Virtual Database Screening Research Articles

Identification of novel leads as potent inhibitors of HDAC3 using ligand-based pharmacophore modeling and MD simulation

In the landscape of epigenetic regulation, histone deacetylase 3 (HDAC3) has emerged as a prominent therapeutic target for the design and development of candidate drugs against various types of cancers and other human disorders. Herein, we have performed ligand-based pharmacophore modeling, virtual screening, molecular docking, and MD simulations to design potent and selective inhibitors against HDAC3. The predicted best pharmacophore model ‘Hypo 1’ showed excellent correlation (R2 = 0.994), lowest RMSD (0.373), lowest total cost value (102.519), and highest cost difference (124.08). Hypo 1 consists of four salient pharmacophore features viz. one hydrogen bond acceptor (HBA), one ring aromatic (RA), and two hydrophobic (HYP). Hypo 1 was validated by Fischer's randomization with a 95% of confidence level and the external test set of 60 compounds with a good correlation coefficient (R2 = 0.970). The virtual screening of chemical databases, drug-like properties calculations followed by molecular docking resulted in identifying 22 representative hit compounds. Performed 50 ns of MD simulations on top three hits were retained the salient π-stacking, Zn2+ coordination, hydrogen bonding, and hydrophobic interactions with catalytic residues from the active site pocket of HDAC3. Total binding energy calculated by MM-PBSA showed that the Hit 1 and Hit 2 formed stable complexes with HDAC3 as compared to reference TSA. Further, the PLIP analysis showed a close resemblance between the salient pharmacophore features of Hypo 1 and the presence of molecular interactions in co-crystallized FDA-approved drugs. We conclude that the screened hit compounds may act as potent inhibitors of HDAC3 and further preclinical and clinical studies may pave the way for developing them as effective therapeutic agents for the treatment of different cancers and neurodegenerative disorders.

The design of TOPK inhibitors using structure-based pharmacophore modeling and molecular docking based on an MD-refined homology model.

The TOPK enzyme (also known as PBK) is a serine-threonine protein kinase that is rarely detected in normal tissues yet is found to be overexpressed and activated in a variety of cancers such as lung, colorectal, breast, and esophageal cancer. Its prevalence in cancerous cells is associated with their poor prognosis and responsiveness to treatment. This enzyme plays a vital role in cell division, specifically in regulating cytokinesis. Unlike drugs targeting early phases in mitosis, inhibition of cytokinesis by targeting biomolecules that are unique to multiplying cells poses no threat to the normal function of non-multiplying cells. Studies have shown that inhibition of cytokinesis is promising in suppressing the growth of proliferating cancerous cells as exemplified by the complete tumor regression seen with the suppression of TOPK. Herein, we report the identification of potent TOPK inhibitors with anticancer potential using a structure-based drug design approach. The only available crystal structure of TOPK corresponds to a double mutant (T9E and T198E) dimer with a distorted N-lobe conformation, thus 3D homology modeling was implemented to rebuild the enzyme's native conformation. The resulting refined model was used to generate 3D pharmacophore models for the virtual screening of small molecules databases. Retrieved hits were filtered, docked into the ATP binding site of the enzyme, rescored, and the binding free energies for the top consensually scoring hits were calculated. Consequently, 45 compounds were selected and their in vitro inhibitory activity against TOPK was tested revealing four potential hits with the most active compound having an IC50 of 3.85µM. This compound will be chosen as a lead compound to synthesize analogs aiming to enhance potency and drug-like properties and to enrich the SAR data.

Identification of autophagic target RAB13 with small-molecule inhibitor in low-grade glioma via integrated multi-omics approaches coupled with virtual screening of traditional Chinese medicine databases.

