A computational investigation of thymidylate synthase inhibitors through a combined approach of 3D-QSAR and pharmacophore modelling.

Thymidylate synthase (TS) is a crucial target of cancer drug discovery and is mainly involved in the De novo synthesis of the DNA precursor thymine. In the present study, to generate reliable models and identify a few promising molecules, we combined QSAR modelling with the pharmacophore hypothesis-generating technique. Input molecules were clustered on their similarity, and a cluster of 74 molecules with a pyrimidine moiety was chosen as the set for 3D-QSAR and pharmacophore modelling. Atom-based and field-based 3D-QSAR models were generated and statistically validated with R2 > 0.90 and Q2 > 0.75. The common pharmacophore hypothesis(CPH) generation identified the best six-point model ADHRRR. Using these best models, a library of FDA-approved drugs was screened for activity and filtered via molecular docking, ADME profiling, and molecular dynamics simulations. The top ten promising TS-inhibiting candidates were identified, and their chemical features profitable for TS inhibitors were explored.Communicated by Ramaswamy H. Sarma.

Field-based 3D-QSAR for tyrosine protein kinase JAK-2 inhibitors.

The present work aimed to develop a Field-based 3D-QSAR model with existing JAK-2 inhibitors. The JAK-STAT pathway is known to play a role in the development of autoimmune diseases, including rheumatoid arthritis, ulcerative colitis, and Crohn's disease. Dysregulation of JAK-STAT is also linked to the development of myelofibrosis and other myeloproliferative diseases. JAK antagonists can be used in many areas of medicine. There are many compounds that already show inhibition of Jak-2. We have developed a Field-based 3D QSAR model which showed good correlation values (r2 0.884 and q2 0.67) with an external test set regression pred_r2 0.562. Various properties, such as electronegativity, electro positivity, hydrophobicity, and shape features, were studied under the activity atlas to determine the inhibitory potential of ligands. These were also identified as important structural features responsible for biological activity. We performed virtual screening based on the pharmacophore features of the co-crystal ligand (PDB ID: 3KRR) and a dataset of NPS was selected with a RMSD value less than 0.8. The developed 3D QSAR model was used to screen ligands and calculate the predicted JAK-2 inhibition activity (pKi). The results of the virtual screening were validated using molecular docking and molecular dynamics simulations. SNP1 (SN00154718) and SNP2 (SN00213825) showed binding affinity of -11.16 and -11.08 kcal/mol, respectively, which were very close to the crystal ligand of 3KRR, -11.67 kcal/mol. The RMSD plot of the protein-ligand complex of SNP1 and 3KRR showed stable interactions with an average RMSD of 2.89 Å. Thus, a statistically robust 3D QSAR model could reveal more inhibitors and aid in the design of novel JAK-2 inhibitors.

Rationalization of the activity Profile of Pyruvate Kinase Isozyme M2 (PKM2) Inhibitors using 3D QSAR.

INTRODUCTION: Pyruvate kinase isozyme M2 (PKM2) was observed to be overexpressed and play a key role in cell growth and cancer cells' metabolism. During the past years, phytochemicals have been developed as new treatment options for chemoprevention and cancer therapy. Natural resources, like shikonin (naphthoquinone) and its derivatives, have emerged to be high potential therapeutics in cancer treatment. METHODS: Our study aimed to design novel anti-tumour agents (PKM2 inhibitors) focusing on the shikonin scaffold with a better activity using computational methods. We applied a three-dimensional quantitative structure-activity relationship (3D-QSAR) approach using Field-based QSAR. RESULTS: The Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) were performed on a series of forty shikonin derivatives, including shikonin, to develop robust models and rationalize the PKM2 inhibitory activity profile by building a correlation between structural features and activity. CONCLUSION: These predictive computational models will further help the design and synthesis of potent PKM2 inhibitors and their fast biological assessment at a low cost.

Profiling the structural determinants of aminoketone derivatives as hNET and hDAT reuptake inhibitors by field-based QSAR based on molecular docking.

BACKGROUND: Bupropion, one of the dual norepinephrine and dopamine reuptake inhibitors (NDRIs), is an aminoketone derivative performed effect in improving cognitive function for depression. However, its therapeutic effect is unsatisfactory due to poor clinical response, and there are only few derivatives in pre-clinical settings. OBJECTIVE: This work attempted to elucidate the essential structural features for the activity and designed a series of novel derivatives with good inhibitive ability, pharmacokinetic and medicinal chemistry properties. METHODS: The field-based QSAR of aminoketone derivatives of two targets were established based on docking poses, and the essential structural properties for designing novel compounds were supplied by comparing contour maps. RESULTS: The selected two models performed good predictability and reliability with R2 of 0.8479 and 0.8040 for training set, Q2 of 0.7352 and 0.6266 for test set respectively, and the designed 29 novel derivatives performed no less than the highest active compound with good ADME/T pharmacokinetic properties and medicinal chemistry friendliness. CONCLUSIONS: Bulky groups in R1, bulky groups with weak hydrophobicity in R3, and potent hydrophobic substituted group with electronegative in R2 from contour maps provided important insights for assessing and designing 29 novel NDRIs, which were considered as candidates for cognitive dysfunction with depression or other related neurodegenerative disorders.

Docking field-based QSAR and pharmacophore studies on the substituted pyrimidine derivatives targeting HIV-1 reverse transcriptase.

HIV-1 reverse transcriptase (RT) is one of the most important enzymes required for viral replication, thus acting as an attractive target for antiretroviral therapy. Pyrimidine analogues reportedly have selective inhibition on HIV-1 RT with favorable antiviral activities in our previous study. To further explore the relationship between inhibitory activity and pharmacophoric characteristics, field-based QSAR models were generated and validated using Schrodinger Suite (correlation coefficient of .8078, cross-validated value of 0.5397 for training set and Q2 of 0.4669, Pearson's r of .7357 for test set). Docking, pocket surfaces, and pharmacophore study were also investigated to define the binding pattern and pharmacophoric features, including (i) π-π interaction with residue Tyr181, Tyr188, and Trp229 and p-π interaction with His235 and (ii) hydrogen bond with residue Lys101 and halogen bond with residue Tyr188. The pharmacophore features of six-point hypothesis AADRRR.184, AAADRR.38, and AADRRR.26 further complimented to the docking and QSAR results. We also found that the protein-ligand complex exhibited high relative binding free energy. These observations could be potentially utilized to guide the rational design and optimization of novel HIV-1 RT inhibitors.

The effect of various atomic partial charge schemes to elucidate consensus activity-correlating molecular regions: a test case of diverse QSAR models.

The estimation of atomic partial charges of the small molecules to calculate molecular interaction fields (MIFs) is an important process in field-based quantitative structure-activity relationship (QSAR). Several studies showed the influence of partial charge schemes that drastically affects the prediction accuracy of the QSAR model and focused on the selection of appropriate charge models that provide highest cross-validated correlation coefficient ([Formula: see text] or q(2)) to explain the variation in chemical structures against biological endpoints. This study shift this focus in a direction to understand the molecular regions deemed to explain SAR in various charge models and recognize a consensus picture of activity-correlating molecular regions. We selected eleven diverse dataset and developed MIF-based QSAR models using various charge schemes including Gasteiger-Marsili, Del Re, Merck Molecular Force Field, Hückel, Gasteiger-Hückel, and Pullman. The generalized resultant QSAR models were then compared with Open3DQSAR model to interpret the MIF descriptors decisively. We suggest the regions of activity contribution or optimization can be effectively determined by studying various charge-based models to understand SAR precisely.