A Combination of Receptor-Based Pharmacophore Modeling & QM Techniques for Identification of Human Chymase Inhibitors

Inhibition of chymase is likely to divulge therapeutic ways for the treatment of cardiovascular diseases, and fibrotic disorders. To find novel and potent chymase inhibitors and to provide a new idea for drug design, we used both ligand-based and structure-based methods to perform the virtual screening(VS) of commercially available databases. Different pharmacophore models generated from various crystal structures of enzyme may depict diverse inhibitor binding modes. Therefore, multiple pharmacophore-based approach is applied in this study. X-ray crystallographic data of chymase in complex with different inhibitors were used to generate four structure–based pharmacophore models. One ligand–based pharmacophore model was also developed from experimentally known inhibitors. After successful validation, all pharmacophore models were employed in database screening to retrieve hits with novel chemical scaffolds. Drug-like hit compounds were subjected to molecular docking using GOLD and AutoDock. Finally four structurally diverse compounds with high GOLD score and binding affinity for several crystal structures of chymase were selected as final hits. Identification of final hits by three different pharmacophore models necessitates the use of multiple pharmacophore-based approach in VS process. Quantum mechanical calculation is also conducted for analysis of electrostatic characteristics of compounds which illustrates their significant role in driving the inhibitor to adopt a suitable bioactive conformation oriented in the active site of enzyme. In general, this study is used as example to illustrate how multiple pharmacophore approach can be useful in identifying structurally diverse hits which may bind to all possible bioactive conformations available in the active site of enzyme. The strategy used in the current study could be appropriate to design drugs for other enzymes as well.     

Combined structure- and ligand-based pharmacophore modeling and molecular dynamics simulation studies to identify selective inhibitors of MMP-8

Matrix metalloproteinase-8 (MMP-8) is the key mediator in initiating type I collagen degradation and is associated with rheumatoid arthritis. In the present study, a pharmacophore hypothesis was developed based on selective non zinc binding inhibitors of MMP-8. The pharmacophore hypothesis was refined manually and validated by observing structures and the interactions of MMP-8 inhibitors. The refined pharmacophore model was able to discriminate the non-zinc binding inhibitors of MMP-8 with respect to other inhibitors. Hence this study proposes a combined structure- and ligand-based pharmacophore model that is suitable for retrieving the novel inhibitors of MMP-8. The pharmacophore hypothesis AADRH was used as query for retrieving potential compounds from the Zinc database and hits were selected based on the catalytic selective amino acid residues of Arg 222, and Tyr 227. We identified six compounds as potent inhibitors and their selectivity profile were checked against different subtypes of MMPs using the cross-docking method. Molecular dynamics results indicated that ZINC 00673680 forms a stable interaction with the key amino acid residues and avoids the zinc atom with a distance of 5.49?Å. Our computational study might be useful for further development of selective MMP-8 inhibitors.     

An Innovative Strategy for Dual Inhibitor Design and Its Application in Dual Inhibition of Human Thymidylate Synthase and Dihydrofolate Reductase Enzymes

Due to the diligence of inherent redundancy and robustness in many biological networks and pathways, multitarget inhibitors present a new prospect in the pharmaceutical industry for treatment of complex diseases. Nevertheless, to design multitarget inhibitors is concurrently a great challenge for medicinal chemists. We have developed a novel computational approach by integrating the affinity predictions from structure-based virtual screening with dual ligand-based pharmacophore to discover potential dual inhibitors of human Thymidylate synthase (hTS) and human dihydrofolate reductase (hDHFR). These are the key enzymes in folate metabolic pathway that is necessary for the biosynthesis of RNA, DNA, and protein. Their inhibition has found clinical utility as antitumor, antimicrobial, and antiprotozoal agents. A druglike database was utilized to perform dual-target docking studies. Hits identified through docking experiments were mapped over a dual pharmacophore which was developed from experimentally known dual inhibitors of hTS and hDHFR. Pharmacophore mapping procedure helped us in eliminating the compounds which do not possess basic chemical features necessary for dual inhibition. Finally, three structurally diverse hit compounds that showed key interactions at both active sites, mapped well upon the dual pharmacophore, and exhibited lowest binding energies were regarded as possible dual inhibitors of hTS and hDHFR. Furthermore, optimization studies were performed for final dual hit compound and eight optimized dual hits demonstrating excellent binding features at target systems were also regarded as possible dual inhibitors of hTS and hDHFR. In general, the strategy used in the current study could be a promising computational approach and may be generally applicable to other dual target drug designs.     