ObjectivesAutophagy, a highly conserved lysosomal degradation process in eukaryotic cells, has been widely reported closely related to the progression of many types of human cancers, including LGG; however, the intricate relationship between autophagy and LGG remains to be clarified.Materials and methodsMulti‐omics methods were used to integrate omics data to determine potential autophagy regulators in LGG. The expression of ZFP36L2 and RAB13 in SW1088 cells was experimentally manipulated using cDNAs and small interfering RNAs (siRNA). RT‐qPCR detects RNAi gene knockout and cDNA overexpression efficiency. The expression levels of proteins in SW1088 cells were evaluated using Western blot analysis and immunofluorescence analysis. Homology modelling and molecular docking were used to identify compounds from Multi‐Traditional Chinese Medicine (TCM) Databases. The apoptosis ratios were determined by flow cytometry analysis of Annexin‐V/PI double staining. We detect the number of autophagosomes by GFP‐MRFP‐LC3 plasmid transfection to verify the process of autophagy flow.ResultsWe integrated various omics data from LGG, including EXP, MET and CNA data, with the SNF method and the LASSO algorithm, and identified ZFP36L2 and RAB13 as positive regulators of autophagy, which are closely related to the core autophagy regulators. Both transcription level and protein expression level of the four autophagy regulators, including ULK1, FIP200, ATG16L1 and ATG2B, and LC3 puncta were increased by ZFP36L2 and RAB13 overexpression. In addition, RAB13 participates in autophagy through ATG2B, FIP200, ULK1, ATG16L1 and Beclin‐1. Finally, we screened multi‐TCM databases and identified gallic acid as a novel potential RAB13 inhibitor, which was confirmed to negatively regulate autophagy as well as to induce cell death in SW1088 cells.ConclusionOur study identified the key autophagic regulators ZFP36L2 and Rab13 in LGG progression, and demonstrated that gallic acid is a small molecular inhibitor of RAB13, which negatively regulates autophagy and provides a possible small molecular medicine for the subsequent treatment of LGG.

CHK1 kinase inhibition: identification of allosteric hits using MD simulations, pharmacophore modeling, docking and MM-PBSA calculations.

The CHK1 kinase plays a pivotal role in the DNA damage response pathway. Hence, inhibition of CHK1 appeared as a promising strategy to overcome the resistance problem of chemotherapeutic agents resulting from the overexpression of CHK1 that enables cancerous cells to repair their chemotherapy-induced DNA damage. In this study, different computational drug design techniques were employed to identify new CHK1 inhibitors targeting its allosteric pocket. A 1μs MD simulation of the apo form of the enzyme was run to study its native dynamics. The resulting trajectory was analyzed to select a frame where the ATP binding pocket is most occluded while its allosteric counterpart is most exposed to be used in the design of potential allosteric inhibitors that could trap the enzyme in such nearly inactive state. Besides the selected frame, another three crystal structures of CHK1 complexed with allosteric inhibitors were utilized to generate structure-based pharmacophore models. Seven pharmacophores were generated and utilized in virtual screening of different databases. The retrieved hits were filtered and then docked into the allosteric pocket. Finally, the binding energies of the top-ranked docked hits were calculated. Twenty compounds were selected as candidates for biological evaluation against CHK1 enzyme. The biological screening results showed moderate activities where the percentage of CHK1 inhibition ranged from zero to 28.26%. Four of the tested compounds showed percentage of CHK1 inhibition greater than 20%, of which, two compounds were identified as allosteric hits that upon further optimization could be converted into lead-like compounds.

In silico identification of Tretinoin as a SARS-CoV-2 envelope (E) protein ion channel inhibitor

Viroporins are oligomeric, pore forming, viral proteins that play critical roles in the life cycle of pathogenic viruses. Viroporins like HIV-1 Vpu, Alphavirus 6 K, Influenza M2, HCV p7, and Picornavirus 2B, form discrete aqueous passageways which mediate ion and small molecule transport in infected cells. The alterations in host membrane structures induced by viroporins is essential for key steps in the virus life cycle like entry, replication and egress. Any disruption in viroporin functionality severely compromises viral pathogenesis. The envelope (E) protein encoded by coronaviruses is a viroporin with ion channel activity and has been shown to be crucial for the assembly and pathophysiology of coronaviruses. We used a combination of virtual database screening, molecular docking, all-atom molecular dynamics simulation and MM-PBSA analysis to test four FDA approved drugs - Tretinoin, Mefenamic Acid, Ondansetron and Artemether - as potential inhibitors of ion channels formed by SARS-CoV-2 E protein. Interaction and binding energy analysis showed that electrostatic interactions and polar solvation energy were the major driving forces for binding of the drugs, with Tretinoin being the most promising inhibitor. Tretinoin bound within the lumen of the channel formed by E protein, which is lined by hydrophobic residues like Phe, Val and Ala, indicating its potential for blocking the channel and inhibiting the viroporin functionality of E. In control simulations, tretinoin demonstrated a lower binding energy with a known target as compared to SARS-CoV-2 E protein. This work thus highlights the possibility of exploring Tretinoin as a potential SARS-CoV-2 E protein ion channel blocker and virus assembly inhibitor, which could be an important therapeutic strategy in the treatment for coronaviruses.

