Virtual Screening in Lead Discovery: A Viewpoint

Chemoinformatics and machine learning techniques to identify novel inhibitors of the lemur tyrosine kinase-3 receptor involved in breast cancer

Breast cancer is still the largest cause of cancer death in women, and around 70% of primary breast cancer patients are estrogen receptor (ER)-positive, which is the most frequent kind of breast cancer. The lemur tyrosine kinase-3 (LMTK3) receptor has been linked to estrogen responsiveness in breast cancer. However, the function of LMTK3 in reaction to cytotoxic chemotherapy has yet to be studied. Breast cancer therapy research remains tricky due to a paucity of structural investigations on LMTK3. We performed structural investigations on LMTK3 using molecular docking and molecular dynamics (MD) simulations of the LMTK3 receptor in complex with the top three inhibitor molecules along with a control inhibitor. Analysis revealed the top three compounds show the best binding affinities during docking simulations. Interactive analysis of hydrogen bonds inferred hotspot residues Tyr163, Asn138, Asp133, Tyr56, Glu52, Ser132, Asp313, and Asp151. Some other residues in the 5-Å region determined strong alkyl bonds and conventional hydrogen bond linkages. Furthermore, protein dynamics analysis revealed significant modifications among the top complexes and the control system. There was a transition from a loop to a-helix conformation in the protein-top1 complex, and in contrast, in complexes top2 and top3, the formation of a stabilizing sheet in the C chain was observed, which limited significant mobility and increased complex stability. Significant structural alterations were observed in the protein-top complexes, including a shorter helix region and the creation of some loop regions in comparison to the control system. Interestingly, binding free energies, including MMGB/PBSA WaterSwap analysis estimation, reveals that the top1 complex system was more stable than other systems, especially in comparison to the control inhibitor complex system. These results suggest a the plausible mode of action for the novel inhibitors. Therefore, the current investigation contributes to understanding the mechanism of action, serving as a basis for future experimental studies.

Identification of terpenoids as dihydropteroate synthase and dihydrofolate reductase inhibitors through structure-based virtual screening and molecular dynamic simulations

Bacterial infections are rising, and antimicrobial resistance (AMR) in bacteria has worsened the scenario, requiring extensive research to find alternative therapeutic agents. Terpenoids play an essential role in protecting plants from herbivores and pathogens. The present study was designed to focus on in silico evaluation of terpenoids for their affinity towards two necessary enzymes, i.e. DHFR and DHPS, which are involved in forming 5, 6, 7, 8-tetrahydrofolate, a key component in bacterial DNA synthesis proteins. Additionally, to account for activity against resistant bacteria, their affinity towards the L28R mutant of DHFR was also assessed in the study. The structure-based drug design approach was used to screen the compound library of terpenes for their interaction with active sites of DHFR and DHPS. Further, compounds were screened based on their dock score, pharmacokinetic properties, and binding affinities. A total of five compounds for each target protein were screened, having dock scores better than their respective standard drug molecules. CNP0169378 (-8.4 kcal/mol) and CNP0309455 (-6.5 kcal/mol) have been identified as molecules with a higher affinity toward the targets of DHFR and DHPS, respectively. At the same time, one molecule CNP0298407 (-5.8 kcal/mol for DHPS, -7.6 kcal/mol for DHFR, -6.1 kcal/mol for the L28R variant), has affinity for both proteins (6XG5 and 6XG4). All the molecules have good pharmacokinetic properties. We further validated the docking study by binding free energy calculations using the MM/GBSA approach and molecular dynamics simulations.Communicated by Ramaswamy H. Sarma.

Structure-based discovery of conformationally selective inhibitors of the serotonin transporter

The serotonin transporter (SERT) removes synaptic serotonin and is the target of anti-depressant drugs. SERT adopts three conformations: outward-open, occluded, and inward-open. All known inhibitors target the outward-open state except ibogaine, which has unusual anti-depressant and substance-withdrawal effects, and stabilizes the inward-open conformation. Unfortunately, ibogaine's promiscuity and cardiotoxicity limit the understanding of inward-open state ligands. We docked over 200 million small molecules against the inward-open state of the SERT. Thirty-six top-ranking compounds were synthesized, and thirteen inhibited; further structure-based optimization led to the selection of two potent (low nanomolar) inhibitors. These stabilized an outward-closed state of the SERT with little activity against common off-targets. A cryo-EM structure of one of these bound to the SERT confirmed the predicted geometry. In mouse behavioral assays, both compounds had anxiolytic- and anti-depressant-like activity, with potencies up to 200-fold better than fluoxetine (Prozac), and one substantially reversed morphine withdrawal effects.

