Drug efficiency indices for improvement of molecular docking scoring functions

Putative mechanism of a multivitamin treatment against insulin resistance

Insulin resistance is caused by the abnormal secretion of proinflammatory cytokines in adipose tissue, which is induced by an increase in lipid accumulation in adipocytes, hepatocytes, and myocytes. The inflammatory pathway involves multiple targets such as nuclear factor kappa B, inhibitor of nuclear factor κ-B kinase, and mitogen-activated protein kinase. Vitamins are micronutrients with anti-inflammatory activities that have unclear mechanisms. The present study aimed to describe the putative mechanisms of vitamins involved in the inflammatory pathway of insulin resistance. The strategy to achieve this goal was to integrate data mining and analysis, target prediction, and molecular docking simulation calculations to support our hypotheses. Our results suggest that the multitarget activity of vitamins A, B1, B2, B3, B5, B6, B7, B12, C, D3, and E inhibits nuclear factor kappa B and mitogen-activated protein kinase, in addition to vitamins A and B12 against inhibitor of nuclear factor κ-B kinase. The findings of this study highlight the pharmacological potential of using an anti-inflammatory and multitarget treatment based on vitamins and open new perspectives to evaluate the inhibitory activity of vitamins against nuclear factor kappa B, mitogen-activated protein kinase, and inhibitor of nuclear factor κ-B kinase in an insulin-resistant context.

Structure‐Based Approaches for the Prediction of Alzheimer's Disease Inhibitors: Comparative Interactions Analysis, Pharmacophore Modeling and Molecular Dynamics Simulations

Due to its significant role in neurodegeneration, Cyclin‐dependent kinase 5 (CDK5) has emerged as a potential target for addressing neuropathological disorders, including Alzheimer's disease (AD). The application of CDK5 inhibitors has demonstrated promise in the treatment of AD. This prompted us to model this interesting target using a computational workflow named Docking‐based Comparative Intermolecular Contacts Analysis (dbCICA). Approaches including 3D‐QSAR, genetic algorithm, and pharmacophore modeling were employed to discover new CDK inhibitors.

Modeling disruption of Apis mellifera (honey bee) odorant-binding protein function with high-affinity binders

Chemical toxins pose a great threat to honey bee health because they affect memory and cognition, diminish immunity, and increase susceptibility to infection, resulting in decreased colony performance, reproduction, and survival. Although the behavioral effects of sub-lethal chemical exposure on honey bees have been intensively studied, how xenobiotics affect olfaction, at the molecular level, still needs to be elucidated. In the present work, in silico tools, such as molecular docking, binding free energy calculations, and molecular dynamics simulations are used to predict if environmental chemicals have stronger binding affinities to honey bee antennal odorant-binding protein 14 (OBP14) than the representative floral odors citralva, eugenol, and the fluorescent probe 1-N-phenylnaphthylamine. Based on structural analysis, 21 chemicals from crop pesticides, household appliances, cosmetics, food, public health-related products, and other sources, many of which are pervasive in the hive environment, have higher binding affinities than the floral odors. These results suggest that chemical exposures are likely to interfere with the honey bee's sense of smell and this disruptive mechanism may be responsible for the lower associative learning and memory based on olfaction found in bees exposed to pesticides. Moreover, bees mainly rely on olfactory cues to perceive their environment and orient themselves as well as to discriminate and identify their food, predators, nestmates, and diseased individuals that need to be removed with hygienic behavior. In summary, sub-lethal exposure to environmental toxins can contribute to colony collapse in several ways from the disruption of proper olfaction functioning.

Molecular docking-based virtual screening: Challenges in hits identification for Anti-SARS-Cov-2 activity

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) requires finding new drugs or repurposing drugs for clinical use. Molecular docking belongs to structure-based drug design providing a fast method for identifying the hit compounds with antiviral activity against SARS-Cov-2. However, the weakness of the docking method is compounded by the limited crystallographic information and comparison drugs due to the novelty of this virus can present challenges in identifying hits of anti-SARS-Cov-2. In the current review, we highlighted several aspects, especially those related to the target structure, docking validation, and virtual hit selection, that need to be considered to obtain reliable docking results. Here, we discussed several cases pertaining to the issue highlighted and approaches that could be used to solve them.

