Truly Target-Focused Pharmacophore Modeling: A Novel Tool for Mapping Intermolecular Surfaces

Watermelon: setup and validation of an in silico fragment-based approach

We present a new computational approach, named Watermelon, designed for the development of pharmacophore models based on receptor structures. The methodology involves the sampling of potential hotspots for ligand interactions within a protein target's binding site, utilising molecular fragments as probes. By employing docking and molecular dynamics (MD) simulations, the most significant interactions formed by these probes within distinct regions of the binding site are identified. These interactions are subsequently transformed into pharmacophore features that delineates key anchoring sites for potential ligands. The reliability of the approach was experimentally validated using the monoacylglycerol lipase (MAGL) enzyme. The generated pharmacophore model captured features representing ligand-MAGL interactions observed in various X-ray co-crystal structures and was employed to screen a database of commercially available compounds, in combination with consensus docking and MD simulations. The screening successfully identified two new MAGL inhibitors with micromolar potency, thus confirming the reliability of the Watermelon approach.

PheSA: An Open-Source Tool for Pharmacophore-Enhanced Shape Alignment

PheSA is an open-source pharmacophore- and shape-based screening and molecular alignment tool that is fully open-source as part of OpenChemLib. Supporting standard ligand-based screening, flexible refinement of alignments, and receptor-guided shape docking, PheSA is a very flexible tool and can be used for different use cases in structure-based drug design. We present the algorithm and different benchmark studies that investigate the screening performance and also the quality of the generated alignments and the pose prediction performance of the receptor-guided PheSA algorithm. An important finding is the effect of the type of similarity metric used for measuring screening enrichment (symmetric Tanimoto versus asymmetric Tversky), whereby we could observe improved enrichment rates by using Tversky. PheSA exhibits enrichments on the DUD-E that are on par with commercial methods.

Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-Based Featurization Method

Protein-ligand interactions are essential to drug discovery and drug development efforts. Desirable on-target or multitarget interactions are the first step in finding an effective therapeutic, while undesirable off-target interactions are the first step in assessing safety. In this work, we introduce a novel ligand-based featurization and mapping of human protein pockets to identify closely related protein targets and to project novel drugs into a hybrid protein-ligand feature space to identify their likely protein interactions. Using structure-based template matches from PDB, protein pockets are featured by the ligands that bind to their best co-complex template matches. The simplicity and interpretability of this approach provide a granular characterization of the human proteome at the protein-pocket level instead of the traditional protein-level characterization by family, function, or pathway. We demonstrate the power of this featurization method by clustering a subset of the human proteome and evaluating the predicted cluster associations of over 7000 compounds.

CommonNNClustering─A Python Package for Generic Common-Nearest-Neighbor Clustering

Density-based clustering procedures are widely used in a variety of data science applications. Their advantage lies in the capability to find arbitrarily shaped and sized clusters and robustness against outliers. In particular, they proved effective in the analysis of molecular dynamics simulations, where they serve to identify relevant, low-energetic molecular conformations. As such, they can provide a convenient basis for the construction of kinetic (core-set) Markov-state models. Here we present the open-source Python project CommonNNClustering, which provides an easy-to-use and efficient reimplementation of the common-nearest-neighbor (CommonNN) method. The package provides functionalities for hierarchical clustering and an evaluation of the results. We put our emphasis on a generic API design to keep the implementation flexible and open for customization.

Dehydrovomifoliol Alleviates Nonalcoholic Fatty Liver Disease via the E2F1/AKT/mTOR Axis: Pharmacophore Modeling and Molecular Docking Study

Objective

Herbal medicine discovery is a complex and time-consuming process, while pharmacophore modeling and molecular docking methods enable simple and economic studies. The pharmacophore model provides an abstract description of essential intermolecular interactions between chemical structures, and the molecular docking technology can identify novel compounds of therapeutic interests and predict the ligand-target interaction at the molecular level. This study was based on the two methods to elucidate the mechanism of dehydrovomifoliol, an active ingredient extracted from Artemisia frigida willd, in nonalcoholic fatty liver disease (NAFLD).

Methods

Bioinformatics analysis was performed to screen target genes of dehydrovomifoliol in NAFLD treatment, which were thus intersected with NAFLD-related differentially expressed genes (DEGs) and NAFLD-related genes. Venn diagram was used to identify candidate DEGs. A pharmacophore model was then generated, and molecular docking was performed. A protein-protein interaction (PPI) network was constructed to identify core genes, which were evaluated using GO and the KEGG enrichment analyses.

