Bioisosteric Replacement and Scaffold Hopping in Lead Generation and Optimization

Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening

Docking tools to predict whether and how a small molecule binds to a target can be applied if a structural model of such target is available. The reliability of docking depends, however, on the accuracy of the adopted scoring function (SF). Despite intense research over the years, improving the accuracy of SFs for structure-based binding affinity prediction or virtual screening has proven to be a challenging task for any class of method. New SFs based on modern machine-learning regression models, which do not impose a predetermined functional form and thus are able to exploit effectively much larger amounts of experimental data, have recently been introduced. These machine-learning SFs have been shown to outperform a wide range of classical SFs at both binding affinity prediction and virtual screening. The emerging picture from these studies is that the classical approach of using linear regression with a small number of expert-selected structural features can be strongly improved by a machine-learning approach based on nonlinear regression allied with comprehensive data-driven feature selection. Furthermore, the performance of classical SFs does not grow with larger training datasets and hence this performance gap is expected to widen as more training data becomes available in the future. Other topics covered in this review include predicting the reliability of a SF on a particular target class, generating synthetic data to improve predictive performance and modeling guidelines for SF development. WIREs Comput Mol Sci 2015, 5:405-424. doi: 10.1002/wcms.1225 For further resources related to this article, please visit the WIREs website.

Development of highly sensitive and selective antibodies for the detection of the explosive pentaerythritol tetranitrate (PETN) by bioisosteric replacement

An improved antibody against the explosive pentaerythritol tetranitrate (PETN) was developed. The immunogen was designed by the concept of bioisosteric replacement, which led to an excellent polyclonal antibody with extreme selectivity and immunoassays of very good sensitivity. Compounds such as nitroglycerine, 2,4,6-trinitrotoluene, 1,3,5-trinitrobenzene, hexogen (RDX), 2,4,6-trinitroaniline, 1,3-dinitrobenzene, octogen (HMX), triacetone triperoxide, ammonium nitrate, 2,4,6-trinitrophenol and nitrobenzene were tested for potential cross-reactivity. The detection limit of a competitive enzyme-linked immunosorbent assay was determined to be around 0.5 µg/l. The dynamic range of the assay was found to be between 1 and 1000 µg/l, covering a concentration range of three decades. This work shows the successful application of the bioisosteric concept in immunochemistry by exchange of a nitroester to a carbonate diester. The antiserum might be used for the development of quick tests, biosensors, microtitration plate immunoassays, microarrays and other analytical methods for the highly sensitive detection of PETN, an explosive frequently used by terrorists, exploiting the extreme difficulty of its detection.

Design and synthesis of novel hydroxyanthraquinone nitrogen mustard derivatives as potential anticancer agents via a bioisostere approach

A series of hydroxyanthraquinones having an alkylating N-mustard pharmacophore at 1'-position were synthesized via a bioisostere approach to evaluate their cytotoxicity against four tumor cell lines (MDA-MB-231, HeLa, MCF-7 and A549). These compounds displayed significant in vitro cytotoxicity against MDA-MB-231 and MCF-7 cells, reflecting the excellent selectivity for the human breast cancer. Among them, compound 5k was the most cytotoxic with IC50 value of 0.263 nM and is more potent than DXR (IC50 = 0.294 nM) in inhibiting the growth of MCF-7 cells. The excellent cytotoxicity and good selectivity of compound 5k suggest that it could be a promising lead for further design and development of anticancer agents, especially for breast cancer.

Evaluation of a semi-automated workflow for fragment growing

Intelligent Automatic Design (IADE) is an expert system developed at Novartis to identify nonclassical bioisosteres. In addition to bioisostere searching, one could also use IADE to grow a fragment bound to a protein. Here we report an evaluation of IADE as a tool for fragment growing. Three examples from the literature served as test cases. In all three cases, IADE generated close analogues of the published compounds and reproduced their crystallographic binding modes. This exercise validated the use of the IADE system for fragment growing. We have also gained experience in optimizing the performance of IADE for this type of application.

