Come for the atmosphere, stay for the interactions: Deciphering small molecule partitioning into biomolecular condensates.

Optimizing pharmacokinetic properties remains challenging but is generally guided by a set of structural rules. However, no such rule set exists for intracellular distribution. Kilgore et al.1 have examined small molecule partitioning within biomolecular condensates, yielding findings that could open a new window in the drug design and discovery process.

In Silico Optimization of Frizzled-8 Receptor Inhibition Activity of Carbamazepine: Designing New Anti-Cancer Agent.

BACKGROUND: Frizzled-8 (FZD8) receptor is a therapeutic target for cancer treatment and recent research has shown that carbamazepine (CBZ) can inhibit this receptor. OBJECTIVE: In this work, it has been tried to optimize CBZ to enhance its binding capacity to the N6W binding site of FZD8 by using structure-based drug design methods. METHODS: CBZ and its 83 derivatives were docked to the N6W binding site of FZD8. RESULTS: Docking results show that two compounds 79 and 82 have the smallest binding energies and are fitted to the N6W binding site. Compounds C79 and C82 have been synthesized by replacing a hydrogen atom of the seven-membered ring in CBZ with benzoate and nicotinate groups, respectively. In addition, docking results show that a trifluoromethyl on one of the phenyl rings is favorable for improving the FZD8 inhibition activity of the molecule. CONCLUSION: Both molecules C79 and C82 were subjected to molecular dynamics (MD) simulation. MD results show that FZD8-C82 complex is stable and this compound binds to the N6W binding site more strongly than compounds C79 and CBZ.

Development linear and non-linear QSAR models for predicting AXL kinase inhibitory activity of N-[4-(quinolin-4-yloxy)phenyl]benzenesulfonamides.

In this research, we used CoMFA, LSSVM and FFANN for creating QSAR models for predicting AXL Kinase inhibitory activity of N-[4-(Quinolin-4-yloxy)phenyl]benzenesulfonamides. A CoMFA model with three components was developed and CoMFA contour maps were interpreted to extract chemical features that influence the inhibitory activity of these molecules. R2 for train and test set of CoMFA model were 0.8900 and 0.8171, respectively. Model created by five Dragon descriptors and LSSVM model showed slightly better predictive power with respect to CoMFA model. R2 for train, test set of created LSSVM model were 0.0.8477 and 0.8218, respectively. Also, a FFANN model, using the same five descriptors, was developed with 2 neurons in its hidden layer and R2 for its train and test sets were 0.8314 and 0.8522, respectively. All created models were validated by calculating several statistical parameters and their applicability domain were investigated by calculating leverage. Furthermore, a homology model was built for Axl structure and molecules with the lowest and the greatest activity were docked to it and their interactions with Axl were investigated.

Towards the In-silico Design of New HSP90 Inhibitors: Molecular Docking and 3D-QSAR CoMFA Studies of Tetrahydropyrido [4, 3-d] Pyrimidine Derivatives as HSP90 Inhibitors.

BACKGROUND: HSP90 is necessary for the conformational maturation of proteins, proteins disaggregation, folding newly synthesized peptides and the refolding of denatured proteins. The inhibition of HSP90 leads to proteasomal degradations of client proteins that finally kill cancer cells. METHODS: In this research, molecular docking and comparative molecular field analysis (CoMFA) were used to investigate the interactions of tetrahydropyrido[4,3-d]pyrimidine derivatives with the N-terminal domain binding site of the HSP90 and predicting their inhibitory activities. RESULTS: A CoMFA model with five components and q2 of 0.81 was developed. R2 for training and test sets were 0.96 and 0.79, respectively. Based on extracted Contour maps for this CoMFA model, three new inhibitors with greater pIC50 with respect to the greatest active molecule in the data-set were designed by modifying molecule m45. Molecule m45 and designed inhibitors were docked to the N-terminal domain binding site of the HSP90. Designed inhibitors obtained lower binding energy with respect to m45. CONCLUSION: Based on extracted CoMFA contour maps, bulky substituents are favored for the R1 group and in R3 group, short and bulky substituents increase the activity of molecules. Less bulky and longer substituents are favored for R2. The molecular docking analysis of compound m45 with the N-terminal domain binding site of the HSP90 show hydroxyl group on phenyl ring is necessary to form hydrogen bonding with hydrophilic residues in binding site and a conserved water molecule. Molecule m45 has Pi-Sigma interaction with phenyl ring in the side chain of Phenylalanine 138 via isopropyl substituent on meta position of the phenyl ring. Also, Molecule m45 forms carbon-hydrogen bond with oxygen atoms at the side chain of Aspartic acid 54 and Asparagine 51 via its dimethylamine group. Others are Van der Waals interactions.

