Sorin Avram

Quantitative estimation of pesticide-likeness for agrochemical discovery

BackgroundThe design of chemical libraries, an early step in agrochemical discovery programs, is frequently addressed by means of qualitative physicochemical and/or topological rule-based methods. The aim of this study is to develop quantitative estimates of herbicide- (QEH), insecticide- (QEI), fungicide- (QEF), and, finally, pesticide-likeness (QEP).In the assessment of these definitions, we relied on the concept of desirability functions.ResultsWe found a simple function, shared by the three classes of pesticides, parameterized particularly, for six, easy to compute, independent and interpretable, molecular properties: molecular weight, logP, number of hydrogen bond acceptors, number of hydrogen bond donors, number of rotatable bounds and number of aromatic rings. Subsequently, we describe the scoring of each pesticide class by the corresponding quantitative estimate. In a comparative study, we assessed the performance of the scoring functions using extensive datasets of patented pesticides.ConclusionsThe hereby-established quantitative assessment has the ability to rank compounds whether they fail well-established pesticide-likeness rules or not, and offer an efficient way to prioritize (class-specific) pesticides. These findings are valuable for the efficient estimation of pesticide-likeness of vast chemical libraries in the field of agrochemical discovery.

PLS-DA - Docking Optimized Combined Energetic Terms (PLSDA-DOCET) Protocol: A Brief Evaluation

Docking studies have become popular approaches in drug design, where the binding energy of the ligand in the active site of the protein is estimated by a scoring function. Many promising techniques were developed to enhance the performance of scoring functions including the fusion of multiple scoring functions outcomes into a so-called consensus scoring function. Hereby, we evaluated the target oriented consensus technique using the energetic terms of several scoring functions. The approach was denoted PLSDA-DOCET. Optimization strategies for consensus energetic terms and scoring functions based on ROC metric were compared to classical rigid docking and to ligand-based similarity search methods comprising 2D fingerprints and ROCS. The ROCS results indicate large performance variations depending on the biological target. The AUC-based strategy of PLSDA-DOCET outperformed the other docking approaches regarding simple retrieval and scaffold-hopping. The superior performance of PLSDA-DOCET protocol relative to single and combined scoring functions was validated on an external test set. We found a relative low mean correlation of the ranks of the chemotypes retrieved by the PLSDA-DOCET protocol and all the other methods employed here.

Exploring the biological promiscuity of high-throughput screening hits through DFT calculations

The goal of this study is the understanding of biologically promiscuous compounds (frequent hitters) in HTS outcomes through their chemical behavior estimated via reactivity descriptors. Chemical reactivity is often an undesirable property due to the lack in biological selectivity of compounds comprised in HTS libraries. In this study the reactivity indexes have been computed within the DFT formalism, at different levels of theory, for two classes of representative compounds compiled from PubChem database, one comprising frequent hitters and the second one comprising rare hitters (biologically more selective compounds). We found that frequent hitters exert increased reactivity, mainly due to their electrophilic character, compared to the more selective class of compounds.

ColBioS-FlavRC: A Collection of Bioselective Flavonoids and Related Compounds Filtered from High-Throughput Screening Outcomes

Flavonoids, the vastest class of natural polyphenols, are extensively investigated for their multiple benefits on human health. Due to their physicochemical or biological properties, many representatives are considered to exhibit low selectivity among various protein targets or to plague high-throughput screening (HTS) outcomes. The aim of this study is to highlight reliable, bioselective compounds sharing flavonoidic scaffolds in HTS experiments. A filtering scheme was applied to remove undesired flavonoids (and related compounds) from confirmatory PubChem bioassays. A number of 433 compounds addressing various protein targets form the core of the collection of bioselective flavonoids and related compounds (ColBioS-FlavRC). With an additional set of 2908 inactive related compounds, ColBioS-FlavRC offers the grounds for method optimization and validation. We exemplified the use of ColBioS-FlavRC by pharmacophore modeling, subsequently (externally) validated for virtual screening purposes. The early enrichment capabilities of the pharmacophore hypotheses were measured by means of the median exponential retriever operating curve enrichment (MeROCE), a suited metric in comparative evaluations of virtual screening methods. ColBioS-FlavRC is available in the Supporting Information and is freely accessible for further studies.

Retrospective group fusion similarity search based on eROCE evaluation metric.

In this study, a simple evaluation metric, denoted as eROCE was proposed to measure the early enrichment of predictive methods. We demonstrated the superior robustness of eROCE compared to other known metrics throughout several active to inactive ratios ranging from 1:10 to 1:1000. Group fusion similarity search was investigated by varying 16 similarity coefficients, five molecular representations (binary and non-binary) and two group fusion rules using two reference structure set sizes. We used a dataset of 3478 actives and 43,938 inactive molecules and the enrichment was analyzed by means of eROCE. This retrospective study provides optimal similarity search parameters in the case of ALDH1A1 inhibitors.