Amide–triazole isosteric substitution for tuning self-assembly and incorporating new functions into soft supramolecular materials

We report the proof-of-concept demonstration for the synthesis of novel supramolecular soft gel materials based on amide–triazole isometric substitution.

De novo design - hop(p)ing against hope

Current trends in computational de novo design provide a fresh approach to 'scaffold-hopping' in drug discovery. The methodological repertoire is no longer limited to receptor-based methods, but specifically ligand-based techniques that consider multiple properties in parallel, including the synthetic feasibility of the computer-generated molecules and their polypharmacology, provide innovative ideas for the discovery of new chemical entities. The concept of fragment-based and virtual reaction-driven design enables rapid compound optimization from scratch with a manageable complexity of the search. Starting from known drugs as a reference, such algorithms suggest drug-like molecules with motivated scaffold variations, and advanced mathematical models of structure-activity landscapes and multi-objective design techniques have created new opportunities for hit and lead finding.

5-alkyl-1,3-oxazole derivatives of 6-amino-nicotinic acids as alkyl ester bioisosteres are antagonists of the P2Y12 receptor

BACKGROUND

Recently, we reported ethyl nicotinates as antagonists of the P2Y12 receptor, which is an important target in antiplatelet therapies. A potential liability of these compounds was their generally high in vivo clearance due to ethyl ester hydrolysis.

RESULTS

Shape and electrostatic similarity matching was used to select five-membered heterocycles to replace the ethyl ester functionality. The 5-methyl and 5-ethyl-oxazole bioisosteres retained the sub-micromolar potency levels of the parent ethyl esters. Many oxazoles showed a higher CYP450 dependent microsomal metabolism than the corresponding ethyl esters. Structure activity relationship investigations supported by ab initio calculations suggested that a correctly positioned alkyl substituent and a strong hydrogen bond acceptor were necessary structural motifs for binding. In rat pharmacokinetics, the low clearance was retained upon replacement of an ethyl ester with a 5-ethyl-oxazole.

CONCLUSION

The use of shape and electrostatic similarity led to the successful replacement of a metabolically labile ethyl ester functionality with 5-alkyl-oxazole bioisosteres.

inSARa: intuitive and interactive SAR interpretation by reduced graphs and hierarchical MCS-based network navigation

The analysis of Structure-Activity-Relationships (SAR) of small molecules is a fundamental task in drug discovery. Although a large number of methods are already published, there is still a strong need for novel intuitive approaches. The inSARa (intuitive networks for Structure-Activity Relationships analysis) method introduced herein takes advantage of the synergistic combination of reduced graphs (RG) and the intuitive maximum common substructure (MCS) concept. The main feature of the inSARa concept is a hierarchical network structure of clearly defined substructure relationships based on common pharmacophoric features. Thus, straightforward SAR interpretation is possible by interactive network navigation. When focusing on a set of active molecules at one single target, the resulting inSARa networks are shown to be valuable for various essential tasks in SAR analysis, such as the identification of activity cliffs or "activity switches", bioisosteric exchanges, common pharmacophoric features, or "SAR hotspots".

Scaffold-switching: an exploration of 5,6-fused bicyclic heteroaromatics systems to afford antituberculosis activity akin to the imidazo[1,2-a]pyridine-3-carboxylates

