SHOP: scaffold HOPping by GRID-based similarity searches

Scaffold hopping approaches for dual-target antitumor drug discovery: opportunities and challenges

INTRODUCTION

Scaffold hopping has emerged as a practical tactic to enrich the synthetic bank of small molecule antitumor agents. Specifically, it enables the chemist to refine the lead compound's pharmacodynamic, pharmacokinetic, and physiochemical properties. Scaffold hopping opens up fresh molecular territory beyond established patented chemical domains.

AREA COVERED

The authors present the scaffold hopping-based drug design strategies for dual inhibitory antitumor structural templates in this review. Minor modifications, structure rigidification and simplification (ring-closing and opening), and complete structural overhauls were the strategies employed by the medicinal chemist to generate a library of bifunctional inhibitors. In addition, the review presents an overview of the computational methods of scaffold hopping (software and programs) and organopalladium catalysis leveraged for the synthesis of templates designed via scaffold hopping.

EXPERT OPINION

The medicinal chemist has demonstrated remarkable prowess in furnishing dual inhibitory antitumor chemical architectures. Scaffold hopping-based drug design strategies have yielded a plethora of pharmacodynamically superior dual modulatory antitumor agents. An integrated approach involving computational advancements, synthetic methodology advancements, and conventional drug design strategies is required to increase the number of scaffold-hopping-assisted drug discovery campaigns.

FragExplorer: GRID-Based Fragment Growing and Replacement

Understanding which chemical modifications can be made to known ligands is a key aspect of structure-based drug design and one that was pioneered by the software GRID. We developed FragExplorer with the explicit aim of showing GRID users which fragments would best match the GRID molecular interaction fields in a protein binding site, given a bound ligand as a starting point. Users can grow ligands or replace existing moieties; the R-Group Exploration mode identifies all potential R-Groups and searches for replacements automatically; the Scaffold Exploration mode does the same for all potential scaffolds. For a ligand with three points of variation, R-Group Exploration will typically explore a chemical space of 10¹⁶ potential molecules; including Scaffold Exploration increases this to 10²². FragExplorer was designed to be integrated within an interactive 3D Editor/Designer; therefore, the speed of computation was an important consideration; a typical fragment search takes 20 seconds. In a fragment reprediction test, FragExplorer demonstrates an overall fragment retrieval rate of 55%, increasing to 69% for smaller fragments. At a 90% substructural match, the retrieval rate increases to ∼80%. We also show how the approach could have been used to hop from olmesartan to azilsartan or to optimize a p38 MAP kinase lead to a compound that bears similarity to a known nanomolar inhibitor.

Advances in L-Type Calcium Channel Structures, Functions and Molecular Modeling

L-type Calcium Channels (LTCCs), also termed as Ca_v1, belong to voltage-gated calcium channels (VGCCs/Ca_vs), which play a critical role in a wide spectrum of physiological processes, including neurotransmission, cell cycle, muscular contraction, cardiac action potential and gene expression. Aberrant regulation of calcium channels is involved in neurological, cardiovascular, muscular and psychiatric disorders. Accordingly, LTCCs have been regarded as important drug targets, and a number of LTCC drugs are in clinical use. In this review, the recent development of structures and biological functions of LTCCs are introduced. Moreover, the representative modulators and ligand binding sites of LTCCs are discussed. Finally, molecular modeling and Computer-aided Drug Design (CADD) methods for understanding structure-function relations of LTCCs are summarized.

Molecular Scaffold Hopping via Holistic Molecular Representation

Molecular descriptors encode a variety of molecular representations for computer-assisted drug discovery. Here, we focus on the Weighted Holistic Atom Localization and Entity Shape (WHALES) descriptors, which were originally designed for scaffold hopping from natural products to synthetic molecules. WHALES descriptors capture molecular shape and partial charges simultaneously. We introduce the key aspects of the WHALES concept and provide a step-by-step guide on how to use these descriptors for virtual compound screening and scaffold hopping. The results presented can be reproduced by using the code freely available from URL: github.com/ETHmodlab/scaffold_hopping_whales .