Discovery of promising FtsZ inhibitors by E-pharmacophore, 3D-QSAR,molecular docking study,and molecular dynamics simulation

Filamentous temperature-sensitive protein Z (FtsZ), playing a key role in bacterial cell division, is regarded as a promising target for the design of antimicrobial agent. This study is looking for potential high-efficiency FtsZ inhibitors. Ligand-based pharmacophore and E-pharmacophore, virtual screening and molecular docking were used to detect promising FtsZ inhibitors, and molecular dynamics simulation was used to study the stability of protein-ligand complexes in this paper. Sixty-three inhibitors from published literatures with pIC₅₀ ranging from 2.483 to 5.678 were collected to develop ligand-based pharmacophore model. 4DXD bound with 9PC was selected to develop the E-pharmacophore model. The pharmacophore models validated by test set method and decoy set were employed for virtual screening to exclude inactive compounds against ZINC database. After molecular docking, ADME analysis, IFD docking and MM-GBSA, 8 hits were identified as potent FtsZ inhibitors. A 50?ns molecular dynamics simulation was implemented on the compounds to assess the stability between potent inhibitors and FtsZ. The results indicated that the candidate compounds had a high docking score and were strongly combined with FtsZ by forming hydrogen bonding interactions with key amino acid residues, and van der Waals forces and hydrophobic interactions had significant contribution to the stability of the binding. Molecular dynamics simulation results showed that the protein-ligand compounds performed well in both the stability and flexibility of the simulation process.     

Pharmacoinformatics analysis to identify inhibitors of Mtb-ASADH

Aspartate-semialdehyde dehydrogenase (ASADH; EC 1.2.1.11) is a key enzyme in the biosynthesis of essential amino acids in prokaryotes and fungi, inhibition of ASADH leads to the development of novel antitubercular agents. In the present work, a combined structure and ligand-based pharmacophore modeling, molecular docking, and molecular dynamics (MD) approaches were employed to identify potent inhibitors of mycobacterium tuberculosis (Mtb)-ASADH. The structure-based pharmacophore hypothesis consists of three hydrogen bond acceptor (HBA), two negatively ionizable, and one positively ionizable center, while ligand-based pharmacophore consists of additional one HBA and one hydrogen bond donor features. The validated pharmacophore models were used to screen the chemical databases (ZINC and NCI). The screened hits were subjected to ADME and toxicity filters, and subsequently to the molecular docking analysis. Best-docked 25 compounds carry the characteristics of highly electronegative functional groups (–COOH and –NO₂) on both sides and exhibited the H-bonding interactions with highly conserved residues Arg99, Arg249, and His256. For further validation of docking results, MD simulation studies were carried out on two representative compounds NSC51108 and ZINC04203124. Both the compounds remain bound to the key active residues of Mtb-ASADH during the MD simulations. These identified hits can be further used for lead optimization and in the design more potent inhibitors against Mtb-ASADH.     

Discovery of potent inhibitors for interleukin-2-inducible T-cell kinase: structure-based virtual screening and molecular dynamics simulation approaches

In our study, a structure-based virtual screening study was conducted to identify potent ITK inhibitors, as ITK is considered to play an important role in the treatment of inflammatory diseases. We developed a structure-based pharmacophore model using the crystal structure (PDB ID: 3MJ2) of ITK complexed with BMS-50944. The most predictive model, SB-Hypo1, consisted of six features: three hydrogen-bond acceptors (HBA), one hydrogen-bond donor (HBD), one ring aromatic (RA), and one hydrophobic (HY). The statistical significance of SB-Hypo1 was validated using wide range of test set molecules and a decoy set. The resulting well-validated model could then be confidently used as a 3D query to screen for drug-like molecules in a database, in order to retrieve new chemical scaffolds that may be potent ITK inhibitors. The hits retrieved from this search were filtered based on the maximum fit value, drug-likeness, and ADMET properties, and the hits that were retained were used in a molecular docking study to find the binding mode and molecular interactions with crucial residues at the active site of the protein. These hits were then fed into a molecular dynamics simulation to study the flexibility of the activation loop of ITK upon ligand binding. This combination of methodologies is a valuable tool for identifying structurally diverse molecules with desired biological activities, and for designing new classes of selective ITK inhibitors.

