Comparison of Combinatorial Fragment Spaces and Its Application to Ultralarge Make-on-Demand Compound Catalogs.

The set of chemical compounds shared by two or more chemical libraries is assessed routinely as means of comparing these libraries for various applications. Traditionally this is achieved by comparing the members of the chemical libraries individually for identity. This approach becomes impractical when operating on chemical libraries exceeding billions or even trillions of compounds in size. As a result, no such analysis exists for ultralarge chemical spaces like the Enamine REAL Space containing over 20 billion compounds. In this work, we present a novel tool called SpaceCompare for the overlap calculation of large, nonenumerable combinatorial fragment spaces. In contrast to existing methods, SpaceCompare utilizes topological fingerprints and the combinatorial character of these chemical spaces. The tool is able to determine the exact overlap of prominent spaces like Enamine's REAL Space, WuXi's GalaXi Space, and Otava's CHEMriya for the first time.

FastGrow: on-the-fly growing and its application to DYRK1A.

Fragment-based drug design is an established routine approach in both experimental and computational spheres. Growing fragment hits into viable ligands has increasingly shifted into the spotlight. FastGrow is an application based on a shape search algorithm that addresses this challenge at high speeds of a few milliseconds per fragment. It further features a pharmacophoric interaction description, ensemble flexibility, as well as geometry optimization to become a fully fledged structure-based modeling tool. All features were evaluated in detail on a previously reported collection of fragment growing scenarios extracted from crystallographic data. FastGrow was also shown to perform competitively versus established docking software. A case study on the DYRK1A kinase, using recently reported new chemotypes, illustrates FastGrow's features in practice and its ability to identify active fragments. FastGrow is freely available to the public as a web server at https://fastgrow.plus/ and is part of the SeeSAR 3D software package.

Shape-Based Descriptors for Efficient Structure-Based Fragment Growing.

Structure-based fragment growing is one of the key techniques in fragment-based drug design. Fragment growing is commonly practiced based on structural and biophysical data. Computational workflows are employed to predict which fragment elaborations could lead to high-affinity binders. Several such workflows exist but many are designed to be long running noninteractive systems. Shape-based descriptors have been proven to be fast and perform well at virtual-screening tasks. They could, therefore, be applied to the fragment-growing problem to enable an interactive fragment-growing workflow. In this work, we describe and analyze the use of specific shape-based directional descriptors for the task of fragment growing. The performance of these descriptors that we call ray volume matrices (RVMs) is evaluated on two data sets containing protein-ligand complexes. While the first set focuses on self-growing, the second measures practical performance in a cross-growing scenario. The runtime of screenings using RVMs as well as their robustness to three dimensional perturbations is also investigated. Overall, it can be shown that RVMs are useful to prefilter fragment candidates. For up to 84% of the 3299 generated self-growing cases and for up to 66% of the 326 generated cross-growing cases, RVMs could create poses with less than 2 Å root-mean-square deviation to the crystal structure with average query speeds of around 30,000 conformations per second. This opens the door for fast explorative screenings of fragment libraries.

SAR by Space: Enriching Hit Sets from the Chemical Space.

We introduce SAR-by-Space, a concept to drastically accelerate structure-activity relationship (SAR) elucidation by synthesizing neighboring compounds that originate from vast chemical spaces. The space navigation is accomplished within minutes on affordable standard computer hardware using a tree-based molecule descriptor and dynamic programming. Maximizing the synthetic accessibility of the results from the computer is achieved by applying a careful selection of building blocks in combination with suitably chosen reactions; a decade of in-house quality control shows that this is a crucial part in the process. The REAL Space is the largest chemical space of commercially available compounds, counting 11 billion molecules as of today. It was used to mine actives against bromodomain 4 (BRD4). Before synthesis, compounds were docked into the binding site using a scoring function, which incorporates intrinsic desolvation terms, thus avoiding time-consuming simulations. Five micromolecular hits have been identified and verified within less than six weeks, including the measurement of IC50 values. We conclude that this procedure is a substantial time-saver, accelerating both ligand- and structure-based approaches in hit generation and lead optimization stages.

New Caspase-1 inhibitor by scaffold hopping into bio-inspired 3D-fragment space.