FragRep: A Web Server for Structure-Based Drug Design by Fragment Replacement

The design of efficient computational tools for structure-guided ligand design is essential for the drug discovery process. We hereby present FragRep, a new web server for structure-based ligand design by fragment replacement. The input is a protein and a ligand structure, either from protein data bank or from molecular docking. Users can choose specific substructures they want to modify. The server tries to find suitable fragments that not only meet the geometric requirements of the remaining part of the ligand but also fit well with local protein environments. FragRep is a powerful computational tool for the rapid generation of ligand design ideas; either in scaffold hopping or bioisosteric replacing. The FragRep Server is freely available to researchers and can be accessed at http://xundrug.cn/fragrep.

Characterization of PD-L1 binding sites by a combined FMO/GRID-DRY approach

The programmed cell death protein 1 (PD-1) and its ligand, PD-L1, constitute an important co-inhibitory immune checkpoint leading to downregulation of immune system. Tumor cells developed a strategy to trigger PD-1/PD-L1 pathway reducing the T cell anticancer activity. Anti-PD-L1 small drugs, generally with improved pharmacokinetic and technological profiles than monoclonal antibodies, became an attractive research topic. Nevertheless, still few works have been published on the chemical features of possible binding sites. In this work, we applied a novel computational protocol based on the combination of the ab initio Fragment Molecular Orbital (FMO) method and a newly developed GRID-DRY approach in order to characterize the PD-L1 binding sites, starting from PD-1/PD-L1 and PD-L1/BMS-ligands (Bristol-Mayers Squibb ligands) complexes. The FMO method allows the calculation of the pair-residues as well as the ligand-residues interactions with ab initio accuracy, whereas the GRID-DRY approach is an effective tool to investigate hydrophobic interactions, not easily detectable by ab initio methods. The present GRID-DRY protocol is able to determine the energy contributions of each ligand atoms to each hydrophobic interaction, both qualitatively and quantitatively. We were also able to identify the three specific hot regions involved in PD-1/PD-L1 protein-protein interaction and in PD-L1/BMS-ligand interactions, in agreement with preceding theoretical/experimental results, and to suggest a specific pharmacophore for PD-L1 inhibitors.

Considerations for Docking of Selective Angiotensin-Converting Enzyme Inhibitors

The angiotensin-converting enzyme (ACE) is a two-domain dipeptidylcarboxypeptidase, which has a direct involvement in the control of blood pressure by performing the hydrolysis of angiotensin I to produce angiotensin II. At the same time, ACE hydrolyzes other substrates such as the vasodilator peptide bradykinin and the anti-inflammatory peptide N-acetyl-SDKP. In this sense, ACE inhibitors are bioactive substances with potential use as medicinal products for treatment or prevention of hypertension, heart failures, myocardial infarction, and other important diseases. This review examined the most recent literature reporting ACE inhibitors with the help of molecular modeling. The examples exposed here demonstrate that molecular modeling methods, including docking, molecular dynamics (MD) simulations, quantitative structure-activity relationship (QSAR), etc, are essential for a complete structural picture of the mode of action of ACE inhibitors, where molecular docking has a key role. Examples show that too many works identified ACE inhibitory activities of natural peptides and peptides obtained from hydrolysates. In addition, other works report non-peptide compounds extracted from natural sources and synthetic compounds. In all these cases, molecular docking was used to provide explanation of the chemical interactions between inhibitors and the ACE binding sites. For docking applications, most of the examples exposed here do not consider that: (i) ACE has two domains (nACE and cACE) with available X-ray structures, which are relevant for the design of selective inhibitors, and (ii) nACE and cACE binding sites have large dimensions, which leads to non-reliable solutions during docking calculations. In support of the solution of these problems, the structural information found in Protein Data Bank (PDB) was used to perform an interaction fingerprints (IFPs) analysis applied on both nACE and cACE domains. This analysis provides plots that identify the chemical interactions between ligands and both ACE binding sites, which can be used to guide docking experiments in the search of selective natural components or novel drugs. In addition, the use of hydrogen bond constraints in the S₂ and S₂′ subsites of nACE and cACE are suggested to guarantee that docking solutions are reliable.