Ligand based virtual screening and biological evaluation of inhibitors of chorismate mutase (Rv1885c) from Mycobacterium tuberculosis H37Rv

We have identified new lead candidates that possess inhibitory activity against Mycobacterium tuberculosis H37Rv chorismate mutase by a ligand-based virtual screening optimized for lead evaluation in combination with in vitro enzymatic assay. The initial virtual screening using a ligand-based pharmacophore model identified 95 compounds from an in-house small molecule database of 15,452 compounds. The obtained hits were further evaluated by molecular docking and 15 compounds were short listed based on docking scores and the other scoring functions and subjected to biological assay. Chorismate mutase activity assays identified four compounds as inhibitors of M. tuberculosis chorismate mutase (MtCM) with low K(i) values. The structural models for these ligands in the chorismate mutase binding site will facilitate medicinal chemistry efforts for lead optimization against this protein.     

Identification of Pak1 inhibitors using water thermodynamic analysis

Abstract

p21-activated kinases (Paks) play an integral component in various cellular diverse processes. The full activation of Pak is dependent upon several serine residues present in the N-terminal region, a threonine present at the activation loop, and finally the phosphorylation of these residues ensure the complete activation of Pak1. The present study deals with the identification of novel potent candidates of Pak1 using computational methods as anti-cancer compounds. A diverse energy based pharmacophore (e-pharmacophore) was developed using four co-crystal inhibitors of Pak1 having pharmacophore features of 5 (DRDRR), 6 (DRHADR), and 7 (RRARDRP and DRRDADH) hypotheses. These models were used for rigorous screening against e-molecule database. The obtained hits were filtered using ADME/T and molecular docking to identify the high affinity binders. These hits were subjected to hierarchical clustering using dendritic fingerprint inorder to identify structurally diverse molecules. The diverse hits were scored against generated water maps to obtain WM/MM ΔG binding energy. Furthermore, molecular dynamics simulation and density functional theory calculations were performed on the final hits to understand the stability of the complexes. Five structurally diverse novel Pak1 inhibitors (4835785, 32198676, 32407813, 76038049, and 32945545) were obtained from virtual screening, water thermodynamics and WM/MM ΔG binding energy. All hits revealed similar mode of binding pattern with the hinge region residues replacing the unstable water molecules in the binding site. The obtained novel hits could be used as a platform to design potent drugs that could be experimentally tested against cancer patients having increased Pak1 expression.     

Computer-aided discovery of novel non-peptide inhibitors against amyloid-beta (Aβ) peptide aggregation for treating Alzheimer's disease

A non-peptide inhibitor that is metabolically stable, orally active and capable of crossing the blood–brain barrier has been a popular option for treating Alzheimer's disease (AD). To identify novel non-peptide inhibitors for AD drug development, a structure-based pharmacophore model (SBPM) was developed using the representative docked conformation of the recently discovered peptide inhibitor PGKLVYA in the potential binding site on the Aβ(17–42) protofibril. The best SBPM, consisting of two hydrophobic, one hydrogen bond donor, and one positive ionisable feature, was further validated using ligand pharmacophore mapping studies. The well-validated SBPM was then used as the 3D query in virtual screening to identify potential hits from the National Cancer Institute database. These hits were subsequently filtered by toxicity prediction and molecular docking, and their binding stabilities and affinities were validated by 20-ns molecular dynamics simulations and molecular mechanics Poisson–Boltzmann surface area analysis, respectively. Finally, two Hits (NSC35984 and NSC102747) were identified as potential leads, which exhibited higher binding stability and affinity towards Aβ compared with PGKVYA. Our results also suggest that these two Hits have the ability to prevent Aβ adopting toxic β-sheet structures, and can be easily synthesised and have structural novelty, indicating that they are promising candidates for treating AD.     

Identification of Novel HIV 1- Protease Inhibitors: Application of Ligand and Structure Based Pharmacophore Mapping and Virtual Screening

