CACHE (Critical Assessment of Computational Hit-finding Experiments): A public-private partnership benchmarking initiative to enable the development of computational methods for hit-finding.

One aspirational goal of computational chemistry is to predict potent and drug-like binders for any protein, such that only those that bind are synthesized. In this Roadmap, we describe the launch of Critical Assessment of Computational Hit-finding Experiments (CACHE), a public benchmarking project to compare and improve small molecule hit-finding algorithms through cycles of prediction and experimental testing. Participants will predict small molecule binders for new and biologically relevant protein targets representing different prediction scenarios. Predicted compounds will be tested rigorously in an experimental hub, and all predicted binders as well as all experimental screening data, including the chemical structures of experimentally tested compounds, will be made publicly available, and not subject to any intellectual property restrictions. The ability of a range of computational approaches to find novel binders will be evaluated, compared, and openly published. CACHE will launch 3 new benchmarking exercises every year. The outcomes will be better prediction methods, new small molecule binders for target proteins of importance for fundamental biology or drug discovery, and a major technological step towards achieving the goal of Target 2035, a global initiative to identify pharmacological probes for all human proteins.

High-Throughput Screen for Inhibitors of Klebsiella pneumoniae Virulence Using a Tetrahymena pyriformis Co-Culture Surrogate Host Model.

The continuing emergence of antibacterial resistance reduces the effectiveness of antibiotics and drives an ongoing search for effective replacements. Screening compound libraries for antibacterial activity in standard growth media has been extensively explored and may be showing diminishing returns. Inhibition of bacterial targets that are selectively important under in vivo (infection) conditions and, therefore, would be missed by conventional in vitro screens might be an alternative. Surrogate host models of infection, however, are often not suitable for high-throughput screens. Here, we adapted a medium-throughput Tetrahymena pyriformis surrogate host model that was successfully used to identify inhibitors of a hyperviscous Klebsiella pneumoniae strain to a high-throughput format and screened circa 1.2 million compounds. The screen was robust and identified confirmed hits from different chemical classes with potent inhibition of K. pneumoniae growth in the presence of T. pyriformis that lacked any appreciable direct antibacterial activity. Several of these appeared to inhibit capsule/mucoidy, which are key virulence factors in hypervirulent K. pneumoniae. A weakly antibacterial inhibitor of LpxC (essential for the synthesis of the lipid A moiety of lipopolysaccharides) also appeared to be more active in the presence of T. pyriformis, which is consistent with the role of LPS in virulence as well as viability in K. pneumoniae.

Evolution of Novartis' Small Molecule Screening Deck Design.

This article summarizes the evolution of the screening deck at the Novartis Institutes for BioMedical Research (NIBR). Historically, the screening deck was an assembly of all available compounds. In 2015, we designed a first deck to facilitate access to diverse subsets with optimized properties. We allocated the compounds as plated subsets on a 2D grid with property based ranking in one dimension and increasing structural redundancy in the other. The learnings from the 2015 screening deck were applied to the design of a next generation in 2019. We found that using traditional leadlikeness criteria (mainly MW, clogP) reduces the hit rates of attractive chemical starting points in subset screening. Consequently, the 2019 deck relies on solubility and permeability to select preferred compounds. The 2019 design also uses NIBR's experimental assay data and inferred biological activity profiles in addition to structural diversity to define redundancy across the compound sets.

An Artificial Intelligence Approach to Proactively Inspire Drug Discovery with Recommendations.

Artificial intelligence (AI) is becoming established in drug discovery. For example, many in the industry are applying machine learning approaches to target discovery or to optimize compound synthesis. While our organization is certainly applying these sorts of approaches, we propose an additional approach: using AI to augment human intelligence. We have been working on a series of recommendation systems that take advantage of our existing laboratory processes, both wet and computational, in order to provide inspiration to our chemists, suggest next steps in their work, and automate existing workflows. We will describe five such systems in various stages of deployment within the Novartis Institutes for BioMedical Research. While each of these systems addresses different stages of the discovery pipeline, all of them share three common features: a trigger that initiates the recommendation, an analysis that leverages our existing systems with AI, and the delivery of a recommendation. The goal of all of these systems is to inspire and accelerate the drug discovery process.

