Simple but highly effective three-dimensional chemical-feature-based pharmacophore model for diketo acid derivatives as hepatitis C virus RNA-dependent RNA polymerase inhibitors

Effect of non-covalent interactions on the stability and structural properties of 2,4-dioxo-4-phenylbutanoic complex: a computational analysis

CONTEXT

The 2,4-dioxo-4-phenylbutanoic acid (DPBA) is a subject of interest in pharmaceutical research, particularly in developing new drugs targeting viral and bacterial infections. Complexation with metal ions can improve the stability and solubility of organic compounds. The present study uses quantum chemical calculations to explore the structural and electronic results arising from the interaction between the metal cation (Fe2+) and the π-system of DPBA in different solvents. For this purpose, the analyses of atoms in molecules (AIM) and natural bond orbital (NBO) are employed to comprehend the interaction features and the charge delocalization during the process of complexation. The results demonstrate that the strongest/weakest interactions are evident when the complex is situated in non-polar/polar solvents, respectively. In addition, the investigated complex exhibits two intramolecular hydrogen bonds (IMHBs) characterized by the O-H···O motif. The results indicate that the HBs present in the complex fall within the category of weak to medium HBs. Moreover, the O-H···O HBs are influenced by cation-π interactions, which can increase/decrease their strength in polar/non-polar solvents. To enhance understanding of the interactions above, an examination is conducted on various physical properties including the energy gap, electronic chemical potential, chemical hardness, softness, and electrophilicity power.

METHOD

All calculations are conducted within the density functional theory (DFT) using the ωB97XD functional and 6-311 +  + G(d,p) basis set. The computations are performed using the quantum chemistry package GAMESS, and the obtained results are visualized by employing the GaussView program.

Lessons learnt from broad-spectrum coronavirus antiviral drug discovery

INTRODUCTION

Highly pathogenic coronaviruses (CoVs), such as severe acute respiratory syndrome CoV (SARS-CoV), Middle East respiratory syndrome CoV (MERS-CoV), and the most recent SARS-CoV-2 responsible for the COVID-19 pandemic, pose significant threats to human populations over the past two decades. These CoVs have caused a broad spectrum of clinical manifestations ranging from asymptomatic to severe distress syndromes (ARDS), resulting in high morbidity and mortality.

AREAS COVERED

The accelerated advancements in antiviral drug discovery, spurred by the COVID-19 pandemic, have shed new light on the imperative to develop treatments effective against a broad spectrum of CoVs. This perspective discusses strategies and lessons learnt in targeting viral non-structural proteins, structural proteins, drug repurposing, and combinational approaches for the development of antivirals against CoVs.

EXPERT OPINION

Drawing lessons from the pandemic, it becomes evident that the absence of efficient broad-spectrum antiviral drugs increases the vulnerability of public health systems to the potential onslaught by highly pathogenic CoVs. The rapid and sustained spread of novel CoVs can have devastating consequences without effective and specifically targeted treatments. Prioritizing the effective development of broad-spectrum antivirals is imperative for bolstering the resilience of public health systems and mitigating the potential impact of future highly pathogenic CoVs.

Facilitating SARS CoV-2 RNA-Dependent RNA polymerase (RdRp) drug discovery by the aid of HCV NS5B palm subdomain binders: In silico approaches and benchmarking

Corona Virus 2019 Disease (COVID-19) is a rapidly emerging pandemic caused by a newly discovered beta coronavirus, called Sever Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2). SARS CoV-2 is an enveloped, single stranded RNA virus that depends on RNA-dependent RNA polymerase (RdRp) to replicate. Therefore, SARS CoV-2 RdRp is considered as a promising target to cease virus replication. SARS CoV-2 polymerase shows high structural similarity to Hepatitis C Virus-1b genotype (HCV-1b) polymerase.