Network Pharmacology- and Molecular Docking-Based Identification of Potential Phytocompounds from Argyreia capitiformis in the Treatment of Inflammation

The methanolic extract of Argyreia capitiformis stem was examined for anti-inflammatory activities following network pharmacology analysis and molecular docking study. Based on gas chromatography-mass spectrometry (GC-MS) analysis, 49 compounds were identified from the methanolic extract of A. capitiformis stem. A network pharmacology analysis was conducted against the identified compounds, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Ontology analysis of biological processes and molecular functions were performed. Six proteins (IL1R1, IRAK4, MYD88, TIRAP, TLR4, and TRAF6) were identified from the KEGG pathway analysis and subjected to molecular docking study. Additionally, six best ligand efficiency compounds and positive control (aspirin) from each protein were evaluated for their stability using the molecular dynamics simulation study. Our study suggested that IL1R1, IRAK4, MYD88, TIRAP, TLR4, and TRAF6 proteins may be targeted by compounds in the methanolic extract of A. capitiformis stem to provide anti-inflammatory effects.

Improving Structure-Based Virtual Screening with Ensemble Docking and Machine Learning

One of the main challenges of structure-based virtual screening (SBVS) is the incorporation of the receptor's flexibility, as its explicit representation in every docking run implies a high computational cost. Therefore, a common alternative to include the receptor's flexibility is the approach known as ensemble docking. Ensemble docking consists of using a set of receptor conformations and performing the docking assays over each of them. However, there is still no agreement on how to combine the ensemble docking results to obtain the final ligand ranking. A common choice is to use consensus strategies to aggregate the ensemble docking scores, but these strategies exhibit slight improvement regarding the single-structure approach. Here, we claim that using machine learning (ML) methodologies over the ensemble docking results could improve the predictive power of SBVS. To test this hypothesis, four proteins were selected as study cases: CDK2, FXa, EGFR, and HSP90. Protein conformational ensembles were built from crystallographic structures, whereas the evaluated compound library comprised up to three benchmarking data sets (DUD, DEKOIS 2.0, and CSAR-2012) and cocrystallized molecules. Ensemble docking results were processed through 30 repetitions of 4-fold cross-validation to train and validate two ML classifiers: logistic regression and gradient boosting trees. Our results indicate that the ML classifiers significantly outperform traditional consensus strategies and even the best performance case achieved with single-structure docking. We provide statistical evidence that supports the effectiveness of ML to improve the ensemble docking performance.

In Vivo and In Silico Studies of Flavonoids Isolated from Pistacia integerrima as Potential Antidiarrheal Agents

Pistacia integerrima leaf galls are used in several traditional medicines to cure many diseases such as diarrhea, asthma, fever, cough, vomiting, and hepatitis. The main goal of the present investigation was to assess the antidiarrheal effect of the Pistacia integerrima extracts/fractions and four isolated flavonoid compounds (1-4) on mice. An in vivo assay involving castor-oil-induced diarrhea was used to evaluate the antidiarrheal potential of extracts/fractions at 100, 200, and 400 mg/kg p.o., as well as isolated compounds at 5, 10, and 20 mg/kg p.o. Pretreatment of mice with extracts/fractions significantly attenuated castor-oil-induced diarrhea in a dose-dependent manner. Among all crude extracts and fractions, the ethyl acetate extract was the most effective with 100% protection against diarrhea followed by chloroform (75% protection) at 400 mg/kg p.o. Although all the isolated compounds exhibited strong antidiarrheal activity, isolated compounds 1 and 4 demonstrated 100% protection against diarrhea. Moreover, docking models were performed using the Molecular Operating Environment (MOE) and AutoDock software and suggested that the extracts and isolated compounds exert antidiarrheal activity by inhibiting mu-opioid and delta-opioid receptors. Therefore, our finding affords a strong pharmacological basis for the traditional use of P. integerrima galls in the treatment of diarrhea.