A set of 5,6-fused bicyclic heteroaromatic scaffolds were investigated for their in vitro anti-tubercular activity versus replicating and non-replicating strains of Mycobacterium tuberculosis (Mtb) in an attempt to find an alternative scaffold to the imidazo[1,2-a]pyridine and imidazo[1,2-a]pyrimidines that were previously shown to have potent activity against replicating and drug resistant Mtb. The five new bicyclic heteroaromatic scaffolds explored in this study include a 2,6-dimethylimidazo[1,2-b]pyridazine-3-carboxamide (7), a 2,6-dimethyl-1H-indole-3-carboxamide (8), a 6-methyl-1H-indazole-3-carboxamide (9), a 7-methyl-[1,2,4]triazolo[4,3-a]pyridine-3-carboxamide (10), and a 5,7-dimethyl-[1,2,4]triazolo[1,5-a]pyrimidine-2-carboxamide (11). Additionally, imidazo[1,2-a]pyridines isomers (2 and 12) and a homologous imidazo[1,2-a]pyrimidine isomer (6) were prepared and compared. Compounds 2 and 6 were found to be the most potent against H37Rv Mtb (MIC's of 0.1 μM and 1.3 μM) and were inactive (MIC >128 μM) against Staphylococcus aureus, Escherichia coli and Candida albicans. Against other non-tubercular mycobacteria strains, compounds 2 and 6 had activity against Mycobacterium avium (16 and 122 μM, respectively), Mycobacterium kansasii (4 and 19 μM, respectively), Mycobacterium bovis BCG (1 and 8 μM, respectively) while all the other scaffolds were inactive (>128 μM).

Novel GlyT1 inhibitor chemotypes by scaffold hopping. Part 2: development of a [3.3.0]-based series and other piperidine bioisosteres

This Letter describes the development and SAR of a novel series of GlyT1 inhibitors derived from a scaffold hopping approach, in lieu of an HTS campaign, which provided intellectual property position. Members within this new [3.3.0]-based series displayed excellent GlyT1 potency, selectivity, free fraction, and modest CNS penetration. Moreover, enantioselective GlyT1 inhibition was observed, within this novel series and a number of other piperidine bioisosteric cores.

Rational hopping of a peptidic scaffold into non-peptidic scaffolds: structurally novel potent proteasome inhibitors derived from a natural product, belactosin A

Rational scaffold hopping of a natural product belactosin A derivative based on the pharmacophore model constructed resulted in the identification of the significantly simplified highly potent non-peptide derivatives.

Assessment of the genotoxic and carcinogenic potentials of 3-aminothiophene derivatives using in vitro and in silico methodologies

Thiophene derivatives, a class of compounds widely used in products such as pharmaceuticals, agrochemicals or dyestuffs, represent chemicals of concern. Indeed, the thiophene ring is often considered as a structural moiety that may be involved in toxic effects in humans. We primarily focus on the genotoxic/mutagenic and carcinogenic potentials of the methyl 3-amino-4-methylthiophene-2-carboxylate (1), a precursor of the articaine local anesthetic (4) which falls within the scope of the European REACH (Registration, Evaluation, Authorisation and restriction of CHemicals) legislation. To discern some structure-toxicity relationships, we also studied two related compounds, namely the 3-amino 4-methylthiophene (2) and the 2-acetyl 4-chlorothiophene (3). Techniques employed to assess mutagenic and DNA-damaging effects involved the Salmonella mutagenicity assay (or Ames test) and the single-cell gel electrophoresis assay (or Comet assay). In the range of tested doses, none of these derivatives led to a positive response in the Ames tests and DNA damage was only observed in the Comet assay after high concentration exposure of 2. The study of their carcinogenic potential using the in vitro SHE (Syrian Hamster Embryo) cell transformation assay (CTA) highlighted the activity of compound 2. A combination of experimental data with in silico predictions of the reactivity of thiophene derivatives towards cytochrome P450 (CYP450), enabled us to hypothesize possible pathways leading to these toxicological profiles.

Scaffold-focused virtual screening: prospective application to the discovery of TTK inhibitors

We describe and apply a scaffold-focused virtual screen based upon scaffold trees to the mitotic kinase TTK (MPS1). Using level 1 of the scaffold tree, we perform both 2D and 3D similarity searches between a query scaffold and a level 1 scaffold library derived from a 2 million compound library; 98 compounds from 27 unique top-ranked level 1 scaffolds are selected for biochemical screening. We show that this scaffold-focused virtual screen prospectively identifies eight confirmed active compounds that are structurally differentiated from the query compound. In comparison, 100 compounds were selected for biochemical screening using a virtual screen based upon whole molecule similarity resulting in 12 confirmed active compounds that are structurally similar to the query compound. We elucidated the binding mode for four of the eight confirmed scaffold hops to TTK by determining their protein-ligand crystal structures; each represents a ligand-efficient scaffold for inhibitor design.