Virtual fragmentation of a library of 12,000 compounds inspired by natural products led to a dataset of 153,000 fragments that was used as a source to identify effective P2-P3 scaffold replacement solutions for peptidic Caspase-1 inhibitors. Our strategy led to the identification of an original 2-azabicyclo-octane scaffold (2-ABO) that was further elaborated into the potent Caspase-1 inhibitor CD10847 (IC50 = 17 nM). The crystal structure of Caspase-1 in complex with CD10847 was obtained, and its binding mode was shown to be similar to the one predicted by docking and in good agreement with other known inhibitors.

Navigating large chemical spaces in early-phase drug discovery.

The size of actionable chemical spaces is surging, owing to a variety of novel techniques, both computational and experimental. As a consequence, novel molecular matter is now at our fingertips that cannot and should not be neglected in early-phase drug discovery. Huge, combinatorial, make-on-demand chemical spaces with high probability of synthetic success rise exponentially in content, generative machine learning models go hand in hand with synthesis prediction, and DNA-encoded libraries offer new ways of hit structure discovery. These technologies enable to search for new chemical matter in a much broader and deeper manner with less effort and fewer financial resources. These transformational developments require new cheminformatics approaches to make huge chemical spaces searchable and analyzable with low resources, and with as little energy consumption as possible. Substantial progress has been made in the past years with respect to computation as well as organic synthesis. First examples of bioactive compounds resulting from the successful use of these novel technologies demonstrate their power to contribute to tomorrow's drug discovery programs. This article gives a compact overview of the state-of-the-art.

Neurotensin(8-13) analogs as dual NTS1 and NTS2 receptor ligands with enhanced effects on a mouse model of Parkinson's disease.

The modulatory interactions between neurotensin (NT) and the dopaminergic neurotransmitter system in the brain suggest that NT may be associated with the progression of Parkinson's disease (PD). NT exerts its neurophysiological effects by interactions with the human NT receptors type 1 (hNTS1) and 2 (hNTS2). Therefore, both receptor subtypes are promising targets for the development of novel NT-based analogs for the treatment of PD. In this study, we used a virtually guided molecular modeling approach to predict the activity of NT(8-13) analogs by investigating the docking models of ligands designed for binding to the human NTS1 and NTS2 receptors. The importance of the residues at positions 8 and/or 9 for hNTS1 and hNTS2 receptor binding affinity was experimentally confirmed by radioligand binding assays. Further in vitro ADME profiling and in vivo studies revealed that, compared to the parent peptide NT(8-13), compound 10 exhibited improved stability and BBB permeability combined with a significant enhancement of the motor function and memory in a mouse model of PD. The herein reported NTS1/NTS2 dual-specific NT(8-13) analogs represent an attractive tool for the development of therapeutic strategies against PD and potentially other CNS disorders.

Substantial improvements in large-scale redocking and screening using the novel HYDE scoring function.

The HYDE scoring function consistently describes hydrogen bonding, the hydrophobic effect and desolvation. It relies on HYdration and DEsolvation terms which are calibrated using octanol/water partition coefficients of small molecules. We do not use affinity data for calibration, therefore HYDE is generally applicable to all protein targets. HYDE reflects the Gibbs free energy of binding while only considering the essential interactions of protein-ligand complexes. The greatest benefit of HYDE is that it yields a very intuitive atom-based score, which can be mapped onto the ligand and protein atoms. This allows the direct visualization of the score and consequently facilitates analysis of protein-ligand complexes during the lead optimization process. In this study, we validated our new scoring function by applying it in large-scale docking experiments. We could successfully predict the correct binding mode in 93% of complexes in redocking calculations on the Astex diverse set, while our performance in virtual screening experiments using the DUD dataset showed significant enrichment values with a mean AUC of 0.77 across all protein targets with little or no structural defects. As part of these studies, we also carried out a very detailed analysis of the data that revealed interesting pitfalls, which we highlight here and which should be addressed in future benchmark datasets.

Magnet for the Needle in Haystack: "Crystal Structure First" Fragment Hits Unlock Active Chemical Matter Using Targeted Exploration of Vast Chemical Spaces.

Fragment-based drug discovery (FBDD) has successfully led to approved therapeutics for challenging and "undruggable" targets. In the context of FBDD, we introduce a novel, multidisciplinary method to identify active molecules from purchasable chemical space. Starting from four small-molecule fragment complexes of protein kinase A (PKA), a template-based docking screen using Enamine's multibillion REAL Space was performed. A total of 93 molecules out of 106 selected compounds were successfully synthesized. Forty compounds were active in at least one validation assay with the most active follow-up having a 13,500-fold gain in affinity. Crystal structures for six of the most promising binders were rapidly obtained, verifying the binding mode. The overall success rate for this novel fragment-to-hit approach was 40%, accomplished in only 9 weeks. The results challenge the established fragment prescreening paradigm since the standard industrial filters for fragment hit identification in a thermal shift assay would have missed the initial fragments.