Predictive and Descriptive CoMFA Models: The Effect of Variable Selection.

Aims & Scope: In this research, 8 variable selection approaches were used to investigate the effect of variable selection on the predictive power and stability of CoMFA models. MATERIALS & METHODS: Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors and 57 ATAD2 bromodomain inhibitors were modelled by CoMFA. First of all, for all three data sets, CoMFA models with all CoMFA descriptors were created then by applying each variable selection method a new CoMFA model was developed so for each data set, 9 CoMFA models were built. Obtained results show noisy and uninformative variables affect CoMFA results. Based on created models, applying 5 variable selection approaches including FFD, SRD-FFD, IVE-PLS, SRD-UVEPLS and SPA-jackknife increases the predictive power and stability of CoMFA models significantly. RESULT & CONCLUSION: Among them, SPA-jackknife removes most of the variables while FFD retains most of them. FFD and IVE-PLS are time consuming process while SRD-FFD and SRD-UVE-PLS run need to few seconds. Also applying FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS protect CoMFA countor maps information for both fields.

Molecular docking and CoMFA studies of thiazoloquin(az)olin(on)es as CD38 inhibitors: determination of inhibitory mechanism, pharmacophore interactions, and design of new inhibitors.

In this research, molecular docking and 3D-QSAR studies were carried out on a series of 79 thiazoloquin(az)olin(on)es as CD38 inhibitors. Based on docking results, four interactions including hydrogen bonding with main chain of GLU-226 (H-M-GLU-226), Van der Waals interactions with side chain of TRP-125 (V-S-TRP-125), TRP-189 (V-S-TRP-189), and THR-221 (V-S-THR-221) were considered as pharmacological interactions. Active conformation of each ligand was extracted from docking studies and was used for carrying out 3D-QSAR modeling. Comparative molecular field analysis (CoMFA) was performed on CD38 inhibitory activities of these compounds on human and mouse. We developed CoMFA models with five components as optimum models for both data-sets. For human data-set, a model with high predictive power was developed. R2, RMSE, and F-test values for training set of this model were .94, .24, and 179.58, respectively, and R2 and RMSE for its test set were .92 and .32, respectively. The q2 and RMSE values for leave-one-out cross validation test on training set were .78 and .46, respectively, that demonstrate created model is robust. Based on extracted steric and electrostatic contour maps for this model, three inhibitors with pIC50 larger than 8.85 were designed.

Investigation of retention behavior of polychlorinated biphenyl congeners on 18 different HRGC columns using molecular surface average local ionization energy descriptors.

In this paper, based on the general interaction properties function (GIPF) family descriptors computed at the B3LYP/6-31G* level in Gaussian98 software, a significant quantitative structure-retention relationship (QSRR) models for the high resolution gas chromatographic relative retention time (HRGC-RRT) of all PCB congeners on 18 different HRGC capillary columns were constructed by using multiple linear regression (MLR) analysis, following the guidelines for development and validation of QSRR models. By means of the elimination selection stepwise regression algorithms, the molecular surface average local ionization energy was selected as one-parameter univariate linear regression to develop a QSRR model for prediction of GC-RRT of PCBs on each stationary phase. The accuracy of all developed models was confirmed using different types of internal and external procedures. A successful interpretation of the complex relationship between HRGC-RRTs of PCBs and the chemical structures was achieved by QSRR.