interval estimate as an additional measure, namely, the prediction interval (PI), which can be estimated from the error distribution of the predictions. The benefits of using the interval estimate are (1) it provides the uncertainty range in the predicted activities, which is important in prospective applications, and (2) a true null model with well-defined PI can be established. We provide one such example termed the Gaussian Random Affinity Model (GRAM), which is based on the empirical observation that the affinity change in a typical lead optimization effort has the tendency to distribute normally N (0, σ). Having an analytically defined PI that only depends on the variation in the activities, GRAM should, in principle, allow us to compare the performance of relative binding affinity prediction methods in a standard way, ultimately critical to measuring the progress made in algorithm development.

CADD medicine: design is the potion that can cure my disease.

The acronym "CADD" is often used interchangeably to refer to "Computer Aided Drug Discovery" and "Computer Aided Drug Design". While the former definition implies the use of a computer to impact one or more aspects of discovering a drug, in this paper we contend that computational chemists are most effective when they enable teams to apply true design principles as they strive to create medicines to treat human disease. We argue that teams must bring to bear multiple sub-disciplines of computational chemistry in an integrated manner in order to utilize these principles to address the multi-objective nature of the drug discovery problem. Impact, resourcing principles, and future directions for the field are also discussed, including areas of future opportunity as well as a cautionary note about hype and hubris.

Collaborating to improve the use of free-energy and other quantitative methods in drug discovery.

In May and August, 2016, several pharmaceutical companies convened to discuss and compare experiences with Free Energy Perturbation (FEP). This unusual synchronization of interest was prompted by Schrödinger's FEP+ implementation and offered the opportunity to share fresh studies with FEP and enable broader discussions on the topic. This article summarizes key conclusions of the meetings, including a path forward of actions for this group to aid the accelerated evaluation, application and development of free energy and related quantitative, structure-based design methods.

Novel pyrrolyl 2-aminopyridines as potent and selective human beta-secretase (BACE1) inhibitors.

The proteolytic enzyme beta-secretase (BACE1) plays a central role in the synthesis of the pathogenic beta-amyloid in Alzheimer's disease. Recently, we reported small molecule acylguanidines as potent BACE1 inhibitors. However, many of these acylguanidines have a high polar surface area (e.g. as measured by the topological polar surface area or TPSA), which is unfavorable for crossing the blood-brain barrier. Herein, we describe the identification of the 2-aminopyridine moiety as a bioisosteric replacement of the acylguanidine moiety, which resulted in inhibitors with lower TPSA values and superior brain penetration. X-ray crystallographic studies indicated that the 2-aminopyridine moiety interacts directly with the catalytic aspartic acids Asp32 and Asp228 via a hydrogen-bonding network.

Discovery and initial optimization of 5,5'-disubstituted aminohydantoins as potent beta-secretase (BACE1) inhibitors.

8,8-Diphenyl-2,3,4,8-tetrahydroimidazo[1,5-a]pyrimidin-6-amine (1) was identified through HTS, as a weak (micromolar) inhibitor of BACE1. X-Ray crystallographic studies indicate the 2-aminoimidazole ring forms key H-bonding interactions with Asp32 and Asp228 in the catalytic site of BACE1. Lead optimization using structure-based focused libraries led to the identification of low nanomolar BACE1 inhibitors such as 20b with substituents which extend from the S(1) to the S(3) pocket.

Discovery of potent, selective sulfonylfuran urea endothelial lipase inhibitors.

Endothelial lipase (EL) activity has been implicated in HDL catabolism, vascular inflammation, and atherogenesis, and inhibitors are therefore expected to be useful for the treatment of cardiovascular disease. Sulfonylfuran urea 1 was identified in a high-throughput screening campaign as a potent and non-selective EL inhibitor. A lead optimization effort was undertaken to improve potency and selectivity, and modifications leading to improved LPL selectivity were identified. Radiolabeling studies were undertaken to establish the mechanism of action for these inhibitors, which were ultimately demonstrated to be irreversible inhibitors.

Dual acting norepinephrine reuptake inhibitors and 5-HT(2A) receptor antagonists: Identification, synthesis and activity of novel 4-aminoethyl-3-(phenylsulfonyl)-1H-indoles.

The discovery of a series of 4-aminoethyl-3-(phenylsulfonyl)-1H-indoles, dual acting norepinephrine reuptake inhibitors (NRIs) and 5-HT(2A) receptor antagonists, is described. The synthesis and structure-activity relationship (SAR) of this novel series of compounds is also presented.

Acylguanidine inhibitors of beta-secretase: optimization of the pyrrole ring substituents extending into the S1' substrate binding pocket.

The proteolytic enzyme beta-secretase (BACE-1) produces amyloid beta (Abeta) peptide, the primary constituent of neurofibrillary plaques, implicated in Alzheimer's disease, by cleavage of the amyloid precursor protein. A small molecule inhibitor of BACE-1, (diaminomethylene)-2,5-diphenyl-1H-pyrrole-1-acetamide (1, BACE-1 IC(50)=3.7 microM), was recently described, representing a new small molecule lead. Initial SAR investigation demonstrated the potential of accessing the nearby S(3) and S(1)(') substrate binding pockets of the BACE-1 enzyme by building substituents off one of the phenyl substituents and guanidinyl functional group. We report here the optimization of guanidinyl functional group substituents on 1, leading to potent submicromolar BACE-1 inhibitors.