Comparative Analyses of Medicinal Chemistry and Cheminformatics Filters with Accessible Implementation in Konstanz Information Miner (KNIME)

High-throughput virtual screening (HTVS) is, in conjunction with rapid advances in computer hardware, becoming a staple in drug design research campaigns and cheminformatics. In this context, virtual compound library design becomes crucial as it generally constitutes the first step where quality filtered databases are essential for the efficient downstream research. Therefore, multiple filters for compound library design were devised and reported in the scientific literature. We collected the most common filters in medicinal chemistry (PAINS, REOS, Aggregators, van de Waterbeemd, Oprea, Fichert, Ghose, Mozzicconacci, Muegge, Egan, Murcko, Veber, Ro3, Ro4, and Ro5) to facilitate their open access use and compared them. Then, we implemented these filters in the open platform Konstanz Information Miner (KNIME) as a freely accessible and simple workflow compatible with small or large compound databases for the benefit of the readers and for the help in the early drug design steps.

Structure-based pharmacophore mapping and virtual screening of natural products to identify polypharmacological inhibitor against c-MET/EGFR/VEGFR-2

Three receptor tyrosine kinases (RTKs), c-MET, EGFR, and VEGFR-2 have been identified as potential oncogenic targets involved in tumor development, metastasis, and invasion. Designing inhibitors that can simultaneously interact with multiple targets is a promising approach, therefore, inhibiting these three RTKs with a single chemical component might give an effective chemotherapeutic strategy for addressing the disease while limiting adverse effects. The in-silico methods have been developed to identify the polypharmacological inhibitors particularly for drug repurposing and multitarget drug design. Here, to find a viable inhibitor from natural source against these three RTKs, structure-based pharmacophore mapping and virtual screening of SN-II database were carried out. The filtered compound SN00020821, identified as Cedeodarin, from different computational approaches, demonstrated good interactions with all the three targets, c-MET/EGFR/VEGFR-2, with interaction energies of -42.35 kcal/mol, -49.32 kcal/mol and -44.83 kcal/mol, respectively. SN00020821displayed stable key interactions with critical amino acids of all the three receptors' kinase catalytic domains including "DFG motif" explored through the MD simulations. Furthermore, it also met the ADMET requirements and was determined to be drug-like as predicted from the Lipinski's rule of five and Veber's rule. Finally, SN00020821 provides a novel molecular scaffold that could be investigated further as a polypharmacological anticancer therapeutic candidate that targets the three RTKs.Communicated by Ramaswamy H. Sarma.

Hybrid In Silico Approach Reveals Novel Inhibitors of Multiple SARS-CoV-2 Variants

The National Center for Advancing Translational Sciences (NCATS) has been actively generating SARS-CoV-2 high-throughput screening data and disseminates it through the OpenData Portal (https://opendata.ncats.nih.gov/covid19/). Here, we provide a hybrid approach that utilizes NCATS screening data from the SARS-CoV-2 cytopathic effect reduction assay to build predictive models, using both machine learning and pharmacophore-based modeling. Optimized models were used to perform two iterative rounds of virtual screening to predict small molecules active against SARS-CoV-2. Experimental testing with live virus provided 100 (∼16% of predicted hits) active compounds (efficacy > 30%, IC50 ≤ 15 μM). Systematic clustering analysis of active compounds revealed three promising chemotypes which have not been previously identified as inhibitors of SARS-CoV-2 infection. Further investigation resulted in the identification of allosteric binders to host receptor angiotensin-converting enzyme 2; these compounds were then shown to inhibit the entry of pseudoparticles bearing spike protein of wild-type SARS-CoV-2, as well as South African B.1.351 and UK B.1.1.7 variants.

Bioactive Isolates of Morus Species as Antibacterial Agents and their In Silico Profiling

Background:

The genus Morus is one of the rich sources of phytomedicine and considered a beneficial natural source for drugs with potential antimicrobial effect under the traditional system of medicine.

Introduction:

In the present study, three bioactive compounds isolated from the leaves of two species of genus Morus and their antibacterial effect against selective pathogens were assessed.

Methods:

The inhibitory effects of the three molecules isolated were assessed for their minimum inhibition concentration (MIC) and minimum bactericidal concentration (MBC) against selected pathogens. The in-silico studies provided the toxicity profile and the binding interactions with glucosamine- 6-phosphate synthase for all the isolates.

Results:

Among the three compounds tested, cathafuran-B showed a prominent bacteriostatic and bactericidal effect, which is supported by the results of in-silico analysis suggesting that cathafuran- B could be a potential glucosamine-6-phosphate synthase inhibitor.

Conclusion:

The biomolecule isolated from less explored Morus laevigata exhibiting higher antibacterial effect among the compounds tested warranted opening a new prospect in phytomedicinal research for exploring its pharmacological properties and lowering the utilization load present on highly explored Morus alba.

