Enhanced Calculation of Property Distributions in Chemical Fragment Spaces.

Chemical fragment spaces exceed traditional virtual compound libraries by orders of magnitude, making them ideal search spaces for drug design projects. However, due to their immense size, they are not compatible with traditional analysis and search algorithms that rely on the enumeration of molecules. In this paper, we present SpaceProp2, an evolution of the SpaceProp algorithm, which enables the calculation of exact property distributions for chemical fragment spaces without enumerating them. We extend the original algorithm by the capabilities to compute distributions for the TPSA, the number of rotatable bonds, and the occurrence of user-defined molecular structures in the form of SMARTS patterns. Furthermore, SpaceProp2 produces example molecules for every property bin, enabling a detailed interpretation of the distributions. We demonstrate SpaceProp2 on six established make-on-demand chemical fragment spaces as well as BICLAIM, the in-house fragment space of Boehringer Ingelheim. The possibility to search multiple SMARTS patterns simultaneously as well as the produced example molecules offers previously impossible insights into the composition of these vast combinatorial molecule collections, making it an ideal tool for the analysis and design of chemical fragment spaces.

Target 2035 - an update on private sector contributions.

Target 2035, an international federation of biomedical scientists from the public and private sectors, is leveraging 'open' principles to develop a pharmacological tool for every human protein. These tools are important reagents for scientists studying human health and disease and will facilitate the development of new medicines. It is therefore not surprising that pharmaceutical companies are joining Target 2035, contributing both knowledge and reagents to study novel proteins. Here, we present a brief progress update on Target 2035 and highlight some of industry's contributions.

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.

Identification of Highly Selective Orexin 1 Receptor Antagonists Driven by Structure-Based Design.

OX1 receptor antagonists are of interest to treat, for example, substance abuse disorders, personality disorders, eating disorders, or anxiety-related disorders. However, known dual OX1/OX2 receptor antagonists are not suitable due to their sleep-inducing effects; therefore, we were interested in identifying a highly OX1 selective antagonist with a sufficient window to OX2-mediated effects. Herein, we describe the design of highly selective OX1 receptor antagonists driven by the X-ray structure of OX1 with suvorexant, a dual OX1/OX2 receptor antagonist. Moderately selective OX1 antagonists comprising a [2.2.1]-bicyclic scaffold served as our starting point. Based on our binding mode hypothesis, we postulated which part of the scaffold points toward one of the regions where the two binding pockets differ. Structural changes in this part resulted in a modified core with higher inherent selectivity compared to the [2.2.1]-bicyclic template. The structure-based design, synthesis, and hit-to-lead evaluation of this novel OX1 receptor-selective scaffold are discussed herein.

Comparison of Large Chemical Spaces.

Chemical libraries are commonplace in computer-aided drug discovery, and assessing their overlap/complementarity is a routine task. For this purpose, different techniques are applied, ranging from exact matching to comparing physicochemical properties. However, these techniques are applicable only if the compound sets are not too big. Particularly for chemical spaces, containing billions of compounds, alternative ways of assessment are required. Random subsets could be enumerated and compared one-to-one, but given the vast sizes of the chemical spaces assessed here, such samples can at best provide a rough estimate of any overlap. Here we describe a novel way to compare chemical spaces utilizing a panel of query compounds. We applied this technique to three different types of spaces and obtained insight into their structural overlap, their coverage of the chemical universe, and their density. As chemical feasibility of virtual compounds is particularly important, we included related in silico predictions in our assessment.

The power of combining phenotypic and target-focused drug discovery.

A fierce dispute has arisen between the supporters of phenotypic and target-focused screening regarding which path grants the higher probability of successful drug development. A chance to reconcile these two approaches lies in successful target deconvolution (TD) after phenotypic screens. But, despite the panoply of available in vitro TD methods, the task of matching a phenotypically active compound with a biomolecular target remains challenging. Consequently, this review details the latest developments of in silico techniques that expedite TD. Ultimately, the deconvoluted target allows us to reap the benefits of the phenotypic and target-focused approaches.

Peptidic Macrocycles - Conformational Sampling and Thermodynamic Characterization.

Macrocycles are of considerable interest as highly specific drug candidates, yet they challenge standard conformer generators with their large number of rotatable bonds and conformational restrictions. Here, we present a molecular dynamics-based routine that bypasses current limitations in conformational sampling and extensively profiles the free energy landscape of peptidic macrocycles in solution. We perform accelerated molecular dynamics simulations to capture a diverse conformational ensemble. By applying an energetic cutoff, followed by geometric clustering, we demonstrate the striking robustness and efficiency of the approach in identifying highly populated conformational states of cyclic peptides. The resulting structural and thermodynamic information is benchmarked against interproton distances from NMR experiments and conformational states identified by X-ray crystallography. Using three different model systems of varying size and flexibility, we show that the method reliably reproduces experimentally determined structural ensembles and is capable of identifying key conformational states that include the bioactive conformation. Thus, the described approach is a robust method to generate conformations of peptidic macrocycles and holds promise for structure-based drug design.