Acylguanidine inhibitors of beta-secretase: optimization of the pyrrole ring substituents extending into the S1 and S3 substrate binding pockets.

Proteolytic cleavage of amyloid precursor protein by beta-secretase (BACE-1) and gamma-secretase leads to formation of beta-amyloid (A beta) a key component of amyloid plaques, which are considered the hallmark of Alzheimer's disease. Small molecule inhibitors of BACE-1 may reduce levels of A beta and thus have therapeutic potential for treating Alzheimer's disease. We recently reported the identification of a novel small molecule BACE-1 inhibitor N-[2-(2,5-diphenyl-pyrrol-1-yl)-acetyl]guanidine (3.a.1). We report here the initial hit-to-lead optimization of this hit and the SAR around the aryl groups occupying the S(1) and S(2') pockets leading to submicromolar BACE-1 inhibitors.

Modeling the value of predictive affinity scoring in preclinical drug discovery.

Drug discovery is widely recognized to be a difficult and costly activity in large part due to the challenge of identifying chemical matter which simultaneously optimizes multiple properties, one of which is affinity for the primary biological target. Further, many of these properties are difficult to predict ahead of expensive and time-consuming compound synthesis and experimental testing. Here we highlight recent work to develop compound affinity prediction models, and extensively investigate the value such models may provide to preclinical drug discovery. We demonstrate that the ability of these models to improve the overall probability of success is crucially dependent on the shape of the error distribution, not just the root-mean-square error. In particular, while scoring more molecule ideas generally improves the probability of project success when the error distribution is Gaussian, fat-tail distributions such as a Cauchy distribution, can lead to a situation where scoring more ideas actually decreases the overall probability of success.

Critical evaluation of methods to incorporate entropy loss upon binding in high-throughput docking.

Proper accounting of the positional/orientational/conformational entropy loss associated with protein-ligand binding is important to obtain reliable predictions of binding affinity. Herein, we critically examine two simplified statistical mechanics-based approaches, namely a constant penalty per rotor method, and a more rigorous method, referred to here as the partition function-based scoring (PFS) method, to account for such entropy losses in high-throughput docking calculations. Our results on the estrogen receptor beta and dihydrofolate reductase proteins demonstrate that, while the constant penalty method over-penalizes molecules for their conformational flexibility, the PFS method behaves in a more "DeltaG-like" manner by penalizing different rotors differently depending on their residual entropy in the bound state. Furthermore, in contrast to no entropic penalty or the constant penalty approximation, the PFS method does not exhibit any bias towards either rigid or flexible molecules in the hit list. Preliminary enrichment studies using a lead-like random molecular database suggest that an accurate representation of the "true" energy landscape of the protein-ligand complex is critical for reliable predictions of relative binding affinities by the PFS method.

Hydrates and solid-state reactivity: a survey of beta-lactam antibiotics.

Crystalline hydrates of hydrolytically susceptible pharmaceuticals are commonly encountered, and are particularly prevalent in the beta-lactam class of antibiotics. In order to rationalize how the apparent chemical incompatibility between water and beta-lactams is reduced through crystallization, a review of the published literature and available structural information on the solid state stability was undertaken. A search in the CSD yielded a total of 32 crystal structures of water-containing beta-lactams which were examined and classified in terms of hydrogen-bonded networks. In most cases the waters of hydration in the single crystal structures were found to fulfill structural roles and were not sufficiently close in proximity to react with the beta-lactam ring. Published data for the solid-state of several hydrates were also considered. In general, the stability data indicate high thermal stability for the crystalline hydrates. Moreover, even when water molecules are in appropriate proximity and orientation with respect to the beta-lactam moiety for a reaction to occur, the crystalline solids remain stable. The use of the crystal structure information along with computational modeling suggests that a combination of proximal relationships, steric and mechanistic arguments can explain the observed solid-state stability of crystalline beta-lactam hydrates.

Chemoselective One-Pot Synthesis of Functionalized Amino-azaheterocycles Enabled by COware.

Functionalized bicyclic amino-azaheterocycles are rapidly accessed in a one-pot cross-coupling/reduction sequence enabled by the use of COware. Incompatible reagents are physically separated in a single reaction vessel to effect two chemoselective transformations-Suzuki-Miyaura cross-coupling and heteroarene reduction. The developed method allows access to novel heterocyclic templates, including semisaturated Hedgehog and dual PI3K/mTOR inhibitors, which show enhanced physicochemical properties compared to their unsaturated counterparts.

A Perspective on Water Site Prediction Methods for Structure Based Drug Design.