Arising from the high similarity between SARS CoV-2 RdRp and HCV NS5B, we utilized the reported small-molecule binders to the palm subdomain of HCV NS5B (genotype 1b) to generate a high-quality DEKOIS 2.0 benchmark set and conducted a benchmarking analysis against HCV NS5B. The three highly cited and publicly available docking tools AutoDock Vina, FRED and PLANTS were benchmarked. Based on the benchmarking results and analysis via pROC-Chemotype plot, PLANTS showed the best screening performance and can recognize potent binders at the early enrichment. Accordingly, we used PLANTS in a prospective virtual screening to repurpose both the FDA-approved drugs (DrugBank) and the HCV-NS5B palm subdomain binders (BindingDB) for SARS CoV-2 RdRp palm subdomain. Further assessment by molecular dynamics simulations for 50 ns recommended diosmin (from DrugBank) and compound 3 (from BindingDB) to be the best potential binders to SARS CoV-2 RdRp palm subdomain. The best predicted compounds are recommended to be biologically investigated against COVID-19. In conclusion, this work provides in-silico analysis to propose possible SARS CoV-2 RdRp palm subdomain binders recommended as a remedy for COVID-19. Up-to-our knowledge, this study is the first to propose binders at the palm subdomain of SARS CoV2 RdRp. Furthermore, this study delivers an example of how to make use of a high quality custom-made DEKOIS 2.0 benchmark set as a procedure to elevate the virtual screening success rate against a vital target of the rapidly emerging pandemic.

Coronavirus helicases: attractive and unique targets of antiviral drug-development and therapeutic patents

Introduction: Coronaviruses encode a helicase that is essential for viral replication and represents an excellent antiviral target. However, only a few coronavirus helicase inhibitors have been patented. These patents include drug-like compound SSYA10-001, aryl diketo acids (ADK), and dihydroxychromones. Additionally, adamantane-derived bananins, natural flavonoids, one acrylamide derivative [(E)-3-(furan-2-yl)-N-(4-sulfamoylphenyl)acrylamide], a purine derivative (7-ethyl-8-mercapto-3-methyl-3,7-dihydro-1 H-purine-2,6-dione), and a few bismuth complexes. The IC₅₀ of patented inhibitors ranges between 0.82 μM and 8.95 μM, depending upon the assays used. Considering the urgency of clinical interventions against Coronavirus Disease-19 (COVID-19), it is important to consider developing antiviral portfolios consisting of small molecules.

Areas covered: This review examines coronavirus helicases as antiviral targets, and the potential of previously patented and experimental compounds to inhibit the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) helicase.

Expert opinion: Small molecule coronavirus helicase inhibitors represent attractive pharmacological modalities for the treatment of coronaviruses such as SARS-CoV and SARS-CoV-2. Rightfully so, the current emphasis is focused upon the development of vaccines. However, vaccines may not work for everyone and broad-based adoption of vaccinations is an increasingly challenging societal endeavor. Therefore, it is important to develop additional pharmacological antivirals against the highly conserved coronavirus helicases to broadly protect against this and subsequent coronavirus epidemics.

Targeting HIV/HCV Coinfection Using a Machine Learning-Based Multiple Quantitative Structure-Activity Relationships (Multiple QSAR) Method

Human immunodeficiency virus type-1 and hepatitis C virus (HIV/HCV) coinfection occurs when a patient is simultaneously infected with both human immunodeficiency virus type-1 (HIV-1) and hepatitis C virus (HCV), which is common today in certain populations. However, the treatment of coinfection is a challenge because of the special considerations needed to ensure hepatic safety and avoid drug–drug interactions. Multitarget inhibitors with less toxicity may provide a promising therapeutic strategy for HIV/HCV coinfection. However, the identification of one molecule that acts on multiple targets simultaneously by experimental evaluation is costly and time-consuming. In silico target prediction tools provide more opportunities for the development of multitarget inhibitors. In this study, by combining Naïve Bayes (NB) and support vector machine (SVM) algorithms with two types of molecular fingerprints, MACCS and extended connectivity fingerprints 6 (ECFP6), 60 classification models were constructed to predict compounds that were active against 11 HIV-1 targets and four HCV targets based on a multiple quantitative structure–activity relationships (multiple QSAR) method. Five-fold cross-validation and test set validation were performed to measure the performance of the 60 classification models. Our results show that the 60 multiple QSAR models appeared to have high classification accuracy in terms of the area under the ROC curve (AUC) values, which ranged from 0.83 to 1 with a mean value of 0.97 for the HIV-1 models and from 0.84 to 1 with a mean value of 0.96 for the HCV models. Furthermore, the 60 models were used to comprehensively predict the potential targets of an additional 46 compounds, including 27 approved HIV-1 drugs, 10 approved HCV drugs and nine selected compounds known to be active against one or more targets of HIV-1 or HCV. Finally, 20 hits, including seven approved HIV-1 drugs, four approved HCV drugs, and nine other compounds, were predicted to be HIV/HCV coinfection multitarget inhibitors. The reported bioactivity data confirmed that seven out of nine compounds actually interacted with HIV-1 and HCV targets simultaneously with diverse binding affinities. The remaining predicted hits and chemical-protein interaction pairs with the potential ability to suppress HIV/HCV coinfection are worthy of further experimental investigation. This investigation shows that the multiple QSAR method is useful in predicting chemical-protein interactions for the discovery of multitarget inhibitors and provides a unique strategy for the treatment of HIV/HCV coinfection.

