Integrated design environment: A multi-use platform for design idea capture, evaluation, and tracking in medicinal chemistry.

An integrated design environment (IDE) has been developed that allows the capture of design ideas, virtual compounds, and design hypotheses for medicinal chemistry projects. Specific consideration for rational molecular design, including design strategy and tactics, as well as comparator reference compounds have been incorporated to more easily convey the proposed design idea. A hierarchical tree architecture and customizable layouts allow for facile browsing across multiple programs and rapid examination of both ongoing and newly designed virtual compounds enabling centralized team discussions to ensure the most efficient prosecution of a queue of these target compounds. Additionally, a "whiteboard" module was incorporated for the rapid evaluation of virtual compounds against a suite of computational models enabling real-time design and triage. Finally, aggregation of cross-project design data enables broader analyses that can indicate portfolio-wide design challenges.

Kinase hinge binding scaffolds and their hydrogen bond patterns.

Protein kinases constitute a major class of intracellular signaling molecules, and describe some of the most prominent drug targets. Kinase inhibitors commonly employ small chemical scaffolds that form hydrogen bonds with the kinase hinge residues connecting the N- and C-terminal lobes of the catalytic domain. In general the satisfied hydrogen bonds are required for potent inhibition, therefore constituting a conserved feature in the majority of inhibitor-kinase interactions. From systematically analyzing the kinase scaffolds extracted from Pfizer crystal structure database (CSDb) we recognize that large number of kinase inhibitors of diverse chemical structures are derived from a relatively small number of common scaffolds. Depending on specific substitution patterns, scaffolds may demonstrate versatile binding capacities to interact with kinase hinge. Afforded by thousands of ligand-protein binary complexes, the hinge hydrogen bond patterns were analyzed with a focus on their three-dimensional configurations. Most of the compounds engage H6 NH for hinge recognition. Dual hydrogen bonds are commonly observed with additional recruitment of H4 CO upstream and/or H6 CO downstream. Triple hydrogen bonds accounts for small number of binary complexes. An unusual hydrogen bond with a non-canonical H5 conformation is observed, requiring a peptide bond flip by a glycine residue at the H6 position. Additional hydrogen bonds to kinase hinge do not necessarily correlate with an increase in potency; conversely they appear to compromise kinase selectivity. Such learnings could enhance the prospect of successful therapy design.

Probing the chemical 'reactome' with high-throughput experimentation data.

High-throughput experimentation (HTE) has the potential to improve our understanding of organic chemistry by systematically interrogating reactivity across diverse chemical spaces. Notable bottlenecks include few publicly available large-scale datasets and the need for facile interpretation of these data's hidden chemical insights. Here we report the development of a high-throughput experimentation analyser, a robust and statistically rigorous framework, which is applicable to any HTE dataset regardless of size, scope or target reaction outcome, which yields interpretable correlations between starting material(s), reagents and outcomes. We improve the HTE data landscape with the disclosure of 39,000+ previously proprietary HTE reactions that cover a breadth of chemistry, including cross-coupling reactions and chiral salt resolutions. The high-throughput experimentation analyser was validated on cross-coupling and hydrogenation datasets, showcasing the elucidation of statistically significant hidden relationships between reaction components and outcomes, as well as highlighting areas of dataset bias and the specific reaction spaces that necessitate further investigation.

Identification of novel series of pyrazole and indole-urea based DFG-out PYK2 inhibitors.

Previous drug discovery efforts identified classical PYK2 kinase inhibitors such as 2 and 3 that possess selectivity for PYK2 over its intra-family isoform FAK. Efforts to identify more kinome-selective chemical matter that stabilize a DFG-out conformation of the enzyme are described herein. Two sub-series of PYK2 inhibitors, an indole carboxamide-urea and a pyrazole-urea have been identified and found to have different binding interactions with the hinge region of PYK2. These leads proved to be more selective than the original classical inhibitors.

Shaping a screening file for maximal lead discovery efficiency and effectiveness: elimination of molecular redundancy.

High Throughput Screening (HTS) is a successful strategy for finding hits and leads that have the opportunity to be converted into drugs. In this paper we highlight novel computational methods used to select compounds to build a new screening file at Pfizer and the analytical methods we used to assess their quality. We also introduce the novel concept of molecular redundancy to help decide on the density of compounds required in any region of chemical space in order to be confident of running successful HTS campaigns.

Integrated in silico-in vitro strategy for addressing cytochrome P450 3A4 time-dependent inhibition.

Throughout the past decade, the expectations from the regulatory agencies for safety, drug-drug interactions (DDIs), pharmacokinetic, and disposition characterization of new chemical entities (NCEs) by pharmaceutical companies seeking registration have increased. DDIs are frequently assessed using in silico, in vitro, and in vivo methodologies. However, a key gap in this screening paradigm is a full structural understanding of time-dependent inhibition (TDI) on the cytochrome P450 systems, particularly P450 3A4. To address this, a number of high-throughput in vitro assays have been developed. This work describes an automated assay for TDI using two concentrations at two time points (2 + 2 assay). Data generated with this assay for over 2000 compounds from multiple therapeutic programs were used to generate in silico Bayesian classification models of P450 3A4-mediated TDI. These in silico models were validated using several external test sets and multiple random group testing (receiver operator curve value >0.847). We identified a number of substructures that were likely to elicit TDI, the majority containing indazole rings. These in vitro and in silico approaches have been implemented as a part of the Pfizer screening paradigm. The Bayesian models are available on the intranet to guide synthetic strategy, predict whether a NCE is likely to cause a TDI via P450 3A4, filter for in vitro testing, and identify substructures important for TDI as well as those that do not cause TDI. This represents an integrated in silico-in vitro strategy for addressing P450 3A4 TDI and improving the efficiency of screening.

