An important mission of our group is to advance the science of computational chemistry. We have embarked on new research to try to shed light on problems that have been elusive to the drug discovery scientific community. We have published on kinetics, solvation and non-equilibrium conditions, to mention a few. Our most recent efforts are centered on membrane simulations with the overarching goal of understanding membranes at an atomic level. Membranes are key to many biological processes including absorption, distribution, metabolism, and excretion (ADME) profiles of drug candidates.

This presentation will focus on the use of massive molecular dynamics simulations to understand ligand binding to membranes (or membrane partition), the effect compounds partition on membrane biophysics and structure-kinetics relationships of passive membrane permeation from multiscale modeling.

Structural information of drug targets has proven a valuable asset to drug discovery projects. MOE is a prestigious software package for handling structural information of both proteins and ligands. One unique feature of MOE is that MOE provides a broad platform allowing users to extend and enhance its functionalities. I will discuss a case study, where fragmentation function provided by MOE is combined with data mining of the Cambridge Structure Database (CSD), and leading to discovery of novel XIAP inhibitors. In another case study, pharmacophore models generated with MOE are subjected to a genetic algorithm guided optimization, which leads to a highly selective pharmacophore model.

HIV Integrase catalyzes the incorporation of viral DNA into the host genome and is a critical part of the viral life cycle. As a result, integrase has been a protein of therapeutic interest for several decades and, to date, three integrase inhibitors have been approved for clinical use (raltegravir, dolutegravir, and elvitegravir). Clinically significant resistance mutations to drugs targeting HIV integrase have emerged with integrase inhibitor-containing treatment regimens and efforts to develop novel inhibitors of HIV integrase that overcome viral resistance have resulted in the identification of compounds with allosteric binding sites and non-canonical mechanisms of inhibition. An example of these efforts is the broad class of compounds which bind in the LEDGF/p75 binding site on HIV integrase and disrupt the association of these two proteins. Integrase inhibitors displaying this mechanism of action have been shown to possess significant antiviral potency, motivating additional discovery efforts in this area. We describe virtual screening, crystallographic, and additional computational efforts directed towards the identification of novel, allosteric integrase inhibitors.

Multiple ligand based in silico quantitative structure activity relationship (QSAR) statistical modeling approaches and tools were used to comprehend the in vitro binding affinities (IC₅₀) of diverse small molecule human β-secretase 1 (hBACE-1) inhibitors reported in scientific literature. Departing from the standard tradition, only 230 (~13%) small molecules were used for training the system and the prediction performance evaluated using an external validation set of 1476 (~87%) inhibitors. Overall, tangible and systematic improvements were observed as the descriptive information content and complexity of the modeling technique increased. The current results demonstrate that useful and productive models is within reach by choosing appropriate modeling techniques in spite of small datasets and diverse chemical classes, a scenario typical in HTS triaging or patent busting activities.

Managing and analyzing structure activity/property relationship data in medicinal chemistry projects is becoming ever more challenging, with larger data sets and parallel development of different structural series. Tools and methods for the efficient visualization, analysis and profiling of structures therefore remain of deep interest. Here, we will describe a new application, MOEsaic, which enhances typical medicinal chemistry workflows aimed at interrogating the SAR data through the use of interactive MMP analysis and R-group profiling, for guiding a campaign in its development.

Even with advances in structural biology, many important protein targets cannot be fully characterized by structural experiments alone. The new classes of selective combination drugs for treating Hepatitis C represent some of the most potent and effective antivirals ever developed. This talk will summarize our development and use of multi-scale informatics workflows to build multiple receptor/drug models and test for ones that best correlate with our collaborators internal chemogenomic screening data. The resulting methods/models are robust and explain SAR for diverse chemotypes and genotypes outside our original training set.

As part of our ongoing efforts in understanding muscle biology, we investigated the use of docking and scoring to optimize our efforts in a small molecule muscle contractility program. Enabled by multiple structures, and a larger well-controlled dataset, our follow-on program offered a unique opportunity to test the abilities of recent advances in machine-learning assisted docking, and field test their use. Herein are described some of the advantages and pitfalls of such an approach.