WebMetabase: cleavage sites analysis tool for natural and unnatural substrates from diverse data source

SUMMARY

More than 150 peptide therapeutics are globally in clinical development. Many enzymatic barriers should be crossed by a successful drug to be prosperous in such a process. Therefore, the new peptide drugs must be designed preventing the potential protease cleavage to make the compound less susceptible to protease reaction. We present a new data analysis tool developed in WebMetabase, an approach that stores the information from liquid chromatography mass spectrometry-based experimental data or from external sources such as the MEROPS database. The tool is a chemically aware system where each peptide substrate is presented as a sequence of structural blocks (SBs) connected by amide bonds and not being limited to the natural amino acids. Each SB is characterized by its pharmacophoric and physicochemical properties including a similarity score that describes likelihood between a SB and each one of the other SBs in the database. This methodology can be used to perform a frequency analysis to discover the most frequent cleavage sites for similar amide bonds, defined based on the similarity of the SB that participate in such a bond within the experimentally derived and/or public database.

AVAILABILITY AND IMPLEMENTATION

http://webmetabase.com:8182/WebMetabaseBioinformatics/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Protein-ligand interaction-guided discovery of novel VEGFR-2 inhibitors

As an effective target in abnormal angiogenesis-related tumor treatment, VEGFR-2 has small-molecule inhibitors of various scaffolds being approved for treating diseases such as renal carcinoma, non-small cell lung cancer, etc. However, endogenous and acquired drug resistance are still considered to be the main contributors for the failure of VEGFR-2 clinical candidates. Therefore, development of novel VEGFR-2 inhibitors is still urgently needed in the market but also challenging. In this work, residues including Asp1046, Ile1025, HIS1026, Cys919 and Lys868 were identified as the most important residues for Hbonded interaction, while His1026, Asp1046, Glu885, Ile1025 and Leu840 exhibited critical role for the nonbonded interactions through a comprehensive analysis of protein-ligand interactions, which plays critical roles in the binding of compounds and targets. Guided by the analysis of binding interactions, a total of 10 novel VEGFR-2 inhibitors based on N-methyl-4-oxo-N-propyl-1,4-dihydroquinoline-2-carboxamide scaffold were discovered through fragment-based drug design and structure-based virtual screening, which expands the chemical space of current VEGFR-2 inhibitors. Biological activity evaluation showed that even though the enzymatic activity of these compounds against VEGFR-2 were inferior to that of the positive controls sorafenib and motesanib, compound I-10 showed moderate HepG2 cell inhibitory activity with an IC₅₀ value of 33.65 μM and eight compounds exhibited moderate or higher HUVEC inhibitory activity in the range of 19.54-57.98 μM compared to the controls. Particularly, the HUVEC inhibitory activity of compound I-6 (IC₅₀ = 19.54 μM) outperformed motesanib and can be used as starting points for further optimization and development for cancer treatment.Communicated by Ramaswamy H. Sarma.

QSAR modeling and in silico design of small-molecule inhibitors targeting the interaction between E3 ligase VHL and HIF-1α

Protein-protein interactions (PPIs) have attracted much attention recently because of their preponderant role in most biological processes. The prevention of the interaction between E3 ligase VHL and HIF-1[Formula: see text] may improve tolerance to hypoxia and ameliorate the prognosis of many diseases. To obtain novel potent inhibitors of VHL/HIF-1[Formula: see text] interaction, a series of hydroxyproline-based inhibitors were investigated for structural optimization using a combination of QSAR modeling and molecular docking. Here, 2D- and 3D-QSAR models were developed by genetic function approximation (GFA) and comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) methods, respectively. The top-ranked models with strict validation revealed satisfactory statistical parameters (CoMFA with [Formula: see text], 0.637; [Formula: see text], 0.955; [Formula: see text], 0.944; CoMSIA with [Formula: see text], 0.649; [Formula: see text], 0.954; [Formula: see text], 0.911; GFA with [Formula: see text], 0.721; [Formula: see text], 0.801; [Formula: see text], 0.861). The selected five 2D-QSAR descriptors were in good accordance with the 3D-QSAR results, and contour maps gave the visualization of feature requirements for inhibitory activity. A new diverse molecular database was created by molecular fragment replacement and BREED techniques for subsequent virtual screening. Eventually, 31 novel hydroxyproline derivatives stood out as potential VHL/HIF-1[Formula: see text] inhibitors with favorable predictions by the CoMFA, CoMSIA and GFA models. The reliability of this protocol suggests that it could also be applied to the exploration of lead optimization of other PPI targets.