Identification of novel EED-EZH2 PPI inhibitors using an in silico fragment mapping method

Enhancer of zeste homolog 2 (EZH2) is a histone lysine methyltransferase that is overexpressed in many cancers. Numerous EZH2 inhibitors have been developed as anticancer agents, but recent studies have also focused on protein-protein interaction (PPI) between embryonic ectoderm development (EED) and EZH2 as a novel drug discovery target. Because EED indirectly enhances EZH2 enzymatic activity, EED-EZH2 PPI inhibitors suppress the methyltransferase activity and inhibit cancer growth. By contrast to the numerous promising EZH2 inhibitors, there are a paucity of EED-EZH2 PPI inhibitors reported in the literature. Here, we aimed to discover novel EED-EZH2 PPI inhibitors by first identifying possible binders of EED using an in-house knowledge-based in silico fragment mapping method. Next, 3D pharmacophore models were constructed from the arrangement pattern of the potential binders mapped onto the EED surface. In all, 16 compounds were selected by 3D pharmacophore-based virtual screening followed by docking-based virtual screening. In vitro evaluation revealed that five of these compounds exhibited inhibitory activities. This study has provided structural insights into the discovery and the molecular design of novel EED-EZH2 PPI inhibitors using an in silico fragment mapping method.

Development of novel anti-malarial from structurally diverse library of molecules, targeting plant-like CDPK1, a multistage growth regulator of P. falciparum

Upon Plasmodium falciparum merozoites exposure to low [K+] environment in blood plasma, there is escalation of cytosolic [Ca2+] which activates Ca2+-Dependent Protein Kinase 1 (CDPK1), a signaling hub of intra-erythrocytic proliferative stages of parasite. Given its high abundance and multidimensional attributes in parasite life-cycle, this is a lucrative target for designing antimalarials. Towards this, we have virtually screened MyriaScreenII diversity collection of 10,000 drug-like molecules, which resulted in 18 compounds complementing ATP-binding pocket of CDPK1. In vitro screening for toxicity in mammalian cells revealed that these compounds are non-toxic in nature. Furthermore, SPR analysis demonstrated differential binding affinity of these compounds towards recombinantly purified CDPK1 protein. Selection of lead compound 1 was performed by evaluating their inhibitory effects on phosphorylation and ATP binding activities of CDPK1. Furthermore, in vitro biophysical evaluations by ITC and FS revealed that binding of compound 1 is driven by formation of energetically favorable non-covalent interactions, with different binding constants in presence and absence of Ca2+, and TSA authenticated stability of compound 1 bound CDPK1 complex. Finally, compound 1 strongly inhibited intra-erythrocytic growth of P. falciparum in vitro. Conceivably, we propose a novel CDPK1-selective inhibitor, step towards developing pan-CDPK kinase inhibitors, prerequisite for cross-stage anti-malarial protection.

Ester and amide derivatives of rhodamine B exert cytotoxic effects on different human tumor cell lines

Three esters of rhodamine B (1–3) differing in their alkyl chain lengths as well as several rhodamine B amides (4–9) were synthesized in good yields and tested for their cytotoxicity in SRB assays employing several human tumor cell lines. The rhodamine B esters were unselective but showed cytotoxicity of as low as EC50 = 0.15 ± 0.02 µM. The rhodamine B amides were slightly less cytotoxic but showed good selectivity against MCF-7 and A2780 tumor cell lines. Especially a morpholinyl derivative 4 was ~20 time more cytotoxic for MCF-7 than for nonmalignant NIH 3T3 cells.

Computational approaches for drug discovery against trypanosomatid-caused diseases

During three decades, only about 20 new drugs have been developed for malaria, tuberculosis and all neglected tropical diseases (NTDs). This critical situation was reached because NTDs represent only 10% of health research investments; however, they comprise about 90% of the global disease burden. Computational simulations applied in virtual screening (VS) strategies are very efficient tools to identify pharmacologically active compounds or new indications for drugs already administered for other diseases. One of the advantages of this approach is the low time-consuming and low-budget first stage, which filters for testing experimentally a group of candidate compounds with high chances of binding to the target and present trypanocidal activity. In this work, we review the most common VS strategies that have been used for the identification of new drugs with special emphasis on those applied to trypanosomiasis and leishmaniasis. Computational simulations based on the selected protein targets or their ligands are explained, including the method selection criteria, examples of successful VS campaigns applied to NTDs, a list of validated molecular targets for drug development and repositioned drugs for trypanosomatid-caused diseases. Thereby, here we present the state-of-the-art of VS and drug repurposing to conclude pointing out the future perspectives in the field.

A Ligand-Based Virtual Screening Method Using Direct Quantification of Generalization Ability

Machine learning plays an important role in ligand-based virtual screening. However, conventional machine learning approaches tend to be inefficient when dealing with such problems where the data are imbalanced and features describing the chemical characteristic of ligands are high-dimensional. We here describe a machine learning algorithm LBS (local beta screening) for ligand-based virtual screening. The unique characteristic of LBS is that it quantifies the generalization ability of screening directly by a refined loss function, and thus can assess the risk of over-fitting accurately and efficiently for imbalanced and high-dimensional data in ligand-based virtual screening without the help of resampling methods such as cross validation. The robustness of LBS was demonstrated by a simulation study and tests on real datasets, in which LBS outperformed conventional algorithms in terms of screening accuracy and model interpretation. LBS was then used for screening potential activators of HIV-1 integrase multimerization in an independent compound library, and the virtual screening result was experimentally validated. Of the 25 compounds tested, six were proved to be active. The most potent compound in experimental validation showed an EC₅₀ value of 0.71 µM.