Results

Seven target genes of dehydrovomifoliol in NAFLD treatment were screened out, namely E2F1, MERTK, SOX17, MMP9, SULT2A1, VEGFA, and BLVRA. The pharmacophore model and molecular docking of candidate DEGs and dehydrovomifoliol were successfully constructed. E2F1 was identified as a core gene of dehydrovomifoliol in NAFLD treatment. Further enrichment analysis indicated the regulatory role of E2F1 in fat metabolism was associated with the regulation of the AKT/mTOR signaling pathway.

Conclusion

Overall, this study illustrates the anti-NAFLD mechanism of dehydrovomifoliol, which could be a useful compound for developing novel drugs in the treatment of NAFLD.

Structure based Drug Designing Approaches in SARS-CoV-2 Spike Inhibitor Design

The COVID-19 outbreak and the pandemic situation have hastened the research community to design a novel drug and vaccine against its causative organism, the SARS-CoV-2. The spike glycoprotein present on the surface of this pathogenic organism plays an immense role in viral entry and antigenicity. Hence, it is considered an important drug target in COVID-19 drug design. Several three-dimensional crystal structures of this SARS-CoV-2 spike protein have been identified and deposited in the Protein DataBank during the pandemic period. This accelerated the research in computer- aided drug designing, especially in the field of structure-based drug designing. This review summarizes various structure-based drug design approaches applied to this SARS-CoV-2 spike protein and its findings. Specifically, it is focused on different structure-based approaches such as molecular docking, high-throughput virtual screening, molecular dynamics simulation, drug repurposing, and target-based pharmacophore modelling and screening. These structural approaches have been applied to different ligands and datasets such as FDA-approved drugs, small molecular chemical compounds, chemical libraries, chemical databases, structural analogs, and natural compounds, which resulted in the prediction of spike inhibitors, spike-ACE-2 interface inhibitors, and allosteric inhibitors.

Apo2ph4: A Versatile Workflow for the Generation of Receptor-based Pharmacophore Models for Virtual Screening

Pharmacophore models are widely used as efficient virtual screening (VS) filters for the target-directed enrichment of large compound libraries. However, the generation of pharmacophore models that have the power to discriminate between active and inactive molecules traditionally requires structural information about ligand-target complexes or at the very least knowledge of one active ligand. The fact that the discovery of the first known active ligand of a newly investigated target represents a major hurdle at the beginning of every drug discovery project underscores the need for methods that are able to derive high-quality pharmacophore models even without the prior knowledge of any active ligand structures. In this work, we introduce a novel workflow, called apo2ph4, that enables the rapid derivation of pharmacophore models solely from the three-dimensional structure of the target receptor. The utility of this workflow is demonstrated retrospectively for the generation of a pharmacophore model for the M2 muscarinic acetylcholine receptor. Furthermore, in order to show the general applicability of apo2ph4, the workflow was employed for all 15 targets of the recently published LIT-PCBA dataset. Pharmacophore-based VS runs using the apo2ph4-derived models achieved a significant enrichment of actives for 13 targets. In the last presented example, a pharmacophore model derived from the etomidate site of the α1β2γ2 GABA_A receptor was used in VS campaigns. Subsequent in vitro testing of selected hits revealed that 19 out of 20 (95%) tested compounds were able to significantly enhance GABA currents, which impressively demonstrates the applicability of apo2ph4 for real-world drug design projects.

New Drug Design Avenues Targeting Alzheimer's Disease by Pharmacoinformatics-Aided Tools

Neurodegenerative diseases (NDD) have been of great interest to scientists for a long time due to their multifactorial character. Among these pathologies, Alzheimer’s disease (AD) is of special relevance, and despite the existence of approved drugs for its treatment, there is still no efficient pharmacological therapy to stop, slow, or repair neurodegeneration. Existing drugs have certain disadvantages, such as lack of efficacy and side effects. Therefore, there is a real need to discover new drugs that can deal with this problem. However, as AD is multifactorial in nature with so many physiological pathways involved, the most effective approach to modulate more than one of them in a relevant manner and without undesirable consequences is through polypharmacology. In this field, there has been significant progress in recent years in terms of pharmacoinformatics tools that allow the discovery of bioactive molecules with polypharmacological profiles without the need to spend a long time and excessive resources on complex experimental designs, making the drug design and development pipeline more efficient. In this review, we present from different perspectives how pharmacoinformatics tools can be useful when drug design programs are designed to tackle complex diseases such as AD, highlighting essential concepts, showing the relevance of artificial intelligence and new trends, as well as different databases and software with their main results, emphasizing the importance of coupling wet and dry approaches in drug design and development processes.