Over the last decade, a number of computational methods have been developed, which attempt to evaluate the thermodynamic properties of individual water molecules at the solute-solvent interface, in order to assess contributions to protein-ligand binding. In some cases, these tools tell us what we already know, e.g. that hydrophobic pockets prefer lipophilic substituents, and in other cases the methods only seem to add clarity when retrospectively applied. Hence we have grappled with how to utilize such approaches to understand non-intuitive results and to generate chemistry ideas that otherwise would not have been developed. Here we provide our perspective on these methods and describe how results have been interpreted and applied. We include examples from GSK and elsewhere that highlight how water methods have been (1) utilized retrospectively to explain non-intuitive structure- activity relationships and (2) applied prospectively for chemistry design. Finally, we discuss where this field of study could lead to maximal impact in drug discovery research.

Investigation of a Bicyclo[1.1.1]pentane as a Phenyl Replacement within an LpPLA2 Inhibitor.

We describe the incorporation of a bicyclo[1.1.1]pentane moiety within two known LpPLA2 inhibitors to act as bioisosteric phenyl replacements. An efficient synthesis to the target compounds was enabled with a dichlorocarbene insertion into a bicyclo[1.1.0]butane system being the key transformation. Potency, physicochemical, and X-ray crystallographic data were obtained to compare the known inhibitors to their bioisosteric counterparts, which showed the isostere was well tolerated and positively impacted on the physicochemical profile.

Acylguanidines as small-molecule beta-secretase inhibitors.

BACE1 is an aspartyl protease responsible for cleaving amyloid precursor protein to liberate Abeta, which aggregates leading to plaque deposits implicated in Alzheimer's disease. We have identified small-molecule acylguanidine inhibitors of BACE1. Crystallographic studies show that these compounds form unique hydrogen-bonding interactions with the catalytic site aspartic acids and stabilize the protein in a flap-open conformation. Structure-based optimization led to the identification of potent analogs, such as 10d (BACE1 IC(50) = 110 nM).

Understanding the selectivity of genistein for human estrogen receptor-beta using X-ray crystallography and computational methods.

We present X-ray crystallographic and molecular modeling studies of estrogen receptors-alpha and -beta complexed with the estrogen receptor-beta-selective phytoestrogen genistein, and coactivator-derived NR box peptides containing an LXXLL motif. We demonstrate that the ligand binding mode is essentially identical when genistein is bound to both isoforms, despite the considerably weaker affinity of this ligand for estrogen receptor-alpha. In addition, we examine subtle differences between binding site residues, providing an explanation for why genistein is modestly selective for the beta isoform. To this end, we also present the results of quantum chemical studies and thermodynamic arguments that yield insight to the nature of the interactions leading to estrogen receptor-beta selectivity. The importance of our analysis to structure-based drug design is discussed.

Fragment-Based Approach to the Development of an Orally Bioavailable Lactam Inhibitor of Lipoprotein-Associated Phospholipase A2 (Lp-PLA2).

Lp-PLA2 has been explored as a target for a number of inflammation associated diseases, including cardiovascular disease and dementia. This article describes the discovery of a new fragment derived chemotype that interacts with the active site of Lp-PLA2. The starting fragment hit was discovered through an X-ray fragment screen and showed no activity in the bioassay (IC50 > 1 mM). The fragment hit was optimized using a variety of structure-based drug design techniques, including virtual screening, fragment merging, and improvement of shape complementarity. A novel series of Lp-PLA2 inhibitors was generated with low lipophilicity and a promising pharmacokinetic profile.

Exploitation of a Novel Binding Pocket in Human Lipoprotein-Associated Phospholipase A2 (Lp-PLA2) Discovered through X-ray Fragment Screening.

Elevated levels of human lipoprotein-associated phospholipase A2 (Lp-PLA2) are associated with cardiovascular disease and dementia. A fragment screen was conducted against Lp-PLA2 in order to identify novel inhibitors. Multiple fragment hits were observed in different regions of the active site, including some hits that bound in a pocket created by movement of a protein side chain (approximately 13 Å from the catalytic residue Ser273). Using structure guided design, we optimized a fragment that bound in this pocket to generate a novel low nanomolar chemotype, which did not interact with the catalytic residues.

ERbeta ligands. 3. Exploiting two binding orientations of the 2-phenylnaphthalene scaffold to achieve ERbeta selectivity.

The 2-phenylnaphthalene scaffold was explored as a simplified version of genistein in order to identify ER selective ligands. With the aid of docking studies, positions 1, 4, and 8 of the 2-phenylnaphthalene template were predicted to be the most potentially influential positions to enhance ER selectivity using two different binding orientations. Both orientations have the phenol moiety mimicking the A-ring of genistein. Several compounds predicted to adopt orientations similar to that of genistein when bound to ERbeta were observed to have slightly higher ER affinity and selectivity than genistein. The second orientation we exploited, which was different from that of genistein when bound to ERbeta, resulted in the discovery of several compounds that had superior ER selectivity and affinity versus genistein. X-ray structures of two ER selective compounds (i.e., 15 and 47) confirmed the alternate binding mode and suggested that substituents at positions 1 and 8 were responsible for inducing selectivity. One compound (i.e., 47, WAY-202196) was further examined and found to be effective in two models of inflammation, suggesting that targeting ER may be therapeutically useful in treating certain chronic inflammatory diseases.