Ever since the first rational approaches to the discovery of promising lead candidate structures were applied, it has been a challenge for both medicinal and computational chemists to assess, generate, and combine promising structural motifs to form new and potent chemical entities for biological screening against potential drug targets. Many scientists have committed themselves to the analysis and identification of valuable chemical building blocks and have also developed strategies on how to best recombine them. In this context, the retrosynthetic fragmentation and recombination of chemical motifs derived from known inhibitors is a common and well-known procedure. Meanwhile, fragment-based approaches have become established and valuable processes in pharmaceutical lead discovery and validation. Several application studies have yielded promising lead candidates. [2] Chemical space is huge. Corporate as well as public databases are in the millions and are still increasing in size in order to cover a larger part of the chemical universe. For several good reasons, there is the common trend to standardize experimental and computational protocols in pharmaceutical research. This trend demands systematic and consistent approaches, although they can hardly match the creativity and intuition of medicinal chemists. Consequently, they can and should not substitute, but rather assist, the expert in this task. The most prominent automated example for fragment generation is the retrosynthetic combinatorial analysis procedure (RECAP).[3] It was the first of its kind to apply 11 distinct rules that were supposed to model chemical motifs that could easily be formed by combinatorial chemistry. In this context, the "fragment space" concept was introduced. In contrast to a fragment library, such a space consists not only of a set of fragments, but also of a set of rules that specifies how to recombine fragments by fusing the respective chemical motifs.RECAP is widely used and often referred to, yet even though authors frequently state to have used modified improved versions of the original, actual publications that communicate the extensions that were carried out are rare. An extension of the fragment space concept was recently published, but with a focus on obtaining scaffolds and not on retaining supposedly 'drug-like' substituents or functional groups.[4] Apart from that, the question remains what a 'drug-like' fragment space actually is, and whether or not 'drug-likeness' depends on the origin of the fragments: that is, if they necessarily have to be derived from drugs. In this context, it is highly interesting and important to measure the extent and accuracy with which current models and methods are able to represent the available chemical space.In an attempt to improve existing approaches for the automatic decomposition of molecules into fragments, we compiled a new and more elaborate set of rules for the breaking of retrosynthetically interesting chemical substructures (BRICS) and used this for obtaining fragments from biol...

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We present a new molecular design program, FlexNovo, for structure-based searching within large fragment spaces following a sequential growth strategy. The fragment spaces consist of several thousands of chemical fragments and a corresponding set of rules that specify how the fragments can be connected. FlexNovo is based on the FlexX molecular docking software and makes use of its incremental construction algorithm and the underlying chemical models. Interaction energies are calculated by using standard scoring functions. Several placement geometry, physicochemical property (drug-likeness), and diversity filter criteria are directly integrated into the "build-up" process. FlexNovo has been used to design potential inhibitors for four targets of pharmaceutical interest (dihydrofolate reductase, cyclin-dependant kinase 2, cyclooxygenase-2, and the estrogen receptor). We have carried out calculations using different diversity parameters for each of these targets and generated solution sets containing up to 50 molecules. The compounds obtained show that FlexNovo is able to generate a diverse set of reasonable molecules with drug-like properties. The results, including an automated similarity analysis with the Feature Tree program, indicate that FlexNovo often reproduces structural motifs as well as the corresponding binding modes seen in known active structures.

For computational de novo design, a general retrospective validation work is a very challenging task. Here we propose a comprehensive workflow to de novo design driven by the needs of computational and medicinal chemists and, at the same time, we propose a general validation scheme for this technique. The study was conducted combining a suite of already published programs developed within the framework of the NovoBench project, which involved three different pharmaceutical companies and four groups of developers. Based on 188 PDB protein-ligand complexes with diverse functions, the study involved the ligand reconstruction by means of a fragment-based de-novo design approach. The structure-based de novo search engine FlexNovo showed in five out of eight total cases the ability to reconstruct native ligands and to rank them in four cases out of five within the first five candidates. The generated structures were ranked according to their synthetic accessibilities evaluated by the program SYLVIA. This investigation showed that the final candidate molecules have about the same synthetic complexity as the respective reference ligands. Furthermore, the plausibility of being true actives was assessed through literature searches.

We present a new algorithm for the enumeration of chemical fragment spaces under constraints. Fragment spaces consist of a set of molecular fragments and a set of rules that specifies how fragments can be combined. Although fragment spaces typically cover an infinite number of molecules, they can be enumerated in case that a physicochemical profile of the requested compounds is given. By using min-max ranges for a number of corresponding properties, our algorithm is able to enumerate all molecules which obey these properties. To speed up the calculation, the given ranges are used directly during the build-up process to guide the selection of fragments. Furthermore, a topology based fragment filter is used to skip most of the redundant fragment combinations. We applied the algorithm to 40 different target classes. For each of these, we generated tailored fragment spaces from sets of known inhibitors and additionally derived ranges for several physicochemical properties. We characterized the target-specific fragment spaces and were able to enumerate the complete chemical subspaces for most of the targets.