Incorporating Target-Specific Pharmacophoric Information into Deep Generative Models for Fragment Elaboration

Despite recent interest in deep generative models for scaffold elaboration, their applicability to fragment-to-lead campaigns has so far been limited. This is primarily due to their inability to account for local protein structure or a user's design hypothesis. We propose a novel method for fragment elaboration, STRIFE, that overcomes these issues. STRIFE takes as input fragment hotspot maps (FHMs) extracted from a protein target and processes them to provide meaningful and interpretable structural information to its generative model, which in turn is able to rapidly generate elaborations with complementary pharmacophores to the protein. In a large-scale evaluation, STRIFE outperforms existing, structure-unaware, fragment elaboration methods in proposing highly ligand-efficient elaborations. In addition to automatically extracting pharmacophoric information from a protein target's FHM, STRIFE optionally allows the user to specify their own design hypotheses.

Solvent accessibility of E1α and E1β residues with known missense mutations causing pyruvate dehydrogenase complex (PDC) deficiency: Impact on PDC-E1 structure and function

Pyruvate dehydrogenase complex deficiency is a major cause of primary lactic acidemia resulting in high morbidity and mortality, with limited therapeutic options. PDHA1 mutations are responsible for >82% of cases. The E1 component of PDC is a symmetric dimer of heterodimers (αβ/α'β') encoded by PDHA1 and PDHB. We measured solvent accessibility surface area (SASA), utilized nearest-neighbor analysis, incorporated sequence changes using mutagenesis tool in PyMOL, and performed molecular modeling with SWISS-MODEL, to investigate the impact of residues with disease-causing missense variants (DMVs) on E1 structure and function. We reviewed 166 and 13 genetically resolved cases due to PDHA1 and PDHB, respectively, from variant databases. We expanded on 102 E1α and 13 E1β nonduplicate DMVs. DMVs of E1α Arg112-Arg224 stretch (exons 5-7) and of E1α Arg residues constituted 40% and 39% of cases, respectively, with invariant Arg349 accounting for 22% of arginine replacements. SASA analysis showed that 86% and 84% of residues with nonduplicate DMVs of E1α and E1β, respectively, are solvent inaccessible ("buried"). Furthermore, 30% of E1α buried residues with DMVs are deleterious through perturbation of subunit-subunit interface contact (SSIC), with 73% located in the Arg112-Arg224 stretch. E1α Arg349 represented 74% of buried E1α Arg residues involved in SSIC. Structural perturbations resulting from residue replacements in some matched neighboring pairs of amino acids on different subunits involved in SSIC at 2.9-4.0 Å interatomic distance apart, exhibit similar clinical phenotype. Collectively, this work provides insight for future target-based advanced molecular modeling studies, with implications for development of novel therapeutics for specific recurrent DMVs of E1α.

Molecular Modeling Techniques Applied to the Design of Multitarget Drugs: Methods and Applications

Multifactorial diseases, such as cancer and diabetes present a challenge for the traditional "one-target, one disease" paradigm due to their complex pathogenic mechanisms. Although a combination of drugs can be used, a multitarget drug may be a better choice face of its efficacy, lower adverse effects and lower chance of resistance development. The computer-based design of these multitarget drugs can explore the same techniques used for single-target drug design, but the difficulties associated to the obtention of drugs that are capable of modulating two or more targets with similar efficacy impose new challenges, whose solutions involve the adaptation of known techniques and also to the development of new ones, including machine-learning approaches. In this review, some SBDD and LBDD techniques for the multitarget drug design are discussed, together with some cases where the application of such techniques led to effective multitarget ligands.

Recent Advances in In Silico Target Fishing

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.

Network pharmacology-based analysis of the anti-hyperglycemic active ingredients of roselle and experimental validation

Diabetes mellitus is one of the top four leading causes of death among noncommunicable diseases worldwide, according to the World Hibiscus sabdariffa 2019. Roselle (Hibiscus sabdariffa L.), a traditional herbal medicine, has shown significant clinical anti-hyperglycemic efficacy. However, the mechanism of the treatment is not yet clear. We found that Roselle has a certain protective effect on vascular endothelial cells through this study. This study was based on network pharmacology and experimental validation. The present study made a comprehensive analysis by combining active ingredient screening, target prediction and signaling pathway analysis to elucidate the active ingredients and possible molecular mechanism of roselle for the first time, which provided theoretical and experimental basis for the development and application of roselle as an antidiabetic drug.