Predictive Models for Fast and Effective Profiling of Kinase Inhibitors

In this study we developed two-dimensional pharmacophore-based random forest models for the effective profiling of kinase inhibitors. One hundred seven prediction models were developed to address distinct kinases spanning over all kinase groups. Rigorous external validation demonstrates excellent virtual screening and classification potential of the predictors and, more importantly, the capacity to prioritize novel chemical scaffolds in large chemical libraries. The models built upon more diverse and more potent compounds tend to exert the highest predictive power. The analysis of ColBioS-FlavRC (Collection of Bioselective Flavonoids and Related Compounds) highlighted several potentially promiscuous derivatives with undesirable selectivity against kinases. The prediction models can be downloaded from www.chembioinf.ro .

PLS and shape-based similarity analysis of maleimides – GSK-3 inhibitors

Abstract Context: Glycogen synthase kinase-3 (GSK-3) overactivity was correlated with several pathologies including type 2 diabetes mellitus, Alzheimer’s disease, cancer, inflammation, obesity, etc. Objective: The aim of the current investigation was to model the inhibitory activity of maleimide derivatives – inhibitors of GSK-3, to evaluate the impact of alignment on statistical performances of the Quantitative Structure--Activity Relationship (QSAR) and the effect of the template on shape-similarity – binding affinity relationship. Materials and methods: Dragon descriptors were used to generate Projection to Latent Structures (PLS) models in order to identify the structural prerequisites of maleimides to inhibit GSK-3. Additionally, shape/volume structural analysis of binding site interactions was evaluated. Results: Reliable statistics  = 0.938/0.920,  = 0.866/0.838 for aligned and alignment free QSAR models and significant (Pearson, Kendall and Spearman) correlations between shape/volume similarity and affinities were obtained. Discussion and conclusions: The crucial structural features modulating the activity of maleimides include topology, charge, geometry, 2D autocorrelations, 3D-MoRSE as well as shape/volume and molecular flexibility.

Design, synthesis and pharmaco-toxicological assessment of 5-mercapto-1,2,4-triazole derivatives with antibacterial and antiproliferative activity

The extensive biochemical research of multiple types of cancer has revealed important enzymatic signaling pathways responsible for tumor occurrence and progression, thus compelling the need for the discovery of new means with which to block these signaling cascades. The phosphoinositide 3-kinase/protein kinase B (PI3K/AKT) pathway, which plays an important role in maintaining relevant cellular functions, exhibits various alterations in common human cancers, thus representing a suitable target in cancer treatment. Molecules bearing the 1,2,4-triazole moiety are known to possess multiple biological activities, including anticancer activity. The current study used molecular docking in the design of 5-mercapto-1,2,4-triazole derivatives with antiproliferative activity targeting the PI3K/AKT pathway. Three structures emerged as the result of this method, which indicated for these a highly favorable accommodation within the active binding site of PI3K protein, thus acting as potential PI3K inhibitors, and hence interfering with the above-mentioned pathway. The molecules were synthesized and their chemical structure was confirmed. The antiproliferative activity of these compounds was tested on 4 cancer cell lines (A375, B164A5, MDA-MB-231 and A549) and on normal human keratinocytes (HaCaT) by in vitro alamarBlue assay. The 3 compounds revealed antitumor activity against the breast cancer cell line (MDA-MB-231) and reduced toxicity on the normal cell line. The antibacterial activity of the compounds was also tested in vitro on Gram-positive and Gram-negative bacterial strains, revealing moderate activity.

Modeling of 2-Pyridin-3-yl-Benzo(d)(1,3)Oxazin-4-one Derivatives by Several Conformational Searching Tools and Molecular Docking

Neutrophil elastase, a serine proteinase from the chymotrypsin family, has been the object of comprehensive experimental and theoretical studies to develop efficient human neutrophil elastase inhibitors. The serine protease has been linked to the pathology of a variety of inflammatory diseases, making it an attractive target for the development of anti-inflammatory compounds. In this work, we have built a common binding model of the 2-pyridin-3-yl-benzo[d][1,3]oxazin-4-one derivatives into the human neutrophil elastase binding site. This was accomplished through a comparative conformational analysis (using OMEGA, HYPERCHEM, and MOPAC software) of 2-pyridin-3-yl-benzo[d][1,3]oxazin-4-one inhibitors followed by rigid and flexible molecular docking (by the FRED and GLIDE programs) into the target protein. We conclude that OMEGA software generates the most representative conformers to model the protein-ligand interactions.

MTD-PLS and docking study for a series of substituted 2-phenylindole derivatives with oestrogenic activity

A series of 36 substituted 2-phenylindoles was analysed using minimal topological difference-projections in latent structures variant (MTD-PLS) and molecular docking, using fast rigid exhaustive docking (FRED) and AutoDock Vina programs. For quantitative structure activity relationships (QSAR) validation, a sphere exclusion algorithm in the multi-dimensional descriptor space was used to construct training and test sets. Docking procedures were based on X-ray crystallography studies using the human alpha oestrogen receptor-17β-oestradiol complex. The ranking abilities of the different scoring functions of the FRED package were presented, and the most suitable scoring function (Chemgauss3) for the oestrogen receptor was chosen. Although the series studied contains only a limited number of compounds, the MTD-PLS method and the docking procedure provided coherent results in concordance with the X-ray diffraction data for different ligand-oestrogen receptor complexes.