QSAR methods for the discovery of new inflammatory bowel disease drugs

INTRODUCTION

Inflammatory bowel disease (IBD) represents an important class of chronic gastrointestinal tract disease. And although there are already several useful treatments to reduce and control the symptoms, there is still no cure. One drug discovery technique used is the computer-aided (in silico) discovery approach which has largely demonstrated efficacy. Computational techniques, when used in combination with traditional drug discovery methodology, greatly increase the chance of drug discovery in a sustainable and economical fashion.

AREAS COVERED

This review aims to provide the most recent and important advances of in silico IBD drug discovery. While this review is mainly focused on QSAR methods, especially those based on molecular topology (MT), additional topics, such as docking or comparative field analysis are also addressed.

EXPERT OPINION

IBD is a worldwide growing health concern that can only be currently treated in symptomatic and palliative way; thus, the search for new drugs is imperative. Computer-aided methods, which focus on the drug-receptor interaction, are essential tool in this regard. It is noted, however that a major problem is that although there are many known receptors associated with IBD, none of these have yet been found essential. The use of other approaches, including QSAR methodology, is certainly a complementary and attractive alternative; especially QSAR methods based on MT, which has proven successful in other drug discovery.

Chemoisosterism in the proteome

The concept of chemoisosterism of protein environments is introduced as the complementary property to bioisosterism of chemical fragments. In the same way that two chemical fragments are considered bioisosteric if they can bind to the same protein environment, two protein environments will be considered chemoisosteric if they can interact with the same chemical fragment. The basis for the identification of chemoisosteric relationships among protein environments was the increasing amount of crystal structures available currently for protein-ligand complexes. It is shown that one can recover the right location and orientation of chemical fragments constituting the native ligand in a nuclear receptor structure by using only chemoisosteric environments present in enzyme structures. Examples of the potential applicability of chemoisosterism in fragment-based drug discovery are provided.

A cell-based fascin bioassay identifies compounds with potential anti-metastasis or cognition-enhancing functions

The actin-bundling protein fascin is a key mediator of tumor invasion and metastasis and its activity drives filopodia formation, cell-shape changes and cell migration. Small-molecule inhibitors of fascin block tumor metastasis in animal models. Conversely, fascin deficiency might underlie the pathogenesis of some developmental brain disorders. To identify fascin-pathway modulators we devised a cell-based assay for fascin function and used it in a bidirectional drug screen. The screen utilized cultured fascin-deficient mutant Drosophila neurons, whose neurite arbors manifest the 'filagree' phenotype. Taking a repurposing approach, we screened a library of 1040 known compounds, many of them FDA-approved drugs, for filagree modifiers. Based on scaffold distribution, molecular-fingerprint similarities, and chemical-space distribution, this library has high structural diversity, supporting its utility as a screening tool. We identified 34 fascin-pathway blockers (with potential anti-metastasis activity) and 48 fascin-pathway enhancers (with potential cognitive-enhancer activity). The structural diversity of the active compounds suggests multiple molecular targets. Comparisons of active and inactive compounds provided preliminary structure-activity relationship information. The screen also revealed diverse neurotoxic effects of other drugs, notably the 'beads-on-a-string' defect, which is induced solely by statins. Statin-induced neurotoxicity is enhanced by fascin deficiency. In summary, we provide evidence that primary neuron culture using a genetic model organism can be valuable for early-stage drug discovery and developmental neurotoxicity testing. Furthermore, we propose that, given an appropriate assay for target-pathway function, bidirectional screening for brain-development disorders and invasive cancers represents an efficient, multipurpose strategy for drug discovery.