Exploiting Water Dynamics for Pharmacophore Screening

Three-dimensional pharmacophore models have been proven extremely valuable in exploring novel chemical space through virtual screening. However, traditional pharmacophore-based approaches need ligand information and rely on static snapshots of highly dynamic systems. In this chapter, we describe PyRod, a novel tool to generate three-dimensional pharmacophore models based on water traces of a molecular dynamics simulation of an apo-protein.The protocol described herein was successfully applied for the discovery of novel drug-like inhibitors of West Nile virus NS2B-NS3 protease. By using this recent example, we highlight the key steps of the generation and validation of PyRod-derived pharmacophore models and their application for virtual screening.

Inhibition analysis of aflatoxin by in silico targeting the thioesterase domain of polyketide synthase enzyme in Aspergillus ssp

The spread of fungal growth causes enormous economic, agricultural, and health problems for humans, such as Aspergillus sp., which produce aflatoxins. Thus, the inhibition of aflatoxin production became a precious target. In this research, the thioesterase (TE) domain from Polyketide synthase enzyme was selected to employ the in silico docking, using AutoDock Vina, against 623 natural compounds from the South African natural compound database (SANCDB), to identify potential inhibitors that can selectively inhibit thioesterase domain. The top ten inhibitors components were pinocembrin, typhaphthalide, p-coumaroylputrescine, dilemmaone A, 9-angelylplatynecine, 2,4,6-octatrienal, 4,8-dichloro-3,7-dimethyl-, (2e,4z,6e)-, lilacinobiose, 1,3,7-octatriene, 5,6-dichloro-2-(dichloromethyl)-6-methyl-, [r*,s*-(e)]-(-)- (9ci), lilacinobiose, 1,3,7-octatriene, 5,6-dichloro-2-(dichloromethyl)-6-methyl-, [r*,s*-(e)]-(-)- (9ci), 1,3,7-octatriene, 1,5,6-trichloro-2-(dichloromethyl)-6-methyl-, [r*,s*-(z,e)] and 9-angelylhastanecine and that depending on the lowest binding energy, the best chemical interactions and the best drug-likeness. The results of those components gave successful inhibition with the thioesterase domain. So, they can be used for inhibition and controlling aflatoxin contamination of agriculture crop yields, specially, pinocembrin which gave promising results.Communicated by Ramaswamy H. Sarma.

Applications of the Pharmacophore Concept in Natural Product inspired Drug Design

Pharmacophore-based techniques are nowadays an important part of many computer-aided drug design workflows and have been successfully applied for tasks such as virtual screening, lead optimization and de novo design. Natural products, on the other hand, can serve as a valuable source for unconventional molecular scaffolds that stimulate ideas for novel lead compounds in a more diverse chemical space that does not follow the rules of traditional medicinal chemistry. The first part of this review provides a brief introduction to the pharmacophore concept, the methods for pharmacophore model generation, and their applications. The second, concluding part, presents examples for recent, pharmacophore method related research in the field of natural product chemistry. The selected examples show, that pharmacophore-based methods which get mainly applied on synthetic drug-like molecules work equally well in the realm of natural products and thus can serve as a valuable tool for researchers in the field of natural product inspired drug design.

Flexi-pharma: a molecule-ranking strategy for virtual screening using pharmacophores from ligand-free conformational ensembles

Computer-aided strategies are useful for reducing the costs and increasing the success-rate in drug discovery. Among these strategies, methods based on pharmacophores (an ensemble of electronic and steric features representing the target active site) are efficient to implement over large compound libraries. However, traditional pharmacophore-based methods require knowledge of active compounds or ligand-receptor structures, and only few ones account for target flexibility. Here, we developed a pharmacophore-based virtual screening protocol, Flexi-pharma, that overcomes these limitations. The protocol uses molecular dynamics (MD) simulations to explore receptor flexibility, and performs a pharmacophore-based virtual screening over a set of MD conformations without requiring prior knowledge about known ligands or ligand-receptor structures for building the pharmacophores. The results from the different receptor conformations are combined using a "voting" approach, where a vote is given to each molecule that matches at least one pharmacophore from each MD conformation. Contrarily to other approaches that reduce the pharmacophore ensemble to some representative models and score according to the matching models or molecule conformers, the Flexi-pharma approach takes directly into account the receptor flexibility by scoring in regards to the receptor conformations. We tested the method over twenty systems, finding an enrichment of the dataset for 19 of them. Flexi-pharma is computationally efficient allowing for the screening of thousands of compounds in minutes on a single CPU core. Moreover, the ranking of molecules by vote is a general strategy that can be applied with any pharmacophore-filtering program.