Repurposing of the approved small molecule drugs in order to inhibit SARS-CoV-2 S protein and human ACE2 interaction through virtual screening approaches

Most recently, the new coronavirus (SARS-CoV-2) has been recognized as a pandemic by the World Health Organization (WHO) while this virus shares substantial similarity with SARS-CoV. So far, no definitive vaccine or drug has been developed to cure Covid-19 disease, since many important aspects about Covid-19 such as pathogenesis and proliferation pathways are still unclear. It was proven that human ACE2 is the main receptor for the entry of Covid-19 into lower respiratory tract epithelial cells through interaction with SARS-CoV-2 S protein. Based on this observation, it is expected that the virus infection can be inhibited if protein-protein interaction is prevented. In this study, using structure-based virtual screening of FDA databases, several lead drugs were discovered based on the ACE2-binding pocket of SARS-CoV-2 S protein. Then, binding affinity, binding modes, critical interactions, and pharmaceutical properties of the lead drugs were evaluated. Among the previously approved drugs, Diammonium Glycyrrhizinate, Digitoxin, Ivermectin, Rapamycin, Rifaximin, and Amphotericin B represented the most desirable features, and can be possible candidates for Covid-19 therapies. Furthermore, molecular dynamics (MD) simulation was accomplished for three S protein/drug complexes with the highest binding affinity and best conformation and binding free energies were also computed with the Molecular Mechanics/Poisson–Boltzmann Surface Area (MM/PBSA) method. Results demonstrated the stable binding of these compounds to the S protein; however, in order to confirm the curative effect of these drugs, clinical trials must be done.

Epigenetic-based cancer therapeutics: new potential HDAC8 inhibitors

Designing dual small molecule inhibitors against enzymes associated with cancer has turned into a new strategy in cancer chemotherapy. Targeting DNA methyltransferase (DNMT) and histone deacetylase (HDAC) enzymes, involved in epigenetic modifications, are considered as promising treatments for a wide range of cancers, due to their association with the initiation, proliferation, and survival of cancer cells. In this study, for the first time, the dual inhibitors of the histone deacetylases 8 (HDAC8) and DNA methyltransferase 1 (DNMT1) has introduced as novel potential candidates for epigenetic-based cancer therapeutics. This research has been facilitated by employing pharmacophore-based virtual screening of ZINC and Maybridge databases, as well as performing molecular docking, molecular dynamics simulations and free binding energy calculation on the top derived compound. Results have demonstrated that the suggested compounds not only adopt highly favorable conformations but also possess strong binding interaction with the HDAC8 enzyme. Additionally, the obtained results from the experimental assay confirmed the predicted behavior of inhibitors from virtual screening. These results can be used for further optimization to yield promising more effective candidates for the treatment of cancer. Communicated by Ramaswamy H. Sarma

Docking based screening and molecular dynamics simulations to identify potential selective PDE4B inhibitor

Inhibition of phosphodiesterase 4 (PDE4) is a promising therapeutic approach for the treatment of inflammatory pulmonary disorders, i.e. asthma and chronic obstructive pulmonary disease. However, the treatment with non-selective PDE4 inhibitors is associated with side effects such as nausea and vomiting. Among the subtypes of PDE4 inhibited by these inhibitors, PDE4B is expressed in immune, inflammatory and airway smooth muscle cells, whereas, PDE4D is expressed in the area postrema and nucleus of the solitary tract. Thus, PDE4D inhibition is responsible for the emetic response. In this regard, a selective PDE4B inhibitor is expected to be a potential drug candidate for the treatment of inflammatory pulmonary disorders. Therefore, a shared feature pharmacophore model was developed and used as a query for the virtual screening of Maybridge and SPECS databases. A number of filters were applied to ensure only compounds with drug-like properties were selected. Accordingly, nine compounds have been identified as final hits, where HTS04529 showed the highest affinity and selectivity for PDE4B over PDE4D in molecular docking. The docked complexes of HTS04529 with PDE4B and PDE4D were subjected to molecular dynamics simulations for 100ns to assess their binding stability. The results showed that HTS04529 was bound tightly to PDE4B and formed a more stable complex with it than with PDE4D.