The next level in chemical space navigation: going far beyond enumerable compound libraries.

Recent innovations have brought pharmacophore-driven methods for navigating virtual chemical spaces, the size of which can reach into the billions of molecules, to the fingertips of every chemist. There has been a paradigm shift in the underlying computational chemistry that drives chemical space search applications, incorporating intelligent reaction knowledge into their core so that they can readily deliver commercially available molecules as nearest neighbor hits from within giant virtual spaces. These vast resources enable medicinal chemists to execute rapid scaffold-hopping experiments, rapid hit expansion, and structure-activity relationship (SAR) exploitation in largely intellectual property (IP)-free territory and at unparalleled low cost.

Carboxamides vs. methanimines: Crystal structures, binding interactions, photophysical studies, and biological evaluation of (indazole-5-yl)methanimines as monoamine oxidase B and acetylcholinesterase inhibitors.

A comprehensive study was performed for the first time to compare two structurally related substance classes, namely indazole-5-carboxamides (11-16) and (indazole-5-yl)methanimines (17-22). Both chemical entities are potent, selective and reversible MAO-B inhibitors and, therefore, may serve as promising lead structures for the development of drug candidates against Parkinson's disease (PD) and other neurological disorders. Compounds 15 (Ki = 170 pM, SI = 25907) and 17 (Ki = 270 pM, SI = 16340) were the most potent and selective MAO-B inhibitors in both series. To investigate the multi-target inhibitory activity, all compounds were further screened for their potency against human AChE and BuChE enzymes. Compound 15 was found to be the most potent and selective AChE inhibitor in all series (hAChE IC50 = 78.3 ±â€¯1.7 µM). Moreover, compounds 11 and 17 showed no risk of drug-induced hepatotoxicity and a wider safety window, as determined in preliminary cytotoxicity screening. Molecular modeling studies into the human MAO-B enzyme-binding site supported by a HYDE analysis suggested that the imine linker similarly contributes to the total binding energy in methanimines 17-22 as the amide spacer in their carboxamide analogs 11-16. Amplified photophysical evaluation of compounds 17 and 20, including single X-ray analysis, photochemical experiments, and quantum-chemical calculations, provided insights into their more favourable isomeric forms and structural features, which contribute to their biologically active form and promising drug-like properties.

Crystal structures, binding interactions, and ADME evaluation of brain penetrant N-substituted indazole-5-carboxamides as subnanomolar, selective monoamine oxidase B and dual MAO-A/B inhibitors.

The pharmacological and physicochemical analysis of structurally optimized N-alkyl-substituted indazole-5-carboxamides, developed as potential drug and radioligand candidates for the treatment and diagnosis of Parkinson's disease (PD) and other neurological disorders, is reported. Recent efforts have been focused on the development of subnanomolar potent, selective MAO-B (N1-alkyl-substituted compounds 12a-14a and 15) and dual active MAO-A/B (N2-methylated compounds 12b-14b) inhibitors with nanomolar potency towards MAO-B and moderately active against MAO-A enzyme, respectively. The most promising drug-like derivatives in both series were N-(3-chloro-4-fluorophenyl)-1-methyl-1H-indazole-5-carboxamide (13a, NTZ-1441, IC50 hMAO-B 0.662 nM, >15000-fold selective versus MAO-A) and N-(3-chloro-4-fluorophenyl)-2-methyl-2H-indazole-5-carboxamide (13b, NTZ-1442, IC50 hMAO-B 8.08 nM, IC50 hMAO-A 0.56 µM, SI = 70). Moreover, compounds 13a and 13b were predicted to cross both the gastrointestinal tract (at pH 2.0, 5.5, and 7,4) and the blood-brain barrier (BBB) in vitro with appropriate drug-like properties required for CNS active drugs. Combined single X-ray/molecular modeling studies provided insights into the enzyme-inhibitor interactions within both MAO isoforms and the rationale for their inhibitory activity with controlled MAO-A/B selectivity - despite their small structural differences. The binding modes of 12a,b and 13a,b confirmed that the major interactions with hMAO-B were established via the flexible carbonyl group of the carboxamide linkage and the electron-donating nitrogens N1 or N2 of the indazole moiety, allowing further exploration of the alkyl side chain for next step lead optimization efforts.