The Molecule Cloud - compact visualization of large collections of molecules

BACKGROUND

Analysis and visualization of large collections of molecules is one of the most frequent challenges cheminformatics experts in pharmaceutical industry are facing. Various sophisticated methods are available to perform this task, including clustering, dimensionality reduction or scaffold frequency analysis. In any case, however, viewing and analyzing large tables with molecular structures is necessary. We present a new visualization technique, providing basic information about the composition of molecular data sets at a single glance.

SUMMARY

A method is presented here allowing visual representation of the most common structural features of chemical databases in a form of a cloud diagram. The frequency of molecules containing particular substructure is indicated by the size of respective structural image. The method is useful to quickly perceive the most prominent structural features present in the data set. This approach was inspired by popular word cloud diagrams that are used to visualize textual information in a compact form. Therefore we call this approach "Molecule Cloud". The method also supports visualization of additional information, for example biological activity of molecules containing this scaffold or the protein target class typical for particular scaffolds, by color coding. Detailed description of the algorithm is provided, allowing easy implementation of the method by any cheminformatics toolkit. The layout algorithm is available as open source Java code.

CONCLUSIONS

Visualization of large molecular data sets using the Molecule Cloud approach allows scientists to get information about the composition of molecular databases and their most frequent structural features easily. The method may be used in the areas where analysis of large molecular collections is needed, for example processing of high throughput screening results, virtual screening or compound purchasing. Several example visualizations of large data sets, including PubChem, ChEMBL and ZINC databases using the Molecule Cloud diagrams are provided.

Classification of scaffold-hopping approaches

The general goal of drug discovery is to identify novel compounds that are active against a preselected biological target with acceptable pharmacological properties defined by marketed drugs. Scaffold hopping has been widely applied by medicinal chemists to discover equipotent compounds with novel backbones that have improved properties. In this article we classify scaffold hopping into four major categories, namely heterocycle replacements, ring opening or closure, peptidomimetics and topology-based hopping. We review the structural diversity of original and final scaffolds with respect to each category. We discuss the advantages and limitations of small, medium and large-step scaffold hopping. Finally, we summarize software that is frequently used to facilitate different kinds of scaffold-hopping methods.

DOGS: reaction-driven de novo design of bioactive compounds

We present a computational method for the reaction-based de novo design of drug-like molecules. The software DOGS (Design of Genuine Structures) features a ligand-based strategy for automated ‘in silico’ assembly of potentially novel bioactive compounds. The quality of the designed compounds is assessed by a graph kernel method measuring their similarity to known bioactive reference ligands in terms of structural and pharmacophoric features. We implemented a deterministic compound construction procedure that explicitly considers compound synthesizability, based on a compilation of 25'144 readily available synthetic building blocks and 58 established reaction principles. This enables the software to suggest a synthesis route for each designed compound. Two prospective case studies are presented together with details on the algorithm and its implementation. De novo designed ligand candidates for the human histamine H₄ receptor and γ-secretase were synthesized as suggested by the software. The computational approach proved to be suitable for scaffold-hopping from known ligands to novel chemotypes, and for generating bioactive molecules with drug-like properties.

The computer program DOGS aims at the automated generation of new bioactive compounds. Only a single known reference compound is required to have the computer come up with suggestions for potentially isofunctional molecules. A specific feature of the algorithm is its capability to propose a synthesis plan for each designed compound, based on a large set of readily available molecular building blocks and established reaction protocols. The de novo design software provides rapid access to tool compounds and starting points for the development of a lead candidate structure. The manuscript gives a detailed description of the algorithm. Theoretical analysis and prospective case studies demonstrate its ability to propose bioactive, plausible and chemically accessible compounds.