Scaffold-hopping from xanthines to tricyclic guanines: A case study of dipeptidyl peptidase 4 (DPP4) inhibitors

Molecular modeling of unbound tricyclic guanine scaffolds indicated that they can serve as effective bioisosteric replacements of xanthines. This notion was further confirmed by a combination of X-ray crystallography and SAR studies, indicating that tricyclic guanine DPP4 inhibitors mimic the binding mode of xanthine inhibitors, exemplified by linagliptin. Realization of the bioisosteric relationship between these scaffolds potentially will lead to a wider application of cyclic guanines as xanthine replacements in drug discovery programs for a variety of biological targets. Newly designed DPP4 inhibitors achieved sub-nanomolar potency range and demonstrated oral activity in vivo in mouse glucose tolerance test.

Fragment-based design for the development of N-domain-selective angiotensin-1-converting enzyme inhibitors

ACE (angiotensin-1-converting enzyme) is a zinc metallopeptidase that plays a prominent role in blood pressure regulation and electrolyte homeostasis. ACE consists of two homologous domains that despite similarities of sequence and topology display differences in substrate processing and inhibitor binding. The design of inhibitors that selectively inhibit the N-domain (N-selective) could be useful in treating conditions of tissue injury and fibrosis due to build-up of N-domain-specific substrate Ac-SDKP (N-acetyl-Ser-Asp-Lys-Pro). Using a receptor-based SHOP (scaffold hopping) approach with N-selective inhibitor RXP407, a shortlist of scaffolds that consisted of modified RXP407 backbones with novel chemotypes was generated. These scaffolds were selected on the basis of enhanced predicted interaction energies with N-domain residues that differed from their C-domain counterparts. One scaffold was synthesized and inhibitory binding tested using a fluorogenic ACE assay. A molecule incorporating a tetrazole moiety in the P2 position (compound 33RE) displayed potent inhibition (K(i)=11.21±0.74 nM) and was 927-fold more selective for the N-domain than the C-domain. A crystal structure of compound 33RE in complex with the N-domain revealed its mode of binding through aromatic stacking with His388 and a direct hydrogen bond with the hydroxy group of the N-domain specific Tyr369. This work further elucidates the molecular basis for N-domain-selective inhibition and assists in the design of novel N-selective ACE inhibitors that could be employed in treatment of fibrosis disorders.

Do not hesitate to use Tversky-and other hints for successful active analogue searches with feature count descriptors