In silico discovery and biological validation of ligands of FAD synthase, a promising new antimicrobial target

New treatments for diseases caused by antimicrobial-resistant microorganisms can be developed by identifying unexplored therapeutic targets and by designing efficient drug screening protocols. In this study, we have screened a library of compounds to find ligands for the flavin-adenine dinucleotide synthase (FADS) -a potential target for drug design against tuberculosis and pneumonia- by implementing a new and efficient virtual screening protocol. The protocol has been developed for the in silico search of ligands of unexplored therapeutic targets, for which limited information about ligands or ligand-receptor structures is available. It implements an integrative funnel-like strategy with filtering layers that increase in computational accuracy. The protocol starts with a pharmacophore-based virtual screening strategy that uses ligand-free receptor conformations from molecular dynamics (MD) simulations. Then, it performs a molecular docking stage using several docking programs and an exponential consensus ranking strategy. The last filter, samples the conformations of compounds bound to the target using MD simulations. The MD conformations are scored using several traditional scoring functions in combination with a newly-proposed score that takes into account the fluctuations of the molecule with a Morse-based potential. The protocol was optimized and validated using a compound library with known ligands of the Corynebacterium ammoniagenes FADS. Then, it was used to find new FADS ligands from a compound library of 14,000 molecules. A small set of 17 in silico filtered molecules were tested experimentally. We identified five inhibitors of the activity of the flavin adenylyl transferase module of the FADS, and some of them were able to inhibit growth of three bacterial species: C. ammoniagenes, Mycobacterium tuberculosis, and Streptococcus pneumoniae, where the last two are human pathogens. Overall, the results show that the integrative VS protocol is a cost-effective solution for the discovery of ligands of unexplored therapeutic targets.

AutoPH4: An Automated Method for Generating Pharmacophore Models from Protein Binding Pockets

Pharmacophore models are widely used in computational drug discovery (e.g., in the virtual screening of drug molecules) to capture essential information about interactions between ligands and a target protein. Generating pharmacophore models from protein structures is typically a manual process, but there has been growing interest in automated pharmacophore generation methods. Automation makes feasible the processing of large numbers of protein conformations, such as those generated by molecular dynamics (MD) simulations, and thus may help achieve the longstanding goal of incorporating protein flexibility into virtual screening workflows. Here, we present AutoPH4, a new automated method for generating pharmacophore models based on protein structures; we show that a virtual screening workflow incorporating AutoPH4 ranks compounds more accurately than any other pharmacophore-based virtual screening workflow for which results on a public benchmark have been reported. The strong performance of the virtual screening workflow indicates that the AutoPH4 component of the workflow generates high-quality pharmacophores, making AutoPH4 promising for use in future virtual screening workflows as well, such as ones that use conformations generated by MD simulations.

Molecular docking of novel 5-O-benzoylpinostrobin derivatives as wild type and L858R/T790M/V948R mutant EGFR inhibitor

Background Previous studies have shown that 5-O-benzoylpinostrobin derivatives is a potential anti-breast cancer, with the highest potential being the HER2 inhibitors, is a protein's member of the epidermal growth factor receptor (EGFR) family. Overexpression of EGFR itself is known to be one of the causes of other cancer, including non-small cell lung cancer (NSCLC). Thus, it is possible that 5-O-benzoylpinostrobin derivatives can also inhibit the overexpression of EGFR in NSCLC. In the case of NSCLC, mutations of EGFR are often found in several amino acids, such as L858R, T790M, and V948R. This study aimed to determine the potential of 5-O-benzoylpinostrobin derivatives as an inhibitor of wild type and L858R/T790M/V948R-mutant EGFR. Methods Docking was performed using AutoDock Vina 1.1.2 on both wild type and L858R/T790M/V948R-mutant EGFR. Parameters observed, consisted of free energy of binding (ΔG) and amino acid interactions of each ligand. Results Docking results showed that all 5-O-benzoylpinostrobin derivatives showed a lower ΔG for both wild type and L858R/T790M/V948R-mutant EGFR, with the lowest ΔG shown by 4-methyl-5-O-benzoylpinostrobin and 4-trifluoromethyl-5-O-benzoylpinostrobin. Both the ligands have the similarity of interacting amino acids compared to reference ligands between 76.47 and 88.24%. Specifically, the ΔG of all test ligands was lower in mutant EGFR than in the wild type, which indicates the potential of the ligand as EGFR inhibitors where a mutation to EGFR occurs. Conclusions These results confirm that 5-O-benzoylpinostrobin derivatives have the potential to inhibit EGFR in both wild type and L858R/T790M/V948R-mutant.