Anti-Inflammatory, Thrombolytic and Hair-Growth Promoting Activity of the n-Hexane Fraction of the Methanol Extract of Leea indica Leaves

The anti-inflammatory, thrombolytic, and hair growth-promoting activity of the n-hexane fraction from the methanol extract of Leea indica (NFLI) leaves was investigated. NFLI showed significant inhibition of hemolysis and protein denaturation, and exhibited a concentration-dependent thrombolytic activity. When applied topically to mice at concentrations of 10, 1, 0.1%, NFLI demonstrated a significant increase in average hair length (p < 0.001) compared with untreated animals. NFLI (1% concentration) exhibited the highest percentage of hair regrowth on day 7, 14 and 21 (81.24, 65.60, and 62.5%, respectively). An in silico study was further conducted to predict the binding affinity of phytochemicals previously reported in L. indica towards PGD₂ synthase (PDB ID: 2VD1), an enzyme that catalyses the isomerisation of prostaglandin H₂ to PGD₂ which is involved in hair loss. Phthalic acid, farnesol, n-tricosane, n-tetracosane, and n-heptacosane showed the best ligand efficiencies towards PGD₂ synthase and their intermolecular interactions were visualised using BIOVIA Discovery Studio Visualizer. Our results indicate that L. indica could represent a promising natural alternative to tackle alopecia.

Antibiofilm Activity of Heather and Manuka Honeys and Antivirulence Potential of Some of Their Constituents on the DsbA1 Enzyme of Pseudomonas aeruginosa

Heather honey was tested for its effect on the formation of biofilms by Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, Klebsiella pneumoniae, Enterococcus faecalis, Salmonella Enteriditis and Acinetobacter baumanii in comparison with Manuka honey. At 0.25 mg/mL, Heather honey inhibited biofilm formation in S. aureus, A. baumanii, E. coli, S. Enteriditis and P. aeruginosa, but promoted the growth of E. faecalis and K. pneumoniae biofilms. Manuka honey inhibited biofilm formation in K. pneumoniae, E. faecalis, and S. Enteriditis, A. baumanii, E. coli and P. aeruginosa, but promoted S. aureus biofilm formation. Molecular docking with Autodock Vina was performed to calculate the predictive binding affinities and ligand efficiencies of Manuka and Heather honey constituents for PaDsbA1, the main enzyme controlling the correct folding of virulence proteins in Pseudomonas aeruginosa. A number of constituents, including benzoic acid and methylglyoxal, present in Heather and/or Manuka honey, revealed high ligand efficiencies for the target enzyme. This helps support, to some extent, the decrease in P. aeruginosa biofilm formation observed for such honeys.

Antinociceptive and sedative activity of Vernonia patula and predictive interactions of its phenolic compounds with the cannabinoid type 1 receptor

When tested in the acetic acid-induced writhing and formalin-induced paw-licking tests, the ethanol extract of Vernonia patula (VP) aerial parts showed significant antinociceptive activity. In neuropharmacological tests, it also significantly delayed the onset of sleep, increased the duration of sleeping time, and significantly reduced the locomotor activity and exploratory behaviour of mice. Five phenolic compounds, namely gallic acid, vanillic acid, caffeic acid, quercetin and kaempferol, were detected in VP following HPLC-DAD analysis. The presence of these phenolic compounds in VP provides some support for the observed antinociceptive and sedative effects. A computational study was performed to predict the binding affinity of gallic acid, vanillic acid, caffeic acid, quercetin and kaempferol towards the cannabinoid type 1 (CB1) receptor. Caffeic and vanillic acid showed the highest probable ligand efficiency indices towards the CB1 target. Vanillic acid displayed the best blood-brain barrier penetration prediction score. These findings provide some evidence for the traditional use of VP to treat pain.

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-src Tyrosine Kinase

A prototype of a family of at least nine members, cellular Src tyrosine kinase is a therapeutically interesting target because its inhibition might be of interest not only in a number of malignancies, but also in a diverse array of conditions, from neurodegenerative pathologies to certain viral infections. Computational methods in drug discovery are considerably cheaper than conventional methods and offer opportunities of screening very large numbers of compounds in conditions that would be simply impossible within the wet lab experimental settings. We explored the use of global quantitative structure-activity relationship (QSAR) models and molecular ligand docking in the discovery of new c-src tyrosine kinase inhibitors. Using a dataset of 1038 compounds from ChEMBL database, we developed over 350 QSAR classification models. A total of 49 models with reasonably good performance were selected and the models were assembled by stacking with a simple majority vote and used for the virtual screening of over 100,000 compounds. A total of 744 compounds were predicted by at least 50% of the QSAR models as active, 147 compounds were within the applicability domain and predicted by at least 75% of the models to be active. The latter 147 compounds were submitted to molecular ligand docking using AutoDock Vina and LeDock, and 89 were predicted to be active based on the energy of binding.