Identification of some novel pyrazolo[1,5-a]pyrimidine derivatives as InhA inhibitors through pharmacophore-based virtual screening and molecular docking

The InhA inhibitors play key role in mycolic acid synthesis by preventing the fatty acid biosynthesis pathway. In this present article, Pharmacophore modelling and molecular docking study followed by in silico virtual screening could be considered as effective strategy to identify newer enoyl-ACP reductase inhibitors. Pyrrolidine carboxamide derivatives were opted to generate pharmacophore models using HypoGen algorithm in Discovery studio 2.1. Further it was employed to screen Zinc and Minimaybridge databases to identify and design newer potent hit molecules. The retrieved newer hits were further evaluated for their drug likeliness and docked against enoyl acyl carrier protein reductase. Here, novel pyrazolo[1,5-a]pyrimidine analogues were designed and synthesized with good yields. Structural elucidation of synthesized final molecules was perform through IR, MASS, ¹H-NMR, ¹³C-NMR spectroscopy and further tested for its in vitro anti-tubercular activity against H37Rv strain using Microplate Alamar blue assay (MABA) method. Most of the synthesized compounds displayed strong anti-tubercular activities. Further, these potent compounds were gauged for MDR-TB, XDR-TB and cytotoxic study.

Inhibitors for the hepatitis C virus RNA polymerase explored by SAR with advanced machine learning methods

Hepatitis C virus (HCV) is a global health challenge, affecting approximately 200 million people worldwide. In this study we developed SAR models with advanced machine learning classifiers Random Forest and k Nearest Neighbor Simulated Annealing for 679 small molecules with measured inhibition activity for NS5B genotype 1b. The activity was expressed as a binary value (active/inactive), where actives were considered molecules with IC50 ≤0.95 μM. We applied our SAR models to various drug-like databases and identified novel chemical scaffolds for NS5B inhibitors. Subsequent in vitro antiviral assays suggested a new activity for an existing prodrug, Candesartan cilexetil, which is currently used to treat hypertension and heart failure but has not been previously tested for anti-HCV activity. We also identified NS5B inhibitors with two novel non-nucleoside chemical motifs.

Multiple virtual screening approaches for finding new hepatitis C virus RNA-dependent RNA polymerase inhibitors: structure-based screens and molecular dynamics for the pursue of new poly pharmacological inhibitors

The RNA polymerase NS5B of Hepatitis C virus (HCV) is a well-characterised drug target with an active site and four allosteric binding sites. This work presents a workflow for virtual screening and its application to Drug Bank screening targeting the Hepatitis C Virus (HCV) RNA polymerase non-nucleoside binding sites. Potential polypharmacological drugs are sought with predicted active inhibition on viral replication, and with proven positive pharmaco-clinical profiles. The approach adopted was receptor-based. Docking screens, guided with contact pharmacophores and neural-network activity prediction models on all allosteric binding sites and MD simulations, constituted our analysis workflow for identification of potential hits. Steps included: 1) using a two-phase docking screen with Surflex and Glide Xp. 2) Ranking based on scores, and important H interactions. 3) a machine-learning target-trained artificial neural network PIC prediction model used for ranking. This provided a better correlation of IC50 values of the training sets for each site with different docking scores and sub-scores. 4) interaction pharmacophores-through retrospective analysis of protein-inhibitor complex X-ray structures for the interaction pharmacophore (common interaction modes) of inhibitors for the five non-nucleoside binding sites were constructed. These were used for filtering the hits according to the critical binding feature of formerly reported inhibitors. This filtration process resulted in identification of potential new inhibitors as well as formerly reported ones for the thumb II and Palm I sites (HCV-81) NS5B binding sites. Eventually molecular dynamics simulations were carried out, confirming the binding hypothesis and resulting in 4 hits.