Molecular modeling simulation studies reveal new potential inhibitors against HPV E6 protein

High-risk strains of human papillomavirus (HPV) have been identified as the etiologic agent of some anogenital tract, head, and neck cancers. Although prophylactic HPV vaccines have been approved; it is still necessary a drug-based treatment against the infection and its oncogenic effects. The E6 oncoprotein is one of the most studied therapeutic targets of HPV, it has been identified as a key factor in cell immortalization and tumor progression in HPV-positive cells. E6 can promote the degradation of p53, a tumor suppressor protein, through the interaction with the cellular ubiquitin ligase E6AP. Therefore, preventing the formation of the E6-E6AP complex is one of the main strategies to inhibit the viability and proliferation of infected cells. Herein, we propose an in silico pipeline to identify small-molecule inhibitors of the E6-E6AP interaction. Virtual screening was carried out by predicting the ADME properties of the molecules and performing ensemble-based docking simulations to E6 protein followed by binding free energy estimation through MM/PB(GB)SA methods. Finally, the top-three compounds were selected, and their stability in the E6 docked complex and their effect in the inhibition of the E6-E6AP interaction was corroborated by molecular dynamics simulation. Therefore, this pipeline and the identified molecules represent a new starting point in the development of anti-HPV drugs.

Novel natural non-nucleoside inhibitors of HIV-1 reverse transcriptase identified by shape- and structure-based virtual screening techniques

In this work we report a parallel application of both docking- and shape-based virtual screening (VS) methods, followed by Molecular Dynamics simulations (MDs), for discovering new compounds able to inhibit the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT) RNA-dependent DNA polymerase activity. Specifically, we screened more than 143000 natural compounds commercially available in the ZINC database against the best five RT crystallographic models, taking into account the five approved NNRTIs as query compounds. As a result, 20 hit molecules were selected and tested on biochemical assays for the inhibition of the RNA dependent DNA polymerase RT function and, among them, an indoline pyrrolidine (hit1), an indonyl piperazine (hit2) and an indolyl indolinone (hit3) derivatives were identified as novel non-nucleoside RT inhibitors in the low micromolar range.

Strategies of Virtual Screening in Medicinal Chemistry

Virtual screening represents an effective computational strategy to rise-up the chances of finding new bioactive compounds by accelerating the time needed to move from an initial intuition to market. Classically, the most pursued approaches rely on ligand- and structure-based studies, the former employed when structural data information about the target is missing while the latter employed when X-ray/NMR solved or homology models are instead available for the target. The authors will focus on the most advanced techniques applied in this area. In particular, they will survey the key concepts of virtual screening by discussing how to properly select chemical libraries, how to make database curation, how to applying and- and structure-based techniques, how to wisely use post-processing methods. Emphasis will be also given to the most meaningful databases used in VS protocols. For the ease of discussion several examples will be presented.

Lipoxygenase directed anti-inflammatory and anti-cancerous secondary metabolites: ADMET-based screening, molecular docking and dynamics simulation

Lipoxygenases (LOXs), key enzymes involved in the biosynthesis of leukotrienes, are well known to participate in the inflammatory and immune responses. With the recent reports of involvement of 5-LOX (one of the isozymes of LOX in human) in cancer, there is a need to find out selective inhibitors of 5-LOX for their therapeutic application. In the present study, plant-derived 300 anti-inflammatory and anti-cancerous secondary metabolites (100 each of alkaloids, flavonoids and terpenoids) have been screened for their pharmacokinetic properties and subsequently docked for identification of potent inhibitors of 5-LOX. Pharmacokinetic analyses revealed that only 18 alkaloids, 26 flavonoids, and 9 terpenoids were found to fulfill all the absorption, distribution, metabolism, excretion, and toxicity descriptors as well as those of Lipinski's Rule of Five. Docking analyses of pharmacokinetically screened metabolites and their comparison with a known inhibitor (drug), namely zileuton revealed that only three alkaloids, six flavonoids and three terpenoids were found to dock successfully with 5-LOX with the flavonoid, velutin being the most potent inhibitor among all. The results of the docking analyses were further validated by performing molecular dynamics simulation and binding energy calculations for the complexes of 5-LOX with velutin, galangin, chrysin (in order of LibDock scores), and zileuton. The data revealed stabilization of all the complexes within 15 ns of simulation with velutin complex exhibiting least root-mean-square deviation value (.285 ± .007 nm) as well as least binding energy (ΔG_bind = -203.169 kJ/mol) as compared to others during the stabilization phase of simulation.