Predictive Binding Affinity of Plant-Derived Natural Products Towards the Protein Kinase G Enzyme of Mycobacterium tuberculosis (MtPknG)

Tuberculosis (TB), caused by Mycobacterium tuberculosis, is a growing public health concern worldwide, especially with the emerging challenge of drug resistance to the current drugs. Efforts to discover and develop novel, more effective, and safer anti-TB drugs are urgently needed. Products from natural sources, such as medicinal plants, have played an important role in traditional medicine and continue to provide some inspiring templates for the design of new drugs. Protein kinase G, produced by M. tuberculosis (MtPKnG), is a serine/threonine kinase, that has been reported to prevent phagosome-lysosome fusion and help prolong M. tuberculosis survival within the host’s macrophages. Here, we used an in silico, target-based approach (docking) to predict the interactions between MtPknG and 84 chemical constituents from two medicinal plants (Pelargonium reniforme and Pelargonium sidoides) that have a well-documented historical use as natural remedies for TB. Docking scores for ligands towards the target protein were calculated using AutoDock Vina as the predicted binding free energies. Ten flavonoids present in the aerial parts of P. reniforme and/or P.sidoides showed docking scores ranging from −11.1 to −13.2 kcal/mol. Upon calculation of all ligand efficiency indices, we observed that the (−ΔG/MW) ligand efficiency index for flavonoids (4), (5) and (7) was similar to the one obtained for the AX20017 control. When taking all compounds into account, we observed that the best (−ΔG/MW) efficiency index was obtained for coumaric acid, coumaraldehyde, p-hydroxyphenyl acetic acid and p-hydroxybenzyl alcohol. We found that methyl gallate and myricetin had ligand efficiency indices superior and equal to the AX20017 control efficiency, respectively. It remains to be seen if any of the compounds screened in this study exert an effect in M. tuberculosis-infected macrophages.

KRAS Binders Hidden in Nature

Natural products have proven to be a rich source of molecular architectures for drugs. Here, an integrated approach to natural product screening is proposed, which uncovered eight new natural product scaffolds for KRAS-the most frequently mutated oncogenic driver in human cancers, which has remained thus far undrugged. The approach combines aspects of virtual screening, fragment-based screening, structure-activity relationships (SAR) by NMR, and structure-based drug discovery to overcome the limitations in traditional natural product approaches. By using our approach, a new "snugness of fit" scoring function and the first crystal-soaking system of the active form of KRASG12D , the protein-ligand X-ray structures of a tricyclic indolopyrrole fungal alkaloid and an indoloisoquinolinone have been successfully elucidated. The natural product KRAS hits discovered provide fruitful ground for the optimization of highly potent natural-product-based inhibitors of the active form of oncogenic RAS. This integrated approach for screening natural products also holds promise for other "undruggable" targets.

Data Mining and Machine Learning Models for Predicting Drug Likeness and Their Disease or Organ Category

Data mining approaches can uncover underlying patterns in chemical and pharmacological property space decisive for drug discovery and development. Two of the most common approaches are visualization and machine learning methods. Visualization methods use dimensionality reduction techniques in order to reduce multi-dimension data into 2D or 3D representations with a minimal loss of information. Machine learning attempts to find correlations between specific activities or classifications for a set of compounds and their features by means of recurring mathematical models. Both models take advantage of the different and deep relationships that can exist between features of compounds, and helpfully provide classification of compounds based on such features or in case of visualization methods uncover underlying patterns in the feature space. Drug-likeness has been studied from several viewpoints, but here we provide the first implementation in chemoinformatics of the t-Distributed Stochastic Neighbor Embedding (t-SNE) method for the visualization and the representation of chemical space, and the use of different machine learning methods separately and together to form a new ensemble learning method called AL Boost. The models obtained from AL Boost synergistically combine decision tree, random forests (RF), support vector machine (SVM), artificial neural network (ANN), k nearest neighbors (kNN), and logistic regression models. In this work, we show that together they form a predictive model that not only improves the predictive force but also decreases bias. This resulted in a corrected classification rate of over 0.81, as well as higher sensitivity and specificity rates for the models. In addition, separation and good models were also achieved for disease categories such as antineoplastic compounds and nervous system diseases, among others. Such models can be used to guide decision on the feature landscape of compounds and their likeness to either drugs or other characteristics, such as specific or multiple disease-category(ies) or organ(s) of action of a molecule.