2,6-Bis-arylmethyloxy-5-hydroxychromones with antiviral activity against both hepatitis C virus (HCV) and SARS-associated coronavirus (SCV)

In this study, as a bioisosteric alternative scaffold of the antiviral aryl diketoacids (ADKs), we used 5-hydroxychromone on which two arylmethyloxy substituents were installed. The 5-hydroxychromones (5b–5g) thus prepared showed anti-HCV activity and, depending on the aromatic substituents on the 2-arylmethyloxy moiety, some of the derivatives (5b–5f) were also active against SCV. In addition, unlike the ADKs which showed selective inhibition against the helicase activity of the SCV NTPase/helicase, the 5-hydroxychromones (5b–5f) were active against both NTPase and helicase activities of the target enzyme. Among those, 3-iodobenzyloxy-substituted derivative 5e showed the most potent activity against HCV (EC₅₀ = 4 μM) as well as SCV (IC₅₀ = 4 μM for ATPase activity, 11 μM for helicase activity) and this might be used as a platform structure for future development of the multi-target or broad-spectrum antivirals.

Synthesis and SAR optimization of diketo acid pharmacophore for HCV NS5B polymerase inhibition

Hepatitis C virus (HCV) NS5B polymerase is a key target for anti-HCV therapeutics development. Here we report the synthesis and biological evaluation of a new series of α,γ-diketo acids (DKAs) as NS5B polymerase inhibitors. We initiated structure-activity relationship (SAR) optimization around the furan moiety of compound 1a [IC(50) = 21.8 μM] to achieve more active NS5B inhibitors. This yielded compound 3a [IC(50) = 8.2 μM] bearing the 5-bromobenzofuran-2-yl moiety, the first promising lead compound of the series. Varying the furan moiety with thiophene, thiazole and indazole moieties resulted in compound 11a [IC(50) = 7.5 μM] bearing 3-methylthiophen-2-yl moiety. Finally replacement of the thiophene ring with a bioisosteric phenyl ring further improved the inhibitory activity as seen in compounds 21a [IC(50) = 5.2 μM] and 24a [IC(50) = 2.4 μM]. Binding mode of compound 24a using glide docking within the active site of NS5B polymerase will form the basis for future SAR optimization.

3-Aryl-2-[1H-benzotriazol-1-yl]acrylonitriles: a novel class of potent tubulin inhibitors

During a screening for compounds that could act against Mycobacterium tuberculosis, a series of new cellular antiproliferative agents was identified. The most cytotoxic molecules were evaluated against a panel of human cell lines derived from hematological and solid human tumors. In particular, (E)-2-(1H-benzo[d] [1,2,3]triazol-1-yl)-3-(4-methoxyphenyl)acrylonitrile (1) was found to be of a potency comparable to etoposide and greater than 6-mercaptopurine in all cell lines tested. Accordingly, a synthesis of a new series of (E)-2-(5,6-dichloro-1H-benzo[d] [1,2,3]triazol-1-yl)-3-(4-R-phenyl)acrylonitriles was conducted in order to extend the studies of structure-activity relationship (SAR) for this class of molecules. With the aim to evaluate if 3-aryl-2-[1H-benzotriazol-1-yl]acrylonitriles were able to act like tubulin binding agents, the effects on cell cycle distribution of the most active compounds (1, 2a, 3 and 4) were analyzed in K562 cells. A detailed molecular modeling study of the putative binding mode of this series of compounds on tubulin is also reported.

Discovery of potential integrin VLA-4 antagonists using pharmacophore modeling, virtual screening and molecular docking studies

Very late antigen-4 (VLA-4) is an integrin protein, and its antagonists are useful as anti-inflammatory drugs. The aim of this study is to discover novel virtual lead compounds to use them in designing potent VLA-4 antagonists. A best pharmacophore model was generated with correlation coefficient of 0.935, large cost difference of 114.078, comprising two hydrogen bond acceptors and three hydrophobic features. It was further validated and used in database screening for potential VLA-4 antagonists. A homology model of VLA-4 was built and employed in molecular docking of screened hit compounds. Finally, two compounds were identified as potential virtual leads to be deployed in the designing of novel potent VLA-4 antagonists.