Insights of structure-based pharmacophore studies and inhibitor design against Gal3 receptor through molecular dynamics simulations

Our present work studies the structure-based pharmacophore modeling and designing inhibitor against Gal3 receptor through molecular dynamics (MD) simulations extensively. Pharmacophore models play a key role in computer-aided drug discovery like in the case of virtual screening of chemical databases, de novo drug design and lead optimization. Structure-based methods for developing pharmacophore models are important, and there have been a number of studies combining such methods with the use of MD simulations to model protein’s flexibility. The two potential antagonists SNAP 37889 and SNAP 398299 were docked and simulated for 250 ns and the results are analyzed and carried for the structure-based pharmacophore studies. This helped in identification of the subtype selectivity of the binding sites of the Gal3 receptor. Our work mainly focuses on identifying these binding site residues and to design more potent inhibitors compared to the previously available inhibitors through pharmacophore models. The study provides crucial insight into the binding site residues Ala2, Asp3, Ala4, Gln5, Phe24, Gln79, Ala80, Ile82, Tyr83, Trp88, His99, Ile102, Tyr103, Met106, Tyr157, Tyr161, Pro174, Trp176, Arg181, Ala183, Leu184, Asp185, Thr188, Trp248, His251, His252, Ile255, Leu256, Phe258, Trp259, Tyr270, Arg273, Leu274 and His277, which plays a significant role in the conformational changes of the receptor and helps to understand the inhibition mechanism. Communicated by Ramaswamy H. Sarma

Tackling COVID-19: identification of potential main protease inhibitors via structural analysis, virtual screening, molecular docking and MM-PBSA calculations

The widespread of the COVID-19 disease, caused by the novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), had severely affected the entire world. Unfortunately, no successful vaccines or antiviral drugs are currently available which leaves the scientific community under huge pressure to tackle this pandemic. Among the identified promising druggable targets, specific to this virus, is the main protease (Mpro) enzyme, which is vital for viral replication, transcription and packaging within the host cells. In this study, selective inhibition of the Mpro was sought via thorough analysis of its available structural data in the Protein Data Bank. To this end, COVID-19 Mpro crystal complexes were explored and the key interacting residues (KIRs) within its active site, that are expected to be vital for effective ligand binding, were identified. Based on these KIRs, 3D pharmacophore models were generated and used in virtual screening of different databases. Retrieved hits were docked into the active site of the enzyme and their MM-PBSA based free binding energies were calculated. Finally, ADMET descriptors were calculated to aid the selection of top scoring hits with best ADMET properties. Nine compounds with different chemotypes were identified as potential Mpro inhibitors. Further, MD simulations of a virtual complex of Mpro with one of the promising hits revealed stable binding which is indicative of good inhibitory potential. The identified compounds in this study are expected to support the global drug discovery efforts in fighting against this highly contagious virus by narrowing the searchable chemical space for potential effective therapeutics.

Virtual Screening of Natural Products Database.

Constant research on natural products has generated, over time, a large number of compounds with the potential to be evaluated in several biological tests and subsequently have been cataloged in databases that allow other researchers to perform virtual screenings of activity in various biological systems. This considerably reduces the time for the development of new drugs. This review describes the main databases of natural products available for searching bioactive compounds. It also describes the main features of virtual screening strategies for the identification of molecules with the potential to be used as new drugs. In addition, a search was made in the Web of Science database, using the search term "Virtual screening of natural products databases" from 2003 to 2018. The search criterion resulted in 230 articles, which had their abstracts evaluated with pertinence to the criteria required for this work, which are: a) be a research article; b) performing a virtual screening on databases of natural products or containing natural products; and c) works that identified drug candidate molecules. Based on these criteria, the bibliographic review on the topic was excluded. After this analysis, 104 works were selected for this review. We selected relevant papers describing the potential drug candidates that were distributed in 15 classes, of which the anticancer, antibacterial and anti-inflammatory hits were the most abundant. The works showing efforts to search for new molecules against various other diseases in distinct biological systems were also described. In this way, this work shows an overview of several methodologies and we hope they can help and inspire the development of new research to improve people's quality of life.

