Rafał Kurczab

The multiobjective based design, synthesis and evaluation of the arylsulfonamide/amide derivatives of aryloxyethyl- and arylthioethyl- piperidines and pyrrolidines as a novel class of potent 5-HT7 receptor antagonists

An analysis of the virtual combinatorial library was used for refining a pilot set of 34 derivatives and designing a targeted 38-member library of the arylamide and arylsulfonamide derivatives of aryloxyethyl- and arylthioethyl- piperidines and pyrrolidines. All compounds 24-95 were synthesized according to an elaborated parallel solid-phase method and were biologically evaluated for their affinity for 5-HT(7)R. Additionally, the targeted library members were tested for 5-HT(1A), 5-HT(6), and D(2) receptors. Selected compounds of particular interest were examined for their intrinsic activity at 5-HT(7)R in vitro employing a cAMP assay. The study allowed us to identify compound 68 (4-fluoro-N-(1-{2-[(propan-2-yl)phenoxy]ethyl}piperidin-4-yl) benzenesulfonamide) as a potent 5-HT(7)R ligand (K(i) = 0.3 nM) with strong antagonistic properties (K(b) = 1 nM) and a 1450-fold selectivity over 5-HT(1A)Rs.

The development and validation of a novel virtual screening cascade protocol to identify potential serotonin 5-HT7R antagonists

In an attempt to identify new ligands for the 5-HT(7) receptor (5-HT(7)R), we developed and tested a hierarchical multi-step strategy of virtual screening (VS) based on two-dimensional (2D) pharmacophore similarity, physicochemical scalar descriptors, an ADME/Tox filter, three-dimensional (3D) pharmacophore searches and a docking protocol. Six chemical classes of 5-HT(7)R antagonists were used as query structures in a double-path virtual screening scheme. The Enamine screening database, consisting of approximately 730,000 commercially available drug-like compounds, was adopted and used as a source of structures. A biological evaluation of 26 finally selected virtual hits resulted in finding two benzodioxane derivatives with significant affinity (K(i)=197 and 265 nM). The approach described in this case study can be easily used as a general rational drug design tool for other biological targets.

The influence of negative training set size on machine learning-based virtual screening

BackgroundThe paper presents a thorough analysis of the influence of the number of negative training examples on the performance of machine learning methods.ResultsThe impact of this rather neglected aspect of machine learning methods application was examined for sets containing a fixed number of positive and a varying number of negative examples randomly selected from the ZINC database. An increase in the ratio of positive to negative training instances was found to greatly influence most of the investigated evaluating parameters of ML methods in simulated virtual screening experiments. In a majority of cases, substantial increases in precision and MCC were observed in conjunction with some decreases in hit recall. The analysis of dynamics of those variations let us recommend an optimal composition of training data. The study was performed on several protein targets, 5 machine learning algorithms (SMO, Naïve Bayes, Ibk, J48 and Random Forest) and 2 types of molecular fingerprints (MACCS and CDK FP). The most effective classification was provided by the combination of CDK FP with SMO or Random Forest algorithms. The Naïve Bayes models appeared to be hardly sensitive to changes in the number of negative instances in the training set.ConclusionsIn conclusion, the ratio of positive to negative training instances should be taken into account during the preparation of machine learning experiments, as it might significantly influence the performance of particular classifier. What is more, the optimization of negative training set size can be applied as a boosting-like approach in machine learning-based virtual screening.

Identification of Novel Serotonin Transporter Compounds by Virtual Screening

The serotonin (5-hydroxytryptamine, 5-HT) transporter (SERT) plays an essential role in the termination of serotonergic neurotransmission by removing 5-HT from the synaptic cleft into the presynaptic neuron. It is also of pharmacological importance being targeted by antidepressants and psychostimulant drugs. Here, five commercial databases containing approximately 3.24 million drug-like compounds have been screened using a combination of two-dimensional (2D) fingerprint-based and three-dimensional (3D) pharmacophore-based screening and flexible docking into multiple conformations of the binding pocket detected in an outward-open SERT homology model. Following virtual screening (VS), selected compounds were evaluated using in vitro screening and full binding assays and an in silico hit-to-lead (H2L) screening was performed to obtain analogues of the identified compounds. Using this multistep VS/H2L approach, 74 active compounds, 46 of which had Ki values of ≤1000 nM, belonging to 16 structural classes, have been identified, and multiple compounds share no structural resemblance with known SERT binders.