Biased Diversity for Effective Virtual Screening.

Virtual screening is no longer merely a matter of identifying the subset of compounds from a large collection likely to be active against a particular endpoint. This viewpoint shares some distinctive practices at Novartis, where virtual screening combines multiple computational tools that marry the competing goals of biasing the selection of compounds toward multiple desired properties, while diversifying the selection to sample the available chemistry space, identifying quality compounds that inform drug discovery. Topics include the various considerations needed for a successful virtual screening practice: triaging, compound quality, accuracy and test sets, activity prediction including multitask modeling, virtual profiling, automation, multiproperty bias, diversity and property spaces, and biased-diversity designs.

D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies.

The Drug Design Data Resource (D3R) aims to identify best practice methods for computer aided drug design through blinded ligand pose prediction and affinity challenges. Herein, we report on the results of Grand Challenge 4 (GC4). GC4 focused on proteins beta secretase 1 and Cathepsin S, and was run in an analogous manner to prior challenges. In Stage 1, participant ability to predict the pose and affinity of BACE1 ligands were assessed. Following the completion of Stage 1, all BACE1 co-crystal structures were released, and Stage 2 tested affinity rankings with co-crystal structures. We provide an analysis of the results and discuss insights into determined best practice methods.

Rethinking drug design in the artificial intelligence era.

Artificial intelligence (AI) tools are increasingly being applied in drug discovery. While some protagonists point to vast opportunities potentially offered by such tools, others remain sceptical, waiting for a clear impact to be shown in drug discovery projects. The reality is probably somewhere in-between these extremes, yet it is clear that AI is providing new challenges not only for the scientists involved but also for the biopharma industry and its established processes for discovering and developing new medicines. This article presents the views of a diverse group of international experts on the 'grand challenges' in small-molecule drug discovery with AI and the approaches to address them.

Size Matters and How You Measure It: A Gram-Negative Antibacterial Example Exceeding Typical Molecular Weight Limits.

Monobactam antibiotic 1 is active against Gram-negative bacteria even though it has a higher molecular weight (MW) than the limit of 600 Da typically applied in designing such compounds. On the basis of 2D NMR data, the compound is able to adopt a compact conformation. The dimensions, projection area, and dipole moment derived from this conformation are compatible with porin permeation, as are locations of polar groups upon superimposition to the crystal structure of ampicillin bound to E. coli OmpF porin. Minimum inhibitory concentration (MIC) shifts in a porin knock-out strain are also consistent with 1 predominately permeating through porins. In conclusion, we describe a carefully characterized case of a molecule outside default design parameters where MW does not adequately represent the 3D shape more directly related to permeability. Leveraging 3D design criteria would open up additional chemical space currently underutilized due to limitations perceived in 2D.

Biased Complement Diversity Selection for Effective Exploration of Chemical Space in Hit-Finding Campaigns.

The success of hit-finding campaigns relies on many factors, including the quality and diversity of the set of compounds that is selected for screening. This paper presents a generalized workflow that guides compound selections from large compound archives with opportunities to bias the selections with available knowledge in order to improve hit quality while still effectively sampling the accessible chemical space. An optional flag in the workflow supports an explicit complement design function where diversity selections complement a given core set of compounds. Results from three project applications as well as a literature case study exemplify the effectiveness of the approach, which is available as a KNIME workflow named Biased Complement Diversity (BCD).

Inhibition of K-RAS4B by a Unique Mechanism of Action: Stabilizing Membrane-Dependent Occlusion of the Effector-Binding Site.

KRAS is frequently mutated in several of the most lethal types of cancer; however, the KRAS protein has proven a challenging drug target. K-RAS4B must be localized to the plasma membrane by prenylation to activate oncogenic signaling, thus we endeavored to target the protein-membrane interface with small-molecule compounds. While all reported lead compounds have low affinity for KRAS in solution, the potency of Cmpd2 was strongly enhanced when prenylated K-RAS4B is associated with a lipid bilayer. We have elucidated a unique mechanism of action of Cmpd2, which simultaneously engages a shallow pocket on KRAS and associates with the lipid bilayer, thereby stabilizing KRAS in an orientation in which the membrane occludes its effector-binding site, reducing RAF binding and impairing activation of RAF. Furthermore, enrichment of Cmpd2 on the bilayer enhances potency by promoting interaction with KRAS. This insight reveals a novel approach to developing inhibitors of membrane-associated proteins.