Molecular modeling approach for designing of amino‐derived anti‐Alzheimer agents: A computational study

AbstractTheoretical chemistry involves number of steps for drug designing, which are cost and time effective. In order to remove these barriers in drug designing, computational studies are helpful. Computer‐assisted molecular modeling is valuable in drug designing. Nowadays, molecular docking is routinely used for prediction of protein−ligand interactions and to help in selecting potent molecules as a part of virtual screening of large databases. In this piece of work, we have proposed eight amino‐based estearses (AChE and BChE) inhibitors (dithiocarbamates). The hypothetical structures were optimized via density functional theory (DFT) studies using B3LYP basis set and calculated their different physical properties, which stated that these compounds may be prepared in the wet lab. The energy gap between HOMO and LUMO was ranged from 0.1517 to 0.1789. The proposed molecules were also docked with MOE, and it was depicted from docking results that they are moderate inhibitors against targeted enzymes. ADMET studies were also done for these compounds in order to check their pharmacological parameters. All these results suggested that dithiocarbamates may be good inhibitors in future.

Development of a generative adversarial neural network for identification of potential HIV-1 inhibitors by deep learning methods

A generative adversarial autoencoder for the rational design of potential HIV-1 entry inhibitors able to block the region of the viral envelope protein gp120 critical for the virus binding to cellular receptor CD4 was developed using deep learning methods. The research were carried out to create the architecture of the neural network, to form virtual compound library of potential anti-HIV-1 agents for training the neural network, to make molecular docking of all compounds from this library with gp120, to calculate the values of binding free energy, to generate molecular fingerprints for chemical compounds from the training dataset. The training the neural network was implemented followed by estimation of the learning outcomes and work of the autoencoder. The validation of the neural network on a wide range of compounds from the ZINC database was carried out. The use of the neural network in combination with virtual screening of chemical databases was shown to form a productive platform for identifying the basic structures promising for the design of novel antiviral drugs that inhibit the early stages of HIV infection.

Ligand and structure based virtual screening of chemical databases to explore potent small molecule inhibitors against breast invasive carcinoma using recent computational technologies

Breast carcinoma is the most common invasive cancer to affect the women in the North America and the world. Cancer of breast is the number one cancer overall with estimated 1.5 lakh new cases during 2016. The success of the current endocrine therapies is often limited due to the development of resistance. Therefore, there is a need to develop new lead compounds for breast cancer treatment. As 70% of breast carcinoma is ER+, and it is well known previously that estrogen receptor alpha (ERα) is overexpressed in ER + cases, so in the current work we attempt to develop some novel potent analogues against ERα. To achieve this, we have adopted an integrative computational approach that involves multiple sequence alignment, virtual screening (ligand and structure based), molecular docking, fingerprint based clustering and molecular dynamics simulation. The approach envisaged vital information about the binding site residues, conserved sequence among different species, ligand and protein conformations, binding energy of compound to bind into the active site of the receptor. Molecular docking analysis revealed that some analogues exhibited significant binding towards ERα. The top docked complexes showing good docking scores, hydrogen bond and hydrophobic interactions were selected for molecular dynamics simulation studies. RMSD revealed that the systems were quite stable with RMSD value below 3 Å. The RMSF analysis calculated residue wise fluctuations and revealed that the residues are flexible enough to interact with the ligand. The residue at C-terminal showed more flexibility as compared to other residues. To confirm binding of these analogues, MMGBSA analysis was performed which revealed binding energy of the ligands. Further, per-residue decomposition energy analysis revealed that Glu353, Leu346, Leu387 and Arg394 contributed towards ligand binding. The results visibly indicated that MMGBSA can act as filter in virtual screening experiments and play a major role in facilitating drug discovery.

