Relative binding affinity prediction is a critical component in computer aided drug design. 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 random in an objective manner. Although many performance metrics, such as correlation coefficient (r2), mean unsigned error (MUE), and room mean square error (RMSE), are frequently used in the literature, a true and non-trivial null model has yet been identified. To address this problem, here we introduce an interval estimate as an additional measure, namely 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; 2) a true null model with well-defined PI can be established. We provide one such example termed 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, s). 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.

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.

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.

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.

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.

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.

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.

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.

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.

A method that identifies the hydration shell structure of proteins and estimates the relative free energies of water molecules within that hydration shell is described. The method, which we call "SPAM" (maps spelled in reverse), utilizes explicit solvent molecular dynamics (MD) simulations to capture discrete hydration sites at the water-protein interface and computes a local free energy measure from the distribution of interaction energies between water and the environment at a specific site. SPAM is able to provide a qualitative estimate of the thermodynamic profile of bound water molecules that correlates nicely with well-studied structure-activity relationships and observed binding "hot spots". This is demonstrated in retrospective analyses of HIV1 protease and hen egg white lysozyme, where the effects of water displacement and solvent binding have been studied extensively. The simplicity and effectiveness of SPAM allow for prospective application during the drug discovery process.