A combined ligand and structure-based drug design approach provides a synergistic advantage over either methods performed individually. Present work bestows a good assembly of ligand and structure-based pharmacophore generation concept. Ligand-oriented study was accomplished by employing the HypoGen module of Catalyst in which we have translated the experimental findings into 3-D pharmacophore models by identifying key features (four point pharmacophore) necessary for interaction of the inhibitors with the active site of HIV-1 protease enzyme using a training set of 33 compounds belonging to the cyclic cyanoguanidines and cyclic urea derivatives. The most predictive pharmacophore model (hypothesis 1), consisting of four features, namely, two hydrogen bond acceptors and two hydrophobic, showed a correlation (r) of 0.90 and a root mean square of 0.71 and cost difference of 56.59 bits between null cost and fixed cost. The model was validated using CatScramble technique, internal and external test set prediction. In the second phase of our study, a structure-based five feature pharmacophore hypothesis was generated which signifies the importance of hydrogen bond donor, hydrogen bond acceptors and hydrophobic interaction between the HIV-1 protease enzyme and its inhibitors. This work has taken a significant step towards the full integration of ligand and structure-based drug design methodologies as pharmacophoric features retrieved from structure-based strategy complemented the features from ligand-based study hence proving the accuracy of the developed models. The ligand-based pharmacophore model was used in virtual screening of Maybridge and NCI compound database resulting in the identification of four structurally diverse druggable compounds with nM activities.     

On scaffold hopping: Challenges in the discovery of sulfated small molecules as mimetics of glycosaminoglycans

The design of sulfated, small, nonsaccharide molecules as modulators of proteins is still in its infancy as standard drug discovery tools such as library of diverse sulfated molecules and in silico docking and scoring protocol have not been firmly established. Databases, such as ZINC, contain too few sulfate-containing nonsaccharide molecules, which severely limits the identification of new hits. Lack of a generally applicable protocol for scaffold hopping limits the development of sulfated small molecules as synthetic mimetics of the highly sulfated glycosaminoglycans. We explored a sequential ligand-based (LBVS) and structure-based virtual screening (SBVS) approach starting from our initial discovery of monosulfated benzofurans to discover alternative scaffolds as allosteric modulators of thrombin, a key coagulation enzyme. Screening the ZINC database containing nearly 1 million nonsulfated small molecules using a pharmacophore developed from the parent sulfated benzofurans followed by a genetic algorithm-based dual-filter docking and scoring screening identified a group of 10 promising hits, of which three top-scoring hits were synthesized. Each was found to selectively inhibit human alpha-thrombin suggesting the possibility of this approach for scaffold hopping. Michaelis–Menten kinetics showed allosteric inhibition mechanism for the best molecule and human plasma studies confirmed good anticoagulation potential as expected. Our simple sequential LBVS and SBVS approach is likely to be useful as a general strategy for identification of sulfated small molecules hits as modulators of glycosaminoglycan–protein interactions.     

Ligand- and structure-based in silico studies to identify kinesin spindle protein (KSP) inhibitors as potential anticancer agents

Kinesin spindle protein (KSP) belongs to the kinesin superfamily of microtubule-based motor proteins. KSP is responsible for the establishment of the bipolar mitotic spindle which mediates cell division. Inhibition of KSP expedites the blockade of the normal cell cycle during mitosis through the generation of monoastral MT arrays that finally cause apoptotic cell death. As KSP is highly expressed in proliferating/cancer cells, it has gained considerable attention as a potential drug target for cancer chemotherapy. Therefore, this study envisaged to design novel KSP inhibitors by employing computational techniques/tools such as pharmacophore modelling, virtual database screening, molecular docking and molecular dynamics. Initially, the pharmacophore models were generated from the data-set of highly potent KSP inhibitors and the pharmacophore models were validated against in house test set ligands. The validated pharmacophore model was then taken for database screening (Maybridge and ChemBridge) to yield hits, which were further filtered for their drug-likeliness. The potential hits retrieved from virtual database screening were docked using CDOCKER to identify the ligand binding landscape. The top-ranked hits obtained from molecular docking were progressed to molecular dynamics (AMBER) simulations to deduce the ligand binding affinity. This study identified MB-41570 and CB-10358 as potential hits and evaluated these experimentally using in vitro KSP ATPase inhibition assays.     

Discovery of potential pancreatic cholesterol esterase inhibitors using pharmacophore modelling, virtual screening, and optimization studies

Pancreatic cholesterol esterase (CEase) is a serine hydrolase involved in the hydrolysis of variety of lipids and transport of free cholesterol. In this study, pharmacophore hypotheses based on known inhibitors were generated using common feature pharmacophore generation protocol available in Discovery Studio program. The best pharmacophore model containing two hydrogen bond acceptor and three hydrophobic features was selected and validated. It was further used in screening three diverse chemical databases. Hit compounds were subjected to drug-likeness and molecular docking studies. Four hits, namely SEW00846, NCI0040784, GK03167, and CD10645, were selected based on the GOLD fitness score and interaction with active site amino acids. All hit compounds were further optimized to improve their binding in the active site. The optimized compounds were found to have improved binding at the active site. Strongly binding optimized hits at the active site can act as virtual leads in potent CEase inhibitor designing.     