Multivariate PLS Modeling of Apicomplexan FabD-Ligand Interaction Space for Mapping Target-Specific Chemical Space and Pharmacophore Fingerprints

Biomolecular recognition underlying drug-target interactions is determined by both binding affinity and specificity. Whilst, quantification of binding efficacy is possible, determining specificity remains a challenge, as it requires affinity data for multiple targets with the same ligand dataset. Thus, understanding the interaction space by mapping the target space to model its complementary chemical space through computational techniques are desirable. In this study, active site architecture of FabD drug target in two apicomplexan parasites viz. Plasmodium falciparum (PfFabD) and Toxoplasma gondii (TgFabD) is explored, followed by consensus docking calculations and identification of fifteen best hit compounds, most of which are found to be derivatives of natural products. Subsequently, machine learning techniques were applied on molecular descriptors of six FabD homologs and sixty ligands to induce distinct multivariate partial-least square models. The biological space of FabD mapped by the various chemical entities explain their interaction space in general. It also highlights the selective variations in FabD of apicomplexan parasites with that of the host. Furthermore, chemometric models revealed the principal chemical scaffolds in PfFabD and TgFabD as pyrrolidines and imidazoles, respectively, which render target specificity and improve binding affinity in combination with other functional descriptors conducive for the design and optimization of the leads.

Virtual Screening for Potential Inhibitors of High-Risk Human Papillomavirus 16 E6 Protein

Human papillomavirus (HPV), a life-threatening infection, is the leading cause of cancer mortality among women worldwide and needs for designing anticancerous drugs. In the present study, we explored specific novel inhibitors against E6 oncoprotein of high-risk HPV 16, known to inactivate tumor suppressor p53 protein. A homology model of HPV 16 E6 was built and validated using bioinformatics approach. A total of 5000 drug-like compounds were downloaded from ZINC database based on the properties similar to the known inhibitor Jaceosidin (5,7-dihydroxy-2-(4-hydroxy-3-methoxyphenyl)-6-methoxy-4H-chromen-4-one). Virtual-ligand-screening approaches were applied to screen appropriate drug-like compounds using molecular docking program AutoDock Vina in PyRx 0.8, and five best novel drug-like compounds were identified as potential competitive inhibitors against HPV 16 E6 compared to Jaceosidin. Two among these five identified most potential inhibitors, N-[(5-methyl-1H-benzimidazol-2-yl)methyl]-4-oxo-3,4-dihydrophthalazine-1-carboxamide and 6-[3-(3-fluoro-4-methyl-phenyl)-1,2,4-oxadiazol-5-yl]-1,4-dihydroquinoxaline-2,3-dione, were found to interact with E6 with binding energy of [Formula: see text] and [Formula: see text] kcal/mol, respectively, and form H-bonds with p53 binding site of E6 protein residues 113-122 (CQKPLCPEEK). These two inhibitors may help restoration of p53 functioning. The bioinformatics approach extends a promising platform for developing anticancerous competitive inhibitors targeting high-risk HPV 16.

Virtual screening for potential inhibitors of high-risk human papillomavirus 16 E6 protein

Human papillomavirus (HPV), a life-threatening infection is the leading cause of cancer mortality among women worldwide and needs for designing anti-cancerous drugs. In the present study, we explored specific novel inhibitors against E6 onco-protein of high risk HPV 16, known to inactivate tumor suppressor p53 protein. A homology model of HPV 16 E6 was built and validated using bioinformatics approach. A total of 5000 drug like compounds were downloaded from ZINC database based on the properties similar to the known inhibitor Jaceosidin (5, 7-Dihydroxy-2-(4-hydroxy-3-methoxyphenyl)-6-methoxy-4H-chromen-4-one). Virtual-ligand-screenings approaches were applied to screen appropriate drug like compounds using molecular docking program Auto Dock Vina in PyRx 0.8 and 5 best novel drug-like compounds were identified as potential competitive inhibitors against HPV 16 E6 compared to Jaceosidin. Two amongst these 5 identified most potential inhibitors, N-[(5-methyl-1Hbenzimidazol-2-yl)methyl]-4-oxo-3, 4-dihydrophthalazine-1-carboxamide and 6-[3-(3-fluoro-4-methyl-phenyl)-1, 2, 4-oxadiazol-5-yl]-1, 4-dihydroquinoxaline-2, 3-dione were found to interact with E6 with binding energy of -7.7 and -7.0 Kcal/mol respectively and form H-bonds with p53 binding site of E6 protein residues 113-122 (CQKPLCPEEK). These two inhibitors may help restoration of p53 functioning. The Bioinformatics approach extends a promising platform for developing anti-cancerous competitive inhibitors targeting high-risk HPV 16.