Ligand efficiency metrics in drug discovery: the pros and cons from a practical perspective

INTRODUCTION

Ligand efficiency metrics are almost universally accepted as a valuable indicator of compound quality and an aid to reduce attrition. Areas covered: In this review, the authors describe ligand efficiency metrics giving a balanced overview on their merits and points of weakness in order to enable the readers to gain an informed opinion. Relevant theoretical breakthroughs and drug-like properties are also illustrated. Several recent exemplary case studies are discussed in order to illustrate the main fields of application of ligand efficiency metrics. Expert opinion: As a medicinal chemist guide, ligand efficiency metrics perform in a context- and chemotype-dependent manner; thus, they should not be used as a magic box. Since the 'big bang' of efficiency metrics occurred more or less ten years ago and the average time to develop a new drug is over the same period, the next few years will give a clearer outlook on the increased rate of success, if any, gained by means of these new intriguing tools.

Design, discovery, modelling, synthesis, and biological evaluation of novel and small, low toxicity s-triazine derivatives as HIV-1 non-nucleoside reverse transcriptase inhibitors

A set of top-ranked compounds from a multi-objective in silico screen was experimentally tested for toxicity and the ability to inhibit the activity of HIV-1 reverse transcriptase (RT) in cell-free assay and in cell-based assay using HIV-1 based virus-like particles. Detailed analysis of a commercial sample that indicated specific inhibition of HIV-1 reverse transcription revealed that a minor component that was structurally similar to that of the main compound was responsible for the strongest inhibition. As a result, novel s-triazine derivatives were proposed, modelled, discovered, and synthesised, and their antiviral activity and cellular toxicity were tested. Compounds 18a and 18b were found to be efficient HIV-1 RT inhibitors, with an IC50 of 5.6±1.1μM and 0.16±0.05μM in a cell-based assay using infectious HIV-1, respectively. Compound 18b also had no detectable toxicity for different human cell lines. Their binding mode and interactions with the RT suggest that there was strong and adaptable binding in a tight (NNRTI) hydrophobic pocket. In summary, this iterative study produced structural clues and led to a group of non-toxic, novel compounds to inhibit HIV-RT with up to nanomolar potency.

Do adsorbed drugs onto P-glycoprotein influence its efflux capability?

The membrane biophysical aspects by which multidrug resistance (MDR) relate to the ABC transporter function still remain largely unknown. Notwithstanding the central role that efflux pumps like P-glycoprotein have in MDR onset, experimental studies classified additionally the lipid micro-environment where P-gp is inserted as a determinant for the increased efflux capability demonstrated in MDR cell lines. Recently, a nonlinear model for drug-membrane interactions showed that, upon drug adsorption, long-range mechanical alterations are predicted to affect the P-gp ATPase function at external drug concentrations of ∼10-100 μM. However, our results also show that drug adsorption may also occur at P-gp nucleotide-binding domains where conformational changes drive the efflux cycle. Thus, we assessed the effect of drug adsorption to both protein-water and lipid-water interfaces by means of molecular dynamics simulations. The results show that free energies of adsorption are lower for modulators in both lipid/water and protein/water interfaces. Important differences in drug-protein interactions, protein dynamics and membrane biophysical characteristics were observed between the different classes. Therefore, we hypothesize that drug adsorption to the protein and lipid-water interface accounts for a complex network of events that affect the ability of transporters to efflux drugs.