New leads of metallo-beta-lactamase inhibitors from structure-based pharmacophore design

We have applied pharmacophore generation, database searching, docking methodologies, and experimental enzyme kinetics to discover new structures for design of di-zinc metallo-beta-lactamase inhibitors. Based on crystal structures of class B1 metallo-beta-lactamases with a succinic acid and a mercapto-carboxylic acid inhibitor bound to the enzyme, two pharmacophore models were constructed. With the Catalyst program, these pharmacophores were used to search the ACD database, which provided a total of 74 hits representing four different chemical classes of compounds: Dicarboxylic acids, phosphonic and sulfonic acid derivatives, and mercapto-carboxylic acids. All hits were docked into different metallo-beta-lactamases (from classes B1 and B3) using the GOLD docking program. A selection scheme based on the GOLD scores, the Catalyst fit and shape values, and the size of the compounds (molecular weight, surface area, and number of rotatable bonds) was developed and thirteen compounds representing all four chemical classes were selected for experimental studies. Three compounds with new scaffolds hitherto not present in metallo-beta-lactamase inhibitors have IC50 values less than 15 microM and may serve as starting points in the design of metallo-beta-lactamase inhibitors.     

Pharmacophore modeling and virtual screening studies to identify new c-Met inhibitors

Mesenchymal epithelial transition factor (c-Met) is an attractive target for cancer therapy. Three-dimensional pharmacophore hypotheses were built based on a set of known structurally diverse c-Met inhibitors. The best pharmacophore model, which identified inhibitors with an associated correlation coefficient of 0.983 between their experimental and estimated IC(50) values, consisted of two hydrogen-bond acceptors, one hydrophobic, and one ring aromatic feature. The highly predictive power of the model was rigorously validated by test set prediction and Fischer's randomization method. The high values of enrichment factor and receiver operating characteristic (ROC) score indicated the model performed fairly well at distinguishing active from inactive compounds. The model was then applied to screen compound database for potential c-Met inhibitors. A filtering protocol, including druggability and molecular docking, were also applied in hits selection. The final 38 molecules, which exhibited good estimated activities, desired binding mode and favorable drug likeness were identified as potential c-Met inhibitors. Their novel backbone structures could be served as scaffolds for further study, which may facilitate the discovery and rational design of potent c-Met kinase inhibitors.     

Lead identification and optimization of novel collagenase inhibitors; pharmacophore and structure based studies

In this study, chemical feature based pharmacophore models of MMP-1, MMP-8 and MMP-13 inhibitors have been developed with the aid of HypoGen module within Catalyst program package. In MMP-1 and MMP-13, all the compounds in the training set mapped HBA and RA, while in MMP-8, the training set mapped HBA and HY. These features revealed responsibility for the high molecular bioactivity, and this is further used as a three dimensional query to screen the knowledge based designed molecules. These pharmacophore models for collagenases picked up some potent and novel inhibitors. Subsequently, docking studies were performed for the potent molecules and novel hits were suggested for further studies based on the docking score and active site interactions in MMP-1, MMP-8 and MMP-13.     

Identification of dual kinase inhibitors of CK2 and GSK3β: combined qualitative and quantitative pharmacophore modeling approach

PTEN, a tumor suppressor protein, gets deactivated by casein kinase 2 (CK2) and glycogen synthase kinase 3β (GSK3β), which are the major causes of PI3K/AKT-driven tumors. To surmount this problem, the multi-target inhibitor strategy may be of great significance. The goal of this study was to design dual-target inhibitors of CK2 and GSK3β using a combination of pharmacophore modeling and molecular docking studies. The common feature-based (qualitative) and 3DQSAR-based (quantitative) pharmacophore models were generated and validated. The best pharmacophore models (Pharm18 and Hypo1) comprised two hydrogen-bond acceptors, one hydrophobic, and one ring aromatic features. The models were used to screen various chemical database and top mapped compounds from each database were selected. They were processed for Lipinski filter, Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) analysis, and docking studies. We have obtained six hits with comparable dock score to the reported inhibitors. We have concluded Hit15 as a competent candidate based on its docking and Density Functional Theory (DFT) calculations. It showed 140.73 and 130.79 dock score in CK2 and GSK3β, respectively. The electronic property of Hit 15 showed the lowest energy gap (0.021) compared to other hits and active ligands which suggest its higher reactivity. In conclusion, this study may assist in the development of new potent dual kinase inhibitors of CK2 and GSK3β. Also, the overture effort of combined qualitative and quantitative modeling for the development of multi-target inhibitors may support the future endeavors.     