Other Related Techniques

With the advances in computational resources, there is an increasing urge among the computational researchers to make the in silico approaches fast, convenient, reproducible, acceptable, and sensible ones. Along with the typical two-dimensional (2D) and three-dimensional (3D) quantitative structure–activity relationship (QSAR) methods, approaches like pharmacophore, structure-based docking studies, and combinations of ligand- and structure-based approaches like comparative residue interaction analysis (CoRIA) and comparative binding energy analysis (COMBINE) have gained a significant popularity in the computational drug design process. A pharmacophore can be developed either in a ligand-based method, by superposing a set of active molecules and extracting common chemical features which are vital for their bioactivity; or in a structure-based manner, by probing probable interaction points between the macromolecular target and ligands. The interaction of protein and ligand molecules with each other is one of the interesting studies in modern molecular biology and molecular recognition. This interaction can well be explained with the conceptof a docking study to show how a molecule can bind to another molecule to exert the bioactivity. Docking and pharmacophore are non-QSAR approaches in in silico drug design that can support the QSAR findings. Approaches like CoRIA and COMBINE can use information generated from the ligand–receptor complexes to extract the critical clue concerning the types of significant interaction at the level of both the receptor and the ligand. Employing the abovementioned ligand- and structure-based methodologies and chemical libraries, virtual screening (VS) emerged as an important tool in the quest to develop novel drug compounds. VS serves as an efficient computational tool that integrates structural data with lead optimization as a cost-effective approach to drug discovery.

Synthesis and structure evaluation of new complex butylarylpiperazin-1-yl derivatives

A series of arylpiperazine derivatives of 1,16-diphenyl-19-azahexacyclo-[14.5.1.0^2,15.0^3,8.0^9,14.0^17,21]docosa-2,3,5,7,8,9,11,13,14-nonaene-18,20,22-trione and 4,10-diphenyl-1H,2H,3H,5H-indeno[1,2-f]isoindole-1,3,5-trione was synthesized. The pharmacological profile of compound 4 at the 5-HT_1A receptor was measured by binding assay. The title compounds were tested in cell-based assay against the human immunodeficiency virus type-1. The X-ray crystallographic studies of derivatives 2, 6, 7, 11, 19, and 20 were presented.

Drug repurposing: far beyond new targets for old drugs

Repurposing drugs requires finding novel therapeutic indications compared to the ones for which they were already approved. This is an increasingly utilized strategy for finding novel medicines, one that capitalizes on previous investments while derisking clinical activities. This approach is of interest primarily because we continue to face significant gaps in the drug-target interactions matrix and to accumulate safety and efficacy data during clinical studies. Collecting and making publicly available as much data as possible on the target profile of drugs offer opportunities for drug repurposing, but may limit the commercial applications by patent applications. Certain clinical applications may be more feasible for repurposing than others because of marked differences in side effect tolerance. Other factors that ought to be considered when assessing drug repurposing opportunities include relevance to the disease in question and the intellectual property landscape. These activities go far beyond the identification of new targets for old drugs.

Early phase drug discovery: cheminformatics and computational techniques in identifying lead series

Early drug discovery processes rely on hit finding procedures followed by extensive experimental confirmation in order to select high priority hit series which then undergo further scrutiny in hit-to-lead studies. The experimental cost and the risk associated with poor selection of lead series can be greatly reduced by the use of many different computational and cheminformatic techniques to sort and prioritize compounds. We describe the steps in typical hit identification and hit-to-lead programs and then describe how cheminformatic analysis assists this process. In particular, scaffold analysis, clustering and property calculations assist in the design of high-throughput screening libraries, the early analysis of hits and then organizing compounds into series for their progression from hits to leads. Additionally, these computational tools can be used in virtual screening to design hit-finding libraries and as procedures to help with early SAR exploration.

Of possible cheminformatics futures

For over a decade, cheminformatics has contributed to a wide array of scientific tasks from analytical chemistry and biochemistry to pharmacology and drug discovery; and although its contributions to decision making are recognized, the challenge is how it would contribute to faster development of novel, better products. Here we address the future of cheminformatics with primary focus on innovation. Cheminformatics developers often need to choose between "mainstream" (i.e., accepted, expected) and novel, leading-edge tools, with an increasing trend for open science. Possible futures for cheminformatics include the worst case scenario (lack of funding, no creative usage), as well as the best case scenario (complete integration, from systems biology to virtual physiology). As "-omics" technologies advance, and computer hardware improves, compounds will no longer be profiled at the molecular level, but also in terms of genetic and clinical effects. Among potentially novel tools, we anticipate machine learning models based on free text processing, an increased performance in environmental cheminformatics, significant decision-making support, as well as the emergence of robot scientists conducting automated drug discovery research. Furthermore, cheminformatics is anticipated to expand the frontiers of knowledge and evolve in an open-ended, extensible manner, allowing us to explore multiple research scenarios in order to avoid epistemological "local information minimum trap".