Improving the use of ranking in virtual screening against HIV-1 integrase with triangular numbers and including ligand profiling with antitargets

A delicate balance exists between a drug molecule's toxicity and its activity. Indeed, efficacy, toxicity, and side effect problems are a common cause for the termination of drug candidate compounds and development projects. To address this, an antitarget interaction profile is built and combined with virtual screening and cross docking for new inhibitors of HIV-1 integrase, in order to consider possible off-target interactions as early as possible in a drug or hit discovery program. New ranking techniques using triangular numbers improve ranking information on the compounds and recovery of known inhibitors into the top compounds using different docking programs. This improved ranking arises from using consensus of ranks between docking programs and ligand efficiencies to derive a new rank, instead of using absolute score values, or average of ranks. The triangular number rerank also allowed the objective combination of results from several protein targets or screen conditions and several programs. Triangular number reranking conserves more information than other reranking methods such as average of scores or averages of ranks. In addition, the use of triangular numbers for reranking makes possible the use of thresholds with a justified leeway based on the number of available known inhibitors, so that the majority of the compounds above the threshold in ranks compare to the compounds that have known experimentally determined biological activity. The battery of anti- or off-targets can be tailored to specific molecular or drug design challenges. In silico filters can thus be deployed in successive stages, for prefiltering, activity profiling, and for further analysis and triaging of libraries of compounds.

Peptide-ligand binding modeling of siRNA with cell-penetrating peptides

The binding affinity of a series of cell-penetrating peptides (CPP) was modeled through docking and making use of the number of intermolecular hydrogen bonds, lipophilic contacts, and the number of sp3 molecular orbital hybridization carbons. The new ranking of the peptides is consistent with the experimentally determined efficiency in the downregulation of luciferase activity, which includes the peptides' ability to bind and deliver the siRNA into the cell. The predicted structures of the complexes of peptides to siRNA were stable throughout 10 ns long, explicit water molecular dynamics simulations. The stability and binding affinity of peptide-siRNA complexes was related to the sidechains and modifications of the CPPs, with the stearyl and quinoline groups improving affinity and stability. The reranking of the peptides docked to siRNA, together with explicit water molecular dynamics simulations, appears to be well suited to describe and predict the interaction of CPPs with siRNA.

The use of docking-based comparative intermolecular contacts analysis to identify optimal docking conditions within glucokinase and to discover of new GK activators

Glucokinase (GK) is involved in normal glucose homeostasis and therefore it is a valid target for drug design and discovery efforts. GK activators (GKAs) have excellent potential as treatments of hyperglycemia and diabetes. The combined recent interest in GKAs, together with docking limitations and shortages of docking validation methods prompted us to use our new 3D-QSAR analysis, namely, docking-based comparative intermolecular contacts analysis (dbCICA), to validate docking configurations performed on a group of GKAs within GK binding site. dbCICA assesses the consistency of docking by assessing the correlation between ligands' affinities and their contacts with binding site spots. Optimal dbCICA models were validated by receiver operating characteristic curve analysis and comparative molecular field analysis. dbCICA models were also converted into valid pharmacophores that were used as search queries to mine 3D structural databases for new GKAs. The search yielded several potent bioactivators that experimentally increased GK bioactivity up to 7.5-folds at 10 μM.

Anti-aggregating effect of the naturally occurring dipeptide carnosine on aβ1-42 fibril formation

Carnosine is an endogenous dipeptide abundant in the central nervous system, where by acting as intracellular pH buffering molecule, Zn/Cu ion chelator, antioxidant and anti-crosslinking agent, it exerts a well-recognized multi-protective homeostatic function for neuronal and non-neuronal cells. Carnosine seems to counteract proteotoxicity and protein accumulation in neurodegenerative conditions, such as Alzheimer's Disease (AD). However, its direct impact on the dynamics of AD-related fibril formation remains uninvestigated. We considered the effects of carnosine on the formation of fibrils/aggregates of the amyloidogenic peptide fragment Aβ1-42, a major hallmark of AD injury. Atomic force microscopy and thioflavin T assays showed inhibition of Aβ1-42 fibrillogenesis in vitro and differences in the aggregation state of Aβ1-42 small pre-fibrillar structures (monomers and small oligomers) in the presence of carnosine. in silico molecular docking supported the experimental data, calculating possible conformational carnosine/Aβ1-42 interactions. Overall, our results suggest an effective role of carnosine against Aβ1-42 aggregation.