Inhibitors of pantothenate synthetase of Mycobacterium tuberculosis - a medicinal chemist perspective.

Tuberculosis (TB), one of the most prevalent infections, is on the rise today. Although there are drugs available in the market to combat this lethal disorder, there are several shortcomings with the current drug regimen, such as prolonged treatment period, drug resistance, high cost, etc. Hence, it is inevitable for the current researchers across the globe to embark on new strategies for TB drug discovery, which will yield highly active low cost drugs with a shorter treatment period. To achieve this, novel strategies need to be adopted to discover new drugs. Pantothenate Synthetase (PS) is one such striking drug target in Mycobacterium tuberculosis (MTB). It was observed that the pantothenate biosynthetic pathway is crucial for the pathogenicity of MTB. Pantothenate is absent in mammals and needs to be obtained from dietary sources. Hence, the pantothenate biosynthesis pathway is an impending target for emerging new therapeutics to treat TB. Worldwide, several approaches have been implemented by researchers in the quest for these inhibitors such as high-throughput screening, simulating the reaction intermediate pantoyl adenylate, use of vibrant combinatorial chemistry, hybridization approach, virtual screening of databases, inhibitors based on the crystal structure of MTB PS, etc. The present review recapitulates current developments in PS inhibitors, important analogues of numerous metabolic intermediates, and newly established inhibitors with innumerable chemical structures.

Identification of CHK1 Kinase Inhibitors Using Structure-Based Pharmacophore Modeling and Molecular Docking

The checkpoint kinase, CHK1 plays an important role in repairing DNA damage induced by genotoxic agents for cancer treatment, which renders the cells resistant to such treatment modalities. Therefore, its inhibition appeared as a highly attractive strategy to re-sensitize cancer cells to the DNA damaging chemo and radiotherapies. In this study, a structure-based drug design approach was employed to identify potential ATP competitive CHK1 inhibitors. To this end, 3 crystal structures of CHK1 in complex with ATP competitive inhibitors were utilized to generate structure-based pharmacophore models that were subsequently used for virtual screening of commercial databases. Retrieved hits were filtered, then they were docked into the binding site of the enzyme and the free binding energies of the top-ranked docked hits were calculated. Based on the in silico results, 10 compounds were selected and their CHK1 inhibitory potential was evaluated in vitro. Noteworthy, in this exploratory study compounds 7 and 9 showed moderate but significant inhibition of the catalytic activity of the CHK1 enzyme. These two compounds were identified as promising hits worthy of further optimization towards designing better leads with drug like properties as potential competitive CHK1 inhibitors.

Molecular docking and dynamic simulations for antiviral compounds against SARS-CoV-2: A computational study.

The aim of this study was to develop an appropriate anti-viral drug against the SARS-CoV-2 virus. An immediately qualifying strategy would be to use existing powerful drugs from various virus treatments. The strategy in virtual screening of antiviral databases for possible therapeutic effect would be to identify promising drug molecules, as there is currently no vaccine or treatment approved against COVID-19. Targeting the main protease (pdb id: 6LU7) is gaining importance in anti-CoV drug design. In this conceptual context, an attempt has been made to suggest an in silico computational relationship between US-FDA approved drugs, plant-derived natural drugs, and Coronavirus main protease (6LU7) protein. The evaluation of results was made based on Glide (Schrödinger) dock score. Out of 62 screened compounds, the best docking scores with the targets were found for compounds: lopinavir, amodiaquine, and theaflavin digallate (TFDG). Molecular dynamic (MD) simulation study was also performed for 20 ns to confirm the stability behaviour of the main protease and inhibitor complexes. The MD simulation study validated the stability of three compounds in the protein binding pocket as potent binders.