This study is an exhaustive analysis of the neighborhood behavior over a large coherent data set (ChEMBL target/ligand pairs of known Ki, for 165 targets with >50 associated ligands each). It focuses on similarity-based virtual screening (SVS) success defined by the ascertained optimality index. This is a weighted compromise between purity and retrieval rate of active hits in the neighborhood of an active query. One key issue addressed here is the impact of Tversky asymmetric weighing of query vs candidate features (represented as integer-value ISIDA colored fragment/pharmacophore triplet count descriptor vectors). The nearly a 3/4 million independent SVS runs showed that Tversky scores with a strong bias in favor of query-specific features are, by far, the most successful and the least failure-prone out of a set of nine other dissimilarity scores. These include classical Tanimoto, which failed to defend its privileged status in practical SVS applications. Tversky performance is not significantly conditioned by tuning of its bias parameter α. Both initial "guesses" of α = 0.9 and 0.7 were more successful than Tanimoto (at its turn, better than Euclid). Tversky was eventually tested in exhaustive similarity searching within the library of 1.6 M commercial + bioactive molecules at http://infochim.u-strasbg.fr/webserv/VSEngine.html , comparing favorably to Tanimoto in terms of "scaffold hopping" propensity. Therefore, it should be used at least as often as, perhaps in parallel to Tanimoto in SVS. Analysis with respect to query subclasses highlighted relationships of query complexity (simply expressed in terms of pharmacophore pattern counts) and/or target nature vs SVS success likelihood. SVS using more complex queries are more robust with respect to the choice of their operational premises (descriptors, metric). Yet, they are best handled by "pro-query" Tversky scores at α > 0.5. Among simpler queries, one may distinguish between "growable" (allowing for active analogs with additional features), and a few "conservative" queries not allowing any growth. These (typically bioactive amine transporter ligands) form the specific application domain of "pro-candidate" biased Tversky scores at α < 0.5.

Structure and ligand based drug design strategies in the development of novel 5- LOX inhibitors

Lipoxygenases (LOXs) are non-heme iron containing dioxygenases involved in the oxygenation of polyunsaturated fatty acids (PUFAs) such as arachidonic acid (AA). Depending on the position of insertion of oxygen, LOXs are classified into 5-, 8-, 9-, 12- and 15-LOX. Among these, 5-LOX is the most predominant isoform associated with the formation of 5-hydroperoxyeicosatetraenoic acid (5-HpETE), the precursor of non-peptido (LTB₄) and peptido (LTC₄, LTD₄, and LTE₄) leukotrienes. LTs are involved in inflammatory and allergic diseases like asthma, ulcerative colitis, rhinitis and also in cancer. Consequently 5-LOX has become target for the development of therapeutic molecules for treatment of various inflammatory disorders. Zileuton is one such inhibitor of 5-LOX approved for the treatment of asthma.

In the recent times, computer aided drug design (CADD) strategies have been applied successfully in drug development processes. A comprehensive review on structure based drug design strategies in the development of novel 5-LOX inhibitors is presented in this article. Since the crystal structure of 5-LOX has been recently solved, efforts to develop 5-LOX inhibitors have mostly relied on ligand based rational approaches. The present review provides a comprehensive survey on these strategies in the development of 5-LOX inhibitors.

De novo design of N-(pyridin-4-ylmethyl)aniline derivatives as KDR inhibitors: 3D-QSAR, molecular fragment replacement, protein-ligand interaction fingerprint, and ADMET prediction

Vascular endothelial growth factor (VEGF) and its receptor tyrosine kinase VEGFR-2 or kinase insert domain receptor (KDR) have been identified as promising targets for novel anticancer agents. To achieve new potent inhibitors of KDR, we conducted molecular fragment replacement (MFR) studies for the understanding of 3D-QSAR modeling and the docking investigation of arylphthalazines and 2-((1H-Azol-1-yl)methyl)-N-arylbenzamides-based KDR inhibitors. Two favorable 3D-QSAR models (CoMFA with q(2), 0.671; r(2), 0.969; CoMSIA with q(2), 0.608; r(2), 0.936) have been developed to predict the biological activity of new compounds. The new molecular database generated by MFR was virtually screened using Glide (docking) and further evaluated with CoMFA prediction, protein-ligand interaction fingerprint (PLIF) and ADMET analysis. 44 N-(pyridin-4-ylmethyl)aniline derivatives as novel potential KDR inhibitors were finally obtained. In this paper, the work flow developed could be applied to de novo drug design and virtual screening potential KDR inhibitors, and use hit compounds to further optimize and design new potential KDR inhibitors.

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.