SCRIPDB: a portal for easy access to syntheses, chemicals and reactions in patents

The patent literature is a rich catalog of biologically relevant chemicals; many public and commercial molecular databases contain the structures disclosed in patent claims. However, patents are an equally rich source of metadata about bioactive molecules, including mechanism of action, disease class, homologous experimental series, structural alternatives, or the synthetic pathways used to produce molecules of interest. Unfortunately, this metadata is discarded when chemical structures are deposited separately in databases. SCRIPDB is a chemical structure database designed to make this metadata accessible. SCRIPDB provides the full original patent text, reactions and relationships described within any individual patent, in addition to the molecular files common to structural databases. We discuss how such information is valuable in medical text mining, chemical image analysis, reaction extraction and in silico pharmaceutical lead optimization. SCRIPDB may be searched by exact chemical structure, substructure or molecular similarity and the results may be restricted to patents describing synthetic routes. SCRIPDB is available at http://dcv.uhnres.utoronto.ca/SCRIPDB.

Scaffold diversity of exemplified medicinal chemistry space

The scaffold diversity of 7 representative commercial and proprietary compound libraries is explored for the first time using both Murcko frameworks and Scaffold Trees. We show that Level 1 of the Scaffold Tree is useful for the characterization of scaffold diversity in compound libraries and offers advantages over the use of Murcko frameworks. This analysis also demonstrates that the majority of compounds in the libraries we analyzed contain only a small number of well represented scaffolds and that a high percentage of singleton scaffolds represent the remaining compounds. We use Tree Maps to clearly visualize the scaffold space of representative compound libraries, for example, to display highly populated scaffolds and clusters of structurally similar scaffolds. This study further highlights the need for diversification of compound libraries used in hit discovery by focusing library enrichment on the synthesis of compounds with novel or underrepresented scaffolds.

Target Profile Prediction and Practical Evaluation of a Biginelli-Type Dihydropyrimidine Compound Library

We present a self-organizing map (SOM) approach to predicting macromolecular targets for combinatorial compound libraries. The aim was to study the usefulness of the SOM in combination with a topological pharmacophore representation (CATS) for selecting biologically active compounds from a virtual combinatorial compound collection, taking the multi-component Biginelli dihydropyrimidine reaction as an example. We synthesized a candidate compound from this library, for which the SOM model suggested inhibitory activity against cyclin-dependent kinase 2 (CDK2) and other kinases. The prediction was confirmed in an in vitro panel assay comprising 48 human kinases. We conclude that the computational technique may be used for ligand-based in silico pharmacology studies, off-target prediction, and drug re-purposing, thereby complementing receptor-based approaches.

Benzotriazoles and indazoles are scaffolds with biological activity against Entamoeba histolytica

Parasitic infections caused by Entamoeba histolytica are still major threats against public health, especially in developing countries. Although current therapies exist, the problems associated with parasite resistance and negative side effects make it imperative to search for new therapeutic agents. A systematic scaffold analysis reported herein of a public database containing 474 antiamoebic compounds reveals that benzimidazole is the most active scaffold reported thus far. To gain insights into the antiamoebic activity of novel compounds, the authors report herein the biological activity of 12 compounds, including benzotriazole and indazole derivatives, scaffolds not previously tested against E. histolytica. Compounds with the benzotriazole and indazole scaffolds showed low micromolar activity (IC(50) = 0.304 and 0.339 µM) and are more active than metronidazole, which is the drug of choice used for the treatment of amebiosis. The novel compounds have similar properties to approved drugs. Compounds with novel scaffolds represent promising starting points of an optimization program against E. histolytica.

Large-scale exploration of bioisosteric replacements on the basis of matched molecular pairs

BACKGROUND

Bioisosteric replacements are commonly understood to be replacements of groups of atoms in bioactive compounds that retain their specific activity and retain, or further improve, compound potency. Such chemical modifications are of high interest in medicinal chemistry and are often considered in compound exploration and optimization.

RESULTS

We have applied the matched molecular pair formalism to carry out a large-scale data mining study to identify bioisosteres in publicly available active compounds with similar potency. Our data mining effort has identified a set of 96 nonredundant bioisosteric replacements, approximately half of which were previously unobserved. However, a number of replacements commonly considered to be bioisosteric did not meet our extended bioisostere selection criteria, which included high frequency of occurrence, limited potency alterations and activity across different target families. Furthermore, many commonly known bioisosteric replacements were found to be dependent on the structural context in which they occurred.