Discovery of a nanomolar glyoxalase-I inhibitor using integrated ligand-based pharmacophore modeling and molecular docking

The glyoxalase system, which is composed of two zinc metalloenzymes, glyoxalase I (GLO-I) and glyoxalase II (GLO-II) and a catalytic amount of GSH, has a pivotal role in the detoxification of cytotoxic methylglyoxal (MG), a glycolytic side product. Recent studies have revealed the overexpression of GLO-I in various cancer types such as breast carcinoma, invasive bladder, colon, prostate, and lung cancers. Consequently, GLO-I has become a validated target for the development of novel anticancer agents. In this study we were aiming to identify potent GLO-I inhibitors as potential candidates for the development of effective anticancer therapeutics using an integrated ligand- and structure-based drug design approach. A set of selective pharmacophore models was generated using an in-house tested set of flavonoids and used in virtual screening of Maybridge and Aldrich databases, collectively containing more than 64,000 compounds. Filtration of retained hits resulted in 362 compounds that were docked into the active site of the GLO-I enzyme. Then, the top 30% of docked compounds were visually inspected and 32 compounds were purchased and biologically evaluated. Five compounds showed good to excellent inhibitory activities with the most active one (compound 14, ID number ST018515) showing an IC50 of 336 nM. A high hit rate of actives implies the success of our approach. The five active compounds with considerable structural diversity were identified as novel leads that can be further optimized towards designing potent GLO-I inhibitors.

Prospective computational design and in vitro bio-analytical tests of new chemical entities as potential selective CYP17A1 lyase inhibitors.

The development and advancement of prostate cancer (PCa) into stage 4, where it metastasize, is a major problem mostly in elder males. The growth of PCa cells is stirred up by androgens and androgen receptor (AR). Therefore, therapeutic strategies such as blocking androgens synthesis and inhibiting AR binding have been explored in recent years. However, recently approved drugs (or in clinical phase) failed in improving the expected survival rates for this metastatic-castration resistant prostate cancer (mCRPC) patients. The selective CYP17A1 inhibition of 17,20-lyase route has emerged as a novel strategy. Such inhibition blocks the production of androgens everywhere they are found in the body. In this work, a three dimensional-quantitative structure activity relationship (3D-QSAR) pharmacophore model is developed on a diverse set of non-steroidal inhibitors of CYP17A1 enzyme. Highly active compounds are selected to define a six-point pharmacophore hypothesis with a unique geometrical arrangement fitting the following description: two hydrogen bond acceptors (A), two hydrogen bond donors (D) and two aromatic rings (R). The QSAR model showed adequate predictive statistics. The 3D-QSAR model is further used for database virtual screening of potential inhibitory hit structures. Density functional theory (DFT) optimization provides the electronic properties explaining the reactivity of the hits. Docking simulations discovers hydrogen bonding and hydrophobic interactions as responsible for the binding affinities of hits to the CYP17A1 Protein Data Bank structure. 13 hits from the database search (including five derivatives) are then synthesized in the laboratory as different scaffolds. Ultra high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) in vitro experiments reveals three new chemical entities (NCEs) with half maximal inhibitory concentration (IC50) values against the lyase route at mid-micromolar range with favorable selectivity to the lyase over the hydroxylase route (one of them with null hydroxylase inhibition). Thus, prospective computational design has enabled the design of potential lead lyase-selective inhibitors for further studies.

Advances and Perspectives in Applying Deep Learning for Drug Design and Discovery.

Discovering (or planning) a new drug candidate involves many parameters, which makes this process slow, costly, and leading to failures at the end in some cases. In the last decades, we have witnessed a revolution in the computational area (hardware, software, large-scale computing, etc.), as well as an explosion in data generation (big data), which raises the need for more sophisticated algorithms to analyze this myriad of data. In this scenario, we can highlight the potentialities of artificial intelligence (AI) or computational intelligence (CI) as a powerful tool to analyze medicinal chemistry data. According to IEEE, computational intelligence involves the theory, the design, the application, and the development of biologically and linguistically motivated computational paradigms. In addition, CI encompasses three main methodologies: neural networks (NN), fuzzy systems, and evolutionary computation. In particular, artificial neural networks have been successfully applied in medicinal chemistry studies. A branch of the NN area that has attracted a lot of attention refers to deep learning (DL) due to its generalization power and ability to extract features from data. Therefore, in this mini-review we will briefly outline the present scope, advances, and challenges related to the use of DL in drug design and discovery, describing successful studies involving quantitative structure-activity relationships (QSAR) and virtual screening (VS) of databases containing thousands of compounds.