7-O-Arylmethylgalangin as a novel scaffold for anti-HCV agents

In spite of potent antiviral activity, suboptimal physicochemical properties of aryl diketo acids (ADKs) necessitates modification of the core 1,3-diketo acid functionality into a novel scaffold. As the metal-binding affinity of the diketo acid is the key to the antiviral activity of ADKs, we anticipated 3,5-dihydroxy-4-oxo arrangement of galangin scaffold would serve as an excellent mimic for the diketo acid functionality. In this study, through synthesis and biological evaluation of various galangin derivatives, we have shown that the diketo acid functionality can be successfully replaced with the galangin scaffold by specific combination of the substituents to result in identification of a novel galangin derivative (3s) with anti-HCV activity (EC(50)=0.9 μM) comparable to the ADK counterpart.

Isolation, biological evaluation and 3D-QSAR studies of insecticidal/narcotic sesquiterpene polyol esters

For the first time, a set of (43) natural sesquiterpene polyol esters isolated from the root bark of Celastrus angulatus Maxim and Euonymus japonicus Thunb were subjected to 3D-QSAR comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) studies, with the aim of proposing novel sesquiterpene-based compounds with optimal narcotic or insecticidal activities. The established 3D-QSAR models exhibit reasonable statistical quality and prediction capabilities, with internal cross-validated Q (2) values of ∼ 0.5 and external predicted R (2) values of >0.9, respectively. The relative contributions of the steric/electrostatic fields of the 3D-QSAR models show that the electronic effect governs the narcotic activities of the molecules, but the hybrid effect of the electrostatic and hydrophobic interactions is more influential in the insecticidal activities of the compounds. These findings may have valuable implications for the development of novel natural insecticides.

Pharmacophore modeling, resistant mutant isolation, docking, and MM-PBSA analysis: Combined experimental/computer-assisted approaches to identify new inhibitors of the bovine viral diarrhea virus (BVDV)

Starting from a series of our new 2-phenylbenzimidazole derivatives, shown to be selectively and potently active against the bovine viral diarrhea virus (BVDV), we developed a hierarchical combined experimental/molecular modeling strategy to explore the drug leads for the BVDV RNA-dependent RNA-polymerase. Accordingly, a successful 3D pharmacophore model was developed, characterized by distinct chemical features that may be responsible for the activity of the inhibitors. BVDV mutants resistant to lead compounds in our series were then isolated, and the mutant residues on the viral molecular target, the RNA-dependent RNA-polymerase, were identified. Docking procedures upon pharmacophoric constraints and mutational data were carried out, and the binding affinity of all active compounds for the RdRp were estimated. Given the excellent agreement between in silico and in vitro data, this procedure is currently being employed in the design a new series of more selective and potent BVDV inhibitors.

Aryl diketoacids (ADK) selectively inhibit duplex DNA-unwinding activity of SARS coronavirus NTPase/helicase

As anti-HCV aryl diketoacids (ADK) are good metal chelators, we anticipated that ADKs might serve as potential inhibitors of SARS CoV (SCV) NTPase/helicase (Hel) by mimicking the binding modes of the bismuth complexes which effectively competes for the Zn(2+) ion binding sites in SCV Hel thereby disrupting and inhibiting both the NTPase and helicase activities. Phosphate release assay and FRET-based assay of the ADK analogues showed that the ADKs selectively inhibit the duplex DNA-unwinding activity without significant impact on the helicase ATPase activity. Also, antiviral activities of the ADKs were shown dependent upon the substituent. Taken together, these results suggest that there might be ADK-specific binding site in the SCV Hel, which warrants further investigations with diverse ADKs to provide valuable insights into rational design of specific SCV Hel inhibitors.

Effects of the aryl linker and the aromatic substituent on the anti-HCV activities of aryl diketoacid (ADK) analogues

Based on our pharmacophore model of the aryl diketoacids (ADKs), we designed and prepared a series of novel ADK analogues, which showed potent inhibitory activities against the NS5B polymerase in the submicromolar range. Pharmacophore-guided docking study revealed that the antiviral activities of the ADKs are highly dependent upon the aryl linker as well as the size and position of the aromatic substituent. It is of another importance that, unlike previously reported ADKs, three ADK analogues synthesized in this study effectively blocked Hepatitis C Virus (HCV) replication in the replicon systems.