CONCLUSION

The systematic analysis of public domain compound data presented herein provides an alternative route to the identification of bioisosteric replacements and further extends the spectrum of currently known bioisosteres. We provide a compendium of bioisosteric replacements that are well supported by currently available compound data.

Recent trends and observations in the design of high-quality screening collections

The design of a high-quality screening collection is of utmost importance for the early drug-discovery process and provides, in combination with high-quality assay systems, the foundation of future discoveries. Herein, we review recent trends and observations to successfully expand the access to bioactive chemical space, including the feedback from hit assessment interviews of high-throughput screening campaigns; recent successes with chemogenomics target family approaches, the identification of new relevant target/domain families, diversity-oriented synthesis and new emerging compound classes, and non-classical approaches, such as fragment-based screening and DNA-encoded chemical libraries. The role of in silico library design approaches are emphasized.

Reaction-driven de novo design, synthesis and testing of potential type II kinase inhibitors

BACKGROUND

De novo design of drug-like compounds with a desired pharmacological activity profile has become feasible through innovative computer algorithms. Fragment-based design and simulated chemical reactions allow for the rapid generation of candidate compounds as blueprints for organic synthesis.

METHODS

We used a combination of complementary virtual-screening tools for the analysis of de novo designed compounds that were generated with the aim to inhibit inactive polo-like kinase 1 (Plk1), a target for the development of cancer therapeutics. A homology model of the inactive state of Plk1 was constructed and the nucleotide binding pocket conformations in the DFG-in and DFG-out state were compared. The de novo-designed compounds were analyzed using pharmacophore matching, structure-activity landscape analysis, and automated ligand docking. One compound was synthesized and tested in vitro.

RESULTS

The majority of the designed compounds possess a generic architecture present in known kinase inhibitors. Predictions favor kinases as targets of these compounds but also suggest potential off-target effects. Several bioisosteric replacements were suggested, and de novo designed compounds were assessed by automated docking for potential binding preference toward the inactive (type II inhibitors) over the active conformation (type I inhibitors) of the kinase ATP binding site. One selected compound was successfully synthesized as suggested by the software. The de novo-designed compound exhibited inhibitory activity against inactive Plk1 in vitro, but did not show significant inhibition of active Plk1 and 38 other kinases tested.

CONCLUSIONS

Computer-based de novo design of screening candidates in combination with ligand- and receptor-based virtual screening generates motivated suggestions for focused library design in hit and lead discovery. Attractive, synthetically accessible compounds can be obtained together with predicted on- and off-target profiles and desired activities.

Identification of target family directed bioisosteric replacements

Shown are exemplary replacements of chemical groups that are bioisosteric for individual target families.

Modeling approaches for ligand-based 3D similarity

3D ligand-based similarity approaches are widely used in the early phases of drug discovery for tasks such as hit finding by virtual screening or compound design with quantitative structure–activity relationships. Here in we review widely used software for performing such tasks. Some techniques are based on relatively mature technology, shape-based similarity for instance. Typically, these methods remained in the realm of the expert user, the experienced modeler. However, advances in implementation and speed have improved usability and allow these methods to be applied to databases comprising millions of compounds. There are now many reports of such methods impacting drug-discovery projects. As such, the medicinal chemistry community has become the intended market for some of these new tools, yet they may consider the wide array and choice of approaches somewhat disconcerting. Each method has subtle differences and is better suited to certain tasks than others. In this article we review some of the widely used computational methods via application, provide straightforward background on the underlying theory and provide examples for the interested reader to pursue in more detail. In the new era of preclinical drug discovery there will be ever more pressure to move faster and more efficiently, and computational approaches based on 3D ligand similarity will play an increasing role in in this process.