Graph-based similarity concepts in virtual screening

Applying similarity for finding new promising compounds is a key issue in drug design. Conversely, quantifying similarity between molecules has remained a difficult task despite the numerous approaches. Here, some general aspects along with recent developments regarding similarity criteria are collected. For the purpose of virtual screening, the compounds have to be encoded into a computer-readable format that permits a comparison, according to given similarity criteria, comprising the use of the 3D structure, fingerprints, graph-based and alignment-based approaches. Whereas finding the most common substructures is the most obvious method, more recent approaches take into account chemical modifications that appear throughout existing drugs, from various therapeutic categories and targets.

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.

A novel approach for predicting P-glycoprotein (ABCB1) inhibition using molecular interaction fields

P-glycoprotein (Pgp or ABCB1) is an ABC transporter protein involved in intestinal absorption, drug metabolism, and brain penetration, and its inhibition can seriously alter a drug's bioavailability and safety. In addition, inhibitors of Pgp can be used to overcome multidrug resistance. Given this dual purpose, reliable in silico procedures to predict Pgp inhibition are of great interest. A large and accurate literature collection yielded more than 1200 structures; a model was then constructed using various molecular interaction field-based technologies, considering pharmacophoric features and those physicochemical properties related to membrane partitioning. High accuracy was demonstrated internally with two different validation sets and, moreover, using a number of molecules, for which Pgp inhibition was not experimentally available but was evaluated in-house. All of the validations confirmed the robustness of the model and its suitability to help medicinal chemists in drug discovery. The information derived from the model was rationalized as a pharmacophore for competitive Pgp inhibition.

Pharmacophore-based virtual screening

This chapter is a review of the most recent developments in the field of pharmacophore modeling, covering both methodology and application. Pharmacophore-based virtual screening is nowadays a mature technology, very well accepted in the medicinal chemistry laboratory. Nevertheless, like any empirical approach, it has specific limitations and efforts to improve the methodology are still ongoing. Fundamentally, the core idea of "stripping" functional groups of their actual chemical nature in order to classify them into very few pharmacophore types, according to their dominant physico-chemical features, is both the main advantage and the main drawback of pharmacophore modeling. The advantage is the one of simplicity - the complex nature of noncovalent ligand binding interactions is rendered intuitive and comprehensible by the human mind. Although computers are much better suited for comparisons of pharmacophore patterns, a chemist's intuition is primarily scaffold-oriented. Its underlying simplifications render pharmacophore modeling unable to provide perfect predictions of ligand binding propensities - not even if all its subsisting technical problems would be solved. Each step in pharmacophore modeling and exploitation has specific drawbacks: from insufficient or inaccurate conformational sampling to ambiguities in pharmacophore typing (mainly due to uncertainty regarding the tautomeric/protonation status of compounds), to computer time limitations in complex molecular overlay calculations, and to the choice of inappropriate anchoring points in active sites when ligand cocrystals structures are not available. Yet, imperfections notwithstanding, the approach is accurate enough in order to be practically useful and actually is the most used virtual screening technique in medicinal chemistry - notably for "scaffold hopping" approaches, allowing the discovery of new chemical classes carriers of a desired biological activity.

Methods for combinatorial and parallel library design

Diversity has historically played a critical role in design of combinatorial libraries, screening sets and corporate collections for lead discovery. Large library design dominated the field in the 1990s with methods ranging anywhere from purely arbitrary through property based reagent selection to product based approaches. In recent years, however, there has been a downward trend in library size. This was due to increased information about the desirable targets gleaned from the genomics revolution and to the ever growing availability of target protein structures from crystallography and homology modeling. Creation of libraries directed toward families of receptors such as GPCRs, kinases, nuclear hormone receptors, proteases, etc., replaced the generation of libraries based primarily on diversity while single target focused library design has remained an important objective. Concurrently, computing grids and cpu clusters have facilitated the development of structure based tools that screen hundreds of thousands of molecules. Smaller "smarter" combinatorial and focused parallel libraries replaced those early un-focused large libraries in the twenty-first century drug design paradigm. While diversity still plays a role in lead discovery, the focus of current library design methods has shifted to receptor based methods, scaffold hopping/bio-isostere searching, and a much needed emphasis on synthetic feasibility. Methods such as "privileged substructures based design" and pharmacophore based design still are important methods for parallel and small combinatorial library design. This chapter discusses some of the possible design methods and presents examples where they are available.