The FlexX database docking environment--rational extraction of receptor based pharmacophores.

We present an integrated docking environment that allows for iterative and interactive detailed analysis of many docking solutions. All docking information is stored in an ORACLE database. New scoring schemes (e.g. target-specific scoring functions) as well as various types of filters can be easily defined and tested within this environment. As an example application we investigated the validity of the following hypothesis: If a docking procedure can lead to enrichments significantly better than random then a bias towards (partially) correct placements should be detectable. Such bias in terms of a preference for certain interacting groups within the active site can be used to select a set of receptor-based pharmacophore constraints, which in turn might be used to enhance the docking procedure. As a proof of concept for this approach we performed docking studies on three targets: thrombin, the cyclin-dependent kinase 2 (CDK2) and the angiotensin converting enzyme (ACE). We docked a set of known active compounds with standard FlexX and derived three sets of target-specific receptor-based pharmacophore constraints by statistical analysis of the predicted placements. Applying these receptor-based constraints in a virtual screening protocol utilizing FlexX-Pharm led to significantly improved enrichments.

Indazole- and indole-5-carboxamides: selective and reversible monoamine oxidase B inhibitors with subnanomolar potency.

Indazole- and indole-carboxamides were discovered as highly potent, selective, competitive, and reversible inhibitors of monoamine oxidase B (MAO-B). The compounds are easily accessible by standard synthetic procedures with high overall yields. The most potent derivatives were N-(3,4-dichlorophenyl)-1-methyl-1H-indazole-5-carboxamide (38a, PSB-1491, IC50 human MAO-B 0.386 nM, >25000-fold selective versus MAO-A) and N-(3,4-dichlorophenyl)-1H-indole-5-carboxamide (53, PSB-1410, IC50 human MAO-B 0.227 nM, >5700-fold selective versus MAO-A). Replacement of the carboxamide linker with a methanimine spacer leading to (E)-N-(3,4-dichlorophenyl)-1-(1H-indazol-5-yl)methanimine (58) represents a further novel class of highly potent and selective MAO-B inhibitors (IC50 human MAO-B 0.612 nM, >16000-fold selective versus MAO-A). In N-(3,4-difluorophenyl-1H-indazole-5-carboxamide (30, PSB-1434, IC50 human MAO-B 1.59 nM, selectivity versus MAO-A>6000-fold), high potency and selectivity are optimally combined with superior physicochemical properties. Computational docking studies provided insights into the inhibitors' interaction with the enzyme binding site and a rationale for their high potency despite their small molecular size.

Ultrafast de novo docking combining pharmacophores and combinatorics.

We report on a successful de novo design approach which relies on the combination of multi-million compound combinatorial docking under receptor-based pharmacophore constraints. Inspired by a rationale by A.R. Leach et al., we document on the unification of two steps into one for ligand assembly. In the original work, fragments known to bind in protein active sites were connected forming novel ligand compounds by means of generic skeleton linkers and following a combinatorial approach. In our approach, the knowledge of fragments binding to the protein has been expressed in terms of a receptor-based pharmacophore definition. The combinatorial linking step is performed in situ during docking, starting from combinatorial libraries. Three sample scenarios growing in size and complexity (combinatorial libraries of 1 million, 1.3 million, and 22.4 million compounds) have been created to illustrate the method. Docking could be accomplished between minutes and several hours depending on the outset; the results were throughout promising. Technically, a module compatibility between FlexX(C) and FlexX-Pharm has been established. The background is explained, and the crucial points from an information scientist's perspective are highlighted.

Automated drawing of structural molecular formulas under constraints.

In this paper, we present a new algorithm for automated drawing of 2D structural formulas of molecules. The algorithm is based on the classical scheme of a drawing queue placing the molecular fragments in a sequential way. We extend the concept of so-called prefabricated units developed for complex ring systems to automatically created drawing units for chains and rings which will then be assembled in a sequential fashion. The approach is fast and can be naturally extended to the problem of drawing molecules with common core structures. Further on, we present an algorithm that allows the drawing of 2D structural formulas under directional constraints assigned to a subset of bonds. Since no numerical optimization is necessary, the algorithm creates drawings of small organic molecules on the order of 500 structures per second. The new algorithm is relevant for all kinds of prediction and analysis software presenting a large number of probably similar molecular structures to the user of the software.