Identification of new potent GPR119 agonists by combining virtual screening and combinatorial chemistry.

Virtual screening in a huge collection of virtual combinatorial libraries has led to the identification of two new structural classes of GPR119 agonists with submicromolar in vitro potencies. Herein, we describe the virtual screening process involving feature trees fragment space searches followed by a 3D postprocessing step. The in silico findings were then filtered and prioritized, and finally, combinatorial libraries of target molecules were synthesized. Furthermore the so-called "activity-anchor principle" is introduced as an element to increase the chance to generate true hits. An activity anchor is a structural element expected to provide key contributions to a certain biological activity. Application of this technique has led to the discovery of two new GPR119-agonist hit series, one of which was further optimized to progress as a novel lead class.

Design of combinatorial libraries for the exploration of virtual hits from fragment space searches with LoFT.

A case study is presented illustrating the design of a focused CDK2 library. The scaffold of the library was detected by a feature trees search in a fragment space based on reactions from combinatorial chemistry. For the design the software LoFT (Library optimizer using Feature Trees) was used. The special feature called FTMatch was applied to restrict the parts of the queries where the reagents are permitted to match. This way a 3D scoring function could be simulated. Results were compared with alternative designs by GOLD docking and ROCS 3D alignments.

Improving similarity-driven library design: customized matching and regioselective feature trees.

Reduced graph descriptors, like feature trees, are frequently applied in cases where the relative arrangement of functional groups is more important than exact substructure matches. Due to their ability to deal with fragmented molecules, they are well-suited for fragment space search and library design. We recently presented LoFT, a novel focused library design approach based on feature trees. During evaluation two drawbacks of the reduced graph descriptor were discovered: First, regioisomeric substructures cannot be distinguished in feature tree mappings which results in a large information loss especially when connecting R-groups to cores. Second, the automatic matching procedure might result in undesired alignments, since the knowledge on what is considered as core by the user is not taken into account. In the following, we will present two approaches to overcome those drawbacks. The generation of the feature trees is modified, so that different arene substitution patterns can be recognized and a customized matching is introduced, allowing the user to determine the parts of the query, where the reagents are allowed to match. Subsequently we investigate the improvements on library design by reviewing the design scenarios which were already used for the evaluation of LoFT.

LoFT: similarity-driven multiobjective focused library design.

We present LoFT, a tool for focused combinatorial library design. LoFT provides a set of algorithms, constructing a focused library from a chemical fragment space under optimization of multiple design criteria. A weighted multiobjective scoring function based on physicochemical descriptors is employed for traversing the chemical search space. The new aspect of LoFT is that a similarity-driven product-based library design approach is provided on fragment level. For this reason the feature tree descriptor is incorporated for similarity comparison of library compounds to given bioactive molecules as well as for diversifying the resulting libraries. The feature tree descriptor abstracts the molecular graph to a tree structure where the nodes are labeled with physicochemical properties. For comparison, the nodes of two trees are mapped onto each other. This strictly hierarchical mechanism is suitable for the efficient comparison of chemical fragments, allowing the evaluation of the resulting products on fragment level without explicitly enumerating them. LoFT was validated, applying three different data sets. Starting with a random reagent selection, we optimized the libraries using maximum similarity to known bioactive molecules and iteratively adding further criteria. Moreover, we compared these results with data we obtained with FTrees-FS.

Searching Fragment Spaces with feature trees.

Virtual combinatorial chemistry easily produces billions of compounds, for which conventional virtual screening cannot be performed even with the fastest methods available. An efficient solution for such a scenario is the generation of Fragment Spaces, which encode huge numbers of virtual compounds by their fragments/reagents and rules of how to combine them. Similarity-based searches can be performed in such spaces without ever fully enumerating all virtual products. Here we describe the generation of a huge Fragment Space encoding about 5 * 10(11) compounds based on established in-house synthesis protocols for combinatorial libraries, i.e., we encode practically evaluated combinatorial chemistry protocols in a machine readable form, rendering them accessible to in silico search methods. We show how such searches in this Fragment Space can be integrated as a first step in an overall workflow. It reduces the extremely huge number of virtual products by several orders of magnitude so that the resulting list of molecules becomes more manageable for further more elaborated and time-consuming analysis steps. Results of a case study are presented and discussed, which lead to some general conclusions for an efficient expansion of the chemical space to be screened in pharmaceutical companies.