Computational medicinal chemistry in fragment-based drug discovery: what, how and when

The use of fragment-based drug discovery (FBDD) has increased in the last decade due to the encouraging results obtained to date. In this scenario, computational approaches, together with experimental information, play an important role to guide and speed up the process. By default, FBDD is generally considered as a constructive approach. However, such additive behavior is not always present, therefore, simple fragment maturation will not always deliver the expected results. In this review, computational approaches utilized in FBDD are reported together with real case studies, where applicability domains are exemplified, in order to analyze them, and then, maximize their performance and reliability. Thus, a proper use of these computational tools can minimize misleading conclusions, keeping the credit on FBDD strategy, as well as achieve higher impact in the drug-discovery process. FBDD goes one step beyond a simple constructive approach. A broad set of computational tools: docking, R group quantitative structure–activity relationship, fragmentation tools, fragments management tools, patents analysis and fragment-hopping, for example, can be utilized in FBDD, providing a clear positive impact if they are utilized in the proper scenario – what, how and when. An initial assessment of additive/non-additive behavior is a critical point to define the most convenient approach for fragments elaboration.

FLAP: GRID molecular interaction fields in virtual screening. validation using the DUD data set

The performance of FLAP (Fingerprints for Ligands and Proteins) in virtual screening is assessed using a subset of the DUD (Directory of Useful Decoys) benchmarking data set containing 13 targets each with more than 15 different chemotype classes. A variety of ligand and receptor-based virtual screening approaches are examined, using combinations of individual templates 2D structures of known actives, a cocrystallized ligand, a receptor structure, or a cocrystallized ligand-biased receptor structure. We examine several data fusion approaches to combine the results of the individual virtual screens. In doing so, we show that excellent chemotype enrichment is achieved in both single target ligand-based and receptor-based approaches, of approximately 17-fold over random on average at a false positive rate of 1%. We also show that using as much starting knowledge as possible improves chemotype enrichment, and that data fusion using Pareto ranking is an effective method to do this giving up to 50% improvement in enrichment over the single methods. Finally we show that if inactivity or decoy data is incorporated, automatically training the scoring function in FLAP improves recovery still further, with almost 2-fold improvement over the enrichments shown by the single methods. The results clearly demonstrate the utility of FLAP for virtual screening when either a limited or wide range of prior knowledge is available.

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.

MORPH: a new tool for ligand design

A frequently employed strategy in drug discovery efforts is to replace aromatic rings in known active compounds with alternative aromatic moieties to create novel compounds with improved potency and/or adsorption, distribution, metabolism, excretion, and toxicity properties. Here we introduce MORPH, which is a simple software tool for systematically modifying aromatic rings in three-dimensional models of molecules without altering the coordinates of the nonhydrogen atoms in the rings. MORPH works on individual rings as well as fused ring systems and additionally provides the ability to filter out modified compounds which do not contain hydrogen-bond donors or acceptors at specific positions on the rings or contain more or less than the desired number of heteroatoms. The MORPH program and its application to two ligands extracted from cocrystal structures with cyclin-dependent kinase 2 (CDK2)/cyclin A and CDK2 are discussed below.