Imidazo[1,2-a]pyridin-6-yl-benzamide analogs as potent RAF inhibitors.

A series of imidazo[1,2-a]pyridin-6-yl-benzamide analogs was designed as inhibitors of B-RAFV600E. Medicinal chemistry techniques were employed to explore the SAR for this series and improve selectivity versus P38 and VEGFR2.

Synthesis, Binding Mode, and Antihyperglycemic Activity of Potent and Selective (5-Imidazol-2-yl-4-phenylpyrimidin-2-yl)[2-(2-pyridylamino)ethyl]amine Inhibitors of Glycogen Synthase Kinase 3.

In an effort to identify new antidiabetic agents, we have discovered a novel family of (5-imidazol-2-yl-4-phenylpyrimidin-2-yl)[2-(2-pyridylamino)ethyl]amine analogues which are inhibitors of human glycogen synthase kinase 3 (GSK3). We developed efficient synthetic routes to explore a wide variety of substitution patterns and convergently access a diverse array of analogues. Compound 1 (CHIR-911, CT-99021, or CHIR-73911) emerged from an exploration of heterocycles at the C-5 position, phenyl groups at C-4, and a variety of differently substituted linker and aminopyridine moieties attached at the C-2 position. These compounds exhibited GSK3 IC50s in the low nanomolar range and excellent selectivity. They activate glycogen synthase in insulin receptor-expressing CHO-IR cells and primary rat hepatocytes. Evaluation of lead compounds 1 and 2 (CHIR-611 or CT-98014) in rodent models of type 2 diabetes revealed that single oral doses lowered hyperglycemia within 60 min, enhanced insulin-stimulated glucose transport, and improved glucose disposal without increasing insulin levels.

Design and Discovery of N-(2-Methyl-5'-morpholino-6'-((tetrahydro-2H-pyran-4-yl)oxy)-[3,3'-bipyridin]-5-yl)-3-(trifluoromethyl)benzamide (RAF709): A Potent, Selective, and Efficacious RAF Inhibitor Targeting RAS Mutant Cancers.

RAS oncogenes have been implicated in >30% of human cancers, all representing high unmet medical need. The exquisite dependency on CRAF kinase in KRAS mutant tumors has been established in genetically engineered mouse models and human tumor cells. To date, many small molecule approaches are under investigation to target CRAF, yet kinase-selective and cellular potent inhibitors remain challenging to identify. Herein, we describe 14 (RAF709) [ Aversa , Biaryl amide compounds as kinase inhibitors and their preparation . WO 2014151616, 2014 ], a selective B/C RAF inhibitor, which was developed through a hypothesis-driven approach focusing on drug-like properties. A key challenge encountered in the medicinal chemistry campaign was maintaining a balance between good solubility and potent cellular activity (suppression of pMEK and proliferation) in KRAS mutant tumor cell lines. We investigated the small molecule crystal structure of lead molecule 7 and hypothesized that disruption of the crystal packing would improve solubility, which led to a change from N-methylpyridone to a tetrahydropyranyl oxy-pyridine derivative. 14 proved to be soluble, kinase selective, and efficacious in a KRAS mutant xenograft model.

Rationally Designed PI3Kα Mutants to Mimic ATR and Their Use to Understand Binding Specificity of ATR Inhibitors.