Structural insight into Mycobacterium tuberculosis maltosyl transferase inhibitors: pharmacophore-based virtual screening,docking, and molecular dynamics simulations

Pharmacophore-based virtual screening, subsequent docking, and molecular dynamics (MD) simulations have been done to identify potential inhibitors of maltosyl transferase of Mycobacterium tuberculosis (mtb GlgE). Ligand and structure-based pharmacophore models representing its primary binding site (pbs) and unique secondary binding site 2 (sbs2), respectively, were constructed based on the three dimensional structure of mtb GlgE. These pharmacophore models were further used for screening of ZINC and antituberculosis compounds database (ATD). Virtually screened molecules satisfying Lipinski’s rule of five were then analyzed using docking studies and have identified 23 molecules with better binding affinity than its natural substrate, maltose. Four top scoring ligands from ZINC and ATD that either binds to pbs or sbs2 have been subjected to 10 ns each MD simulations and binding free energy calculations. Results of these studies have confirmed stable protein ligand binding. Results reported in the article are likely to be helpful in antitubercular therapeutic development research.     

Identification of novel, less toxic PTP-LAR inhibitors using in silico strategies: pharmacophore modeling, SADMET-based virtual screening and docking

Human leukocyte antigen-related (PTP-LAR) is a receptor-like transmembrane phosphatase and a potential target for diabetes, obesity and cancer. In the present study, a sequence of in silico strategies (pharmacophore mapping, a 3D database searching, SADMET screening, and docking and toxicity studies) was performed to identify eight novel nontoxic PTP-LAR inhibitors. Twenty different pharmacophore hypotheses were generated using two methods; the best (hypothesis 2) consisted of three hydrogen-bond acceptor (A), one ring aromatic (R), and one hydrophobic aliphatic (Z) features. This hypothesis was used to screen molecules from several databases, such as Specs, IBS, MiniMaybridge, NCI, and an in-house PTP inhibitor database. In order to overcome the general bioavailability problem associated with phosphatases, the hits obtained were filtered by Lipinski’s rule of five and SADMET properties and validated by molecular docking studies using the available crystal structure 1LAR. These docking studies suggested the ligand binding pattern and interactions required for LAR inhibition. The docking analysis also revealed that sulfonylurea derivatives with an isoquinoline or naphthalene scaffold represent potential LAR drugs. The screening protocol was further validated using ligand pharmacophore mapping studies, which showed that the abovementioned interactions are indeed crucial and that the screened molecules can be presumed to possess potent inhibitory activities.     

Discovery of novel 5α-reductase type II inhibitors by pharmacophore modelling,virtual screening,molecular docking and molecular dynamics simulations

Benign prostatic hyperplasia (BPH) is caused by augmented levels of androgen dihydrotestosterone (DHT) which is involved in the growth of the prostate in humans. 5α-Reductase type II (5αR2) is an intracellular enzyme that catalyses the formation of DHT from testosterone; hence, the inhibition of 5αR2 has emerged as one of the most promising strategies for the treatment of BPH. In this study, a computational approach that integrates ligand-based pharmacophore modelling, virtual screening, molecular docking and molecular dynamics (MD) simulations was adopted to discover novel 5αR2 inhibitors with less side effects. After validating by Fischer's randomisation and Güner–Henry test, the best quantitative pharmacophore model (Hypo1), consisting of two hydrogen-bond acceptors and three hydrophobic features, was subsequently used as a three-dimensional-query in virtual screening to identify potential hits from Maybridge and National Cancer Institute databases. These hits were further filtered by ADMET (absorption, distribution, metabolism, elimination and toxicology) and molecular docking experiments, and their binding stabilities were validated by 10-ns MD simulations. Finally, only one hit was identified as a potential lead based on higher predicted inhibitory activity to 5αR2 compared with the most active inhibitor (finasteride). Our results further suggest that this potential lead could easily be synthesised and has structural novelty, making it a promising candidate for treating BPH.