Synthesis, antifungal and antimycobacterial activities of new bis-imidazole derivatives, and prediction of their binding to P45014DM by molecular docking and MM/PBSA method

New bis-imidazole derivatives have been synthesized and their antifungal and antimycobacterial activity was determined. Almost all compounds exhibited a moderate to good activity against two clinical isolates of Candida albicans 3038 and Candida glabrata 123. The same compounds showed an interesting killing activity against Mycobacterium tuberculosis H(37)Rv reference strain. Docking procedures combined with molecular dynamics simulations in the MM/PBSA framework of theory were applied to predict the binding mode of all compounds in the binding pocket of the cytochrome P450 14alpha-sterol demethylase (14DM) of C. albicans, for which no ligand-protein crystal structure is currently available. The results obtained in silico showed that the active compounds may interact at the active site of protein, and that their binding free energy values are in agreement with the corresponding experimental activity values.

In silico pharmacology for drug discovery: applications to targets and beyond

Computational (in silico) methods have been developed and widely applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, similarity searching, pharmacophores, homology models and other molecular modeling, machine learning, data mining, network analysis tools and data analysis tools that use a computer. Such methods have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The first part of this review discussed the methods that have been used for virtual ligand and target-based screening and profiling to predict biological activity. The aim of this second part of the review is to illustrate some of the varied applications of in silico methods for pharmacology in terms of the targets addressed. We will also discuss some of the advantages and disadvantages of in silico methods with respect to in vitro and in vivo methods for pharmacology research. Our conclusion is that the in silico pharmacology paradigm is ongoing and presents a rich array of opportunities that will assist in expediating the discovery of new targets, and ultimately lead to compounds with predicted biological activity for these novel targets.

Pharmacophore mapping of diverse classes of farnesyltransferase inhibitors

Protein farnesyltransferase (FTase) is a zinc-dependent enzyme that catalyzes the attachment of a farnesyl lipid group to the sulfur atom of a cysteine residue of numerous proteins involved in cell signaling including the oncogenic H-Ras protein. Pharmacophore models were developed by using Catalyst HypoGen program with a training set of 22 farnesyltransferase inhibitors (FTIs), which were carefully selected with great diversity in both molecular structure and bioactivity for discovering new potent FTIs. The best pharmacophore hypothesis (Hypo 1), consisting of four features, namely, one hydrogen-bond acceptor (HBA), one hydrophobic point (HY), and two ring aromatics (RA), has a correlation coefficient of 0.961, a root mean square deviation (RMSD) of 0.885, and a cost difference of 62.436, suggesting that a highly predictive pharmacophore model was successfully obtained. For the test series, a classification scheme was used to distinguish highly active from moderately active and inactive compounds on the basis of activity ranges. Hypo 1 was validated with 181 test set compounds, which has a correlation coefficient of 0.713 between estimated activity and experimentally measured activity. The model was further validated by screening a database spiked with 25 known inhibitors. The model picked up all 25 known inhibitors giving an enrichment factor of 10.892. The results demonstrate that the hypothesis derived in this study can be considered to be a useful and reliable tool in identifying structurally diverse compounds with desired biological activity.

Representation of target-bound drugs by computed conformers: implications for conformational libraries

BACKGROUND

The increasing number of known protein structures provides valuable information about pharmaceutical targets. Drug binding sites are identifiable and suitable lead compounds can be proposed. The flexibility of ligands is a critical point for the selection of potential drugs. Since computed 3D structures of millions of compounds are available, the knowledge of their binding conformations would be a great benefit for the development of efficient screening methods.

RESULTS

Integration of two public databases allowed superposition of conformers for 193 approved drugs with 5507 crystallised target-bound counterparts. The generation of 9600 drug conformers using an atomic force field was carried out to obtain an optimal coverage of the conformational space. Bioactive conformations are best described by a conformational ensemble: half of all drugs exhibit multiple active states, distributed over the entire range of the reachable energy and conformational space.A number of up to 100 conformers per drug enabled us to reproduce the bound states within a similarity threshold of 1.0 angstroms in 70% of all cases. This fraction rises to about 90% for smaller or average sized drugs.

CONCLUSION

Single drugs adopt multiple bioactive conformations if they interact with different target proteins. Due to the structural diversity of binding sites they adopt conformations that are distributed over a broad conformational space and wide energy range. Since the majority of drugs is well represented by a predefined low number of conformers (up to 100) this procedure is a valuable method to compare compounds by three-dimensional features or for fast similarity searches starting with pharmacophores. The underlying 9600 generated drug conformers are downloadable from the Super Drug Web site 1. All superpositions are visualised at the same source. Additional conformers (110,000) of 2400 classified WHO-drugs are also available.