PyRod: Tracing Water Molecules in Molecular Dynamics Simulations

Ligands entering a protein binding pocket essentially compete with water molecules for binding to the protein. Hence, the location and thermodynamic properties of water molecules in protein structures have gained increased attention in the drug design community. Including corresponding data into 3D pharmacophore modeling is essential for efficient high throughput virtual screening. Here, we present PyRod, a free and open-source Python software that allows for visualization of pharmacophoric binding pocket characteristics, identification of hot spots for ligand binding, and subsequent generation of pharmacophore features for virtual screening. The implemented routines analyze the protein environment of water molecules in molecular dynamics (MD) simulations and can differentiate between hydrogen bonded waters as well as waters in a protein environment of hydrophobic, charged, or aromatic atom groups. The gathered information is further processed to generate dynamic molecular interaction fields (dMIFs) for visualization and pharmacophoric features for virtual screening. The described software was applied to 5 therapeutically relevant drug targets, and generated pharmacophores were evaluated using DUD-E benchmarking sets. The best performing pharmacophore was found for the HIV1 protease with an early enrichment factor of 54.6. PyRod adds a new perspective to structure-based screening campaigns by providing easy-to-interpret dMIFs and purely protein-based pharmacophores that are solely based on tracing water molecules in MD simulations. Since structural information about cocrystallized ligands is not needed, screening campaigns can be followed, for which less or no ligand information is available. PyRod is freely available at https://github.com/schallerdavid/pyrod .

All in One: Cavity Detection, Druggability Estimate, Cavity-Based Pharmacophore Perception, and Virtual Screening

Discovering the very first ligands of pharmacologically important targets in a fast and cost-efficient manner is an important issue in drug discovery. In the absence of structural information on either endogenous or synthetic ligands, computational chemists classically identify the very first hits by docking compound libraries to a binding site of interest, with well-known biases arising from the usage of scoring functions. We herewith propose a novel computational method tailored to ligand-free protein structures and consisting in the generation of simple cavity-based pharmacophores to which potential ligands could be aligned by the use of a smooth Gaussian function. The method, embedded in the IChem toolkit, automatically detects ligand-binding cavities, then predicts their structural druggability, and last creates a structure-based pharmacophore for predicted druggable binding sites. A companion tool (Shaper2) was designed to align ligands to cavity-derived pharmacophoric features. The proposed method is as efficient as state-of-the-art virtual screening methods (ROCS, Surflex-Dock) in both posing and virtual screening challenges. Interestingly, IChem-Shaper2 is clearly orthogonal to these latter methods in retrieving unique chemotypes from high-throughput virtual screening data.

The Pharmacophore Concept and Its Applications in Computer-Aided Drug Design

Pharmacophore-based techniques currently are an integral part of many computer-aided drug design workflows and have been successfully and extensively applied for tasks such as virtual screening, de novo design, and lead optimization. Pharmacophore models can be derived both in a receptor-based and in a ligand-based manner, and provide an abstract description of essential non-bonded interactions that typically occur between small-molecule ligands and macromolecular targets. Due to their simplistic and abstract nature, pharmacophores are both perfectly suited for efficient computer processing and easy to comprehend by life and physical scientists. As a consequence, they have also proven to be a valuable tool for communicating between computational and medicinal chemists.This chapter aims to provide a short overview of the pharmacophore concept and its applications in modern computer-aided drug design. The chapter is divided into three distinct parts. The first section contains a brief introduction to the pharmacophore concept. The second section provides a description of the most common nonbonded interaction types and their representation as pharmacophoric features. Furthermore, it gives an overview of the various methods for pharmacophore generation and important pharmacophore-based techniques in drug design. This part concludes with examples for recent pharmacophore concept-related research and development. The last section is dedicated to a review of research in the field of natural product chemistry as carried out by employing pharmacophore-based drug design methods.