Project-focused activity and knowledge tracker: a unified data analysis, collaboration, and workflow tool for medicinal chemistry project teams.

Advances in the field of drug discovery have brought an explosion in the quantity of data available to medicinal chemists and other project team members. New strategies and systems are needed to help these scientists to efficiently gather, organize, analyze, annotate, and share data about potential new drug molecules of interest to their project teams. Herein we describe a suite of integrated services and end-user applications that facilitate these activities throughout the medicinal chemistry design cycle. The Automated Data Presentation (ADP) and Virtual Compound Profiler (VCP) processes automate the gathering, organization, and storage of real and virtual molecules, respectively, and associated data. The Project-Focused Activity and Knowledge Tracker (PFAKT) provides a unified data analysis and collaboration environment, enhancing decision-making, improving team communication, and increasing efficiency.

Molecular Transformer unifies reaction prediction and retrosynthesis across pharma chemical space.

Predicting how a complex molecule reacts with different reagents, and how to synthesise complex molecules from simpler starting materials, are fundamental to organic chemistry. We show that an attention-based machine translation model - Molecular Transformer - tackles both reaction prediction and retrosynthesis by learning from the same dataset. Reagents, reactants and products are represented as SMILES text strings. For reaction prediction, the model "translates" the SMILES of reactants and reagents to product SMILES, and the converse for retrosynthesis. Moreover, a model trained on publicly available data is able to make accurate predictions on proprietary molecules extracted from pharma electronic lab notebooks, demonstrating generalisability across chemical space. We expect our versatile framework to be broadly applicable to problems such as reaction condition prediction, reagent prediction and yield prediction.

Quick Building Blocks (QBB): An Innovative and Efficient Business Model To Speed Medicinal Chemistry Analog Synthesis.

Many pharmaceutical companies have invested millions of dollars in establishing internal chemical stores to provide reliable access to large numbers of building blocks (BB) for the synthesis of new molecules, especially for the timely design and execution of parallel (library) synthesis. Recognizing budget and logistical limitations, we required a more economically scalable process to provide diverse BB. We disclose a novel business partnership that achieves the goals of just-in-time, economical access to commercial BB that increases chemical space coverage and accelerates the synthesis of new drug candidates. We believe that this model can be of benefit to companies of all sizes that are engaged in drug discovery by reducing cost, increasing diversity of analog molecules in a time-conscious manner, and reducing BB inventory. More efficient use of BB by customers may allow commercial vendors to devote a greater portion of their resources to preparing novel BB that increase chemical diversity as opposed to resynthesizing out-of-stock compounds that are inaccessible within company compound collections.

Trifluoromethylpyrimidine-based inhibitors of proline-rich tyrosine kinase 2 (PYK2): structure-activity relationships and strategies for the elimination of reactive metabolite formation.

The synthesis and SAR for a series of diaminopyrimidines as PYK2 inhibitors are described. Using a combination of library and traditional medicinal chemistry techniques, a FAK-selective chemical series was transformed into compounds possessing good PYK2 potency and 10- to 20-fold selectivity against FAK. Subsequent studies found that the majority of the compounds were positive in a reactive metabolite assay, an indicator for potential toxicological liabilities. Based on the proposed mechanism for bioactivation, as well as a combination of structure-based drug design and traditional medicinal chemistry techniques, a follow-up series of PYK2 inhibitors was identified that maintained PYK2 potency, FAK selectivity and HLM stability, yet were negative in the RM assay.

The development of structure-activity relationships for mitochondrial dysfunction: uncoupling of oxidative phosphorylation.

Mitochondrial dysfunction has been implicated as an important factor in the development of idiosyncratic organ toxicity. An ability to predict mitochondrial dysfunction early in the drug development process enables the deselection of those drug candidates with potential safety liabilities, allowing resources to be focused on those compounds with the highest chance of success to the market. A database of greater than 2000 compounds was analyzed to identify structural and physicochemical features associated with the uncoupling of oxidative phosphorylation (herein defined as an increase in basal respiration). Many toxicophores associated with potent uncoupling activity were identified, and these could be divided into two main mechanistic classes, protonophores and redox cyclers. For the protonophores, potent uncoupling activity was often promoted by high lipophilicity and apparent stabilization of the anionic charge resulting from deprotonation of the protonophore. The potency of redox cyclers did not appear to be prone to variations in lipophilicity. Only 11 toxicophores were of sufficient predictive performance that they could be incorporated into a structural-alert model. Each alert was associated with one of three confidence levels (high, medium, and low) depending upon the lipophilicity-activity profile of the structural class. The final model identified over 68% of those compounds with potent uncoupling activity and with a value for specificity above 99%. We discuss the advantages and limitations of this approach and conclude that although structural alert methodology is useful for identifying toxicophores associated with mitochondrial dysfunction, they are not a replacement for the mitochondrial dysfunction assays in early screening paradigms.