Topological virtual screening: a way to find new compounds active in ulcerative colitis by inhibiting NF-κB

Ulcerative colitis and Crohn's disease are chronic, immune-mediated inflammatory diseases of the gastrointestinal tract. Nuclear Factor Kappa B (NF-κB) is a transcription factor that plays a key role in regulating expression of multiple inflammatory and immune genes. In this study, a Topological Virtual Screening study has been carried out to achieve a model capable of finding new compounds active in ulcerative colitis by inhibiting NF-κB. Different topological indices were used as structural descriptors, and their relation to biological activity was determined using linear discriminant analysis. A topological model consisting of two discriminant functions was built up. The first function focused in the discrimination between NF-κB active and inactive compounds, and the second one in distinguishing between compounds active and inactive on ulcerative colitis. The model was then applied sequentially to a large database of compounds with unknown activity. Twenty-eight of such compounds were predicted to be active and selected for in vitro and in vivo testing.

Design, synthesis, cytotoxic evaluation, and QSAR study of some 6H-indolo[2,3-b]quinoxaline derivatives

In the pathway of anticancer drug development, we designed and synthesized some 6H-indolo[2,3-b]quinoxaline derivatives (which act as DNA intercalators) by structural modification. The structure of the 6H-indolo[2,3-b]quinoxaline derivatives was confirmed by IR, NMR, Mass and elemental analysis. The compounds (IDQ-5, IDQ-10, IDQ-11, IDQ-13, and IDQ-14) exhibited significant in vitro activity against a human leukemia (HL-60) cell line. The QSAR derived for modeling the cytotoxic activity of 6H-indolo[2,3-b]quinoxaline derivatives suggests that candidate structures for increased cytotoxic potency should incorporate cyclic substituents or substituents with primary carbon atoms.

In silico screening for non-nucleoside HIV-1 reverse transcriptase inhibitors using physicochemical filters and high-throughput docking followed by in vitro evaluation

Reverse transcriptase, being the pivot in human immunodeficiency virus replication, is one of the most attractive targets for the development of new antiretroviral agents. We applied a virtual screening workflow based on a combination of physicochemical filters with high-throughput rigid molecular docking to discover novel efficient lead scaffolds for human immunodeficiency virus type 1 reverse transcriptase inhibition. In our protocol, different filters were employed to enrich the lead-likeness and improve the ligands efficiency of the filtered compounds. Out of the 238,819 compounds included in the National Cancer Institute database, 500 virtual screening hits were retrieved employing FILTER and FRED (molecular docking engine) softwares. Four compounds from the 20 highest ranking scored hits tested positive in human immunodeficiency virus type 1 reverse transcriptase using non-radioactive colorimetric assay method. These results demonstrate that our virtual screening protocol is able to enrich novel scaffolds for human immunodeficiency virus type 1 reverse transcriptase inhibition that could be useful for drug development in the area of acquired immune-deficiency syndrome treatment.

The Rule of Five revisited: applying log D in place of log P in drug-likeness filters

The much publicized "Rule of 5" has been widely adopted among the pharmaceutical industry. It is used as a first step filter to perform virtual screening of compound libraries, in an effort to quickly eliminate lead candidates that have poor physicochemical properties for oral bioavailabilty. One of the key parameters used therein is log P, which is a useful descriptor, but one that fails to take into account variation in the lipophilicity of a drug with respect to the ionic states present at key biological pH values. Given that the majority of commercial pharmaceuticals contain an ionizable moiety, we propose that log D is a better descriptor for lipophilicity in the context of the Rule of 5. It gives more physiologically relevant results, thereby reducing the number of potential false-negatives incorrectly eliminated in screening. Using a series of commercial compound libraries, this study showed that the adapted Rule of 5 using log D instead of log P provides notable improvement in pass rate for compounds that have the desired lipophilicity at a relevant physiological pH.

Integration of virtual and physical screening

High-throughput screening (HTS) represents the dominant technique for the identification of new lead compounds in current drug discovery. It consists of physical screening (PS) of large libraries of chemicals against one or more specific biological targets. Virtual screening (VS) is a strategy for in silico evaluation of chemical libraries for a given target, and can be integrated to focus the PS process. The present work addresses the integration of both PS and VS, respectively.

Shape Signatures: speeding up computer aided drug discovery

Identifying potential lead molecules is becoming a more automated process. We review Shape Signatures, a tool that is effective and easy to use compared with most computer aided drug design techniques. Laboratory researchers can apply this in silico technique cost-effectively without the need for specialized computer backgrounds. Identifying a potential lead molecule requires database screening, and this becomes rate-limiting once the database becomes too large. The use of Shape Signatures eliminates this concern and offers molecule screening rates that are in advance of any currently available method. Shape Signatures provides a conduit for researchers to conduct rapid identification of potential active molecules, and studies with this tool can be initiated with only one bioactive lead or receptor site.