ATR, a protein kinase in the PIKK family, plays a critical role in the cell DNA-damage response and is an attractive anticancer drug target. Several potent and selective inhibitors of ATR have been reported showing significant antitumor efficacy, with most advanced ones entering clinical trials. However, due to the absence of an experimental ATR structure, the determinants contributing to ATR inhibitors' potency and specificity are not well understood. Here we present the mutations in the ATP-binding site of PI3Kα to progressively transform the pocket to mimic that of ATR. The generated PI3Kα mutants exhibit significantly improved affinity for selective ATR inhibitors in multiple chemical classes. Furthermore, we obtained the X-ray structures of the PI3Kα mutants in complex with the ATR inhibitors. The crystal structures together with the analysis on the inhibitor affinity profile elucidate the roles of individual amino acid residues in the binding of ATR inhibitors, offering key insights for the binding mechanism and revealing the structure features important for the specificity of ATR inhibitors. The ability to obtain structural and binding data for these PI3Kα mutants, together with their ATR-like inhibitor binding profiles, makes these chimeric PI3Kα proteins valuable model systems for structure-based inhibitor design.

Inhibition of prenylated KRAS in a lipid environment.

RAS mutations lead to a constitutively active oncogenic protein that signals through multiple effector pathways. In this chemical biology study, we describe a novel coupled biochemical assay that measures activation of the effector BRAF by prenylated KRASG12V in a lipid-dependent manner. Using this assay, we discovered compounds that block biochemical and cellular functions of KRASG12V with low single-digit micromolar potency. We characterized the structural basis for inhibition using NMR methods and showed that the compounds stabilized the inactive conformation of KRASG12V. Determination of the biophysical affinity of binding using biolayer interferometry demonstrated that the potency of inhibition matches the affinity of binding only when KRAS is in its native state, namely post-translationally modified and in a lipid environment. The assays we describe here provide a first-time alignment across biochemical, biophysical, and cellular KRAS assays through incorporation of key physiological factors regulating RAS biology, namely a negatively charged lipid environment and prenylation, into the in vitro assays. These assays and the ligands we discovered are valuable tools for further study of KRAS inhibition and drug discovery.

Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria.

The first challenge in the 2014 competition launched by the Teach-Discover-Treat (TDT) initiative asked for the development of a tutorial for ligand-based virtual screening, based on data from a primary phenotypic high-throughput screen (HTS) against malaria. The resulting Workflows were applied to select compounds from a commercial database, and a subset of those were purchased and tested experimentally for anti-malaria activity. Here, we present the two most successful Workflows, both using machine-learning approaches, and report the results for the 114 compounds tested in the follow-up screen. Excluding the two known anti-malarials quinidine and amodiaquine and 31 compounds already present in the primary HTS, a high hit rate of 57% was found.

Gravity field of the Orientale basin from the Gravity Recovery and Interior Laboratory Mission.

The Orientale basin is the youngest and best-preserved major impact structure on the Moon. We used the Gravity Recovery and Interior Laboratory (GRAIL) spacecraft to investigate the gravitational field of Orientale at 3- to 5-kilometer (km) horizontal resolution. A volume of at least (3.4 ± 0.2) × 106 km3 of crustal material was removed and redistributed during basin formation. There is no preserved evidence of the transient crater that would reveal the basin's maximum volume, but its diameter may now be inferred to be between 320 and 460 km. The gravity field resolves distinctive structures of Orientale's three rings and suggests the presence of faults associated with the outer two that penetrate to the mantle. The crustal structure of Orientale provides constraints on the formation of multiring basins.

Teach-Discover-Treat (TDT): collaborative computational drug discovery for neglected diseases.

Teach-Discover-Treat (TDT) is an initiative to promote the development and sharing of computational tools solicited through a competition with the aim to impact education and collaborative drug discovery for neglected diseases. Collaboration, multidisciplinary integration, and innovation are essential for successful drug discovery. This requires a workforce that is trained in state-of-the-art workflows and equipped with the ability to collaborate on platforms that are accessible and free. The TDT competition solicits high quality computational workflows for neglected disease targets, using freely available, open access tools.

Design and synthesis of 6,6-fused heterocyclic amides as raf kinase inhibitors.

Compounds belonging to several scaffolds-quinazolines, quinolines and quinoxalines-were designed and synthesized as Raf kinase inhibitors. Scaffolds were assessed for in vitro Braf(V600E) inhibition, and overall kinase selectivity. Pharmacokinetic parameters for one of the scaffolds were also determined.