Molecular shape and medicinal chemistry: a perspective

The eight contributions here provide ample evidence that shape as a volume or as a surface is a vibrant and useful concept when applied to drug discovery. It provides a reliable scaffold for "decoration" with chemical intuition (or bias) for virtual screening and lead optimization but also has its unadorned uses, as in library design, ligand fitting, pose prediction, or active site description. Computing power has facilitated this evolution by allowing shape to be handled precisely without the need to reduce down to point descriptors or approximate metrics, and the diversity of resultant applications argues for this being an important step forward. Certainly, it is encouraging that as computation has enabled our intuition, molecular shape has consistently surprised us in its usefulness and adaptability. The first Aurelius question, "What is the essence of a thing?", seems well answered, however, the third, "What do molecules do?", only partly so. Are the topics covered here exhaustive, or is there more to come? To date, there has been little published on the use of the volumetric definition of shape described here as a QSAR variable, for instance, in the prediction or classification of activity, although other shape definitions have been successful applied, for instance, as embodied in the Compass program described above in "Shape from Surfaces". Crystal packing is a phenomenon much desired to be understood. Although powerful models have been applied to the problem, to what degree is this dominated purely by the shape of a molecule? The shape comparison described here is typically of a global nature, and yet some importance must surely be placed on partial shape matching, just as the substructure matching of chemical graphs has proved useful. The approach of using surfaces, as described here, offers some flavor of this, as does the use of metrics that penalize volume mismatch less than the Tanimoto, e.g., Tversky measures. As yet, there is little to go on as to how useful a paradigm this will be because there is less software and fewer concrete results.Finally, the distance between molecular shapes, or between any shapes defined as volumes or surfaces, is a metric property in the mathematical sense of the word. As yet, there has been little, if any, application of this observation. We cannot know what new application to the design and discovery of pharmaceuticals may yet arise from the simple concept of molecular shape, but it is fair to say that the progress so far is impressive.

Lead generation and optimization based on protein-ligand complementarity

This work proposes a computational procedure for structure-based lead generation and optimization, which relies on the complementarity of the protein-ligand interactions. This procedure takes as input the known structure of a protein-ligand complex. Retaining the positions of the ligand heavy atoms in the protein binding site it designs structurally similar compounds considering all possible combinations of atomic species (N, C, O, CH_3, NH, etc). Compounds are ranked based on a score which incorporates energetic contributions evaluated using molecular mechanics force fields. This procedure was used to design new inhibitor molecules for three serine/threonine protein kinases (p38 MAP kinase, p42 MAP kinase (ERK2), and c-Jun N-terminal kinase 3 (JNK3)). For each enzyme, the calculations produce a set of potential inhibitors whose scores are in agreement with IC50 data and Ki values. Furthermore, the native ligands for each protein target, scored within the five top-ranking compounds predicted by our method, one of the top-ranking compounds predicted to inhibit JNK3 was synthesized and his inhibitory activity confirmed against ATP hydrolysis. Our computational procedure is therefore deemed to be a useful tool for generating chemically diverse molecules active against known target proteins.

LigMatch: a multiple structure-based ligand matching method for 3D virtual screening

We have developed a new virtual screening (VS) method called LigMatch and evaluated its performance on 13 protein targets using a filtered and clustered version of the directory of useful decoys (DUD). The method uses 3D structural comparison to a crystallographically determined ligand in a bioactive 'template' conformation, using a geometric hashing method, in order to prioritize each database compound. We show that LigMatch outperforms several other widely used VS methods on the 13 DUD targets. We go on to demonstrate that improved VS performance can be gained from using multiple, structurally diverse templates rather than a single template ligand for a particular protein target. In this case, a 2D fingerprint-based method is used to select a ligand template from a set of known bioactive conformations. Furthermore, we show that LigMatch performs well even in the absence of 2D similarity to the template ligands, thereby demonstrating its robustness with respect to purely 2D methods and its potential for scaffold hopping.

SHOP: receptor-based scaffold HOPping by GRID-based similarity searches

A new field-derived 3D method for receptor-based scaffold hopping, implemented in the software SHOP, is presented. Information from a protein-ligand complex is utilized to substitute a fragment of the ligand with another fragment from a database of synthetically accessible scaffolds. A GRID-based interaction profile of the receptor and geometrical descriptions of a ligand scaffold are used to obtain new scaffolds with different structural features and are able to replace the original scaffold in the protein-ligand complex. An enrichment study was successfully performed verifying the ability of SHOP to find known active CDK2 scaffolds in a database. Additionally, SHOP was used for suggesting new inhibitors of p38 MAP kinase. Four p38 complexes were used to perform six scaffold searches. Several new scaffolds were suggested, and the resulting compounds were successfully docked into the query proteins.