An efficient and accurate method to evaluate whether a ligand design is strained would be highly useful. With the aim of identifying strain calculators for computational and medicinal chemists, we have evaluated a number of both standard and newly-developed quantum and molecular mechanics methods. Strain energy workflows were constructed using our recently open-sourced modeling package, "Chemalot", which not only enabled us to quickly evaluate methods but also integrate the chosen workflows within MOE. Finally, we will share some applications of using MOE and our strain calculations on small-molecule drug-design teams.

The p21-activated kinases (PAKs) comprise a family of six serine/threonine protein kinases, divided into two subgroups based on sequence and structural homology: group I (PAKs 1-3) and group II (PAKs 4-6). Multiple PAK family members have been shown to interact directly or indirectly with a wide variety of other proteins, and as a result have been demonstrated to regulate a variety of cellular activities, such as cytoskeletal reorganization, cellular motility, and survival.

In recent years, emerging data has pointed to the role of PAK1 signaling in certain human cancers, suggesting that inhibitors of PAK1 kinase activity may have utility as antitumor agents. However, due to the relatively open and flexible ATP-binding site of this kinase, the identification of potent PAK1 inhibitors with high kinase selectivity and drug-like physical properties has remained a challenge. In this presentation, we will discuss our efforts towards this goal, highlighting the discovery and optimization of two structurally distinct chemical series of PAK1 inhibitors.

There remains a significant need for improved therapies to treat inflammatory bowel diseases, including ulcerative colitis (UC). The oral JAK inhibitor tofacitinib has demonstrated clinical effectiveness in treating UC patients in Phase 3 trials, although its usage may be limited by adverse events resulting from systemic drug levels. Here we report the structure-based design and profiling of a series of novel, pan-JAK inhibitors designed to be intestinally-restricted thereby minimizing systemic side effects.

The PI3K/Akt/mTOR (PAM) pathway is an essential signaling regulator of cell growth, proliferation, and metabolism. Aberrant hyperactivation of the pathway through multiple mechanisms, including loss of tumor suppressor PTEN function, amplification or mutation of Akt and exposure to carcinogens leads to dysregulated cell growth and survival. Clinical evidence suggest that the PAM pathway is an attractive therapeutic target because it serves as a convergence point to cellular processes that contribute to the initiation and maintenance of cancer.

Herein, we present the successful optimization of the quinazoline-8-carboxamide (QCA) series of dual p70S6K/Akt inhibitors. The initial lead MSC2120352 was identified in a focused, kinase library screen. MSC2120352 binds in the ATP-binding pocket of the kinase: the QCA amide group forms bidentate interactions with the hinge region (Glu173-Leu175); the electron-rich π system of the benzyl binds in the G-loop, utilizing noncovalent cation-π interactions with the catalytic lysine-123. Using rational structure-based design, MSC2120352 was subsequently optimized to a highly potent, kinase-selective, CNS penetrant p70S6K/Akt inhibitor M2698.

M2698 delivers a novel mode of PAM pathway inhibition with a different mechanism of action, compared to single-node Akt or p70S6K inhibitors or mTOR rapalogs. In a cellular context, inhibition of p70S6K by M2698 leads to potent inhibition of ribosomal protein S6 phosphorylation, while inhibition of Akt blocks the negative effects of a compensatory feedback loop. In addition, M2698 potently inhibits proliferation of multiple solid tumors, including those with PAM pathway genomic alterations. In vivo pharmacokinetic and efficacy studies of M2698 indicated a dose dependent tumor growth inhibition in models with PAM pathway genomic alterations, including, triple negative breast cancer, Her2+ breast cancer, and glioblastoma. On the basis of its superior in vitro and in vivo profile, M2698 was selected for further development and is currently being evaluated in Phase I clinical studies.