The influence of the inactives subset generation on the performance of machine learning methods.

BackgroundA growing popularity of machine learning methods application in virtual screening, in both classification and regression tasks, can be observed in the past few years. However, their effectiveness is strongly dependent on many different factors.ResultsIn this study, the influence of the way of forming the set of inactives on the classification process was examined: random and diverse selection from the ZINC database, MDDR database and libraries generated according to the DUD methodology. All learning methods were tested in two modes: using one test set, the same for each method of inactive molecules generation and using test sets with inactives prepared in an analogous way as for training. The experiments were carried out for 5 different protein targets, 3 fingerprints for molecules representation and 7 classification algorithms with varying parameters. It appeared that the process of inactive set formation had a substantial impact on the machine learning methods performance.ConclusionsThe level of chemical space limitation determined the ability of tested classifiers to select potentially active molecules in virtual screening tasks, as for example DUDs (widely applied in docking experiments) did not provide proper selection of active molecules from databases with diverse structures. The study clearly showed that inactive compounds forming training set should be representative to the highest possible extent for libraries that undergo screening.

Structure-activity relationships and molecular modeling studies of novel arylpiperazinylalkyl 2-benzoxazolones and 2-benzothiazolones as 5-HT7 and 5-HT1A receptor ligands

A novel series of arylpiperazinylalkyl 2-benzoxazolones and 2-benzothiazolones 18-38 was designed, synthesized and tested to evaluate their affinity for the 5-HT7 and 5-HT1A receptors. Compounds with a 2-benzothiazolone nucleus generally had affinity values higher than the corresponding 2-benzoxazolone compounds. In particular, derivatives possessing a six or seven carbon chain linker between 2-benzothiazolone and arylpiperazine had Ki values in the subnanomolar range for the 5-HT1A receptor and in the low nanomolar range for the 5-HT7 receptor, indicating that they may be interesting dual ligands. Molecular modeling studies revealed different docking poses for the investigated compounds in homology models of 5-HT1A and 5-HT7 receptors, which explained their experimentally determined affinities and general low selectivity. Additionally, structural interaction fingerprints analysis identified the important amino acid residues for the specific interactions of long-chain arylpiperazines within the binding pockets of both serotonin receptors.

Molecular mechanism of serotonin transporter inhibition elucidated by a new flexible docking protocol

The two main groups of antidepressant drugs, the tricyclic antidepressants (TCAs) and the selective serotonin reuptake inhibitors (SSRIs), as well as several other compounds, act by inhibiting the serotonin transporter (SERT). However, the binding mode and molecular mechanism of inhibition in SERT are not fully understood. In this study, five classes of SERT inhibitors were docked into an outward-facing SERT homology model using a new 4D ensemble docking protocol. Unlike other docking protocols, where protein flexibility is not considered or is highly dependent on the ligand structure, flexibility was here obtained by side chain sampling of the amino acids of the binding pocket using biased probability Monte Carlo (BPMC) prior to docking. This resulted in the generation of multiple binding pocket conformations that the ligands were docked into. The docking results showed that the inhibitors were stacked between the aromatic amino acids of the extracellular gate (Y176, F335) presumably preventing its closure. The inhibitors interacted with amino acids in both the putative substrate binding site and more extracellular regions of the protein. A general structure-docking-based pharmacophore model was generated to explain binding of all studied classes of SERT inhibitors. Docking of a test set of actives and decoys furthermore showed that the outward-facing ensemble SERT homology model consistently and selectively scored the majority of active compounds above decoys, which indicates its usefulness in virtual screening.

Theoretical description of hydrogen bonding in oxalic acid dimer and trimer based on the combined extended-transition-state energy decomposition analysis and natural orbitals for chemical valence (ETS-NOCV)

AbstractIn the present study we have analyzed hydrogen bonding in dimer and trimer of oxalic acid, based on a recently proposed charge and energy decomposition scheme (ETS-NOCV). In the case of a dimer, two conformations, α and β, were considered. The deformation density contributions originating from NOCV’s revealed that the formation of hydrogen bonding is associated with the electronic charge deformation in both the σ—(Δρσ) and π-networks (Δρπ). It was demonstrated that σ-donation is realized by electron transfer from the lone pair of oxygen on one monomer into the empty

Fingerprint-based consensus virtual screening towards structurally new 5-HT 6 R ligands

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