In silico ADME modelling 2: Computational models to predict human serum albumin binding affinity using ant colony systems

Modelling of in vitro human serum albumin (HSA) binding data of 94 diverse drugs and drug-like compounds is performed to develop global predictive models that are applicable to the whole medicinal chemistry space. For this aim, ant colony systems, a stochastic method along with multiple linear regression (MLR), is employed to exhaustively search and select multivariate linear equations, from a pool of 327 molecular descriptors. This methodology helped us to derive optimal quantitative structure-property relationship (QSPR) models based on five and six descriptors with excellent predictive power. The best five-descriptor model is based on Kier and Hall valence connectivity index--Order 5 (path), Auto-correlation descriptor (Broto-Moreau) weighted by atomic masses--Order 4, Auto-correlation descriptor (Broto-Moreau) weighted by atomic polarizabilities--Order 5, AlogP98, SklogS (calculated buffer water solubility) [R=0.8942, Q=0.86790, F=62.24 and SE=0.2626]; the best six-variable model is based on Kier and Hall valence connectivity index of Order 3 (cluster), Auto-correlation descriptor (Broto-Moreau) weighted by atomic masses--Order 4, Auto-correlation descriptor (Broto-Moreau) weighted by atomic polarizabilities--Order 5, Atomic-Level-Based AI topological descriptors--AIdsCH, AlogP98, SklogS (calculated buffer water solubility) [R=0.9128, Q=0.89220, F=64.09 and SE=0.2411]. From the analysis of the physical meaning of the selected descriptors, it is inferred that the binding affinity of small organic compounds to human serum albumin is principally dependent on the following fundamental properties: (1) hydrophobic interactions, (2) solubility, (3) size and (4) shape. Finally, as the models reported herein are based on computed properties, they appear to be a valuable tool in virtual screening, where selection and prioritisation of candidates is required.

Focus on success: using a probabilistic approach to achieve an optimal balance of compound properties in drug discovery

The success of any drug will depend on how closely it achieves an ideal combination of potency, selectivity, pharmacokinetics and safety. The key to achieving this success efficiently is to consider the overall balance of molecular properties of compounds against the ideal profile for the therapeutic indication from the earliest stages of a drug discovery project. The use of in silico predictive models of absorption, distribution, metabolism and elimination (ADME) and physicochemical properties is a major aid in this exercise, as it enables virtual molecules to be assessed across a broad range of properties from initial library generation, through to candidate selection. Of course, no measurement, whether in silico, in vitro or in vivo, is perfect and the uncertainties in any data should be explicitly taken into account when basing conclusions on test results. In addition, in the early stages of drug discovery, when designing a library that is lead seeking or building compound structure-activity relationships, the quality of any set of molecules should also be balanced against the chemical diversity covered. Here, a scheme is presented for achieving these goals based on a suite of predictive ADME models, probabilistic scoring and multiobjective optimisation for library design. The use of this platform for applications in lead identification and optimisation is illustrated.

VolSurf analysis of pharmacokinetic properties for several antifungal sesquiterpene lactones isolated from Greek Centaurea sp

Sesquiterpene lactones are terpenoid compounds characteristic of the Asteraceae (Compositae) possessing a variety of biological activities, such as cytotoxic, antitumor, antibacterial, and antifungal. The prediction of the pharmacokinetic profile of several antifungal sesquiterpene lactones, isolated from Greek taxa of Centaurea sp., was undertaken in this study using the VolSurf procedure. The molecules were projected on the following pre-calculated ADME models: Caco-2 cell permeability, plasma protein affinity, blood-brain barrier permeation and thermodynamic solubility. The in silico projection revealed a non optimal pharmacokinetic profile for the studied compounds. ADME in silico screening of a semi-synthetic derivatives virtual library has been performed in order to optimize the pharmacokinetic properties. A number of derivatives were proposed as it was predicted to have higher Caco-2 cell permeability, while the pharmacokinetic behaviour regarding BBB penetration, protein binding and solubility was mainly preserved.

In silico ADME modelling: prediction models for blood-brain barrier permeation using a systematic variable selection method

Quantitative Structure-Property Relationship models (QSPR) based on in vivo blood-brain permeation data (logBB) of 88 diverse compounds, 324 descriptors and a systematic variable selection method, namely 'Variable Selection and Modeling method based on the prediction (VSMP)', are reported. Of all the models developed using VSMP, the best three-descriptors model is based on Atomic type E-state index (SsssN), AlogP98 and Van der Waal's surface area (r=0.8425, q=0.8239, F=68.49 and SE=0.4165); the best four-descriptors model is based on Kappa shape index of order 1, Atomic type E-state index (SsssN), Atomic level based AI topological descriptor (AIssssC) and AlogP98 (r=0.8638, q=0.8472, F=60.982 and SE=0.3919). The performance of the models on three test sets taken from the literature is illustrated and compared with the results from other reported computational approaches. Test set III constitutes 91 compounds from the literature with known qualitative BBB indication and is used for virtual screening studies. The success rate of the reported models is 82% in the case of BBB+ compounds and a similar success rate is observed with BBB- compounds. Finally, as the models reported herein are based on computed properties, they appear as a valuable tool in virtual screening, where selection and prioritization of candidates is required.