Drugs, non-drugs, and disease category specificity: organ effects by ligand pharmacology

Important understanding can be gained from using molecular biology-based and chemistry-based techniques together. Bayesian classifiers have thus been developed in the present work using several statistically significant molecular properties of compiled datasets of drugs and non-drugs, including their disease category or organ. The results show they provide a useful classification and simplicity of several different ligand efficiencies and molecular properties. Early recall of drugs among non-drugs using the classifiers as a ranking tool is also provided. As the chemical space of compounds is addressed together with their anatomical characterization, chemical libraries can be improved to select for specific organ or disease. Eventually, by including even finer detail, the method may help in designing libraries with specific pharmacological or toxicological target chemical space. Alternatively, a lack of statistically significant differences in property density distributions may help in further describing compounds with possibility of activity on several organs or disease groups, and given their very similar or considerably overlapping chemical space, therefore wanted or unwanted side-effects. The overlaps between densities for several properties of organs or disease categories were calculated by integrating the area under the curves where they intersect. The naïve Bayesian classifiers are readily built, fast to score, and easily interpretable.

Artemisinin-polypyrrole conjugates: synthesis, DNA binding studies and preliminary antiproliferative evaluation

Greater than the sum of its parts: Artemisinins are currently in phase I-II clinical trials against breast, colorectal and non-small-cell lung cancers. In an attempt to offer increased specificity, a series of hybrid artemisinin-polypyrrole minor groove binder conjugates are described. DNA binding/modelling studies and preliminary biological evaluation give insights into their mechanism of action and the potential of this strategy.

DrugLogit: logistic discrimination between drugs and nondrugs including disease-specificity by assigning probabilities based on molecular properties

The increasing knowledge of both structure and activity of compounds provides a good basis for enhancing the pharmacological characterization of chemical libraries. In addition, pharmacology can be seen as incorporating both advances from molecular biology as well as chemical sciences, with innovative insight provided from studying target-ligand data from a ligand molecular point of view. Predictions and profiling of libraries of drug candidates have previously focused mainly on certain cases of oral bioavailability. Inclusion of other administration routes and disease-specificity would improve the precision of drug profiling. In this work, recent data are extended, and a probability-based approach is introduced for quantitative and gradual classification of compounds into categories of drugs/nondrugs, as well as for disease- or organ-specificity. Using experimental data of over 1067 compounds and multivariate logistic regressions, the classification shows good performance in training and independent test cases. The regressions have high statistical significance in terms of the robustness of coefficients and 95% confidence intervals provided by a 1000-fold bootstrapping resampling. Besides their good predictive power, the classification functions remain chemically interpretable, containing only one to five variables in total, and the physicochemical terms involved can be easily calculated. The present approach is useful for an improved description and filtering of compound libraries. It can also be applied sequentially or in combinations of filters, as well as adapted to particular use cases. The scores and equations may be able to suggest possible routes for compound or library modification. The data is made available for reuse by others, and the equations are freely accessible at http://hermes.chem.ut.ee/~alfx/druglogit.html.

Using free energy of binding calculations to improve the accuracy of virtual screening predictions

Virtual screening of small molecule databases against macromolecular targets was used to identify binding ligands and predict their lowest energy bound conformation (i.e., pose). AutoDock4-generated poses were rescored using mean-field pathway decoupling free energy of binding calculations and evaluated if these calculations improved virtual screening discrimination between bound and nonbound ligands. Two small molecule databases were used to evaluate the effectiveness of the rescoring algorithm in correctly identifying binders of L99A T4 lysozyme. Self-dock calculations of a database containing compounds with known binding free energies and cocrystal structures largely reproduced experimental measurements, although the mean difference between calculated and experimental binding free energies increased as the predicted bound poses diverged from the experimental poses. In addition, free energy rescoring was more accurate than AutoDock4 scores in discriminating between known binders and nonbinders, suggesting free energy rescoring could be a useful approach to reduce false positive predictions in virtual screening experiments.